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Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles

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CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY 2019年 18卷 16-22页

作者： Christopherson, Kaitlin M. Ghosh, Alokananda Sherif, Abdallah Mohamed, Radwan Kamal, Mona Fuller, David Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston TX United States Department of Biostatistics and Data Science University of Texas School of Public Health Houston TX United States Department of Clinical Oncology University of Alexandria Alexandria Egypt Department of Emergency Medicine The University of Texas MD Anderson Cancer Center Houston TX United States Baylor College of Medicine Houston TX United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA United States Department of Radiation Oncology The University of Texas Medical Branch Galveston TX United States Department of Head and Neck Surgery The University of Texas MD Anderson Cancer Center Houston TX United States Department of Thoracic & Head and Neck Oncology The University of Texas MD Anderson Cancer Center Houston TX United States Medical Physics Program The University of Texas Graduate School of Biomedical Sciences Houston TX United States Department of Computer Science University of Illinois at Chicago Chicago IL United States Department of Electrical and Computer Engineering The University of Iowa Iowa City IA United States

Objectives: We sought to model chronic radiation-associated dysphagia (RAD) in patients given intensity-modulated radiation therapy (IMRT) for oropharyngeal squamous cell cancer (OPSCC) as a function of age and dose to non-target swallowing muscles. Methods: We reviewed 300 patients with T1-T4 N0-3 M0 OPSCC given definitive IMRT with concurrent chemotherapy. Chronic RAD was defined as aspiration or stricture on videoflouroscopy/endoscopy, gastrostomy tube, or aspiration pneumonia at >= 12 months after IMRT. Doses to autosegmented regions of interest (ROIs;inferior, middle and superior constrictors, anterior and posterior digastrics, mylo/geniohyoid complex, intrinsic tongue, and gengioglossus) were obtained from DICOM-RT plans and dose-volume histograms. The probability of chronic RAD as a function of mean ROI dose, stratified by age (<50, 50-59, 60-69, or >70 years), was estimated with logistic probability models and subsequent unsupervised nonlinear curves. Results: Chronic RAD was observed in 34 patients (11%). Age was a significant correlate of chronic RAD, both independently and with dose for all muscle groups examined. Distinct muscle-specific dose-response profiles were observed as a function of age (e.g., 5% of patients in their 50 s [but 20% of those 70 +] who received 60 Gy to the superior constrictor had chronic RAD). This effect was stable across all observed muscle ROIs, with a false discovery rate-corrected p < 0.05, for all dose/muscle/age models, suggesting that including age as a covariate improves modeling of chronic RAD. Conclusions: Age at treatment moderates the probability of chronic RAD after chemo-IMRT for OPSCC, with aging muscles showing lower dose thresholds. Uniform dose constraints may not predict toxicity in older patients. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of European Society for Radiotherapy and Oncology.

关键词： Oropharynx IMRT Radiation Toxicity Presbyphagia

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Reproducible brain-wide association studies require thousands of individuals (Mar, 10.1038/s41586-022-04492-9, 2022)

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NATURE 2022年第7911期605卷 E11-E11页

作者： Marek, Scott Tervo-Clemmens, Brenden Calabro, Finnegan J. Montez, David F. Kay, Benjamin P. Hatoum, Alexander S. Donohue, Meghan Rose Foran, William Miller, Ryland L. Hendrickson, Timothy J. Malone, Stephen M. Kandala, Sridhar Feczko, Eric Miranda-Dominguez, Oscar Graham, Alice M. Earl, Eric A. Perrone, Anders J. Cordova, Michaela Doyle, Olivia Moore, Lucille A. Conan, Gregory M. Uriarte, Johnny Snider, Kathy Lynch, Benjamin J. Wilgenbusch, James C. Pengo, Thomas Tam, Angela Chen, Jianzhong Newbold, Dillan J. Zheng, Annie Seider, Nicole A. Van, Andrew N. Metoki, Athanasia Chauvin, Roselyne J. Laumann, Timothy O. Greene, Deanna J. Petersen, Steven E. Garavan, Hugh Thompson, Wesley K. Nichols, Thomas E. Yeo, B. T. Thomas Barch, Deanna M. Luna, Beatriz Fair, Damien A. Dosenbach, Nico U. F. Department of Psychiatry Washington University School of Medicine St Louis MO USA Department of Neurology Washington University School of Medicine St Louis MO USA Department of Psychological and Brain Sciences Washington University in St Louis St Louis MO USA Department of Psychiatry Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Psychology University of Pittsburgh Pittsburgh PA USA Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Department of Bioengineering University of Pittsburgh Pittsburgh PA USA Department of Biomedical Engineering Washington University in St Louis St Louis MO USA Department of Radiology Washington University School of Medicine St Louis MO USA Department of Neurological Surgery Washington University School of Medicine St Louis MO USA Program in Occupational Therapy Washington University School of Medicine St Louis MO USA Department of Pediatrics Washington University School of Medicine St Louis MO USA University of Minnesota Informatics Institute University of Minnesota Minneapolis MN USA Department of Psychology University of Minnesota Minneapolis MN USA Masonic Institute for the Developing Brain University of Minnesota Medical School Minneapolis MN USA Department of Pediatrics University of Minnesota Medical School Minneapolis MN USA Department of Psychiatry Oregon Health and Science University Portland OR USA Minnesota Supercomputing Institute University of Minnesota Minneapolis MN USA Institute of Child Development University of Minnesota Medical School Minneapolis MN USA Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Centre for Sleep and Cognition National University of Singapore Singapore Singapore Centre for Translational MR Research National University of Singapore Singapore Singapore N.1 Institute for Health Institute for Digital Medicine National University of Singapore Singapore Sing

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Heterochromatin drives compartmentalization of inverted and conventional nuclei (vol 570, pg 395, 2019)

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NATURE 2019年第7771期572卷 E22-E22页

作者： Falk, Martin Feodorova, Yana Naumova, Natalia Imakaev, Maxim Lajoie, Bryan R. Leonhardt, Heinrich Joffe, Boris Dekker, Job Fudenberg, Geoffrey Solovei, Irina Mirny, Leonid A. Institute for Medical Engineering and Science and Department of Physics Massachusetts Institute of Technology Cambridge USA Department of Medical Biology Medical University-Plovdiv Plovdiv Bulgaria Biozentrum Ludwig Maximilians University Munich Planegg-Martinsried Germany Howard Hughes Medical Institute and Program in Systems Biology Department of Biochemistry and Molecular Pharmacology University of Massachusetts Medical School Worcester USA Epinomics Inc Menlo Park USA Illumina Inc San Diego USA Gladstone Institutes of Data Science and Biotechnology University of California San Francisco San Francisco USA

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

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DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

arXiv

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arXiv 2019年

作者： Wang, Haidi Chen, Weijie Zeng, Jinzhe Zhang, Linfeng Wang, Han Zhang, Yuzhi Weinan, E. Yuanpei College of Peking University Beijing100871 China School of Electronic Science and Applied Physics Hefei University of Technology Hefei230601 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Beijing Institute of Big Data Research Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Beijing Institute of Big Data Research Beijing100871 China

In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed"on-the-fly" learning procedure [Phys. Rev. Materials 3, 023804] and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN. Copyright © 2019, The Authors. All rights reserved.

关键词： Potential energy

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Efficient construction method for phase diagrams using uncertainty sampling

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Physical Review Materials 2019年第3期3卷 033802-033802页

作者： Kei Terayama Ryo Tamura Yoshitaro Nose Hidenori Hiramatsu Hideo Hosono Yasushi Okuno Koji Tsuda RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan Medical Sciences Innovation Hub Program RIKEN Cluster for Science Technology and Innovation Hub Kanagawa 230-0045 Japan Graduate School of Medicine Kyoto University Kyoto 606-8507 Japan International Center for Materials Nanoarchitectonics (WPI-MANA) National Institute for Materials Science Ibaraki 305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki 305-0047 Japan Graduate School of Frontier Sciences University of Tokyo Chiba 277-8568 Japan Department of Materials Science and Engineering Kyoto University Kyoto 606-8501 Japan Laboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology Yokohama 226-8503 Japan Materials Research Center for Element Strategy Tokyo Institute of Technology Yokohama 226-8503 Japan

We develop a method to efficiently construct phase diagrams using machine learning. Uncertainty sampling (US) in active learning is utilized to intensively sample around phase boundaries. Here, we demonstrate constructions of three known experimental phase diagrams by the US approach. Compared with random sampling, the US approach decreases the number of sampling points to about 20%. In particular, the reduction rate is pronounced in more complicated phase diagrams. Furthermore, we show that using the US approach, undetected new phases can be rapidly found, and smaller numbers of initial sampling points are sufficient. Thus, we conclude that the US approach is useful to construct complicated phase diagrams from scratch and will be an essential tool in materials science.

关键词： Phase diagrams

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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High temperature structure detection in ferromagnets

arXiv

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arXiv 2018年

作者： Cao, Yuan Neykov, Matey Liu, Han Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Department of Statistics & Data Science Carnegie Mellon University PittsburghPA United States Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL United States

This paper studies structure detection problems in high temperature ferromagnetic (positive interaction only) Ising models. The goal is to distinguish whether the underlying graph is empty, i.e., the model consists of independent Rademacher variables, versus the alternative that the underlying graph contains a subgraph of a certain structure. We give matching upper and lower minimax bounds under which testing this problem is possible/impossible respectively. Our results reveal that a key quantity called graph arboricity drives the testability of the problem. On the computational front, under a conjecture of the computational hardness of sparse principal component analysis, we prove that, unless the signal is strong enough, there are no polynomial time linear tests on the sample covariance matrix which are capable of testing this problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Ferromagnetism

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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems 32

End-to-end symmetry preserving inter-atomic potential energy...

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32nd Conference on Neural Information Processing Systems, NeurIPS 2018

作者： Zhang, Linfeng Han, Jiequn Wang, Han Saidi, Wissam A. Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Institute of Applied Physics and Computational Mathematics China CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh United States Department of Chemistry Department of Physics Princeton University United States Princeton Institute for the Science and Technology of Materials Princeton University United States Department of Mathematics Princeton University United States Beijing Institute of Big Data Research China

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity. © 2018 Curran Associates Inc..All rights reserved.

关键词： Potential energy

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Deep neural network for Wannier function centers

arXiv

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arXiv 2019年

作者： Zhang, Linfeng Chen, Mohan Wu, Xifan Wang, Han Weinan, E. Car, Roberto Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States CAPT HEDPS College of Engineering Peking University Beijing100871 China Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States

We introduce a deep neural network (DNN) model that assigns the position of the centers of the electronic charge in the snapshots of a molecular dynamics trajectory. The electronic centers are uniquely specified by the unitary transformation that maps the occupied eigenstates onto maximally localized Wannier functions. In combination with deep potential molecular dynamics, a DNN approach to model the atomic potential energy surface at the ab initio density functional level of theory, the scheme makes possible to predict the dielectric properties of insulators for samples and trajectories inaccessible to direct ab initio simulation, without loss of accuracy. As an example, we report calculations of the infrared absorption spectra of light and heavy water at a dispersion inclusive hybrid functional level of theory, finding good agreement with experiment. Extensions to other spectroscopies, like Raman and sum frequency generation, are discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum chemistry

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Correction: Identifcation and functional analyses of drought stress resistance genes by transcriptomics of the Mongolian grassland plant Chloris virgata

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BMC plant biology 2025年第1期25卷 257页

作者： Ganbayar Namuunaa Baldorj Bujin Ayumi Yamagami Byambajav Bolortuya Shintaro Kawabata Hirotaka Ogawa Asaka Kanatani Minami Shimizu Anzu Minami Keiichi Mochida Takuya Miyakawa Bekh-Ochir Davaapurev Tadao Asami Javzan Batkhuu Takeshi Nakano Laboratory of Plant Chemical Biology Graduate School of Biostudies Kyoto University Sakyo-ku Kyoto 606-8502 Japan. Laboratory of Plant Chemical Biology Graduate School of Biostudies Kyoto University Sakyo-ku Kyoto 606-8502 Japan. yamagami.ayumi.6s@kyoto-u.ac.jp. RIKEN Center for Sustainable Resource Science Tsurumi-ku Yokohama 230 - 0045 Kanagawa Japan. Kihara Institute for Biological Research Yokohama City University Totsuka-ku Yokohama Kanagawa 244-0813 Japan. Baton Zone Program RIKEN Tsurumi-ku Yokohama 230 - 0045 Kanagawa Japan. School of Information and Data Sciences Nagasaki University Bunkyo-machi Nagasaki 852-8521 Japan. School of Engineering and Technology National University of Mongolia Ulaanbaatar 14201 Mongolia. Graduate School of Agricultural and Life Sciences The University of Tokyo Bunkyo-ku Tokyo 113-8657 Japan. Laboratory of Plant Chemical Biology Graduate School of Biostudies Kyoto University Sakyo-ku Kyoto 606-8502 Japan. nakano.takeshi.6x@kyoto-u.ac.jp.

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