检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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Technical note: A fast and monolithic prototype clinical proton radiography system optimized for pencil beam scanning

arXiv

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arXiv 2020年

作者： DeJongh, Ethan A. DeJongh, Don F. Polnyi, Igor Rykalin, Victor Sarosiek, Christina Coutrakon, George Duffin, Kirk L. Karonis, Nicholas T. Ordoñez, Caesar E. Pankuch, Mark Winans, John R. Welsh, James S. ProtonVDA LLC NapervilleIL60563 United States Department of Physics Northern Illinois University DeKalbIL60115 United States Department of Computer Science Northern Illinois University DeKalbIL60115 United States Argonne National Laboratory Data Science and Learning Division ArgonneIL60439 United States Northwestern Medicine Chicago Proton Center WarrenvilleIL60555 United States Edward Hines Jr VA Medical Center Radiation Oncology Service HinesIL60141 United States Department of Radiation Oncology Loyola University Stritch School of Medicine MaywoodIL60153 United States

Purpose: To demonstrate a proton imaging system based on well-established fast scintillator technology to achieve high performance with low cost and complexity, with the potential of a straightforward translation into clinical use. Methods: The system tracks individual protons through one (X, Y) scintillating fiber tracker plane upstream and downstream of the object and into a 13 cm-thick scintillating block residual energy detector. The fibers in the tracker planes are multiplexed into silicon photomultipliers (SiPMs) to reduce the number of electronics channels. The light signal from the residual energy detector is collected by 16 photomultiplier tubes (PMTs). Only four signals from the PMTs are output from each event, which allows for fast signal readout. A robust calibration method of the PMT signal to residual energy has been developed to obtain accurate proton images. The development of patient-specific scan patterns using multiple input energies allows for an image to be produced with minimal excess dose delivered to the patient. Results: The calibration of signals in the energy detector produces accurate residual range measurements limited by intrinsic range straggling. We measured the water-equivalent thickness (WET) of a block of solid water (physical thickness of 6.10 mm) with a proton radiograph. The mean WET from all pixels in the block was 6.13 cm (SD 0.02 cm). The use of patient-specific scan patterns using multiple input energies enables imaging with a compact range detector. Conclusions: We have developed a prototype clinical proton radiography system for pretreatment imaging in proton radiation therapy. We have optimized the system for use with pencil beam scanning systems and have achieved a reduction of size and complexity compared to previous designs. Copyright © 2020, The Authors. All rights reserved.

关键词： Calibration

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Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist

arXiv

引用

arXiv 2023年

作者： Ning, Yilin Teixayavong, Salinelat Shang, Yuqing Savulescu, Julian Nagaraj, Vaishaanth Miao, Di Mertens, Mayli Wei Ting, Daniel Shu Ling Ong, Jasmine Chiat Liu, Mingxuan Cao, Jiuwen Dunn, Michael Vaughan, Roger Hock Ong, Marcus Eng Sung, Joseph Jao-Yiu Topol, Eric J. Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Centre for Biomedical Ethics National University of Singapore Singapore Singapore Wellcome Centre for Ethics and Humanities University of Oxford Oxford United Kingdom School of Medicine Imperial College London London United Kingdom Centre for Ethics Department of Philosophy University of Antwerp Antwerp Belgium Antwerp Center on Responsible AI University of Antwerp Antwerp Belgium Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore SingHealth AI Office Singapore Health Services Singapore Singapore Division of Pharmacy Singapore General Hospital Singapore Singapore Machine Learning and I-Health International Cooperation Base of Zhejiang Province Hangzhou Dianzi University Zhejiang China Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang China Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore State Key Laboratory of Digestive Disease The Chinese University of Hong Kong Hong Kong Scripps Research Translational Institute Scripps Research La Jolla CA United States Institute of Data Science National University of Singapore Singapore Singapore Centre for Quantitative Medicine Duke-NUS Medical School 8 College Road Singapore169857 Singapore

The widespread use of ChatGPT and other emerging technology powered by generative artificial intelligence (GenAI) has drawn much attention to potential ethical issues, especially in high-stakes applications such as healthcare, but ethical discussions are yet to translate into operationalisable solutions. Furthermore, ongoing ethical discussions often neglect other types of GenAI that have been used to synthesise data (e.g., images) for research and practical purposes, which resolved some ethical issues and exposed others. We conduct a scoping review of ethical discussions on GenAI in healthcare to comprehensively analyse gaps in the current research, and further propose to reduce the gaps by developing a checklist for comprehensive assessment and transparent documentation of ethical discussions in GenAI research. The checklist can be readily integrated into the current peer review and publication system to enhance GenAI research, and may be used for ethics-related disclosures for GenAI-powered products, healthcare applications of such products and beyond. © 2023, CC BY.

关键词： Artificial intelligence

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Constructing Impactful Machine learning Research for Astronomy: Best Practices for Researchers and Reviewers

arXiv

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arXiv 2023年

作者： Huppenkothen, Daniela Ntampaka, Michelle Ho, Matthew Fouesneau, Morgan Nord, Brian Peek, J.E.G. Walmsley, Mike Wu, John F. Avestruz, C. Buck, Tobias Brescia, Massimo Finkbeiner, Douglas P. Goulding, Andy D. Kacprzak, T. Melchior, Peter Pasquato, Mario Ramachandra, Nesar Ting, Yuan-Sen van de Ven, Glenn Villar, Soledad Villar, V.A. Zinger, Elad SRON Netherlands Institute for Space Research Niels Bohrweg 4 Leiden2333CA Netherlands Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Space Telescope Science Institute BaltimoreMD21218 United States Department of Physics & Astronomy Johns Hopkins University BaltimoreMD21218 United States UMR 7095 98 bis bd Arago ParisF-75014 France Königstuhl 17 HeidelbergD-69117 Germany Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Jodrell Bank Centre for Astrophysics Department of Physics & Astronomy University of Manchester ManchesterM13 9PL United Kingdom Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Leinweber Center for Theoretical Physics University of Michigan Ann ArborMI48109 United States Department of Physics University of Michigan Ann ArborMI48109 United States Universität Heidelberg Interdisziplinäres Zentrum für Wissenschaftliches Rechnen Im Neuenheimer Feld 205 Heidelberg69120 Germany Universität Heidelberg Zentrum für Astronomie Institut für Theoretische Astrophysik Albert-Ueberle-Straße 2 Heidelberg69120 Germany Department of Physics "E. Pancini " University Federico II of Napoli Via Cinthia 21 NapoliI-80126 Italy INAF Astronomical Observatory of Capodimonte Salita Moiariello 16 NapoliI-80131 Italy Department of Physics Harvard University 17 Oxford St. CambridgeMA02138 United States Harvard-Smithsonian Center for Astrophysics 60 Garden St. CambridgeMA02138 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Swiss Data Science Center Paul Scherrer Institute Villigen5303 Switzerland Center for Statistics & Machine Learn

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Environment scan of generative AI infrastructure for clinical and translational science

Npj health systems

引用

Npj health systems 2025年第1期2卷 4页

作者： Betina Idnay Zihan Xu William G Adams Mohammad Adibuzzaman Nicholas R Anderson Neil Bahroos Douglas S Bell Cody Bumgardner Thomas Campion Mario Castro James J Cimino I Glenn Cohen David Dorr Peter L Elkin Jungwei W Fan Todd Ferris David J Foran David Hanauer Mike Hogarth Kun Huang Jayashree Kalpathy-Cramer Manoj Kandpal Niranjan S Karnik Avnish Katoch Albert M Lai Christophe G Lambert Lang Li Christopher Lindsell Jinze Liu Zhiyong Lu Yuan Luo Peter McGarvey Eneida A Mendonca Parsa Mirhaji Shawn Murphy John D Osborne Ioannis C Paschalidis Paul A Harris Fred Prior Nicholas J Shaheen Nawar Shara Ida Sim Umberto Tachinardi Lemuel R Waitman Rosalind J Wright Adrian H Zai Kai Zheng Sandra Soo-Jin Lee Bradley A Malin Karthik Natarajan W Nicholson Price Ii Rui Zhang Yiye Zhang Hua Xu Jiang Bian Chunhua Weng Yifan Peng Department of Biomedical Informatics Columbia University Irving Medical Center New York NY USA. Department of Population Health Sciences Weill Cornell Medicine New York NY USA. Department of Pediatrics Boston Medical Center Boston MA USA Chobanian & Avedisian School of Medicine Boston University Boston MA USA. Oregon Clinical and Translational Research Institute Oregon Health and Science University Portland OR USA. Department of Public Health Sciences University of California Davis Davis CA USA. Keck School of Medicine University of Southern California Los Angeles CA USA. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Department of Pathology and Laboratory Medicine University of Kentucky College of Medicine Lexington KY USA. Clinical and Translational Science Center Weill Cornell Medicine New York NY USA. Division of Pulmonary Critical Care and Sleep Medicine University of Kansas School of Medicine Kansas City KS USA. Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama Birmingham AL USA. Harvard Law School Petrie-Flom Center for Health Law Policy Biotechnology and Bioethics Harvard University Cambridge MA USA. Department of Biomedical Informatics University at Buffalo Buffalo NY USA. Center for Clinical and Translational Science Mayo Clinic Rochester MN USA. Technology and Digital Solutions Stanford Medicine Stanford University Stanford CA USA. Center for Biomedical Informatics Rutgers Cancer Institute New Brunswick NJ USA. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI USA. Altman Clinical and Translational Research Institute (ACTRI) University of California San Diego La Jolla CA USA. Department of Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN USA. Department of Ophthalmology CCTSI University of Colorado Aurora CO USA. Center for Clinic

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies.

关键词： Health services Public health

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Correction: Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder

引用

BMC medicine 2023年第1期21卷 113页

作者： Shile Qi Vince D Calhoun Daoqiang Zhang Jeremy Miller Zhi-De Deng Katherine L Narr Yvette Sheline Shawn M McClintock Rongtao Jiang Xiao Yang Joel Upston Tom Jones Jing Sui Christopher C Abbott College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China. shile.qi@***. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA. College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China. Department of Psychiatry University of New Mexico Albuquerque NM USA. Noninvasive Neuromodulation Unit Experimental Therapeutics & Pathophysiology Branch National Institute of Mental Health Bethesda MD USA. Departments of Neurology Psychiatry and Biobehavioral Sciences University of California Los Angeles CA USA. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. Division of Psychology Department of Psychiatry UT Southwestern Medical Center Dallas TX USA. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. Huaxi Brain Research Center West China Hospital of Sichuan University Chengdu China. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. jsui@***. Department of Psychiatry University of New Mexico Albuquerque NM USA. cabbott@salud.unm.edu.

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Pandemic drugs at pandemic speed: Infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- And physics-based simulations on high performance computers

arXiv

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arXiv 2021年

作者： Bhati, Agastya P. Wan, Shunzhou Alfè, Dario Clyde, Austin R. Bode, Mathis Tan, Li Titov, Mikhail Merzky, Andre Turilli, Matteo Jha, Shantenu Highfield, Roger R. Rocchia, Walter Scafuri, Nicola Succi, Sauro Kranzlmüller, Dieter Mathias, Gerald Wifling, David Donon, Yann Meglio, Alberto Di Vallecorsa, Sofia Ma, Heng Trifan, Anda Ramanathan, Arvind Brettin, Tom Partin, Alexander Xia, Fangfang Duan, Xiaotan Stevens, Rick Coveney, Peter V. Centre for Computational Science University College London Gordon Street LondonWC1H 0AJ United Kingdom Department of Earth Sciences London Centre for Nanotechnology and Thomas Young Centre University College London University College London Gower Street LondonWC1E 6BT United Kingdom Dipartimento di Fisica Ettore Pancini Università di Napoli Federico II Monte Sant’Angelo NapoliI-80126 Italy Department of Computer Science University of Chicago Institute for Combustion Technology RWTH Aachen University Aachen52056 Germany Brookhaven National Laboratory UptonNY11973 United States Rutgers State University of New Jersey PiscatawayNJ08854 United States Science Museum Exhibition Road LondonSW7 2DD United Kingdom Concept Lab Italian Institute of Technology Via Melen Genova Italy Center for Life Nanosciences at La Sapienza Italian Institute of Technology viale Regina Elena Roma Italy of the Bavarian Academy of Sciences and Humanities Boltzmannstraße 1 Garching bei München85748 Germany OpenLab CERN Geneva Switzerland Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Computing Environment and Life Sciences Directorate Argonne National Laboratory LemontIL60439 United States Institute for Informatics Science Park 904 University of Amsterdam Amsterdam1098 XH Netherlands

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers. © 2021, CC BY.

关键词： Molecular dynamics

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Feature extraction for hyperspectral imagery: The evolution from shallow to deep

arXiv

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arXiv 2020年

作者： Rasti, Behnood Hong, Danfeng Hang, Renlong Ghamisi, Pedram Kang, Xudong Chanussot, Jocelyn Benediktsson, Jon Atli Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany Univ. Grenoble Alpes CNRS Grenoble INP GIPSAlab Grenoble38000 France Jiangsu Key Laboratory of Big Data Analysis Technology School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany College of Electrical and Information Engineering Hunan University Changsha410082 China Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Changsha410082 China Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK GrenobleF-38000 France Faculty of Electrical and Computer Engineering University of Iceland Reykjavik101 Iceland Faculty of Electrical and Computer Engineering University of Iceland Reykjavik107 Iceland

The final version of the paper can be found in IEEE Geoscience and Remote Sensing Magazine. Hyperspectral images provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands) with continuous spectral information that can accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to the conventional techniques (the so-called curse of dimensionality) for accurate analysis of hyperspectral images. Feature extraction, as a vibrant field of research in the hyperspectral community, evolved through decades of research to address this issue and extract informative features suitable for data representation and classification. The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques. This article outlines the advances in feature extraction approaches for hyperspectral imagery by providing a technical overview of the state-of-the-art techniques, providing useful entry points for researchers at different levels, including students, researchers, and senior researchers, willing to explore novel investigations on this challenging topic. In more detail, this paper provides a bird's eye view over shallow (both supervised and unsupervised) and deep feature extraction approaches specifically dedicated to the topic of hyperspectral feature extraction and its application on hyperspectral image classification. Additionally, this paper compares 15 advanced techniques with an emphasis on their methodological foundations in terms of classification accuracies. Furthermore, to push this vibrant field of research forward an impressive amount of codes and libraries is shared at: https://***/Behnoo

关键词： Feature extraction

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Deep generative model driven protein folding simulations

arXiv

引用

arXiv 2019年

作者： Ma, Heng Bhowmik, Debsindhu Lee, Hyungro Turilli, Matteo Young, Michael T. Jha, Shantenu Ramanathan, Arvind Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37830 United States RADICAL ECE Rutgers University PiscatawayNJ08854 United States Brookhaven National Laboratory UptonNY11973 United States Data Science and Learning Argonne National Laboratory LemontIL60439 United States

Significant progress in computer hardware and software have enabled molecular dynamics (MD) simulations to model complex biological phenomena such as protein folding. However, enabling MD simulations to access biologically relevant timescales (e.g., beyond milliseconds) still remains challenging. These limitations include (1) quantifying which set of states have already been (sufficiently) sampled in an ensemble of MD runs, and (2) identifying novel states from which simulations can be initiated to sample rare events (e.g., sampling folding events). With the recent success of deep learning and artificial intelligence techniques in analyzing large datasets, we posit that these techniques can also be used to adaptively guide MD simulations to model such complex biological phenomena. Leveraging our recently developed unsupervised deep learning technique to cluster protein folding trajectories into partially folded intermediates, we build an iterative workflow that enables our generative model to be coupled with all-atom MD simulations to fold small protein systems on emerging high performance computing platforms. We demonstrate our approach in folding Fs-peptide and the ββα (BBA) fold, FSD-EY. Our adaptive workflow enables us to achieve an overall root-mean squared deviation (RMSD) to the native state of 1.6 Å and 4.4 Å respectively for Fs-peptide and FSD-EY. We also highlight some emerging challenges in the context of designing scalable workflows when data intensive deep learning techniques are coupled to compute intensive MD simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Molecular dynamics

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A comparison of proton stopping power measured with proton CT and x-ray CT in fresh post-mortem porcine structures

arXiv

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arXiv 2020年

作者： DeJongh, Don F. DeJongh, Ethan A. Rykalin, Victor DeFillippo, Greg Pankuch, Mark Best, Andrew W. Coutrakon, George Duffin, Kirk L. Karonis, Nicholas T. Ordoñez, Caesar E. Sarosiek, Christina Schulte, Reinhard W. Winans, John R. Block, Alec M. Hentz, Courtney L. Welsh, James S. ProtonVDA LLC NapervilleIL60563 United States Northwestern Medicine Chicago Proton Center WarrenvilleIL60555 United States Department of Physics Northern Illinois University DeKalbIL60115 United States Department of Computer Science Northern Illinois University DeKalbIL60115 United States Argonne National Laboratory Data Science and Learning Division ArgonneIL60439 United States Loma Linda University Loma LindaCA92350 United States Edward Hines Jr VA Medical Center Radiation Oncology Service HinesIL60141 United States Department of Radiation Oncology Loyola University Stritch School of Medicine MaywoodIL60153 United States

Purpose: Currently, calculations of proton range in proton therapy patients are based on a conversion of CT Hounsfield Units of patient tissues into proton relative stopping power. Uncertainties in this conversion necessitate larger proximal and distal planned target volume margins. Proton CT can potentially reduce these uncertainties by directly measuring proton stopping power. We aim to demonstrate proton CT imaging with complex porcine samples, to analyze in detail three-dimensional regions of interest, and to compare proton stopping powers directly measured by proton CT to those determined from x-ray CT scans. Methods: We have used a prototype proton imaging system with single proton tracking to acquire proton radiography and proton CT images of a sample of porcine pectoral girdle and ribs, and a pig's head. We also acquired close in time x-ray CT scans of the same samples, and compared proton stopping power measurements from the two modalities. In the case of the pig's head, we obtained x-ray CT scans from two different scanners, and compared results from high-dose and low-dose settings. Results: Comparing our reconstructed proton CT images with images derived from x-ray CT scans, we find agreement within 1% to 2% for soft tissues, and discrepancies of up to 6% for compact bone. We also observed large discrepancies, up to 40%, for cavitated regions with mixed content of air, soft tissue, and bone, such as sinus cavities or tympanic bullae. Conclusions: Our images and findings from a clinically realistic proton CT scanner demonstrate the potential for proton CT to be used for low-dose treatment planning with reduced margins. Copyright © 2020, The Authors. All rights reserved.

关键词： Computerized tomography

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