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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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Preemptive periodic epidemic control reduces life and healthcare system costs without aggravation of social and economic losses

arXiv

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arXiv 2021年

作者： De Nicolao, Giuseppe Colaneri, Marta Filippo, Alessandro Di Blanchini, Franco Bolzern, Paolo Colaneri, Patrizio Giordano, Giulia Bruno, Raffaele Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Division of Infectious Diseases I Fondazione IRCCS Policlinico San Matteo Pavia Italy Department of Mathematics Computer Science and Physics University of Udine Udine Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Department of Industrial Engineering University of Trento Trento Italy Department of Clinical Surgical Diagnostic and Pediatric Sciences University of Pavia Pavia Italy

Many countries are managing COVID-19 epidemic by switching between lighter and heavier restrictions. While an open-close and a close-open cycle have comparable socio-economic costs, the former leads to a much heavier burden in terms of deaths and pressure on the healthcare system. An empirical demonstration of the toll ensuing from procrastination was recently observed in Israel, where both cycles were enforced from late August to mid-December 2020, yielding some 1,600 deaths with open-close compared to 440 with close-open. © 2021, CC BY.

关键词： Losses

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Biopsy-free Virtual Histology of Skin Using Reflectance Confocal Microscopy and Deep Learning

Biopsy-free Virtual Histology of Skin Using Reflectance Conf...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Jingxi Li Jason Garfinkel Xiaoran Zhang Di Wu Yijie Zhang Kevin de Haan Hongda Wang Tairan Liu Bijie Bai Yair Rivenson Gennady Rubinstein Philip O. Scumpia 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 Dermatology and Laser Centre Studio City CA USA Computer Science Department University of California Los Angeles CA USA Division of Dermatology University of California Los Angeles CA USA Department of Dermatology Veterans Affairs Greater Los Angeles Healthcare System Los Angeles CA USA Department of Surgery University of California Los Angeles CA USA

We report in vivo virtual histology of skin without a biopsy, where deep learning is used to virtually stain tissue and generate hematoxylin and eosin (H&E)-like microscopic images of skin using a reflectance conf... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Reflectivity Deep learning In vivo Histopathology Microscopy Biopsy Lasers and electrooptics

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3D OCT characterization and quantification of refractive indices of bacteria and biofilms with antibiotic interventions

3D OCT characterization and quantification of refractive ind...

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Optical Molecular Probes, Imaging and Drug Delivery, OMP 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

作者： Zaki, Farzana R. Sudhir, Kavya Won, Jungeun Monroy, Guillermo L. Choi, Honggu Chaney, Eric J. Spillman, Darold R. Boppart, Stephen A. Beckman Institute for Advanced Science and Technology 405 North Mathews Avenue UrbanaIL61801 United States University of Illinois at Urbana–Champaign Department of Bioengineering 1304 West Springfield Avenue UrbanaIL61801 United States University of Illinois at Urbana–Champaign Department of Electrical and Computer Engineering 306 North Wright Street UrbanaIL61801 United States Carle Illinois College of Medicine University of Illinois at Urbana-Champaign 807 S. Wright St. ChampaignIL61820 United States

Using 3D OCT, we have quantified the refractive indices of ear-infection causing bacteria and biofilms with antibiotics interventions. This approach reveals changing optical properties of bacteria in biofilms followin...

ISBN: (纸本)9781557528209

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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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ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers

arXiv

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arXiv 2024年

作者： Chen, Zihao Xiao, Zhili Akl, Mahmoud Leugring, Johannes Olajide, Omowuyi Malik, Adil Dennler, Nik Harper, Chad Bose, Subhankar Gonzalez, Hector A. Samaali, Mohamed Liu, Gengting Eshraghian, Jason Pignari, Riccardo Urgese, Gianvito Andreou, Andreas G. Shankar, Sadasivan Mayr, Christian Cauwenberghs, Gert Chakrabartty, Shantanu Department of Electrical and Systems Engineering Washington University in St. Louis One Brookings Drive St. LouisMO63130 United States SpiNNcloud Systems GmbH Freibergerstr. 37 Dresden01067 Germany Department of Bioengineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of Electrical and Electronic Engineering Imperial College London Exhibition Rd LondonSW7 2AZ United Kingdom International Centre for Neuromorphic Engineering Western Sydney University Penrith Second Ave KingswoodNSW2747 Australia Biocomputation Group University of Hertfordshire Exhibition Rd LondonSW7 2AZ United Kingdom Department of Physics University of California Berkeley University Avenue and Oxford St BerkeleyCA94720 United States Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute University of California Berkeley University Avenue and Oxford St BerkeleyCA94720 United States Highly-Parallel VLSI-Systems and Neuro-Microelectronics Technische Universität Dresden Mommsenstraße 12 Dresden01069 Germany Department of Electrical and Computer Engineering University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States Politecnico di Torino Corso Duca degli Abruzzi 24 Torino10129 Italy Department of Electrical and computer engineering Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Materials Science and Engineering Stanford University 450 Jane Stanford Way StanfordCA94305 United States Scads.AI Center for scalable data analytics and artificial intelligence Strehlener Street 12 14 Dresden01069 Germany

We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mechanical tunneling based threshold-annealing process. The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing dynamics onto a network of integrate-and-fire neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer and the resulting spiking dynamics replicates the optimal escape mechanism and convergence of SA, particularly at low-temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved benchmark combinatorial optimization problems such as MAX-CUT and Max Independent Set. Across multiple runs, NeuroSA consistently generates distribution of solutions that are concentrated around the state-of-the-art results (within 99%) or surpass the current state-of-the-art solutions for Max Independent Set benchmarks. Furthermore, NeuroSA is able to achieve these superior distributions without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform. © 2024, CC BY-NC-SA.

关键词： Combinatorial optimization

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Virtual histological staining of unlabeled autopsy tissue

arXiv

引用

arXiv 2023年

作者： Li, Yuzhu Pillar, Nir Li, Jingxi Liu, Tairan Wu, Di Sun, Songyu Ma, Guangdong de Haan, Kevin Huang, Luzhe Hamidi, Sepehr Urisman, Anatoly Haran, Tal Keidar Dean Wallace, William Zuckerman, Jonathan E. Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los Angeles90095 United States University of California Los AngelesCA90095 United States Computer Science Department University of California Los AngelesCA90095 United States School of Physics Xi'an Jiaotong University Shaanxi Xi’an710049 China Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los AngelesCA90095 United States Department of Pathology University of California San FranciscoCA94143 United States Department of Pathology Hadassah Hebrew University Medical Center Jerusalem91120 Israel Department of Pathology Keck School of Medicine University of Southern California Los AngelesCA90033 United States Department of Surgery University of California Los AngelesCA90095 United States

Histological examination is a crucial step in an autopsy;however, the traditional histochemical staining of postmortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining. Copyright © 2023, The Authors. All rights reserved.

关键词： Tissue

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Modified Hamiltonian for a particle in an infinite box that includes wall effects

arXiv

引用

arXiv 2022年

作者： Cohen, Leon Mayato, Rafael Sala Laughlin, Patrick Department of Physics Hunter College of the City University of New York 695 Park Ave. New YorkNY10065 United States Departamento de Física and IUDEA Universidad de La Laguna La Laguna S/C de Tenerife 38204 Spain Departments of Bioengineering and Electrical & Computer Engineering University of Pittsburgh PittsburghPA15261 United States

We give a modified Hamiltonian for a particle in a box with infinite potential walls that takes into account wall effects. The Hamiltonian is expressed in both the position and momentum representation. In the momentum representation the eigenvalue problem for energy is an integral equation. Copyright © 2022, The Authors. All rights reserved.

关键词： Hamiltonians

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An electronically-controlled digital ferrofluidic architecture for scalable and addressable bioanalytical operations 23

An electronically-controlled digital ferrofluidic architectu...

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23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019

作者： Yu, Wenzhuo Wang, Yilian Lin, Haisong Chen, Nathan He, Xu Sun, Kevin Di Carlo, Dino Emaminejad, Sam Department of Electrical and Computer Engineering University of California Los AngelesCA United States Department of Bioengineering University of California Los AngelesCA United States

ISBN: (纸本)9781733419000

We report a scalable and addressable digital microfluidic platform, enabled by the electronically-controlled actuation of biocompatible magnetic fluids, which implements diverse operations, including droplet transport, generation, and dispensing to render sample-to-answer bioanalytical assays. The digital ferrofluidic platform provides a new scalable, cost-effective, and reduced failure-mode approach to automate small-volume fluid handling, thus enabling wide-ranging integrated diagnostic and chemistry operations. © 2019 CBMS-0001.

关键词： Digital microfluidics

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ACCORDION: Clustering and selecting relevant data for guided network extension and query answering

arXiv

引用

arXiv 2020年

作者： Ahmed, Yasmine Telmer, Cheryl Miskov-Zivanov, Natasa Electrical and Computer Engineering Department Department of Biological Sciences Carnegie Mellon University PittsburghPA United States Bioengineering Computational and Systems Biology University of Pittsburgh

Querying new information from knowledge sources, in general, and published literature, in particular, aims to provide precise and quick answers to questions raised about a system under study. In this paper, we present ACCORDION (Automated Clustering Conditional On Relating Data of Interactions tO a Network), a novel tool and a methodology to enable efficient answering of biological questions by automatically assembling new or expanding existing models using published literature. Our approach integrates information extraction and clustering with simulation and formal analysis to allow for automated iterative process that includes assembling, testing and selecting most relevant models, given a set of desired system properties. We demonstrate our methodology on a model of the circuitry that controls T cell differentiation. To evaluate our approach, we compare the model that we obtained, using our automated model extension approach, with the previously published manually extended T cell model. Besides being able to automatically and rapidly reconstruct the manually extended model, ACCORDION can provide multiple viable extended model versions. As such, it replaces large number of tedious or even impractical manual experiments and guides alternative interventions in real biological systems. Copyright © 2020, The Authors. All rights reserved.

关键词： T-cells

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