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SSRN 2024年

作者： Parmar, Nishaal Sirpal, Parikshat Sikora, William A. Dewald, Julius P.A. Refai, Hazem H. Yang, Yuan University of Oklahoma School of Electrical and Computer Engineering Gallogly College of Engineering NormanOK United States University of Oklahoma Stephenson School of Biomedical Engineering NormanOK United States Northwestern University Departments of Physical Therapy and Human Movement Sciences ChicagoIL United States Northwestern University Departments of Biomedical Engineering EvanstonIL United States University of Illinois Urbana-Champaign Department of Bioengineering Grainger College of Engineering UrbanaIL United States Carle Foundation Hospital Stephenson Family Clinical Research Institute Clinical Imaging Research Center UrbanaIL United States University of Illinois Urbana-Champaign Beckman Institute for Advanced Science and Technology UrbanaIL United States

During post-stroke recovery, the brain undergoes reorganization, compensating for loss of ipsilesional corticospinal and corticobulbar pathways, resulting in increased contralesional cortical activity during paretic arm movement tasks. Better understanding underlying mechanisms may improve post-stroke motor rehabilitation, with the study objective being to provide insight into poststroke contralesional cortical adaptation. This proof-of-concept study involves eight healthy controls and ten post-stroke participants. Electroencephalographic (EEG) and deltoid electromyographic (EMG) data were collected during an upper-limb exertion task. Phase coupling between beta-band motor cortex EEG and deltoid EMG was assessed using the Multi-Phase Locking Value (M-PLV) method. The nerve conduction delay from the contralateral motor cortex to the deltoid muscle of the paretic arm was estimated from EEG/EMG phase coherence. Our results show the ipsilateral (contralesional) motor cortex beta-band phase coherence behavior is altered in stroke participants. These show a significant difference in ipsilateral EEG-EMG coherence values, ipsilateral time course percentage above the significance threshold, and ipsilateral time course area above the significance threshold, in comparison to controls. M-PLV phase coherence analysis provides evidence for poststroke contralesional motor adaptation, highlighting its increased role in the paretic shoulder abduction task. Nerve conduction delay between the motor cortexes and deltoid muscle is significantly higher in stroke participants. Beta-band M-PLV phase coherence analysis shows greater phase-coherence distribution convergence between the ipsilateral and contralateral motor cortices in stroke participants, which we conclude may be interpreted as evidence of maladaptive neural adaptation resulting from a greater reliance on the contralesional motor cortex. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

arXiv

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arXiv 2023年

作者： Fleming, Scott L. Lozano, Alejandro Haberkorn, William J. Jindal, Jenelle A. Reis, Eduardo Thapa, Rahul Blankemeier, Louis Genkins, Julian Z. Steinberg, Ethan Nayak, Ashwin Patel, Birju Chiang, Chia-Chun Callahan, Alison Huo, Zepeng Gatidis, Sergios Adams, Scott Fayanju, Oluseyi Shah, Shreya J. Savage, Thomas Goh, Ethan Chaudhari, Akshay S. Aghaeepour, Nima Sharp, Christopher Pfeffer, Michael A. Liang, Percy Chen, Jonathan H. Morse, Keith E. Brunskill, Emma P. Fries, Jason A. Shah, Nigam H. Department of Biomedical Data Science Stanford School of Medicine StanfordCA United States Department of Computer Science Stanford School of Engineering StanfordCA United States Department of Anesthesiology Peri-operative and Pain Medicine Stanford School of Medicine StanfordCA United States Department of Pediatrics Stanford School of Medicine StanfordCA United States Stanford Center for Biomedical Informatics Research Stanford University StanfordCA United States Department of Radiology Stanford School of Medicine StanfordCA United States Stanford University StanfordCA United States Hospital Israelita Albert Einstein SP Sao Paulo Brazil Technology and Digital Solutions Stanford Health Care Palo AltoCA United States Department of Electrical Enginering Stanford School of Engineering StanfordCA United States Department of Medicine Vanderbilt University School of Medicine NashvilleTN United States Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Medicine Stanford School of Medicine StanfordCA United States Department of Neurology Mayo Clinic RochesterMN United States Human-Centered Artificial Intelligence Institute Stanford University StanfordCA United States Division of Hospital Medicine Stanford University StanfordCA United States Clinical Excellence Research Center Stanford School of Medicine StanfordCA United States

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic text generation tasks for healthcare remains challenging. Existing question answering datasets for electronic health record (EHR) data fail to capture the complexity of information needs and documentation burdens experienced by clinicians. To address these challenges, we introduce MedAlign, a benchmark dataset of 983 natural language instructions for EHR data. MedAlign is curated by 15 clinicians (7 specialities), includes clinician-written reference responses for 303 instructions, and provides 276 longitudinal EHRs for grounding instruction-response pairs. We used MedAlign to evaluate 6 general domain LLMs, having clinicians rank the accuracy and quality of each LLM response. We found high error rates, ranging from 35% (GPT-4) to 68% (MPT-7B-Instruct), and 8.3% drop in accuracy moving from 32k to 2k context lengths for GPT-4. Finally, we report correlations between clinician rankings and automated natural language generation metrics as a way to rank LLMs without human review. MedAlign is provided under a research data use agreement1 to enable LLM evaluations on tasks aligned with clinician needs and preferences. © 2023, CC BY.

关键词： Natural language processing systems

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Functional characterization of Alzheimer’s disease genetic variants in microglia

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Nature Genetics 2023年第10期55卷 1735-1744页

作者： Yang, Xiaoyu Wen, Jia Yang, Han Jones, Ian R. Zhu, Xiaodong Liu, Weifang Li, Bingkun Clelland, Claire D. Luo, Wenjie Wong, Man Ying Ren, Xingjie Cui, Xiekui Song, Michael Liu, Hongjiang Chen, Cady Eng, Nicolas Ravichandran, Mirunalini Sun, Yang Lee, David Van Buren, Eric Jiang, Min-Zhi Chan, Candace S. Y. Ye, Chun Jimmie Perera, Rushika M. Gan, Li Li, Yun Shen, Yin Institute for Human Genetics University of California San Francisco San Francisco CA United States Department of Genetics University of North Carolina Chapel Hill NC United States Pharmaceutical Sciences and Pharmacogenomics Graduate Program University of California San Francisco San Francisco CA United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain and Mind Research Institute Weill Cornell Medicine New York City NY United States Department of Biostatistics University of North Carolina Chapel Hill NC United States Department of Neurology University of California San Francisco San Francisco CA United States Weill Institute for Neurosciences University of California San Francisco San Francisco CA United States Department of Anatomy University of California San Francisco San Francisco CA United States Division of Rheumatology Department of Medicine University of California San Francisco San Francisco CA United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States Tetrad Graduate Program University of California San Francisco San Francisco CA United States Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center University of California San Francisco San Francisco CA United States Department of Epidemiology and Biostatistics University of California San Francisco San Francisco CA United States Parker Institute for Cancer Immunotherapy San Francisco CA United States Chan Zuckerberg Biohub San Francisco CA United States Bakar Computational Health Sciences Institute University of California San Francisco San Francisco CA United States Department of Pathology University of California San Francisco San Francisco CA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San Francisco CA United States Department of Computer Science University of North Carolina Chapel Hill NC United States

Candidate cis-regulatory elements (cCREs) in microglia demonstrate the most substantial enrichment for Alzheimer’s disease (AD) heritability compared to other brain cell types. However, whether and how these genome-wide association studies (GWAS) variants contribute to AD remain elusive. Here we prioritize 308 previously unreported AD risk variants at 181 cCREs by integrating genetic information with microglia-specific 3D epigenome annotation. We further establish the link between functional variants and target genes by single-cell CRISPRi screening in microglia. In addition, we show that AD variants exhibit allelic imbalance on target gene expression. In particular, rs7922621 is the effective variant in controlling TSPAN14 expression among other nominated variants in the same cCRE and exerts multiple physiological effects including reduced cell surface ADAM10 and altered soluble TREM2 (sTREM2) shedding. Our work represents a systematic approach to prioritize and characterize AD-associated variants and provides a roadmap for advancing genetic association to experimentally validated cell-type-specific phenotypes and mechanisms. © 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.

关键词： Epigenomics Functional genomics

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MS-net: Multi-site network for improving prostate segmentation with heterogeneous MRI data

arXiv

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arXiv 2020年

作者： Liu, Quande Dou, Qi Yu, Lequan Heng, Pheng Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Radiation Oncology Stanford University StanfordCA94305 United States Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress in this task, usually relying on large amounts of training data. Due to the nature of scarcity for medical images, it is important to effectively aggregate data from multiple sites for robust model training, to alleviate the insufficiency of single-site samples. However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training. In this paper, we propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations, leveraging multiple sources of data. To compensate for the inter-site heterogeneity of different MRI datasets, we develop Domain-Specific Batch Normalization layers in the network backbone, enabling the network to estimate statistics and perform feature normalization for each site separately. Considering the difficulty of capturing the shared knowledge from multiple datasets, a novel learning paradigm, i.e., Multi-site-guided Knowledge Transfer, is proposed to enhance the kernels to extract more generic representations from multi-site data. Extensive experiments on three heterogeneous prostate MRI datasets demonstrate that our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Urology

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Dissecting the mechanisms of spatiotemporal mode-locking

Dissecting the mechanisms of spatiotemporal mode-locking

引用

Nonlinear Photonics, NP 2020

作者： Wright, Logan G. Sidorenko, Pavel Pourbeyram, Hamed Ziegler, Zachary M. Isichenko, Andrei Malomed, Boris A. Menyuk, Curtis R. Christodoulides, Demetrios N. Wise, Frank W. School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Department of Physical Electronics School of Electrical Engineering Faculty of Engineering Center for Light-Matter Interaction Tel Aviv University Tel Aviv69978 Israel ITMO University St. Petersburg197101 Russia Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States CREOL College of Optics and Photonics University of Central Florida OrlandoFL32816 United States

We uncover the mechanisms behind a range of new 3D solitons in multimode lasers using an approach called attractor dissection. Experimental measurements allow us to decompose multimode states containing up to 30 milli...

ISBN: (纸本)9781943580798

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Packaging, containerization, and virtualization of computational omics methods: Advances, challenges, and opportunities

arXiv

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arXiv 2022年

作者： Alser, Mohammed Waymost, Sharon Ayyala, Ram Lawlor, Brendan Abdill, Richard J. Rajkumar, Neha LaPierre, Nathan Brito, Jaqueline Ribeiro-Dos-Santos, André M. Firtina, Can Almadhoun, Nour Sarwal, Varuni Eskin, Eleazar Hu, Qiyang Strong, Derek Kim, Byoung-Do Abedalthagafi, Malak S. Mutlu, Onur Mangul, Serghei Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Department of Computer Science UCLA Los AngelesCA90095 United States Department of Translational Biomedical Informatics University of Southern California Los AngelesCA90089 United States Department of Clinical Pharmacy University of Southern California Los AngelesCA90089 United States Department of Computer Science Munster Technological University CorkT12 P928 Ireland Department of Genetics Cell Biology and Development University of Minnesota MinneapolisMN55417 United States Department of Bioengineering UCLA Los AngelesCA90095 United States Institute for Systems Genetics NYU Grossman School of Medicine New YorkNY10016 United States Department of Computational Medicine University of California Los AngelesCA90095 United States Department of Human Genetics University of California Los AngelesCA90095 United States Office of Advanced Research Computing UCLA Los AngelesCA90095 United States Center for Advanced Research Computing University of Southern California Los AngelesCA90089 United States Genomics Research Department Saudi Human Genome Project King Fahad Medical City King Abdulaziz City for Science and Technology Riyadh Saudi Arabia King Salman Center for Disability Research Riyadh12512 Saudi Arabia Riyadh Saudi Arabia

Omics software tools have reshaped the landscape of modern biology and become an essential component of biomedical research. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging, virtualization, and containerization are different approaches to satisfy this need by wrapping omics tools in additional software that makes the omics tools easier to install and use. Here, we systematically review practices across prominent packaging, virtualization, and containerization platforms. We outline the challenges, advantages, and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers, and system administrators. We also propose principles to make packaging, virtualization, and containerization of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research. © 2022, CC BY.

关键词： Containers

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The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel

The Importance of Gender Specification for Detection of Driv...

引用

Biomedical Engineering International Conference (BMEiCON)

作者： Mohammad Shahbakhti Matin Beiramvand Erfan Nasiri Wei Chen Jordi Solé-Casals Michal Wierzchon Anna Broniec-Wójcik Piotr Augustyniak Vaidotas Marozas Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Faculty of Information Technology and Communication Tampere University Tempere Finland Faculty of Statistics Mathematics and Computer Allameh Tabataba’i University Tehran Iran Center for Intelligent Medical Electronics School of Information Science and Technology the Human Phenome Institute Fudan University Shanghai China Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain Institute of Psychology Jagiellonian University Krakow Poland Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9781665489041

Although detection of the driver fatigue using a single electroencephalography (EEG) channel has been addressed in literature, the gender differentiation for applicability of the model has not been investigated heretofore. Motivated accordingly, we address the detection of driver fatigue based the gender-segregated datasets, where each of them contains 8 subjects. After splitting the EEG signal into its sub-bands (delta, theta, alpha, beta, and gamma) using discrete wavelet transform, the log energy entropy of each band is computed to form the feature vector. Afterwards, the feature vector is randomly split into 50% for training and 50% for the unseen testing, and fed to a support vector machine model. When comparing the classification results of fatigue driving detection between the gender segregated and non-gender segregated datasets, the former achieved the accuracy 78% and 77% for male and female subjects, respectively, than the latter (71%). The obtained results show the importance of gender-specification for the driver fatigue detection.

关键词： Training Support vector machines Computational modeling Transforms Fatigue Brain modeling Electroencephalography

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The status of the human gene catalogue

arXiv

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arXiv 2023年

作者： Amaral, Paulo Carbonell-Sala, Silvia De La Vega, Francisco M. Faial, Tiago Frankish, Adam Gingeras, Thomas Guigo, Roderic Harrow, Jennifer L. Hatzigeorgiou, Artemis G. Johnson, Rory Murphy, Terence D. Pertea, Mihaela Pruitt, Kim D. Pujar, Shashikant Takahashi, Hazuki Ulitsky, Igor Varabyou, Ales Wells, Christine A. Yandell, Mark Carninci, Piero Salzberg, Steven L. INSPER Institute of Education and Research SP São Paulo Brazil Dr. Aiguader 88 Catalonia Barcelona08003 Spain Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Tempus Labs Inc. ChicagoIL United States Nature Genetics San FranciscoCA United States European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Department of Functional Genomics Cold Spring Harbor Laboratory Cold Spring HarborNY United States Catalonia Barcelona Spain Centre for Genomics Research Discovery Sciences AstraZeneca Da Vinci Building. Melbourn Science Park RoystonSG8 6HB United Kingdom Universithy of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece Hellenic Pasteur Institute Athens Greece School of Biology and Environmental Science University College Dublin DublinD04 V1W8 Ireland Conway Institute of Biomedical and Biomolecular Research University College Dublin DublinD04 V1W8 Ireland Department of Medical Oncology Inselspital Bern University Hospital University of Bern Bern3010 Switzerland Department for BioMedical Research University of Bern Bern3008 Switzerland National Center for Biotechnology Information National Library of Medicine National Institutes of Health BethesdaMD20894 United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Laboratory for Transcriptome Technology RIKEN Center for Integrative Medical Sciences Kanagawa Yokohama230-0045 Japan Department of Immunology and Regenerative Biology Department of Molecular Neuroscience Weizmann Institute of Science Rehovot76100 Israel Department of Computer Science Johns Hopkins University BaltimoreMD United States Stem Cell Systems Department of Anatomy and Physiology Faculty of Medicine Dentistry and Health Scien

Scientists have been trying to identify all of the genes in the human genome since the initial draft of the genome was published in 2001. Over the intervening years, much progress has been made in identifying protein-coding genes, and the estimated number has shrunk to fewer than 20,000, although the number of distinct protein-coding isoforms has expanded dramatically. The invention of high-throughput RNA sequencing and other technological breakthroughs have led to an explosion in the number of reported non-coding RNA genes, although most of them do not yet have any known function. A combination of recent advances offers a path forward to identifying these functions and towards eventually completing the human gene catalogue. However, much work remains to be done before we have a universal annotation standard that includes all medically significant genes, maintains their relationships with different reference genomes, and describes clinically relevant genetic variants. © 2023, CC BY.

关键词： Genes

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The concept of augmented reality application for putting alignment in golf 5

The concept of augmented reality application for putting ali...

引用

5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019

作者： Ginters, Egils Burkhardt, Dirk Nazemi, Kawa Merkuryev, Yuri Sociotechnical Systems Engineering Group Department of Modelling and Simulation Faculty of Computer Science and Information Technology Riga Technical University 2 Daugavgrivas Street RigaLV-1048 Latvia Sociotechnical Systems OU Sakala 7-2 Street Tallinn10141 Estonia Human-Computer Interaction and Visual Analytics Research Group Darmstadt University of Applied Sciences Darmstadt Germany

ISBN: (纸本)9788885741423

Virtual and augmented reality (VR / AR) applications have successfully overcome the critical part of the Gartner curve. Investments are made and new products entering the economy. However, a very small percentage of society have also heard about AR glasses, mainly linking these with potential identity threats and personal data breaches. The authors dealt with the design of application of AR to improve golf skills by improving the putting technique. The above solution is complicated by requiring complex object recognition, tracking and advanced AR software designing. © 5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019. All rights reserved.

关键词： Augmented reality

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Using ASR methods for OCR 15

Using ASR methods for OCR

引用

15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Arora, Ashish Garcia, Paola Watanabe, Shinji Manohar, Vimal Shao, Yiwen Khudanpur, Sanjeev Chang, Chun Chieh Rekabdar, Babak Babaali, Bagher Povey, Daniel Etter, David Raj, Desh Hadian, Hossein Trmal, Jan Center for Language and Speech Processing Johns Hopkins University Baltimore United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore United States Department of Computer Engineering Sharif University of Technology Iran School of Mathematics Statistics and Computer Sciences College of Science University of Tehran Iran

ISBN: (纸本)9781728128610

Hybrid deep neural network hidden Markov models (DNN-HMM) have achieved impressive results on large vocabulary continuous speech recognition (LVCSR) tasks. However, the recent approaches using DNN-HMM models are not explored much for text recognition. Inspired by the current work in automatic speech recognition (ASR) and machine translation, we present an open vocabulary sub-word text recognition system. The sub-word lexicon and sub-word language model (LM) helps in overcoming the challenge of recognizing out of vocabulary (OOV) words, and a time delay neural network (TDNN) and convolution neural network (CNN) based DNN-HMM optical model (OM) efficiently models the sequence dependency in the line image. We present results on 12 datasets with training data varying from 6k lines to 600k lines. The system is built for 8 languages, i.e., English, French, Arabic, Chinese, Farsi, Tamil, Russian, and Korean. We report competitive results on several commonly used handwritten and printed text datasets. © 2019 IEEE.

关键词： Hidden Markov models

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