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Lifestyle Medicine 2024年第1期5卷

作者： Kurniawan, Jonas F. Lee, Sanghee Le, Hoang-Kim Nguyen, Andrew K. L. Sit, Nathan L. J. Pham, Timothy Tjhia, Boris Gottlieb, Ryan Li, Carleen Shin, Andrew J. Garcia, Hassler Bueno Wu, Vincent M. Mendoza, Tris R DiPina, Thomas Ornelas, Gladys Chiang, George Jamieson, Christina A. M. Santiago-Lastra, Yahir Coleman, Todd P. Department of Bioengineering University of California San Diego La Jolla CA United States Materials Science and Engineering Program University of California San Diego La Jolla CA United States Department of Bioengineering Stanford University Stanford CA United States Department of Urology University of California San Diego La Jolla CA United States Pediatric Urology Rady Children's Hospital San Diego San Diego CA United States Department of Urology Baylor Scott & White Temple TX United States Department of Physics University of California San Diego La Jolla CA United States Department of Electrical and Computer Engineering University of California San Diego La Jolla CA United States Department of Nanoengineering University of California San Diego La Jolla CA United States Department of Materials Science and Engineering Stanford University Stanford CA United States Division of Pediatric Urology University of California San Diego La Jolla CA United States

We developed a high-resolution and noninvasive multichannel urodynamic system combined with a novel signal-processing algorithm tailored to extract electromyographic (EMG) activity from the urinary bladder. We utilized a flexible noninvasive biopotential recording system to collect an array of EMG signals from the urinary bladder of human subjects. This study also presents the use of an existing artifact rejection methodology to autonomously extract an EMG signal from an environment prone to motion artifacts. © 2023 The Authors. Lifestyle Medicine published by John Wiley & Sons Ltd.

关键词： electrical signal from the urinary bladder flexible electronics noninvasive multichannel urodynamic study voluntary voiding

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cytoNet: Spatiotemporal network analysis of cell communities (vol 18, e1009846, 2022)

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PLOS COMPUTATIONAL BIOLOGY 2022年第11期18卷 e1009846页

作者： Mahadevan, A. S. Long, B. L. Hu, C. W. Ryan, D. T. Grandel, N. E. Department of Bioengineering University of Pennsylvania Philadelphia Pennsylvania United States of America Department of Bioengineering Rice University Houston Texas United States of America Department of Biomedical Engineering University of Texas at San Antonio San Antonio Texas United States of America Department of Computer Science University of Texas at San Antonio San Antonio Texas United States of America Systems Synthetic and Physical Biology Program Rice University Houston Texas United States of America Biophysics Graduate Program University of California Berkeley California United States of America Department of Oral & Maxillofacial Surgery University of Texas Health Science Center at San Antonio San Antonio Texas United States of America Department of Electrical and Computer Engineering Rice University Houston Texas United States of America Department of Biosciences Rice University Houston Texas United States of America UTSA–UT Health Joint Graduate Group in Biomedical Engineering San Antonio Texas United States of America Programs in Integrated Biomedical Sciences Translational Sciences Radiological Sciences University of Texas Health Science Center at San Antonio San Antonio Texas United States of America UTSA AI MATRIX Consortium San Antonio Texas United States of America

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet’s capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo , 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.

关键词： Calcium imaging Fluorescence imaging Growth factors Centrality Cell cycle and cell division Endothelial cells Network analysis Stem cells

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Author Correction: Dynamical flexible inference of nonlinear latent factors and structures in neural population activity

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Nature biomedical engineering 2025年第2期9卷 283页

作者： Hamidreza Abbaspourazad Eray Erturk Bijan Pesaran Maryam M Shanechi Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. Departments of Neurosurgery Neuroscience and Bioengineering University of Pennsylvania Philadelphia PA USA. Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. shanechi@usc.edu. Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA USA. shanechi@usc.edu.

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Author Correction: Miniscope3D: optimized single-shot miniature 3D fluorescence microscopy

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Light, science & applications 2023年第1期12卷 93页

作者： Kyrollos Yanny Nick Antipa William Liberti Sam Dehaeck Kristina Monakhova Fanglin Linda Liu Konlin Shen Ren Ng Laura Waller UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley CA 94720 USA. kyrollosyanny@***. Department of Electrical Engineering & Computer Sciences University of California Berkeley CA 94720 USA. naantipa@***. Department of Electrical Engineering & Computer Sciences University of California Berkeley CA 94720 USA. TIPs Department Université libre de Bruxelles (ULB) 1050 Brussels Belgium. UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley CA 94720 USA.

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Hybrid dedicated and distributed coding in PMd/M1 provides separation and interaction of bilateral arm signals

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PLoS Computational Biology 2021年第11期17卷 e1009615页

作者： Dixon, Tanner C. Merrick, Christina M. Wallis, Joni D. Ivry, Richard B. Carmena, Jose M. UC Berkeley-UCSF Graduate Program in Bioengineering University of California-Berkeley BerkeleyCA United States Department of Psychology University of California-Berkeley BerkeleyCA United States Helen Wills Neuroscience Institute University of California-Berkeley BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California-Berkeley BerkeleyCA United States

Pronounced activity is observed in both hemispheres of the motor cortex during preparation and execution of unimanual movements. The organizational principles of bi-hemispheric signals and the functions they serve throughout motor planning remain unclear. Using an instructed-delay reaching task in monkeys, we identified two components in population responses spanning PMd and M1. A "dedicated"component, which segregated activity at the level of individual units, emerged in PMd during preparation. It was most prominent following movement when M1 became strongly engaged, and principally involved the contralateral hemisphere. In contrast to recent reports, these dedicated signals solely accounted for divergence of arm-specific neural subspaces. The other "distributed"component mixed signals for each arm within units, and the subspace containing it did not discriminate between arms at any stage. The statistics of the population response suggest two functional aspects of the cortical network: One that spans both hemispheres for supporting preparatory and ongoing processes, and another that is predominantly housed in the contralateral hemisphere and specifies unilateral output. © 2021 Dixon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Population statistics

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Plug-and-Play Stability for Intracortical Brain-computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication

arXiv

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arXiv 2023年

作者： Fan, Chaofei Hahn, Nick Kamdar, Foram Avansino, Donald Wilson, Guy H. Hochberg, Leigh Shenoy, Krishna V. Henderson, Jaimie M. Willett, Francis R. Bio-X Program Stanford University United States Department of Neuroscience Stanford University United States Department of Neurosurgery Stanford University United States Department of Neurobiology Stanford University United States Department of Bioengineering Stanford University United States Department of Computer Science Stanford University United States Wu Tsai Neurosciences Institute Stanford University United States Howard Hughes Medical Institute Stanford University United States Department of Electrical Engineering Stanford University United States School of Engineering Carney Institute for Brain Science Brown University United States VA RR&D Center for Neurorestoration and Neurotechnology VA Providence Healthcare System United States Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School United States

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs. Copyright © 2023, The Authors. All rights reserved.

关键词： Brain computer interface

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Dynamic optical contrast imaging for real-time delineation of tumor resection margins using head and neck cancer as a model

arXiv

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arXiv 2022年

作者： Hu, Yong Huang, Shan Han, Albert Y. Moon, Seong Krane, Jeffrey F. Stafsudd, Oscar Grundfest, Warren St. John, Maie A. Los AngelesCA United States Department of Bioengineering UCLA Los AngelesCA United States Department of Materials Science and Engineering UCLA Los AngelesCA United States UCLA Head and Neck Cancer Program UCLA Medical Center Los AngelesCA United States Jonsson Comprehensive Cancer Center UCLA Medical Center Los AngelesCA United States Department of Pathology & Laboratory Medicine UCLA Medical Center Los AngelesCA United States Department of Electrical and Computer Engineering UCLA Los AngelesCA United States

Complete surgical resection of the tumor for Head and neck squamous cell carcinoma (HNSCC) remains challenging, given the devastating side effects of aggressive surgery and the anatomic proximity to vital structures. To address the clinical challenges, we introduce a widefield, label-free imaging tool that can assist surgeons delineate tumor margins real-time. We assume that autofluorescence lifetime is a natural indicator of the health level of tissues, and ratio-metric measurement of the emission-decay state to the emission-peak state of excited fluorophores will enable rapid lifetime mapping of tissues. Here, we describe the principle, instrumentation, characterization of the imager and the intraoperative imaging of resected tissues from 13 patients undergoing head and neck cancer resection. 20 x 20 mm2 imaging takes 2 second/frame with a working distance of 50 mm, and characterization shows that the spatial resolution reached 70 μm and the least distinguishable fluorescence lifetime difference is 0.14 ns. Tissue imaging and Hematoxylin-Eosin stain slides comparison reveals its capability of delineating cancerous boundaries with submillimeter accuracy and a sensitivity of 91.86% and specificity of 84.38%. Copyright © 2022, The Authors. All rights reserved.

关键词： Tumors

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AI-driven 3D Spatial Transcriptomics

arXiv

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arXiv 2025年

作者： Almagro-Pérez, Cristina Song, Andrew H. Weishaupt, Luca Kim, Ahrong Jaume, Guillaume Williamson, Drew F.K. Hemker, Konstantin Lu, Ming Y. Singh, Kritika Chen, Bowen Le, Long Phi Baras, Alexander S. Jiang, Sizun Bashashati, Ali Liu, Jonathan T.C. Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Rutgers Robert Wood Johnson Medical School New BrunswickNJ United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Broad Institute of Harvard and MIT CambridgeMA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology Dana Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications1. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue2–4. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability5–8. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), an AI framework that leverages 3D tissue morphology and minimal 2D ST to predict volumetric 3D ST. By pretraining on diverse 3D morphology–transcriptomic pairs from heterogeneous tissue samples and then fine-tuning on minimal 2D ST data from a specific volume of interest, VORTEX learns both generic tissue-related and sample-specific morphological correlates of gene expression. This approach enables dense, high-throughput, and fast 3D ST, scaling seamlessly to large tissue volumes far beyond the reach of existing 3D ST techniques. By offering a cost-effective and minimally destructive route to obtaining volumetric molecular insights, we anticipate that VORTEX will accelerate biomarker discovery and our understanding of morphomolecular associations and cell states in complex tissues. Interactive 3D ST volumes can be viewed at https://***/. © 2025, CC BY-NC-ND.

关键词： Vortex flow

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TEAM: TRIANGULAR-MESH ADAPTIVE AND MULTISCALE PROTON SPOT GENERATION METHOD

International Journal of Particle Therapy

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International Journal of Particle Therapy 2024年 12卷

作者： Chao Wang Bowen Lin Yuting Lin Suzanne M. Shontz Weizhang Huang Ronald Chen Hao Gao Department Of Radiation Oncology University of Kansas Medical Center Kansas City United States of America Department Of Intervention Medicine The Second Hospital of Shandong University Jinan China Department Of Electrical Engineering And Computer Science Institute For Information Sciences Bioengineering Program University of Kansas Lawrence United States of America Department Of Mathematics University of Kansas Lawrence United States of America

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Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array

arXiv

引用

arXiv 2020年

作者： Monakhova, Kristina Yanny, Kyrollos Aggarwal, Neerja Waller, Laura Department of Electrical Engineering & Computer Sciences University of California BerkeleyCA94720 United States UCB/UCSF Joint Graduate Program in Bioengineering University of California BerkeleyCA94720 United States

Hyperspectral imaging is useful for applications ranging from medical diagnostics to crop monitoring;however, traditional scanning hyperspectral imagers are prohibitively slow and expensive for widespread adoption. Snapshot techniques exist but are often confined to bulky bench-top setups or have low spatio-spectral resolution. In this paper, we propose a novel, compact, and inexpensive computational camera for snapshot hyperspectral imaging. Our system consists of a repeated spectral filter array placed directly on the image sensor and a diffuser placed close to the sensor. Each point in the world maps to a unique pseudorandom pattern on the spectral filter array, which encodes multiplexed spatio-spectral information. A sparsity-constrained inverse problem solver then recovers the hyperspectral volume with good spatio-spectral resolution. By using a spectral filter array, our hyperspectral imaging framework is flexible and can be designed with contiguous or non-contiguous spectral filters that can be chosen for a given application. We provide theory for system design, demonstrate a prototype device, and present experimental results with high spatio-spectral resolution. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement Copyright © 2020, The Authors. All rights reserved.

关键词： Spectroscopy

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