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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jingya Huang Pablo Tostado-Marcos Sowmya Manojna Narasimha Aashish N. Patel Ezequiel. M. Arneodo Timothy Q. Gentner Gal Mishne Vikash Gilja Department of Electrical and Computer Engineering University of California San Diego CA Department of Bioengineering University of California San Diego CA Institute for Neural Computation University of California San Diego CA Neuroscience Graduate Program University of California San Diego CA Department of Physics University of California San Diego CA Department of Psychology University of California San Diego CA Halıcıoğlu Data Science Institute University of California San Diego CA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

State-of-the-art intracortical neuroprostheses currently enable communication at 60+ words per minute for anarthric individuals by training on over 10K sentences to account for phoneme variability in different word contexts. There is limited understanding about whether this performance can be maintained in decoding naturalistic speech with 40K+ word vocabularies across elicited, spontaneous, and conversational speech contexts. We introduce a vocal-unit-level generalization test to explicitly evaluate neural decoder performance with an expanded and more diverse behavioral repertoire. Tested on neural decoders modeling zebra finch vocalization, an analog to human vocal production, we compare three decoders with different input types: spike trains, neural factors, and firing rates. The factors and rates are latent neural features inferred using trained Latent Factor Analysis via Dynamical Systems (LFADS) models that capture the population neural dynamics during vocal production. While the conventional random holdout generalization error measure is similar for all three decoders, factor- and rate-based decoders outperform spike-based decoders when testing vocal-unit-holdout generalization error. These results suggest the later models better adapt to flexible vocalization inference when trained with partial observation of data variation, motivating further exploration of decoders incorporating latent neural and vocalization dynamics.

关键词： Training Adaptation models Vocabulary Firing Measurement uncertainty Neuroprostheses Production Decoding engineering in medicine and biology Testing

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Dynamic Structured Illumination Microscopy with a Neural Space-time Model

arXiv

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arXiv 2022年

作者： Cao, Ruiming Liu, Fanglin Linda Yeh, Li-Hao Waller, Laura The Department of Bioengineering University of California Berkeley BerkeleyCA94720 United States The Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States The Chan Zuckerberg Biohub San FranciscoCA94158 United States

Structured illumination microscopy (SIM) reconstructs a super-resolved image from multiple raw images captured with different illumination patterns;hence, acquisition speed is limited, making it unsuitable for dynamic scenes. We propose a new method, Speckle Flow SIM, that uses static patterned illumination with moving samples and models the sample motion during data capture in order to reconstruct the dynamic scene with super-resolution. Speckle Flow SIM relies on sample motion to capture a sequence of raw images. The spatio-temporal relationship of the dynamic scene is modeled using a neural space-time model with coordinate-based multi-layer perceptrons (MLPs), and the motion dynamics and the super-resolved scene are jointly recovered. We validate Speckle Flow SIM for coherent imaging in simulation and build a simple, inexpensive experimental setup with off-the-shelf components. We demonstrate that Speckle Flow SIM can reconstruct a dynamic scene with deformable motion and 1.88× the diffraction-limited resolution in experiment. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse problems

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Extreme compressed sensing of poisson rates from multiple measurements

arXiv

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arXiv 2021年

作者： Kota, Pavan K. LeJeune, Daniel Drezek, Rebekah A. Baraniuk, Richard G. The Department of Bioengineering Rice University HoustonTX77005 United States The Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

Compressed sensing (CS) is a signal processing technique that enables the efficient recovery of a sparse high-dimensional signal from low-dimensional measurements. In the multiple measurement vector (MMV) framework, a set of signals with the same support must be recovered from their corresponding measurements. Here, we present the first exploration of the MMV problem where signals are independently drawn from a sparse, multivariate Poisson distribution. We are primarily motivated by a suite of biosensing applications of microfluidics where analytes (such as whole cells or biomarkers) are captured in small volume partitions according to a Poisson distribution. We recover the sparse parameter vector of Poisson rates through maximum likelihood estimation with our novel Sparse Poisson Recovery (SPoRe) algorithm. SPoRe uses batch stochastic gradient ascent enabled by Monte Carlo approximations of otherwise intractable gradients. By uniquely leveraging the Poisson structure, SPoRe substantially outperforms a comprehensive set of existing and custom baseline CS algorithms. Notably, SPoRe can exhibit high performance even with one-dimensional measurements and high noise levels. This resource efficiency is not only unprecedented in the field of CS but is also particularly potent for applications in microfluidics in which the number of resolvable measurements per partition is often severely limited. We prove the identifiability property of the Poisson model under such lax conditions, analytically develop insights into system performance, and confirm these insights in simulated experiments. Our findings encourage a new approach to biosensing and are generalizable to other applications featuring spatial and temporal Poisson signals. © 2021, CC BY.

关键词： Monte Carlo methods

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A DIRECT METHOD OF GENERATING QUADRATIC CURVILINEAR TETRAHEDRAL MESHES USING AN ADVANCING FRONT APPROACH 29

A DIRECT METHOD OF GENERATING QUADRATIC CURVILINEAR TETRAHED...

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29th International Meshing Roundtable, IMR 2021

作者： Mohammadi, Fariba Shontz, Suzanne M. Department of Mechanical Engineering Information and Telecommunication Technology Center The University of Kansas 1520 W. 15th Street LawrenceKS66045 United States Department of Electrical Engineering and Computer Science Bioengineering Program Information and Telecommunication Technology Center University of Kansas 1520 W. 15th Street LawrenceKS66045 United States

ISBN: (纸本)9781733489010

Computational modeling and simulation of real-world problems, e.g., various applications in the automotive, aerospace, and biomedical industries, often involve geometric objects which are bounded by curved surfaces. The geometric modeling of such objects can be performed via high-order meshes. Such a mesh, when paired with a high-order partial differential equation (PDE) solver, can realize more accurate solution results with a decreased number of mesh elements (in comparison to a low-order mesh). There are several types of high-order mesh generation approaches, such as direct methods, a posteriori methods, and isogeometric analysis (IGA)-based spline modeling approaches. In this paper, we propose a direct, high-order, curvilinear tetrahedral mesh generation method using an advancing front technique. After generating the mesh, we apply mesh optimization to improve the quality and to take advantage of the degrees of freedom available in the initially straight-sided quadratic elements. Our method aims to generate high-quality tetrahedral mesh elements from various types of boundary representations including the cases where no computer-aided design files are available. Such a method is essential, for example, for generating meshes for various biomedical models where the boundary representation is obtained from medical images instead of CAD files. We present several numerical examples of second-order tetrahedral meshes generated using our method based on input triangular surface meshes. © 2021 held by the authors of the individual papers.

关键词： Mesh generation

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Neural network-based virtual staining of defocused autofluorescence images of label-free tissue

Neural network-based virtual staining of defocused autofluor...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Yijie Zhang Luzhe Huang Tairan Liu Keyi Cheng Kevin de Haan Yuzhu Li Bijie Bai Aydogan Ozcan Electrical and Computer Engineering Department University of California Los Angeles CA USA Bioengineering Department University of California Los Angeles CA USA California NanoSystems Institute (CNSI) University of California Los Angeles CA USA Department of Mathematics University of California Los Angeles CA USA Department of Surgery David Geffen School of Medicine University of California Los Angeles CA USA

We present a virtual staining framework to rapidly stain defocused autofluorescence images of label-free tissue. This technique saves image acquisition time by lowering the autofocusing precision of a microscope witho... 详细信息

ISBN: (纸本)9781957171258

关键词： Autofluorescence imaging Image metrics Image processing Neural networks Structured illumination microscopy Tissue

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Virtual Histological Staining of Label-Free Autopsy Tissue Sections via Deep Learning

Virtual Histological Staining of Label-Free Autopsy Tissue S...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Yuzhu Li Nir Pillar Jingxi Li Tairan Liu Di Wu Songyu Sun Guangdong Ma Kevin De Haan Luzhe Huang Yijie Zhang Sepehr Hamidi Anatoly Urisman Tal Keidar Haran William Dean Wallace Jonathan E. Zuckerman Aydogan Ozcan Electrical and Computer Engineering Department Bioengineering Department California NanoSystems Institute (CNSI) Computer Science Department University of California Los Angeles CA USA School of physics Xi'an Jiaotong University Xi'an Shaanxi China Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles CA USA Department of Pathology University of California San Francisco CA USA Department of Pathology Hadassah Hebrew University Medical Center Jerusalem Israel Department of Pathology Keck School of Medicine University of Southern California Los Angeles CA USA Department of Surgery University of California Los Angeles CA USA

We present deep learning-based virtual staining of label-free autopsy tissue sections, eliminating severe autolysis-induced artifacts caused by delayed fixation inherent in traditional histochemical H&E staining.

ISBN: (纸本)9798350369311

关键词： Deep learning Autopsy Lasers and electrooptics Electro-optical waveguides

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Snapshot hyperspectral imaging using a random phase mask and spectral filter array

Snapshot hyperspectral imaging using a random phase mask and...

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Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

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

We introduce a snapshot hyperspectral imager that uses a random phase mask, repeated spectral filter array, and compressive recovery to achieve high spatial and spectral resolution in a small form factor. © 2020 ... 详细信息

ISBN: (纸本)9781557528209

关键词： Hyperspectral imaging

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Label-free Imaging with Photonic Crystal Surface for Hematopoietic Stem Cell Differentiation

Label-free Imaging with Photonic Crystal Surface for Hematop...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Zhuo, Yue Choi, Ji Sun Marin, Thibault Yu, Hojeong Harley, Brendan A. Cunningham, Brian T. Gordon Center for Medical Imaging Massachusetts General Hospital BostonMA02114 United States Center for Systems Biology Massachusetts General Hospital BostonMA02114 United States Department of Radiology Harvard Medical School BostonMA02114 United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Chemical and Biomolecular Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Micro and Nanotechnology Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States

With the Photonic Resonator Outcoupler Microscopy (PROM), it is possible to detect and monitor weak-adhesive HSC adhesion without labeling. These findings indicate that PROM can be used to quantitatively and dynamical... 详细信息

ISBN: (纸本)9781957171340

关键词： Stem cells

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Author Correction: Endocisternal interfaces for minimally invasive neural stimulation and recording of the brain and spinal cord (Nov, 10.1038/s41551-024-01281-9, 2024)

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NATURE BIOMEDICAL engineering 2025年 1页

作者： Chen, Joshua C. Dhuliyawalla, Abdeali Garcia, Robert Robledo, Ariadna Woods, Joshua E. Alrashdan, Fatima O'Leary, Sean Husain, Adam Price, Anthony Crosby, Scott Felicella, Michelle M. Wakhloo, Ajay K. Karas, Patrick Provenza, Nicole Goodman, Wayne Sheth, Sameer A. Sheth, Sunil A. Robinson, Jacob T. Kan, Peter Department of Bioengineering Rice University Houston TX USA Department of Electrical and Computer Engineering Rice University Houston TX USA Department of Neurosurgery University of Texas Medical Branch Galveston TX USA Neuromonitoring Associates LLC Las Vegas NV USA Department of Pathology University of Texas Medical Branch Galveston TX USA Department of Radiology TUFTS University School of Medicine Boston MA USA Deinde Medical Miramar FL USA Department of Neurosurgery Baylor College of Medicine Houston TX USA Department of Psychiatry Baylor College of Medicine Houston TX USA Department of Neurology McGovern Medical School at UTHealth Houston TX USA

Correction to: Nature Biomedical engineeringhttps://***/10.1038/s41551-024-01281-9, published online 11 November 2024. In the version of this article initially published, the Data availability section did not include the statement "The data that support the findings of this study are openly available via figshare at https://***/10.6084/***.27616083" as is now amended in the HTML and PDF versions of the article. © The Author(s), under exclusive licence to Springer Nature Limited 2025.

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Deep learning-enhanced paper-based vertical flow assay for high-sensitivity troponin detection using nanoparticle amplification

arXiv

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arXiv 2024年

作者： Han, Gyeo-Re Goncharov, Artem Eryilmaz, Merve Joung, Hyou-Arm Ghosh, Rajesh Yim, Geon Chang, Nicole Kim, Minsoo Ngo, Kevin Veszpremi, Marcell Liao, Kun Garner, Omai B. Di Carlo, Dino Ozcan, Aydogan Electrical áComputer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States Department of Chemistry and Biochemistry University of California Los AngelesCA90095 United States Department of Pathology and Laboratory Medicine University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Department of Surgery University of California Los AngelesCA90095 United States

Successful integration of point-of-care testing (POCT) into clinical settings requires improved assay sensitivity and precision to match laboratory standards. Here, we show how innovations in amplified biosensing, imaging, and data processing, coupled with deep learning, can help improve POCT. To demonstrate the performance of our approach, we present a rapid and cost-effective paper-based high-sensitivity vertical flow assay (hs-VFA) for quantitative measurement of cardiac troponin I (cTnI), a biomarker widely used for measuring acute cardiac damage and assessing cardiovascular risk. The hs-VFA includes a colorimetric paper-based sensor, a portable reader with time-lapse imaging, and computational algorithms for digital assay validation and outlier detection. Operating at the level of a rapid at-home test, the hs-VFA enabled the accurate quantification of cTnI using 50 μL of serum within 15 min per test and achieved a detection limit of 0.2 pg/mL, enabled by gold ion amplification chemistry and time-lapse imaging. It also achieved high precision with a coefficient of variation of Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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