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Scanner invariant multiple sclerosis lesion segmentation from MRI

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arXiv 2019年

作者： Aslani, Shahab Murino, Vittorio Dayan, Michael Tam, Roger Sona, Diego Hamarneh, Ghassan Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genoa Italy Human Neuroscience Platform Fondation Campus Biotech Geneva Switzerland NeuroInformatics Laboratory Fondazione Bruno Kessler Trento Italy Computer Science Department University of Verona Italy Department of Radiology School of Biomedical Engineering University of British Columbia Vancouver Canada School of Computing Science Simon Fraser University Vancouver Canada

This paper presents a simple and effective generalization method for magnetic resonance imaging (MRI) segmentation when data is collected from multiple MRI scanning sites and as a consequence is affected by (site-)domain shifts. We propose to integrate a traditional encoder-decoder network with a regularization network. This added network includes an auxiliary loss term which is responsible for the reduction of the domain shift problem and for the resulting improved generalization. The proposed method was evaluated on multiple sclerosis lesion segmentation from MRI data. We tested the proposed model on an in-house clinical dataset including 117 patients from 56 different scanning sites. In the experiments, our method showed better generalization performance than other baseline networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Causal mediation analysis leveraging multiple types of summary statistics data

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arXiv 2019年

作者： Park, Yongjin Sarkar, Abhishek Nguyen, Khoi Kellis, Manolis Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Department of Human Genetics University of Chicago ChicagoIL United States

Summary statistics of genome-wide association studies (GWAS) teach causal relationship between millions of genetic markers and tens and thousands of phenotypes. However, underlying biological mechanisms are yet to be elucidated. We can achieve necessary interpretation of GWAS in a causal mediation framework, looking to establish a sparse set of mediators between genetic and downstream variables, but there are several challenges. Unlike existing methods rely on strong and unrealistic assumptions, we tackle practical challenges within a principled summary-based causal inference framework. We analyzed the proposed methods in extensive simulations generated from real-world genetic data. We demonstrated only our approach can accurately redeem causal genes, even without knowing actual individual-level data, despite the presence of competing non-causal trails. Copyright © 2019, The Authors. All rights reserved.

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Hierarchical contextualized representation for named entity recognition

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arXiv 2019年

作者： Luo, Ying Xiao, Fengshun Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Named entity recognition (NER) models are typically based on the architecture of Bi-directional LSTM (BiLSTM). The constraints of sequential nature and the modeling of single input prevent the full utilization of global information from larger scope, not only in the entire sentence, but also in the entire document (dataset). In this paper, we address these two deficiencies and propose a model augmented with hierarchical contextualized representation: Sentence-level representation and document-level representation. In sentencelevel, we take different contributions of words in a single sentence into consideration to enhance the sentence representation learned from an independent BiLSTM via label embedding attention mechanism. In document-level, the key-value memory network is adopted to record the document-aware information for each unique word which is sensitive to similarity of context information. Our two-level hierarchical contextualized representations are fused with each input token embedding and corresponding hidden state of BiLSTM, respectively. The experimental results on three benchmark NER datasets (CoNLL-2003 and Ontonotes 5.0 English datasets, CoNLL-2002 Spanish dataset) show that we establish new state-of-the-art results. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

Unlocking capacities of viral genomics for the COVID-19 pandemic response

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arXiv 2021年

作者： Knyazev, Sergey Chhugani, Karishma Sarwal, Varuni Ayyala, Ram Singh, Harman Karthikeyan, Smruthi Deshpande, Dhrithi Comarova, Zoia Lu, Angela Porozov, Yuri Wu, Aiping Abedalthagafi, Malak S. Nagaraj, Shivashankar H. Smith, Adam L. Skums, Pavel Ladner, Jason Lam, Tommy Tsan-Yuk Wu, Nicholas C. Zelikovsky, Alex Knight, Rob Crandall, Keith A. Mangul, Serghei Department of Computer Science College of Art and Science Georgia State University 1 Park Place AtlantaGA30303 United States Department of Pharmacology and Pharmaceutical Sciences School of Pharmacy University of Southern California 1985 Zonal Avenue Los AngelesCA90089 United States Department of Computer Science University of California Los Angeles 580 Portola Plaza Los AngelesCA90095 United States Department of Neuroscience College of Life Sciences University of California Los Angeles 580 Portola Plaza Los AngelesCA90095 United States Department of Electrical Engineering Indian Institute of Technology Hauz Khas New Delhi110016 India Department of Pediatrics University of California San Diego San DiegoCA United States Paradigm Environmental 3911 Old Lee Highway FairfaxVA22030 United States Department of Pharmacology and Pharmaceutical Sciences School of Pharmacy University of Southern California 1985 Zonal Avenue Los AngelesCA90089-9121 United States World-Class Research Center Digital Biodesign and Personalized Healthcare I.M. Sechenov First Moscow State Medical University Moscow Russia Department of Computational Biology Sirius University of Science and Technology Sochi Russia Center for Systems Medicine Institute of Basic Medical Sciences Chinese Academy of Medical Sciences Peking Union Medical College Beijing100005 China Suzhou Institute of Systems Medicine Suzhou215123 China Genomics Research Department Saudi Human Genome Project King Fahad Medical City King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Centre for Genomics and Personalised Health Queensland University of Technology BrisbaneQLD4059 Australia Translational Research Institute Brisbane Australia Astani Department of Civil and Environmental Engineering University of Southern California 3620 South Vermont Avenue Los AngelesCA90089 United States Department of Computer Science College of Art and Science Georgia State University 1 Park Place Atlanta

More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19. © 2021, CC BY-NC-ND.

关键词： SARS

The Impact of Genomic Variation on Function (IGVF) Consortium

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arXiv 2023年

作者： Shendure, Jay Ahituv, Nadav Kircher, Martin Agarwal, Vikram Blair, Andrew Chalkiadakis, Theofilos Chardon, Florence M. Dash, Pyaree M. Deng, Chengyu Hamazaki, Nobuhiko Keukeleire, Pia Kubo, Connor Lalanne, Jean-Benoît Maass, Thorben Martin, Beth McDiarmid, Troy Nobuhara, Mai Page, Nicholas F. Regalado, Sam Schubach, Max Sims, Jasmine Ushiki, Aki Zhao, Jingjing Starita, Lea M. Fowler, Douglas M. Best, Sabrina M. Boyle, Gabe Camp, Nathan Casadei, Silvia Da, Estelle Y. Dawood, Moez Dawson, Samantha C. Fayer, Shawn Hamm, Audrey James, Richard G. Jarvik, Gail P. McEwen, Abbye E. Moore, Nick Muffley, Lara A. Pendyala, Sriram Popp, Nicholas A. Post, Mason Rubin, Alan F. Shendure, Jay Smith, Nahum T. Rubin, Alan F. Stone, Jeremy Tejura, Malvika Wang, Ziyu R. Wheelock, Melinda K. Woo, Ivan Zapp, Brendan D. Engreitz, Jesse M. Quertermous, Thomas Amgalan, Dulguun Aradhana, Aradhana Arana, Sophia M. Bassik, Michael C. Bauman, Julia R. Bhattacharya, Asmita Cai, Xiangmeng Shawn Chen, Ziwei Conley, Stephanie Deshpande, Salil Doughty, Benjamin R. Du, Peter P. Gifford, Casey Greenleaf, William J. Gschwind, Andreas R. Guo, Katherine Isobe, Sarasa Jagoda, Evelyn Jain, Nimit Jones, Hank Kang, Helen Y. Kim, Samuel H. Kim, YeEun Klemm, Sandy Kundaje, Anshul Kundu, Soumya Lago-Docampo, Mauro Lee-Yow, Yannick C. Levin-Konigsberg, Roni Li, Daniel Y. Lindenhofer, Dominik Rosa Ma, X. Marinov, Georgi K. Martyn, Gabriella E. Metzl-Raz, Eyal Monteiro, Joao P. Montgomery, Michael T. Mualim, Kristy S. Munger, Chad Munson, Glen Nguyen, Tri C. Nguyen, Trieu Palmisano, Brian T. Pampari, Anusri Park, Chong Y. Rabinovitch, Marlene Ramste, Markus Ray, Judhajeet Roy, Kevin R. Rubio, Oriane M. Schaepe, Julia M. Schnitzler, Gavin Schreiber, Jacob Sharma, Disha Sheth, Maya U. Shi, Huitong Singh, Vasundhara Sinha, Riya Steinmetz, Lars M. Tan, Jason Tan, Anthony Tycko, Josh Valbuena, Raeline C. Pour Amiri, Valeh Valiollah van Kooten, Mariëlle J.F.M. Vaughan-Jackson, Alun Venida, Anthony Weldy, Chad S. Yao, David Zeng, Tony Zhou, Ronghao Vidal, Marc Calderw Department of Genome Sciences University of Washington SeattleWA United States Department of Bioengineering and Therapeutic Sciences Institute for Human Genetics University of California San Francisco San FranciscoCA United States Institute of Human Genetics University Medical Center Schleswig-Holstein University of Lübeck Lübeck23562 Germany Exploratory Diagnostic Sciences Berlin Institute of Health Charité-Universitätsmedizin Berlin Berlin10117 Germany Brotman Baty Institute for Precision Medicine SeattleWA United States Center of immunotherapy and Immunity Seattle Children's Research Institute SeattleWA United States Bioinformatics Division WEHI ParkvilleVIC Australia Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Laboratory Medicine and Pathology University of Washington SeattleWA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Basic Science and Engineering Initiative Stanford Children’s Health Betty Irene Moore Children’s Heart Center StanfordCA United States The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard CambridgeMA United States Division of Cardiovascular Medicine School of Medicine Stanford University United States Department of Computer Science Stanford University School of Medicine StanfordCA United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Department of Pediatrics Stanford University School of Medicine StanfordCA United States Institute for Stem Cell Biology and Regenerative Medicine Stanford University School of Medicine StanfordCA United States Division of Pediatric Cardiology Cardiovascular Institute Stanford University School of Medicine Stanford University United States Genome Biology Unit Heidelberg Germany Department of Biology Stanford University StanfordCA United States Stanford Genome Techn

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations. © 2023, CC BY.

关键词： Genome

An Implicit Brain computer Interface Supported by Gaze Monitoring for Virtual Therapy

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An Implicit Brain Computer Interface Supported by Gaze Monit...

IEEE International Conference on Systems, Man and Cybernetics

作者： David Achanccaray George Mylonas Javier Andreu-Perez Neuro-Robotics Laboratory Tohoku University Sendai Japan Department of Surgery and Cancer Imperial College London Human-centred Automation Robotics and Monitoring for Surgery London UK School of Computer Science and Electronic Engineering University of Essex Colchester UK

Advanced Brain-computer Interface (BCI) paradigms aim to solve some problems as BCI illiteracy and unfamiliarity of the subjects to be able to control their elicited motor imagery (MI) successfully, hence improving training time and performance of BCI systems. This work evaluates the effect and performance of an Implicit BCI supported by the Gaze Monitoring (IBCI-GM) paradigm for virtual rehabilitation therapy of patients suffering from partial or total paralysis of their upper limbs; this paradigm also was compared with alternative forms of advanced BCI methods such as Virtual Reality-based BCI (VR-BCI) with a head-mounted display (HMD) and a computer screen (CS). Eight subjects participated in the experiments; four subjects tested the VR-BCI with a CS, and the rest of them tested both BCI advanced methods (IBCI-GM and VR-BCI with an HMD). The subjects were asked to control a virtual arm through MI of flexion and extension movements. The VR-BCI HMD was the approached best method; however, IBCI-GM had significant results and was more practical for users, but it depends on the ability to perform eye movements to be applied by patients. Therefore, these methods should be tested with more subjects to have definitive results.

关键词： Resists Task analysis Electroencephalography Support vector machines Training Monitoring Brain

Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution

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Nature metabolism 2025年第5期7卷 875-894页

作者： Loft, Anne Emont, Margo P. Weinstock, Ada Divoux, Adeline Ghosh, Adhideb Wagner, Allon Hertzel, Ann V. Maniyadath, Babukrishna Deplancke, Bart Liu, Boxiang Scheele, Camilla Lumeng, Carey Ding, Changhai Ma, Chenkai Wolfrum, Christian Strieder-Barboza, Clarissa Li, Congru Truong, Danh D. Bernlohr, David A. Stener-Victorin, Elisabet Kershaw, Erin E. Yeger-Lotem, Esti Shamsi, Farnaz Hui, Hannah X. Camara, Henrique Zhong, Jiawei Kalucka, Joanna Ludwig, Joseph A. Semon, Julie A. Jalkanen, Jutta Whytock, Katie L. Dumont, Kyle D. Sparks, Lauren M. Muir, Lindsey A. Fang, Lingzhao Massier, Lucas Saraiva, Luis R. Beyer, Marc D. Jeschke, Marc G. Mori, Marcelo A. Boroni, Mariana Walsh, Martin J. Patti, Mary-Elizabeth Lynes, Matthew D. Blüher, Matthias Rydén, Mikael Hamda, Natnael Solimini, Nicole L. Mejhert, Niklas Gao, Peng Gupta, Rana K. Murphy, Rinki Pirouzpanah, Saeed Corvera, Silvia Tang, Su'an Das, Swapan K. Schmidt, Søren F Zhang, Tao Nelson, Theodore M. Efthymiou, Vissarion Wang, Wenjing Tong, Yihan Tseng, Yu-Hua Mandrup, Susanne Rosen, Evan D. Center for Functional Genomics and Tissue Plasticity (ATLAS) Department of Biochemistry and Molecular Biology University of Southern Denmark (SDU) Odense Denmark Section of Endocrinology Diabetes and Metabolism University of Chicago Chicago IL United States Department of Medicine Section of Genetic Medicine University of Chicago Chicago IL United States Translational Research Institute Orlando FL United States Department of Health Sciences and Technology Institute of Food Nutrition and Health ETH Zurich Schwerzenbach Switzerland Department of Electrical Engineering and Computer Sciences University of California Berkeley CA United States Department of Molecular and Cell Biology University of California Berkeley CA United States Center for Computational Biology University of California Berkeley CA United States Department of Biochemistry Molecular Biology and Biophysics Institute on the Biology of Aging and Metabolism University of Minnesota-Twin Cities Minneapolis MN United States Center for Functional Genomics and Tissue Plasticity (ATLAS) Department of Biochemistry and Molecular Biology University of Southern Denmark (SDU) Odense Denmark Laboratory of Systems Biology and Genetics Institute of Bioengineering School of Life Sciences École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Department of Pharmacy and Pharmaceutical Sciences Faculty of Science National University of Singapore Singapore Department of Biomedical Informatics Yong Loo Lin School of Medicine National University of Singapore Singapore Genome Institute of Singapore (GIS) Agency for Science Technology and Research (A*STAR) Singapore Precision Medicine Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Cardiovascular-Metabolic Disease Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore NUS Centre for Cancer Research Yong Loo Lin School of Medicine Nationa

Adipose tissue (AT) is a complex connective tissue with a high relative proportion of adipocytes, which are specialized cells with the ability to store lipids in large droplets. AT is found in multiple discrete depots throughout the body, where it serves as the primary repository for excess calories. In addition, AT has an important role in functions as diverse as insulation, immunity and regulation of metabolic homeostasis. The human Cell Atlas Adipose Bionetwork was established to support the generation of single-cell atlases of human AT as well as the development of unified approaches and consensus for cell annotation. Here, we provide a first roadmap from this bionetwork, including our suggested cell annotations for humans and mice, with the aim of describing the state of the field and providing guidelines for the production, analysis, interpretation and presentation of AT single-cell data. © 2025. Springer Nature Limited.

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Modeling multi-turn conversation with deep utterance aggregation 27

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Modeling multi-turn conversation with deep utterance aggrega...

27th International Conference on Computational Linguistics, COLING 2018

作者： Zhang, Zhuosheng Li, Jiangtong Zhu, Pengfei Zhao, Hai Liu, Gongshen Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China College of Zhiyuan Shanghai Jiao Tong University China School of Cyber Security Shanghai Jiao Tong University China School of Computer Science and Software Engineering East China Normal University China

ISBN: (纸本)9781948087506

Multi-turn conversation understanding is a major challenge for building intelligent dialogue systems. This work focuses on retrieval-based response matching for multi-turn conversation whose related work simply concatenates the conversation utterances, ignoring the interactions among previous utterances for context modeling. In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation. In detail, a self-matching attention is first introduced to route the vital information in each utterance. Then the model matches a response with each refined utterance and the final matching score is obtained after attentive turns aggregation. Experimental results show our model outperforms the state-of-the-art methods on three multi-turn conversation benchmarks, including a newly introduced e-commerce dialogue corpus. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Speech processing

Open named entity modeling from embedding distribution

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arXiv 2019年

作者： Luo, Ying Zhao, Hai Zhang, Zhuosheng Tang, Bingjie The Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Minhang District Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University 800 Dongchuan Road Minhang District Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University 800 Dongchuan Road Minhang District Shanghai China The Department of Computer Science University of Southern California United States

In this paper, we report our discovery on named entity distribution in a general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from human labor and replies on schedule update. Our initial visualization of monolingual word embeddings indicates named entities tend to gather together despite of named entity types and language difference, which enable us to model all named entities using a specific geometric structure inside embedding space, namely, the named entity hypersphere. For monolingual cases, the proposed named entity model gives an open description of diverse named entity types and different languages. For cross-lingual cases, mapping the proposed named entity model provides a novel way to build a named entity dataset for resource-poor languages. At last, the proposed named entity model may be shown as a handy clue to enhance state-of-the-art named entity recognition systems generally. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings