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27th International Conference on DNA Computing and Molecular Programming, DNA 2021

作者： Linder, Johannes Chen, Yuan-Jyue Wong, David Seelig, Georg Ceze, Luis Strauss, Karin University of Washington Paul G. Allen School of Computer Science and Engineering SeattleWA United States Microsoft Research RedmondWA United States University of Washington Department of Bioengineering SeattleWA United States University of Washington Department of Electrical and Computer Engineering SeattleWA United States

ISBN: (纸本)9783959772051

Molecular programming - a paradigm wherein molecules are engineered to perform computation - shows great potential for applications in nanotechnology, disease diagnostics and smart therapeutics. A key challenge is to identify systematic approaches for compiling abstract models of computation to molecules. Due to their wide applicability, one of the most useful abstractions to realize is neural networks. In prior work, real-valued weights were achieved by individually controlling the concentrations of the corresponding "weight" molecules. However, large-scale preparation of reactants with precise concentrations quickly becomes intractable. Here, we propose to bypass this fundamental problem using Binarized Neural Networks (BNNs), a model that is highly scalable in a molecular setting due to the small number of distinct weight values. We devise a noise-tolerant digital molecular circuit that compactly implements a majority voting operation on binary-valued inputs to compute the neuron output. The network is also rate-independent, meaning the speed at which individual reactions occur does not affect the computation, further increasing robustness to noise. We first demonstrate our design on the MNIST classification task by simulating the system as idealized chemical reactions. Next, we map the reactions to DNA strand displacement cascades, providing simulation results that demonstrate the practical feasibility of our approach. We perform extensive noise tolerance simulations, showing that digital molecular neurons are notably more robust to noise in the concentrations of chemical reactants compared to their analog counterparts. Finally, we provide initial experimental results of a single binarized neuron. Our work suggests a solid framework for building even more complex neural network computation. © 2021 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Neural networks

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All-Optical Phase Conjugation Using Diffractive Wavefront Processing

arXiv

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arXiv 2023年

作者： Shen, Che-Yung Li, Jingxi Gan, Tianyi Jarrahi, Mona Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with various applications ranging from imaging to beam focusing. Here, we present the design of a diffractive wavefront processor to approximate all-optical phase conjugation operation for input fields with phase aberrations. Leveraging deep learning, a set of passive diffractive layers was optimized to all-optically process an arbitrary phase-aberrated coherent field from an input aperture, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated the efficacy of this wavefront processor by 3D fabricating diffractive layers trained using deep learning and performing OPC on phase distortions never seen by the diffractive processor during its training. Employing terahertz radiation, our physical diffractive processor successfully performed the OPC task through a shallow spatially-engineered volume that axially spans tens of wavelengths. In addition to this transmissive OPC configuration, we also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and scalable nature, our diffractive wavefront processor can be used for diverse OPC-related applications, e.g., turbidity suppression and aberration correction, and is also adaptable to different parts of the electromagnetic spectrum, especially those where cost-effective wavefront engineering solutions do not exist. Copyright © 2023, The Authors. All rights reserved.

关键词： Wavefronts

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Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning

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Biomedical engineering Frontiers 2022年第1期3卷 422-436页

作者： Bijie Bai Hongda Wang Yuzhu Li Kevin de Haan Francesco Colonnese Yujie Wan Jingyi Zuo Ngan B.Doan Xiaoran Zhang Yijie Zhang Jingxi Li Xilin Yang Wenjie Dong Morgan Angus Darrow Elham Kamangar Han Sung Lee Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos Angeles 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCAUSA Computer Science Department University of CaliforniaLos AngelesCAUSA Physics and Astronomy Department University of CaliforniaLos AngelesCA 90095USA Translational Pathology Core Laboratory University of CaliforniaLos AngelesCA 90095USA Statistics Department University of CaliforniaLos AngelesCA 90095USA Department of Pathology and Laboratory Medicine University of California at DavisSacramentoCA 95817USA Department of Surgery University of CaliforniaLos AngelesCA 90095USA

The immunohistochemical(IHC)staining of the human epidermal growth factor receptor 2(HER2)biomarker is widely practiced in breast tissue analysis,preclinical studies,and diagnostic decisions,guiding cancer treatment and investigation of ***2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist,which typically takes one day to prepare in a laboratory,increasing analysis time and associated ***,we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images,matching the standard HER2 IHC staining that is chemically performed on the same tissue *** efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis,in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images(WSIs)to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts.A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail,membrane clearness,and absence of staining artifacts with respect to their immunohistochemically stained *** virtual HER2 staining framework bypasses the costly,laborious,and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.

关键词： HER2 consuming Deep

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Review of bio-optical imaging systems with a high space-bandwidth product

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Advanced Photonics 2021年第4期3卷 1-18页

作者： Jongchan Park David J.Brady Guoan Zheng Lei Tian Liang Gao University of California Department of BioengineeringLos AngelesCaliforniaUnited States University of Arizona James C.Wyant College of Optical SciencesTucsonArizonaUnited States University of Connecticut Department of Biomedical EngineeringStorrsConnecticutUnited States University of Connecticut Department of Electrical and Computer EngineeringStorrsConnecticutUnited States Boston University Department of Electrical and Computer EngineeringBostonMassachusettsUnited States

Optical imaging has served as a primary method to collect information about biosystems across scales—from functionalities of tissues to morphological structures of cells and even at biomolecular ***,to adequately characterize a complex biosystem,an imaging system with a number of resolvable points,referred to as a space-bandwidth product(SBP),in excess of one billion is typically *** a gigapixel-scale far exceeds the capacity of current optical imagers,compromises must be made to obtain either a low spatial resolution or a narrow field-of-view(FOV).The problem originates from constituent refractive optics—the larger the aperture,the more challenging the correction of lens ***,it is impractical for a conventional optical imaging system to achieve an SBP over hundreds of *** address this unmet need,a variety of high-SBP imagers have emerged over the past decade,enabling an unprecedented resolution and FOV beyond the limit of conventional *** provide a comprehensive survey of high-SBP imaging techniques,exploring their underlying principles and applications in bioimaging.

关键词： space-bandwidth product bioimaging gigapixel imaging high resolution wide field of view

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COMPARING QDESS AND RAFO-4 PERFORMANCE IN 5-MINUTE, SIMULTANEOUS 3D T 2 MAPPING AND MORPHOLOGICAL MR IMAGING

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Osteoarthritis and Cartilage 2025年第6期33卷 792-793页

作者： Krithika Balaji Pedro M. Vicente Simran Kukran Michael Mendoza Anil A. Bharath Peter J. Lally Neal K. Bangerter Department of Bioengineering Imperial College London London Please select an option below United Kingdom Department of Brain Sciences Imperial College London London United Kingdom School of Medicine Case Western Reserve University Cleveland Ohio United States IN-CYPHER Imperial Global: Singapore Singapore Singapore Boise State University Department of Electrical and Computer Engineering Boise Idaho United States

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Computational optical sensor with a paper-based peptide panel assay for point-of-care testing of Lyme disease

Computational optical sensor with a paper-based peptide pane...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Hyou-Arm Joung Rajesh Ghosh Artem Goncharov Kevin Ngo Barath Palanisamy Elizabeth J. Horn Paul M. Arnaboldi Raymond J. Dattwyler Omai B. Garner Dino Di Carlo Aydogan Ozcan Department of Electrical and Computer Engineering California NanoSystems Institute Lyme Disease Biobank Portland Oregon USA Department of Microbiology/Immunology New York Medical College Valhalla New York USA Department of Pathology and Medicine University of California Los Angeles USA Department of Bioengineering

We present a computational paper-based optical sensor for point-of-care serodiagnosis of Lyme disease. Using blindly tested human serum samples, we achieved 91.7% sensitivity and 100% specificity against two-tier sero... 详细信息

ISBN: (纸本)9781957171258

关键词： Gold Image sensors Neural networks Optical sensing

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Fast non-iterative algorithm for 3D point-cloud holography

arXiv

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arXiv 2023年

作者： Ersaro, Nathan Tessema Yalcin, Cem Murray, Liz Kabuli, Leyla Waller, Laura Muller, Rikky Graduate Program in Bioengineering University of California Berkeley University of California San Francisco BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States

Recently developed iterative and deep learning-based approaches to computer-generated holography (CGH) have been shown to achieve high-quality photorealistic 3D images with spatial light modulators. However, such approaches remain overly cumbersome for patterning sparse collections of target points across a photoresponsive volume in applications including biological microscopy and material processing. Specifically, in addition to requiring heavy computation that cannot accommodate real-time operation in mobile or hardware-light settings, existing sampling-dependent 3D CGH methods preclude the ability to place target points with arbitrary precision, limiting accessible depths to a handful of planes. Accordingly, we present a non-iterative point cloud holography algorithm that employs fast deterministic calculations in order to efficiently allocate patches of SLM pixels to different target points in the 3D volume and spread the patterning of all points across multiple time frames. Compared to a matched-performance implementation of the iterative Gerchberg-Saxton algorithm, our algorithm's relative computation speed advantage was found to increase with SLM pixel count, reaching >100,000x at 512x512 array format. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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The Effect of Physical Structural Properties on Electrochemical Properties of Ruthenium Oxide for Neural Stimulating and Recording Electrodes

The Effect of Physical Structural Properties on Electrochemi...

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

作者： Yupeng Wu Miguel Figueroa Hernández Tian Lei Siddarth Jayakumar Rohan R. Lalapet Alexandra Joshi-Imre Mark E. Orazem Kevin J. Otto Stuart F. Cogan Department of Material Science Engineering The University of Texas at Dallas Richardson TX USA Department of Bioengineering The University of Texas at Dallas Richardson TX USA Department of Electrical & Computer Engineering The University of Texas at Dallas Richardson TX USA Office of Research and Innovation The University of Texas at Dallas Richardson TX USA Department of Chemical Engineering University of Florida Gainesville FL USA J. Crayton Pruitt Family Department of Biomedical Engineering University of Florida Gainesville FL USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Recently, there has been a growing interest in ruthenium oxide (RuOx) as an alternative mixed-conductor oxide to SIROF as an electrode coating. RuOx is recognized as a faradic charge-injection coating with high CSCc, long-term pulsing stability, and low impedance. We examined how the structural properties of sputter-deposited RuOx influence its electrochemical performance as an electrode coating for neural stimulation and recording. Thin film RuOx was deposited under various pressures: 5 mTorr, 15 mTorr, 30 mTorr, and 60 mTorr on wafer-based planar test structures. Electrochemical characterizations, including electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and voltage transient (VT), were employed. The structure of RuOx films was characterized by scanning electron microscope (SEM). Our findings revealed that the sputtering pressure significantly influences the growth of the RuOx film, subsequently affecting its electrochemical performance. The results indicate that the electrochemical performance of RuOx can be optimized by adjusting the deposition conditions to achieve a favorable balance between electronic and ionic *** Relevance— This research underscores the potential for optimizing the structural properties of RuOx to enhance its electrochemical capabilities for neural stimulation and recording.

关键词： Electrodes Scanning electron microscopy Ruthenium Films Voltage Recording Coatings Impedance Transient analysis Sputtering

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Direct measurement of DNA bending by quantum magnetic imaging of a nanomechanical torque balance

引用

Physical Review Applied 2024年第6期22卷 064044-064044页

作者： Zeeshawn Kazi Isaac M. Shelby Ruhee Nirodi Joseph Turnbull Hideyuki Watanabe Kohei M. Itoh Paul A. Wiggins Kai-Mei C. Fu Department of Physics University of Washington Seattle Washington 98105 USA Present address: Department of Electrical and Computer Engineering Princeton University Princeton New Jersey 08540 USA. National Institute of Advanced Industrial Science and Technology Tsukuba Central 2 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan School of Fundamental Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama 223-8522 Japan Department of Bioengineering University of Washington Seattle Washington 98105 USA Department of Electrical and Computer Engineering University of Washington Seattle Washington 98105 USA Physical Sciences Division Pacific Northwest National Laboratory Richland Washington 99354 USA

DNA flexibility is a key determinant of biological function, from nucleosome positioning to transcriptional regulation, motivating a direct measurement of the bend-torque response of individual DNA molecules. In this work, DNA bending is detected with the use of a nanomechanical torque balance formed by the tethering of a ferromagnetic nanoparticle probe by an individual DNA molecule to a diamond magnetic field imager. The torque exerted by the DNA in response to bending caused by an applied magnetic torque is measured by wide-field imaging of quantum defects near the surface of the diamond. Qualitative measurements of differences in DNA biomechanical binding configuration are demonstrated, and as a proof of principle, a quantitative measurement of the bend response is made for individual DNA molecules. This quantum-enabled measurement approach could be applied to characterize the bend response of biophysically relevant short DNA molecules as well as the sequence dependence of DNA bending energy.

关键词： Molecules

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Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis

arXiv

引用

arXiv 2024年

作者： Zhao, Haokai Lou, Haowei Yao, Lina Zhang, Yu Engineering Rd UNSW Sydney KensingtonNSW2052 Australia CSIRO’s Data61 Level 5/13 Garden St EveleighNSW2015 Australia Department of Bioengineering Lehigh University BethlehemPA18015 United States Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States

Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is commonly modeled as networks of Regions of Interest (ROIs) and their connections, named functional connectivity, for understanding the brain functions and mental disorders. However, due to the high cost of fMRI data acquisition and labeling, the amount of fMRI data is usually small, which largely limits the performance of recognition models. With the rise of generative models, especially diffusion models, the ability to generate realistic samples close to the real data distribution has been widely used for data augmentations. In this work, we present a transformer-based latent diffusion model for functional connectivity generation and demonstrate the effectiveness of the diffusion model as an augmentation tool for fMRI functional connectivity. Furthermore, extended experiments are conducted to provide detailed analysis of the generation quality and interpretations for the learned feature pattern. Our code will be made public upon acceptance. © 2024, CC BY.

关键词： Diseases

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