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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center 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 New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Combining noise-to-image and image-to-image GANs: Brain MR image augmentation for tumor detection

arXiv

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arXiv 2019年

作者： Han, Changhee Rundo, Leonardo Araki, Ryosuke Nagano, Yudai Furukawa, Yujiro Mauri, Giancarlo Nakayama, Hideki Hayashi, Hideaki Machine Perception Group Graduate School of Information Science and Technology University of Tokyo Tokyo113-8657 Japan Research Center for Medical Big Data National Institute of Informatics Tokyo100-0003 Japan Department of Radiology University of Cambridge CambridgeCB2 0QQ United Kingdom Cancer Research UK Cambridge Centre CambridgeCB2 0RE United Kingdom Department of Informatics Systems and Communication University of Milano-Bicocca Milan20126 Italy Machine Perception and Robotics Group Graduate School of Engineering Chubu University Aichi487-8501 Japan Department of Psychiatry Jikei University School of Medicine Tokyo105-8461 Japan Human Interface Laboratory Department of Advanced Information Technology Kyushu University Fukuoka819-0395 Japan

Convolutional Neural Networks (CNNs) can achieve excellent computer-assisted diagnosis performance, relying on sufficient annotated training data. Unfortunately, most medical imaging datasets, often collected from various scanners, are small and fragmented. In this context, as a Data Augmentation (DA) technique, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution;researchers have improved classification by augmenting images with noise-to-image (e.g., random noise samples to diverse pathological images) or image-to-image GANs (e.g., a benign image to a malignant one). Yet, no research has reported results combining (i) noise-to-image GANs and image-to-image GANs or (ii) GANs and other deep generative models, for further performance boost. Therefore, to maximize the DA effect with the GAN combinations, we propose a two-step GAN-based DA that generates and refines brain MR images with/without tumors separately: (i) Progressive Growing of GANs (PGGANs), multi-stage noise-to-image GAN for highresolution image generation, first generates realistic/diverse 256 256 images-even a physician cannot accurately distinguish them from real ones via Visual Turing Test;(ii) UNsupervised Image-to-image Translation or SimGAN, image-to-image GAN combining GANs/Variational AutoEncoders or using a GAN loss for DA, further refines the texture/shape of the PGGAN-generated images similarly to the real *** thoroughly investigate CNN-based tumor classification results, also considering the influence of pre-training on ImageNet and discarding weird-looking GAN-generated images. The results show that, when combined with classic DA, our two-step GAN-based DA can significantly outperform the classic DA alone, in tumor detection (i.e., boosting sensitivity from 93:63% to 97:53%) and also in other medical imaging tasks. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Special Issue on: A sense of interaction in humans and robots: From visual perception to social cognition

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IEEE Transactions on Cognitive and Developmental systems 2017年第1期9卷 93-93页

作者： Sciutti, Alessandra Noceti, Nicoletta Robotics Brain and Cognitive Science Unit Istituto Italiano di Tecnologia Genova Italy Department of Informatics Bioengineering Robotics and Systems Engineering Universita degli Studi di Genova Genova Italy

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Synthesis of fuzzy, artificial intelligence and neural networks for hierarchical intelligent control

Synthesis of fuzzy, artificial intelligence and neural netwo...

引用

Neural and Fuzzy systems: The Emerging Science of Intelligent Computing 1994

作者： Fukuda, Toshio Shibata, Takanori Dept. of Mechano-Informatics and Systems Nagoya University 1 Furo-cho Chikusa-ku Nagoya464-01 Japan Bio-Robotics Robotics Department Mechanical Engineering Laboratory Ministry of International Trade and Industry 1-2 Namiki Tsukuba305 Japan

This chapter presents a new structure of intelligent control for robotic motion. This structure is analogous to the human cerebral control structure for intelligent control. Therefore, the system has a hierarchical structure as an integrated approach of Neuromorphic and Symbolic control, including an applied neural network for servo control, a knowledge based approximation, and a fuzzy set theory for a human interface. The neural network in the servo control level is numerical manipulation, while the knowledge based part is symbolic manipulation. In the neuromorphic control, the neural network compensates for the nonlinearities of the system and uncertainty in its environment. The knowledge base part develops control strategies symbolically for the servo level with a-priori knowledge. The fuzzy logic combined with the neural network is used between the servo control level and the knowledge based part to link numerals to symbols and express human skills through learning. © 2017 SPIE.

关键词： Fuzzy neural networks

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Context-dependent architecture of brain state dynamics is explained by white matter connectivity and theories of network control

arXiv

引用

arXiv 2018年

作者： Cornblath, Eli J. Ashourvan, Arian Kim, Jason Z. Betzel, Richard F. Ciric, Rastko Baum, Graham L. He, Xiaosong Ruparel, Kosha Moore, Tyler M. Gur, Ruben C. Gur, Raquel E. Shinohara, Russell T. Roalf, David R. Satterthwaite, Theodore D. Bassett, Danielle S. Department of Neuroscience Perelman School of Medicine Department of Bioengineering School of Engineering & Applied Science Department of Psychiatry Perelman School of Medicine Department of Biostatistics Epidemiology & Informatics Perelman School of Medicine Department of Physics & Astronomy College of Arts & Sciences Department of Neurology Perelman School of Medicine Department of Electrical & Systems Engineering School of Engineering & Applied Science Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

A diverse white matter network and finely tuned neuronal membrane properties allow the brain to transition seamlessly between cognitive states. However, it remains unclear how static structural connections guide the temporal progression of large-scale brain activity patterns in different cognitive states. Here, we deploy an unsupervised machine learning algorithm to define brain states as time point level activity patterns from functional magnetic resonance imaging data acquired during passive visual fixation (rest) and an n-back working memory task. We find that brain states are composed of interdigitated functional networks and exhibit context-dependent dynamics. Using diffusion-weighted imaging acquired from the same subjects, we show that structural connectivity constrains the temporal progression of brain states. We also combine tools from network control theory with geometrically conservative null models to demonstrate that brains are wired to support states of high activity in default mode areas, while requiring relatively low energy. Finally, we show that brain state dynamics change throughout development and explain working memory performance. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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Analysis by finite element method of dynamic characteristics of the external ear canal in neonates

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Journal of Biomechanical Science and engineering 2017年第2期12卷 16-00596-16-00596页

作者： Murakoshi, Michio Takeda, Shohei Wada, Hiroshi Department of Mechanical Engineering Kagoshima University 1-21-40 Korimoto Kagoshima890-0065 Japan Department of Bioengineering and Robotics Tohoku University 6-6-01 Aoba-yama Sendai980-8579 Japan Department of Intelligent Information Systems Faculty of Science and Technology Tohoku Bunka Gakuen University 6-45-1 Kunimi Sendai981-8551 Japan

Although early diagnosis and treatment of hearing disorders in neonates is highly effective for realization of lin- guistic competence and intellectual development, the diagnostic accuracy in the initial period of life is low. To overcome this problem, extensive research studies to diagnose the neonatal hearing with tympanometry, multi- frequency tympanometry, the wideband acoustic transfer function technique, the sweep frequency impedance (SFI) technique and others have been conducted. However, since the results obtained are not consistent with those ob- tained in adults/children, diagnosis of hearing in neonates has been problematic. The difference in the dynamic characteristics of the hearing apparatus between adults/children and neonates, especially due to the morphologi- cal difference in the external ear canal, has been suggested as a possible reason for such problem. However, the dynamics of the neonatal hearing system has not yet been clarified. In the present study, therefore, three types of finite-element models of the neonatal external ear were developed and their dynamic characteristics (i.e., natural frequencies and mode shapes) were investigated. Frequency analyses of the constructed models revealed that the neonatal external ear canal wall has a resonance of around 0.3 kHz and that this resonance leads to the volume change of such canal. This result suggests that the neonatal external ear canal wall may exhibit intrinsic oscillatory behavior if conventional tympanometry with a probe tone of 226 Hz is performed, affecting the diagnostic data in neonates. © 2017 The Japan Society of Mechanical Engineers.

关键词： Audition

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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Tissue disorder for label-free diagnosis of biopsies using quantitative phase imaging

Tissue disorder for label-free diagnosis of biopsies using q...

引用

Optics and Photonics Japan, OFJ 2017

作者： Takabayashi, Masanori Majeed, Hassaan Kajdacsy-Balla, Andre Popescu, Gabriel Department of Systems Design and Informatics Kyushu Institute of Technology Japan Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Japan Department of Bioengineering University of Illinois at Urbana-Champaign United States Department of Pathology University of Illinois at Chicago United States

ISBN: (纸本)9781557528209

Using quantitative phase imaging, we measured the tissue disorder strength, which is known as one of the effective markers of early carcinogenesis. We retrieved the disorder parameter from local refractive index fluctuation map obtained by spatial light interference microscopy (SLIM). We show that SLIM imaging combined with the disorder analysis is a valuable approach for screening of benign and malignant breast tissue biopsies. © 2017 OSA - The Optical Society. All rights reserved.

关键词： Tissue

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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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