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arXiv 2023年

作者： Bao, Shunxing Zhu, Sichen Kolachala, Vasantha L. Remedios, Lucas W. Hwang, Yeonjoo Sun, Yutong Deng, Ruining Cui, Can Li, Yike Li, Jia Roland, Joseph T. Liu, Qi Lau, Ken S. Kugathasan, Subra Qiu, Peng Wilson, Keith T. Coburn, Lori A. Landman, Bennett A. Huo, Yuankai Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States Division of Pediatric Gastroenterology Department of Pediatrics Pediatric Research Institute Children's Healthcare of Atlanta Emory University School of Medicine AtlantaGA United States Department of Computer Science Vanderbilt University NashvilleTN United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Surgery Vanderbilt University Medical Center NashvilleTN United States Department of Cell and Developmental Biology Vanderbilt University School of Medicine NashvilleTN United States Division of Gastroenterology Hepatology and Nutrition Department of Medicine Vanderbilt University Medical Center NashvilleTN United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Department of Otolaryngology-Head and Neck Surgery Vanderbilt University Medical Center NashvilleTN United States

Crohn's disease (CD) is a chronic and relapsing inflammatory condition that affects segments of the gastrointestinal tract. CD activity is determined by histological findings, particularly the density of neutrophils observed on Hematoxylin and Eosin stains (H&E) imaging. However, understanding the broader morphometry and local cell arrangement beyond cell counting and tissue morphology remains challenging. To address this, we characterize six distinct cell types from H&E images and develop a novel approach for the local spatial signature of each cell. Specifically, we create a 10-cell neighborhood matrix, representing neighboring cell arrangements for each individual cell. Utilizing t-SNE for non-linear spatial projection in scatter-plot and Kernel Density Estimation contour-plot formats, our study examines patterns of differences in the cellular environment associated with the odds ratio of spatial patterns between active CD and control groups. This analysis is based on data collected at the two research institutes. The findings reveal heterogeneous nearest-neighbor patterns, signifying distinct tendencies of cell clustering, with a particular focus on the rectum region. These variations underscore the impact of data heterogeneity on cell spatial arrangements in CD patients. Moreover, the spatial distribution disparities between the two research sites highlight the significance of collaborative efforts among healthcare organizations. All research analysis pipeline tools are available at https://***/MASILab/cellNN. © 2023, CC BY.

关键词： Cells

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Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology

arXiv

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arXiv 2024年

作者： Remedios, Lucas W. Bao, Shunxing Remedios, Samuel W. Lee, Ho Hin Cai, Leon Y. Li, Thomas Deng, Ruining Newlin, Nancy R. Saunders, Adam M. Cui, Can Li, Jia Liu, Qi Lau, Ken S. Roland, Joseph T. Washington, Mary K. Coburn, Lori A. Wilson, Keith T. Huo, Yuankai Landman, Bennett A. Vanderbilt University Department of Computer Science Nashville United States Vanderbilt University Department of Electrical and Computer Engineering Nashville United States Johns Hopkins University Department of Computer Science Baltimore United States National Institutes of Health Department of Radiology and Imaging Sciences Bethesda United States Vanderbilt University Department of Biomedical Engineering Nashville United States Vanderbilt University Medical Center Department of Biostatistics Nashville United States Vanderbilt University Medical Center Center for Quantitative Sciences Nashville United States Vanderbilt University Medical Center Epithelial Biology Center Nashville United States Vanderbilt University School of Medicine Department of Cell and Developmental Biology Nashville United States Vanderbilt University Medical Center Division of Gastroenterology Hepatology and Nutrition Department of Medicine Nashville United States Vanderbilt University Medical Center Vanderbilt Center for Mucosal Inflammation and Cancer Nashville United States Vanderbilt University School of Medicine Program in Cancer Biology Nashville United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Medical Center Department of Pathology Microbiology and Immunology NashvilleTN United States

Purpose: Cells are building blocks for human physiology;consequently, understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions in both health and disease. Hematoxylin and eosin (H&E) is the standard stain used in histological analysis of tissues. Microscope slides of H&E-stained tissues are widely available in both clinical and research settings. While H&E is ubiquitous and reveals tissue microanatomy, the classification and tracking of cell subtypes often requires expert knowledge and the use of specialized stains, such as immunofluorescence staining. To reduce both the manual annotation burden and reliance on specialized staining technologies, artificial intelligence has been proposed for the automatic classification of cell types on H&E slides. For example, the recent Colon Nucleus Identification and Classification (CoNIC) Challenge focused on labeling 6 cell types on imaging of H&E stains from the human colon. However, this is a very small fraction of the number of potential cell subtypes within the intestines. Specifically, the CoNIC Challenge was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). To approach this problem, we propose to use inter-modality learning to label previously un-labelable cell types on H&E. Approach: We take advantage of the cell classification information inherent in whole slide images (WSIs) of multiplexed immunofluorescence (MxIF) histology to create cell level annotations for 14 subclasses. We performed style transfer on the same MxIF tissues to synthesize realistic virtual H&E which we paired with the MxIF-derived cell subclassification labels. We evaluated the efficacy of using a supervised learning scheme where the input was realistic-quality virtual H&E and the labels were MxIF-derived cell subclasses. We assessed our model on a testing

关键词： Tissue

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Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug Labeling to Enhance Product-Specific Guidance Assessment

arXiv

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arXiv 2022年

作者： Shi, Yiwen Wang, Jing Ren, Ping ValizadehAslani, Taha Zhang, Yi Hu, Meng Liang, Hualou College of Computing and Informatics Drexel University PhiladelphiaPA United States Office of Research and Standards Office of Generic Drugs Center for Drug Evaluation and Research United States Food and Drug Administration Silver Spring MD United States Department of Electrical and Computer Engineering College of Engineering Drexel University PhiladelphiaPA United States School of Biomedical Engineering Science and Health Systems Drexel University PhiladelphiaPA United States

Product-specific guidances (PSGs) recommended by the United States Food and Drug Administration (FDA) are instrumental to promote and guide generic drug product development. To assess a PSG, the FDA assessor needs to take extensive time and effort to manually retrieve supportive drug information of absorption, distribution, metabolism, and excretion (ADME) from the reference listed drug labeling. In this work, we leveraged the state-of-the-art pre-trained language models to automatically label the ADME paragraphs in the pharmacokinetics section from the FDA-approved drug labeling to facilitate PSG assessment. We applied a transfer learning approach by fine-tuning the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model to develop a novel application of ADME semantic labeling, which can automatically retrieve ADME paragraphs from drug labeling instead of manual work. We demonstrated that fine-tuning the pre-trained BERT model can outperform the conventional machine learning techniques, achieving up to 11.6% absolute F1 improvement. To our knowledge, we were the first to successfully apply BERT to solve the ADME semantic labeling task. We further assessed the relative contribution of pre-training and fine-tuning to the overall performance of the BERT model in the ADME semantic labeling task using a series of analysis methods such as attention similarity and layer-based ablations. Our analysis revealed that the information learned via fine-tuning is focused on task-specific knowledge in the top layers of the BERT, whereas the benefit from the pre-trained BERT model is from the bottom layers. © 2022, CC BY.

关键词： Semantics

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Pseudo-linear summation explains neural geometry of multi-finger movements in human premotor cortex

引用

Nature Communications 2025年第1期16卷 1-15页

作者： Shah, Nishal P. Avansino, Donald Kamdar, Foram Nicolas, Claire Kapitonava, Anastasia Vargas-Irwin, Carlos Hochberg, Leigh R. Pandarinath, Chethan Shenoy, Krishna V. Willett, Francis R. Henderson, Jaimie M. Department of Neurosurgery Stanford University Stanford CA United States Department of Electrical and Computer Engineering Rice University Houston TX United States Department of Electrical and Computer Engineering Rice University Houston United States Neuroengineering Initiative Rice University Houston TX United States Howard Hughes Medical Institute at Stanford University Stanford CA United States Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School Boston MA United States VA RR & D Center for Neurorestoration and Neurotechnology Rehabilitation R & D Service Providence VA Medical Center Providence RI United States Department of Neuroscience Brown University Providence RI United States Robert J. and Nancy D. Carney Institute for Brain Science Brown University Providence RI United States School of Engineering Brown University Providence RI United States Wallace H. Coulter Department of Biomedical Engineering Emory University and Georgia Institute of Technology Atlanta GA United States Department of Neurosurgery Emory University Atlanta GA United States Wu Tsai Neurosciences Institute Stanford University Stanford CA United States Bio-X Institute Stanford University Stanford CA United States Department of Electrical Engineering Stanford University Stanford CA United States Department of Bioengineering Stanford University Stanford CA United States Department of Neurobiology Stanford University Stanford CA United States

How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such as hand gestures, or playing the piano)? To address this question, motor cortical activity was recorded using intracortical multi-electrode arrays in two male people with tetraplegia as they attempted single, pairwise and higher-order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity exhibited normalization, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers changed significantly as a result of the movement of more strongly represented fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation. © The Author(s) 2025.

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Relaxation Based Modeling of GMD Induced Cascading Failures in ***

Relaxation Based Modeling of GMD Induced Cascading Failures ...

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2021 North American Power Symposium, NAPS 2021

作者： Mate, Adam Barnes, Arthur K. Morley, Steven K. Friz-Trillo, Jacob A. Cotilla-Sanchez, Eduardo Blake, Sean P. Los Alamos National Laboratory Advanced Network Science Initiative Los AlamosNM87545 United States Los Alamos National Laboratory Space Science and Applications Group Los AlamosNM87545 United States Department of Electrical and Biomedical Engineering University of Vermont BurlingtonVT05405 United States Oregon State University School of Electrical Engineering and Computer Science CorvallisOR97331 United States NASA Goddard Space Flight Center Heliophysics Science Division GreenbeltMD20771 United States

ISBN: (纸本)9781665420815

A major risk of geomagnetic disturbances (GMDs) is cascading failure of electrical grids. The modeling of GMD events and cascading outages in power systems is difficult, both independently and jointly, because of the many different mechanisms and physics involved. This paper introduces a relaxation based modeling of GMD-induced cascading failures: the dc approximation-based DCSIMSEP solver was adapted to simulate cascading as a result of GMDs, the full set of ac power flow equations were relaxed to guarantee optimality, and the reactive power losses were modeled while keeping the problem convex. The developed algorithm was implemented in *** - an open-source software specifically designed to model and analyze geomagnetic hazards - and demonstrated to work on the RTS-GMLC-GIC-EAST synthetic test network. © 2021 IEEE.

关键词： Open source software

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HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation

arXiv

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arXiv 2022年

作者： Heidari, Moein Kazerouni, Amirhossein Soltany, Milad Azad, Reza Aghdam, Ehsan Khodapanah Cohen-Adad, Julien Merhof, Dorit School of Electrical Engineering Iran University of Science and Technology Tehran Iran Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Department of Electrical Engineering Shahid Beheshti University Tehran Iran Functional Neuroimaging Unit CRIUGM University of Montreal Canada NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Canada MILA Quebec AI Institute Montreal Canada Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation in modeling long-range dependencies and spatial correlations due to the nature of convolution operation. Although transformers were first developed to address this issue, they fail to capture low-level features. In contrast, it is demonstrated that both local and global features are crucial for dense prediction, such as segmenting in challenging contexts. In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer for medical image segmentation. Specifically, we design two multi-scale feature representations using the seminal Swin Transformer module and a CNN-based encoder. To secure a fine fusion of global and local features obtained from the two aforementioned representations, we propose a Double-Level Fusion (DLF) module in the skip connection of the encoder-decoder structure. Extensive experiments on various medical image segmentation datasets demonstrate the effectiveness of HiFormer over other CNN-based, transformer-based, and hybrid methods in terms of computational complexity, quantitative and qualitative results. Our code is publicly available at GitHub. © 2022, CC BY.

关键词： Convolution

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Non-Hermitian metasurface with non-trivial topology

Non-Hermitian metasurface with non-trivial topology

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Ciril S. Prasad Frank Yang Weijian Li Rosemary Lach Henry O. Everitt Gururaj V. Naik Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA U.S. Army DEVCOM Army Research Laboratory-South Houston TX USA

The synergy between topology and non-Hermiticity in photonics holds potential for optical devices that are robust against defects. We demonstrate a non-Hermitian plasmonic-dielectric metasurface in the visible with no... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Analytical models Laser theory Optical switches Optical resonators Metasurfaces Laser modes Topology

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Modeling and Passivity Properties of Multi-Producer District Heating Systems

arXiv

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arXiv 2020年

作者： Martinez, Juan Eduardo Machado Cucuzzella, Michele Scherpen, Jacquelien M.A. Faculty of Science and Engineering University of Groningen Netherlands Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

We propose a comprehensive nonlinear ODE-based thermo-hydraulic model of a district heating system featuring several heat producers, consumers and storage devices which are interconnected through a distribution network of meshed topology whose temperature dynamics are explicitly considered. Moreover, we analyze the conditions under which the hydraulic and thermal subsystems of the model exhibit shifted passivity properties. For the hydraulic subsystem, our claims on passivity draw on the monotonicity of the vector field associated to the district heating system’s flow dynamics, which mainly codifies viscous friction effects on the system’s pressures. For the temperature dynamics, we propose a storage function based on the ectropy function of a thermodynamic system, recently used in the passivity analysis of heat exchanger networks. Copyright © 2020, The Authors. All rights reserved.

关键词： District heating

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Multiplexed Serial Imaging of Immunotherapy Response with Serrs Nanoprobes

Multiplexed Serial Imaging of Immunotherapy Response with Se...

引用

IEEE International Symposium on biomedical Imaging

作者： Naxhije Berisha Konstantinos Plakas Matthieu Gigoux Taha Merghoub Chrysafis Andreou Hunter College of the City University of New York New York NY USA PhD Program in Chemistry The Graduate Center of the City University of New York NY USA Memorial Sloan Kettering Cancer Center New York NY USA Department of Chemistry University at Buffalo The State University of New York Buffalo NY USA Weill Cornell Medicine New York NY USA Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

With the advent of new and promising cancer therapies, and with increased interest in precision medicine, there is an urgent need to develop non-invasive technologies that evaluate heterogeneity in treatment response. We demonstrate an 8-plex imaging platform based on surface enhanced resonance Raman scattering nanoparticles (SERRS NPs) to monitor the expression of 4 immunomarkers before and after treatment with checkpoint blockade therapy with anti-CTLA4 and anti-PD-1 antibodies in immunotherapy models of colon (CT26) and breast (4T1) cancers. We found that CT26 tumors showed an early treatment response, while the typically refractory 4T1 tumors demonstrated an unexpected treatment response, suggesting that the nanoparticles may have synergistically contributed to the treatment. Serial imaging of multiple markers during therapy can help predict treatment efficacy and uncover new theranostic adjuvants.

关键词： Nanoparticles Multiplexing Precision medicine Raman scattering Immunotherapy Resonance Surface treatment

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CloudPred: Predicting patient phenotypes from single-cell RNA-seq

arXiv

引用

arXiv 2021年

作者： He, Bryan Thomson, Matthew Subramaniam, Meena Perez, Richard Ye, Chun Jimmie Zou, James Department of Computer Science Stanford University Division of Biology and Biological Engineering California Institute of Technology Beckman Center for Single-Cell Profiling and Engineering Department of Epidemiology and Biostatistics University of California San Francisco United States Department of Electrical Engineering Stanford University Department of Biomedical Data Science Stanford University Chan-Zuckerberg Biohub

Single-cell RNA sequencing (scRNA-seq) has the potential to provide powerful, high-resolution signatures to inform disease prognosis and precision medicine. This paper takes an important first step towards this goal by developing an interpretable machine learning algorithm, CloudPred, to predict individuals' disease phenotypes from their scRNA-seq data. Predicting phenotype from scRNA-seq is challenging for standard machine learning methods-the number of cells measured can vary by orders of magnitude across individuals and the cell populations are also highly heterogeneous. Typical analysis creates pseudo-bulk samples which are biased toward prior annotations and also lose the single cell resolution. CloudPred addresses these challenges via a novel end-to-end differentiable learning algorithm which is coupled with a biologically informed mixture of cell types model. CloudPred automatically infers the cell subpopulation that are salient for the phenotype without prior annotations. We developed a systematic simulation platform to evaluate the performance of CloudPred and several alternative methods we propose, and find that CloudPred outperforms the alternative methods across several settings. We further validated CloudPred on a real scRNA-seq dataset of 142 lupus patients and controls. CloudPred achieves AUROC of 0.98 while identifying a specific subpopulation of CD4 T cells whose presence is highly indicative of lupus. CloudPred is a powerful new framework to predict clinical phenotypes from scRNA-seq data and to identify relevant cells. © 2021, CC BY.

关键词： Forecasting

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