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Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants (vol 11, 955, 2020)

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NATURE COMMUNICATIONS 2020年第1期11卷 955页

作者： Donovan, Margaret K. R. D'Antonio-Chronowska, Agnieszka D'Antonio, Matteo Frazer, Kelly A. University of California San Diego Bioinformatics and Systems Biology Graduate Program La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Biomedical Informatics La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Pediatrics and Rady Children’s Hospital La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Pediatrics and Rady Children’s Hospital La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Institute for Genomic Medicine La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242)

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

关键词： Gene expression Genetic association study RNA sequencing

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Simultaneous Segmentation of Cell and Nucleus in Schizosaccharomyces pombe Images with Focus Gradient

Simultaneous Segmentation of Cell and Nucleus in Schizosacch...

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IEEE International Conference on Healthcare informatics, Imaging and Systems biology (HISB)

作者： Jyh-Ying Peng Yen-Jen Chen Marc D. Green Susan L. Forsburg Chun-Nan Hsu Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Education and Research Taipei City Hospital Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Molecular and Computational Biology Program University of Southern California Los Angeles CA USA Information Sciences Institute University of Southern California Marina del Rey CA USA

Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo [1]. However, performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of multiple images. The first step requires robust segmentation of the cell and the most distinguishable compartments (the nucleus) from images with varying focus conditions and qualities. We developed a segmentation system that can segment transmitted illumination images with focus gradient and varying contrast, and extract cell and nucleus boundaries. Global and locally adaptive corrections for focus gradient are applied to the image to accurately detect cell membrane and cytoplasm pixels. We use the gradient vector flow snake model [2] to segment individual cells, using a novel edge map based on detected cell membrane. We applied our system to multi-channel images of S. pombe, the whole data set contains about 4000 mutant genotypes each with at least three sets of transmitted illumination (bright field), Rad52-YFP and RPA-CFP images. Our system is able to correctly segment a majority of nuclei and cells in almost all images of sufficient quality, and performance is consistent over a wide variety of focus distance, field brightness, relative contrast and phenotypic characteristics. A quantitative evaluation is also performed using a set of hand produced gold standard segmentations of pombe cells, representing different image acquisition conditions and quality. We evaluated the percentage of cells detected, the accuracy of the final snake contours. The whole set of 60 gold standard images contain a total of 14,926 pombe cells, averaging about 249 cells per image, of which 97.5% were detected by nucleus segmentation and pixel classification of cell interior, and 89.0% were accurately segmented (defined as less than 10% pixel

关键词： Image segmentation Proteins Educational institutions Robustness Lighting Cities and towns Hospitals

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Robust cell segmentation for schizosaccharomyces pombe images with focus gradient

Robust cell segmentation for schizosaccharomyces pombe image...

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IEEE International Symposium on biomedical Imaging

ISBN: (纸本)9781467364560

关键词： Image segmentation Lighting Robustness Proteins Genomics Bioinformatics Image edge detection

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Meta-EHR: A meta-learning approach for electronic health records with a high imbalanced ratio and missing rate

Meta-EHR: A meta-learning approach for electronic health rec...

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Annual International Conference of the IEEE Engineering in Medicine and biology Society (EMBC)

作者： Hsu-Hsiang Chang Te-Cheng Hsu Yi-Hsien Hsieh Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Institute of Communications Engineering National Tsing Hua University (NTHU) Hsinchu Taiwan Department of Electrical Engineering National Taiwan University (NTU) Taipei Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

Data imbalance is a practical and crucial issue in deep learning. Moreover, real-world datasets, such as electronic health records (EHR), often suffer from high missing rates. Both issues can be understood as noises in data that may lead to bad generalization results for standard deep-learning algorithms. This paper introduces a novel meta-learning approach to deal with these noise issues in an EHR dataset for a binary classification task. This meta-learning approach leverages the information from a selected subset of balanced, low-missing rate data to automatically assign proper weight to each sample. Such weights would enhance the informative samples and suppress the opposites during training. Furthermore, the meta-learning approach is model-agnostic for deep learning-based architectures that simultaneously handle the high imbalanced ratio and high missing rate problems. Through experiments, we demonstrate that this meta-learning approach is better in extreme cases. In the most extreme one, with an imbalance ratio of 172 and a 74.6% missing rate, our method outperforms the original model without meta-learning by as much as 10.3% of the area under the receiver-operating characteristic curve (AUROC) and 3.2% of the area under the precision-recall curve (AUPRC). Our results mark the first step towards training a robust model for extremely noisy EHR datasets.

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Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time 21

Deep inference of latent dynamics with spatio-temporal super...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Feng Zhu Andrew R. Sedler Harrison A. Grier Nauman Ahad Mark A. Davenport Matthew T. Kaufman Andrea Giovannucci Chethan Pandarinath Neuroscience Graduate Program Emory University Center for Machine Learning Georgia Tech Computational Neuroscience Graduate Program The University of Chicago School of Electrical and Computer Engineering Georgia Tech Dept. of Organismal Biology and Anatomy The University of Chicago and Neuroscience Institute The University of Chicago Joint Dept. of Biomedical Engineering UNC Chapel Hill and NC State University and UNC Chapel Hill Neuroscience Center and NC State/UNC Closed-Loop Engineering for Advanced Rehabilitation (CLEAR) Coulter Dept. of Biomedical Engineering Emory University and Georgia Tech and Dept. of Neurosurgery Emory University

ISBN: (纸本)9781713845393

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of latent dynamics from data in which the set of observed variables changes at each time step. The resulting models are able to infer activity for missing samples by combining observations with learned latent dynamics. We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data. In electrophysiology, SBTT enables accurate inference of neuronal population dynamics with lower interface bandwidths, providing an avenue to significant power savings for implanted neuroelectronic interfaces. In applications to two-photon calcium imaging, SBTT accurately uncovers high-frequency temporal structure underlying neural population activity, substantially outperforming the current state-of-the-art. Finally, we demonstrate that performance could be further improved by using limited, high-bandwidth sampling to pretrain dynamics models, and then using SBTT to adapt these models for sparsely-sampled data.

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Subcytoplasmic Location of Translation Controls Protein Output

SSRN

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SSRN 2022年

作者： Horste, Ellen L. Zhen, Gang Fansler, Mervin M. Chen, Xiuzhen Lee, Flora C.Y. Ule, Jernej Mayr, Christine Gerstner Sloan Kettering Graduate School of Biomedical Sciences New YorkNY10065 United States Cancer Biology and Genetics Program Sloan Kettering Institute New YorkNY10065 United States Tri-Institutional Training Program in Computational Biology and Medicine Weill-Cornell Graduate College New YorkNY10021 United States UK Dementia Research Institute King's College London LondonSE5 9NU United Kingdom The Francis Crick Institute 1 Midland Road LondonNW1 1AT United Kingdom

The cytoplasm is highly compartmentalized, but the extent of subcytopIasmic mRNA localization in non-polarized cells is largely unknown. We used fluorescent particle sorting to determine mRNA enrichment in three unenclosed cytoplasmic compartments: the canonicaI rough endopIasmic reticuIum (CRER), the TIS granule-associated rough endopIasmic reticuIum (TGER), and the cytosol. Focusing our analysis on non-membrane protein-encoding mRNAs, we observed that 53% have a unique subcytoplasmic localization pattern which is determined by a combinatorial code of 3′UTR-bound RNA-binding proteins. Compartment-enriched mRNAs differed in production and degradation rates and the expression levels and functional classes of their encoded proteins. The TGER domain enriches mRNAs that encode transcription factors, the CRER highly expressed proteins, and the cytosol unstable mRNAs. The rough ER environment is stimulatory as redirecting cytosolic mRNAs to the ER increases their protein expression by two-fold, independently of the bound RNA-binding proteins. We show that local translation environments functionally compartmentaIize the *** Information: This work was funded by the NIH Director′s Pioneer Award (DP1-GM123454), the R35GM144046 NIH grant, a grant from the Pershing Square Sohn Cancer Research Alliance, a grant from the G. Harold and Leila Y. Mathers Foundation, and the MSK Core Grant (P30 CA008748) to CM. J.U. received funding from the ERC under the European Union Horizon 2020 Research and Innovation program (835300-RNPdynamics) and The Francis Crick Institute receives its core funding from Cancer Research UK (CC0102), the UK Medical Research Council (CC0102), and the Wellcome Trust (CC0102). An NIH F31 (CA254335-01) fellowship was awarded to *** of Interests: The authors declare no competing interests. © 2022, The Authors. All rights reserved.

关键词： Proteins

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Lysosomal dysfunction and inflammatory sterol metabolism in pulmonary arterial hypertension

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Science (New York, N.Y.) 2025年第6732期387卷 eadn7277页

作者： Harvey, Lloyd D. Alotaibi, Mona Tai, Yi-Yin Tang, Ying Kim, Hee-Jung J Kelly, Neil J. Sun, Wei Woodcock, Chen-Shan C Arshad, Sanya Culley, Miranda K. El Khoury, Wadih Xie, Rong Al Aaraj, Yassmin Zhao, Jingsi Hafeez, Neha Rao, Rashmi J. Jiang, Siyi Negi, Vinny Kirillova, Anna Perk, Dror Watson, Annie M. St Croix, Claudette M. Stolz, Donna B. Lee, Ji Young Cheng, Mary Hongying Zhang, Manling Detmer, Samuel Guzman, Edward Manan, Rajith S. Saggar, Rajan Haley, Kathleen J. Waxman, Aaron B. Okawa, Satoshi Schwantes-An, Tae-Hwi Pauciulo, Michael W. Wang, Bing Webb, Amy Chauvet, Caroline Anderson, Daniel G. Nichols, William C. Desai, Ankit A. Lafyatis, Robert Nouraie, S Mehdi Wu, Haodi McDonald, Jeffrey G. Cheng, Susan Bahar, Ivet Bertero, Thomas Benza, Raymond L. Jain, Mohit Chan, Stephen Y. Medical Scientist Training Program University of Pittsburgh Pittsburgh PA United States Center for Pulmonary Vascular Biology and Medicine Heart Lung Blood Vascular Medicine Institute University of Pittsburgh Pittsburgh PA United States Division of Cardiology University of Pittsburgh School of Medicine Pittsburgh PA United States Division of Pulmonary and Critical Care Medicine University of California San Diego La Jolla CA United States Department of Medicine University of California La Jolla San Diego CA United States Department of Pharmacology University of California La Jolla San Diego CA United States Division of Cardiology Veterans Affairs Pittsburgh Healthcare System Pittsburgh PA United States Center for Biologic Imaging University of Pittsburgh Pittsburgh PA United States Laufer Center for Physical and Quantitative Biology Stony Brook University Stony Brook NY United States Department of Biochemistry and Cell Biology Stony Brook University Stony Brook NY United States Department of Chemistry Massachusetts Institute of Technology Boston MA United States Institute of Medical Engineering and Science Massachusetts Institute of Technology Boston MA United States School of Engineering and Applied Sciences Harvard University Cambridge MA United States Department of Chemical Engineering Massachusetts Institute of Technology Boston MA United States Department of Medicine David Geffen School of Medicine University of California Los Angeles (UCLA) Los Angeles CA USA Department of Pathology David Geffen School of Medicine UCLA Los Angeles CA United States Division of Pulmonary and Critical Care Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston MA United States Department of Computational and Systems Biology University of Pittsburgh Pittsburgh PA United States McGowan Institute for Regenerative Medicine University of Pittsburgh Pittsburgh PA United States Department of Medical and Molecular G

Vascular inflammation regulates endothelial pathophenotypes, particularly in pulmonary arterial hypertension (PAH). Dysregulated lysosomal activity and cholesterol metabolism activate pathogenic inflammation, but their relevance to PAH is unclear. Nuclear receptor coactivator 7 (NCOA7) deficiency in endothelium produced an oxysterol and bile acid signature through lysosomal dysregulation, promoting endothelial pathophenotypes. This oxysterol signature overlapped with a plasma metabolite signature associated with human PAH mortality. Mice deficient for endothelial Ncoa7 or exposed to an inflammatory bile acid developed worsened PAH. Genetic predisposition to NCOA7 deficiency was driven by single-nucleotide polymorphism rs11154337, which alters endothelial immunoactivation and is associated with human PAH mortality. An NCOA7-activating agent reversed endothelial immunoactivation and rodent PAH. Thus, we established a genetic and metabolic paradigm that links lysosomal biology and oxysterol processes to endothelial inflammation and PAH.

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Developmental stage classification of Embryos using two-stream neural network with linear-chain conditional random field

arXiv

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arXiv 2021年

作者： Lukyanenko, Stanislav Jang, Won-Dong Wei, Donglai Struyven, Robbert Kim, Yoon Leahy, Brian Yang, Helen Rush, Alexander Ben-Yosef, Dalit Needleman, Daniel Pfister, Hanspeter Department of Informatics Technical University of Munich Germany School of Engineering and Applied Sciences Harvard University United States Department of Molecular and Cellular Biology Harvard University United States Graduate Program in Biophysics Harvard University United States University College London United Kingdom MIT United States Cornell University United States Center for Computational Biology Flatiron Institute United States Lis Maternity Hospital Tel-Aviv Sourasky Medical Center Israel Cell and Developmental Biology Tel-Aviv University Israel

The developmental process of embryos follows a monotonic order. An embryo can progressively cleave from one cell to multiple cells and finally transform to morula and blastocyst. For time-lapse videos of embryos, most existing developmental stage classification methods conduct per-frame predictions using an image frame at each time step. However, classification using only images suffers from overlapping between cells and imbalance between stages. Temporal information can be valuable in addressing this problem by capturing movements between neighboring frames. In this work, we propose a two-stream model for developmental stage classification. Unlike previous methods, our two-stream model accepts both temporal and image information. We develop a linear-chain conditional random field (CRF) on top of neural network features extracted from the temporal and image streams to make use of both modalities. The linear-chain CRF formulation enables tractable training of global sequential models over multiple frames while also making it possible to inject monotonic development order constraints into the learning process explicitly. We demonstrate our algorithm on two time-lapse embryo video datasets: i) mouse and ii) human embryo datasets. Our method achieves 98.1% and 80.6% for mouse and human embryo stage classification, respectively. Our approach will enable more profound clinical and biological studies and suggests a new direction for developmental stage classification by utilizing temporal information. Copyright © 2021, The Authors. All rights reserved.

关键词： Classification (of information)

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Tumor-derived interleukin-1 receptor antagonist exhibits immunosuppressive functions and promotes pancreatic cancer

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Cell and Bioscience 2023年第1期13卷 1页

作者： Fan, Yu-Ching Fong, Yu-Cin Kuo, Chun-Tse Li, Chia-Wei Chen, Wei-Yu Lin, Jian-Da Bürtin, Florian Linnebacher, Michael Bui, Quoc Thang Lee, Kuan-Der Tsai, Yuan-Chin Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University and Academia Sinica Taipei Taiwan Graduate Institute of Cancer Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan Institute of Biomedical Sciences Academia Sinica Taipei Taiwan Department of Pathology Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Pathology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Biochemical Science and Technology College of Life Science National Taiwan University Taipei City 10617 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei City 10617 Taiwan Clinic of General Surgery University Medical Center Rostock Schillingallee 35 Rostock 18057 Germany Clinic of General Surgery Molecular Oncology and Immunotherapy University Medical Center Rostock Schillingallee 69 Rostock 18057 Germany International Ph.D. Program for Cell Therapy and Regeneration Medicine (IPCTRM) School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Post-Baccalaureate Medicine College of Medicine Natioanl Chung Hsing University Taichung Taiwan Cell Therapy and Regenerative Medicine Center and Comprehensive Cancer Center Taichung Veterans General Hospital Taichung Taiwan Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan

Background: Pancreatic ductal adenocarcinoma (PDA) is a pernicious disease characterized by an immunosuppressive milieu that is unresponsive to current immunotherapies. Interleukin-1 receptor antagonist (IL-1Ra) is a natural anti-inflammatory cytokine;however, its contribution to cancer pathogenesis and immunosuppression remains elusive. In this research, we investigated the role and mechanism of IL-1Ra in malignant progression of PDA. Results: Through analyzing clinical dataset and examining the pathological tumor tissues and serum samples, we have demonstrated that IL-1Ra expression is elevated in human PDA and positively associated with malignant progression of PDA. To study the biological function of IL-1Ra in tumors, we generated a set of mouse pancreatic cancer cell lines with a knockout (KO) of the Il1rn gene, encoding IL-1Ra, and compared the tumor growth rates in immune-competent and immune-deficient mice. We found that the Il1rn KO cells exhibited greater tumor inhibition in immune-competent mice, highlighting the crucial role of a functional immune system in Il1rn KO-mediated anti-tumor response. Consistently, we found an increase in CD8⁺ T cells and a decrease in CD11b⁺Ly6G⁻ immunosuppressive mononuclear population in the tumor microenvironment of Il1rn KO-derived tumors. To monitor the inhibitory effects of IL-1Ra on immune cells, we utilized a luciferase-based reporter CD4⁺ T cell line and splenocytes, which were derived from transgenic mice expressing ovalbumin-specific T cell receptors in CD8⁺ T cells, and mice immunized with ovalbumin. We showed that IL-1Ra suppressed T cell receptor signaling and inhibited antigen-specific interferon-γ (IFN-γ) secretion and cytolytic activity in splenocytes. Conclusions: Our findings illustrate the immunosuppressive properties of the natural anti-inflammatory cytokine IL-1Ra, and provide a rationale for considering IL-1Ra-targeted therapies in the treatment of

关键词： IFN-γ Immunosuppression interleukin-1 receptor antagonist Pancreatic adenocarcinoma Patient-derived xenograft Tumor microenvironment

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How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment

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JMIR Medical Education 2023年 9卷 e45312页

作者： Gilson, Aidan Safranek, Conrad W. Huang, Thomas Socrates, Vimig Chi, Ling Taylor, Richard Andrew Chartash, David Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States School of Medicine University College Dublin National University of Ireland Dublin Ireland

Background: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. Objective: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Methods: We used 2 sets of multiple-choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT’s performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. Results: Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT’s answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001

关键词： artificial intelligence chatbot ChatGPT conversational agent education technology generative pre-trained transformer GPT machine learning medical education MedQA natural language processing NLP

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