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Correction to: CpG‑creating mutations are costly in many human viruses

Evolutionary Ecology 2020年第3期34卷 361-362页

作者： Caudill, Victoria R. Qin, Sarina Winstead, Ryan Kaur, Jasmeen Tisthammer, Kaho Pineda, E. Geo Solis, Caroline Cobey, Sarah Bedford, Trevor Carja, Oana Eggo, Rosalind M. Koelle, Katia Lythgoe, Katrina Regoes, Roland Roy, Scott Allen, Nicole Aviles, Milo Baker, Brittany A. Bauer, William Bermudez, Shannel Carlson, Corey Castellanos, Edgar Catalan, Francisca L. Chemel, Angeline Katia Elliot, Jacob Evans, Dwayne Fiutek, Natalie Fryer, Emily Goodfellow, Samuel Melvin Hecht, Mordecai Hopp, Kellen Hopson, E. Deshawn Jaberi, Amirhossein Kim, Jennifer Kinney, Christen Lao, Derek Le, Adrienne Lo, Jacky Lopez, Alejandro G. López, Andrea Lorenzo, Fernando G. Luu, Gordon T. Mahoney, Andrew R. Melton, Rebecca L. Nascimento, Gabriela Do Pradhananga, Anjani Rodrigues, Nicole S. Shieh, Annie Sims, Jasmine Singh, Rima Sulaeman, Hasan Thu, Ricky Tran, Krystal Tran, Livia Winters, Elizabeth J. Wong, Albert Pennings, Pleuni S. Department of Biology San Francisco State University San Francisco USA Department of Biology University of Oregon Eugene USA Quantitative Systems Biology Univeristy of California Merced USA Department of Ecology and Evolution University of Chicago Chicago USA Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center Seattle USA Department of Computational Biology School of Computer Science Carnegie Mellon University Pittsburgh USA Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London UK Department of Biology Emory University Atlanta USA Big Data Institute University of Oxford Oxford UK Department of Environmental Systems Science ETH Zurich Zurich Switzerland Department of Neurological Surgery University of California San Francisco USA Department of Organismic and Evolutionary Biology Harvard University Cambridge USA Department of Plant Biology Carnegie Institution for Science Stanford USA Health Sciences Center University of New Mexico Albuquerque USA UCSD Biomed Sciences PhD Program University of California San Diego USA Biochemistry Molecular Cellular and Developmental Biology Graduate Group University of California Davis USA UCSF Tetrad Graduate Program University of California San Francisco USA

In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.

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Speech Emotion Recognition using Convolutional Neural Network with Audio Word-based Embedding

Speech Emotion Recognition using Convolutional Neural Networ...

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International Symposium on Chinese Spoken Language Processing

作者： Kun-Yi Huang Chung-Hsien Wu Qian-Bei Hong Ming-Hsiang Su Yuan-Rong Zeng Department of Computer Science and Information Engineering National Cheng Kung University Taiwan PhD Program for Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

ISBN: (纸本)9781538656280;9781538656273

A complete emotional expression typically contains a complex temporal course in a natural conversation. Related research on utterance-level, segment-level and multi-level processing lacks understanding of the underlying relation of emotional speech. In this work, a convolutional neural network (CNN) with audio word-based embedding is proposed for emotion modeling. In this study, vector quantization is first applied to convert the low level features of each speech frame into audio words using k-means algorithm. Word2vec is adopted to convert an input speech utterance into the corresponding audio word vector sequence. Finally, the audio word vector sequences of the training emotional speech data with emotion annotation are used to construct the CNN- based emotion model. The NCKU-ES database, containing seven emotion categories: happiness, boredom, anger, anxiety, sadness, surprise and disgust, was collected and five-fold cross validation was used to evaluate the performance of the proposed CNN-based method for speech emotion recognition. Experimental results show that the proposed method achieved an emotion recognition accuracy of 82.34%, improving by 8.7% compared to the Long Short Term Memory (LSTM)- based method, which faced the challenging issue of long input sequence. Comparing with raw features, the audio word-based embedding achieved an improvement of 3.4% for speech emotion recognition.

关键词： Emotion recognition Feature extraction Speech recognition Acoustics Training Databases Neural networks

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Identification of gene expression signatures for psoriasis classification using machine learning techniques

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Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

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Solving the conjugacy decision problem via machine learning

arXiv

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arXiv 2017年

作者： GRYAK, JONATHAN HARALICK, ROBERT M. KAHROBAEI, DELARAM CUNY Graduate Center PhD Program in Computer Science City University of New York CUNY Graduate Center PhD Program in Computer Science City University of New York CUNY Graduate Center PhD Program in Computer Science and NYCCT Mathematics Department City University of New York

Machine learning and pattern recognition techniques have been successfully applied to algorithmic problems in free groups. In this paper, we seek to extend these techniques to finitely presented non-free groups, with a particular emphasis on polycyclic and metabelian groups that are of interest to non-commutative cryptography. As a prototypical example, we utilize supervised learning methods to construct classifiers that can solve the conjugacy decision problem, i.e., determine whether or not a pair of elements from a specified group are conjugate. The accuracies of classifiers created using decision trees, random forests, and N-tuple neural network models are evaluated for several non-free groups. The very high accuracy of these classifiers suggests an underlying mathematical relationship with respect to conjugacy in the tested groups.20F10, 68T05 Copyright © 2017, The Authors. All rights reserved.

关键词： Decision trees

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Hot air treatment: Alternative annealing of TiO2 nanoparticulate films without substrate deformation

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AIP Conference Proceedings 2020年第1期2279卷

作者： Arisara Panthawan Winai Thongpan Porntipa Pooseekheaw Nidchamon Jumrus Tewasin Kumpika Wattikon Sroila Ekkapong Kantarak Panupong Sanmuangmoon Adisorn Tuantranont Wiradej Thongsuwan Pisith Singjai 1PhD’s Degree Program in Materials Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand 3Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand 2Center of Excellence in Materials Science and Technology Chiang Mai University Chiang Mai 50200 Thailand 4Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand 5Thailand Organic and Printed Electronics Innovation Center National Electronics and Computer Technology Center National Science and Technology Development Agency (Klong Luang Pathumthani 12120 Thailand) 6Center of Advanced Materials of Printed Electronics and Sensors Materials Science Research Center Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand

Titanium dioxide (TiO2) nanoparticulate films was successfully annealed on a glass substrate via alternative hot air treatment (HAT). Interestingly, HAT not only protects the glass substrate deformation but can also reduce cost and time in the annealing process. The annealed films using HAT at 500 °C for 10 min can be compared with the annealed films in conventional furnace heat treatment (FHT) at 500 °C for 60 min. The results showed the sizes of ∼7.9 nm are obtained after the films annealed with both annealing techniques. The surface roughness of the as-deposited, the annealed films using FHT and HAT were 17.37, 23.74 and 23.26 nm, respectively. The energy band gap of the as- deposited films, the annealed films using FHT and HAT were 3.24 eV, 3.1 and 3.19 eV, respectively. Moreover, the annealed films using FHT and HAT techniques show superhydrophilic with a water contact angle of 3.42° and 2.81°, while the as-deposited films was 8.93°. After aging time testing, superhydrophilicity of the annealed films using HAT is greater than FHT. The result is in good agreement with Ti wt% of the as-deposited, the annealed films using FHT and HAT left on the substrate to 0.15, 0.73 and 0.65 nm after testing simulation for 20 years.

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Machine learning for early prediction of circulatory failure in the intensive care unit

arXiv

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arXiv 2019年

作者： Hyland, Stephanie L. Faltys, Martin Hüser, Matthias Lyu, Xinrui Gumbsch, Thomas Esteban, Cristóbal Bock, Christian Horn, Max Moor, Michael Rieck, Bastian Zimmermann, Marc Bodenham, Dean Borgwardt, Karsten Rätsch, Gunnar Merz, Tobias M. Department of Computer Science ETH Zürich Zürich Switzerland Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Medical Informatics Group Zürich University Hospital Zürich Switzerland Department of Intensive Care Medicine University Hospital University of Bern Bern Switzerland Department of Biosystems and Engineering ETH Zürich Basel Switzerland Department of Biology ETH Zürich Zürich Switzerland Swiss Institute for Bioinformatics Lausanne Switzerland Cardiovascular Intensive Care Unit Auckland City Hospital Auckland New Zealand

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient-years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort. Copyright © 2019, The Authors. All rights reserved.

关键词： Intensive care units

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Urban legislation assessment by data analytics with smart city characteristics

Urban legislation assessment by data analytics with smart ci...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Xu Du Diane Liporace Aparna S. Varde Earth and Environmental Studies Environmental Mgmt PhD Program Montclair State University NJ USA Department of Computer Science Montclair State University Montclair NJ USA Department of Computer Science PhD Program Montclair State University NJ USA

ISBN: (纸本)9781538611050

Smart cities receive great attention today especially in conjunction with ubiquitous computing. People feel the need to access information about their cities anywhere anytime. They wish to be actively involved with local government bodies for policy decisions affecting urban lifestyle. Accordingly, this paper describes our research on urban policy management. We analyze urban legislation, more specifically, ordinances or local laws. We categorize ordinances based on smart city characteristics they address. This work deploys data warehousing, XML data management and data mining over categorized ordinances. Interesting findings include relative importance of smart city characteristics considering the focus given by urban agencies. This research helps agencies assess their current ordinance policies with decision support for the future. It also provides urban residents at-a-glance information about their cities and policies with analysis. This work has broader impacts of enhancing smart cities and ubiquitous computing by making useful information widely accessible with suitable inferences.

关键词： XML Smart cities Weapons Databases Data mining Data warehouses

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Practical and secure outsourcing of discrete log group exponentiation to a single malicious server 8

Practical and secure outsourcing of discrete log group expon...

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8th ACM Cloud Computing Security Workshop, CCSW 2017

作者： Crescenzo, Giovanni Di Khodjaeva, Matluba Kahrobaei, Delaram Shpilrain, Vladimir Vencore Labs Basking RidgeNJ07920 United States CUNY Graduate Center New YorkNY10016 United States PhD Program in Computer Science CUNY Graduate Center New YorkNY10016 United States

ISBN: (纸本)9781450353939

Group exponentiation is an important operation used in many public-key cryptosystems and, more generally, cryptographic protocols. To expand the applicability of these solutions to computationally weaker devices, it has been advocated that this operation is outsourced from a computationally weaker client to a computationally stronger server, possibly implemented in a cloud-based architecture. While preliminary solutions to this problem considered mostly honest servers, or multiple separated servers, some of which honest, solving this problem in the case of a single (logical), possibly malicious, server, has remained open since a formal cryptographic model was introduced in [20]. Several later attempts either failed to achieve privacy or only bounded by a constant the (security) probability that a cheating server convinces a client of an incorrect result. In this paper we solve this problem for a large class of cyclic groups, thus making our solutions applicable to many cryptosystems in the literature that are based on the hardness of the discrete logarithm problem or on related assumptions. Our main protocol satisfies natural correctness, security, privacy and efficiency requirements, where the security probability is exponentially small. In our main protocol, with very limited offline computation and server computation, the client can delegate an exponentiation to an exponent of the same length as a group element by performing an exponentiation to an exponent of short length (i.e., the length of a statistical parameter). We also show an extension protocol that further reduces client computation by a constant factor, while increasing offline computation and server computation by about the same factor.

关键词： Group theory

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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