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arXiv 2023年

作者： Zhang, Yuhong Li, Qin Nahata, Sujal Jamal, Tasnia Cheng, Shih-Kuen Cauwenberghs, Gert Jung, Tzyy-Ping The School of Engineering Brown University ProvidenceRI02912 United States Institute for Neural Computation University of California San Diego La Jolla CA92093 United States Department of Radiology and Biomedical Imaging Yale University New HavenCT06520 United States The Department of Bioengineering University of California Los Angeles Los AngelesCA90095 United States The Department of Computer Science and Engineering University of California San Diego La Jolla CA92093 United States The Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States The Institute of Cognitive Neuroscience National Central University Taoyuan32001 Taiwan The Department of Bioengineering Institute for Neural Computation University of California San Diego La Jolla CA92093 United States The Institute for Neural Computation University of California San Diego La Jolla CA92093 United States

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for interdisciplinary research that bridges cognitive science and natural language processing (NLP). This pilot study aims to provide insights into individuals’ neural states during a semantic relation reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading. We also use a feature engineering approach to improve the fixation-related EEG data classification while participants read words with high versus low relevance to the keyword. The best validation accuracy in this word-level classification is over 60% across 12 subjects. Words of high relevance to the inference keyword had significantly more eye fixations per word: 1.0584 compared to 0.6576 when excluding no-fixation words, and 1.5126 compared to 1.4026 when including them. This study represents the first attempt to classify brain states at a word level using LLM knowledge. It provides valuable insights into human cognitive abilities and the realm of Artificial General Intelligence (AGI), and offers guidance for developing potential reading-assisted technologies. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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Deep Learning-based Virtual Immunohistochemical HER2 staining of Label-Free Breast Tissue

Deep Learning-based Virtual Immunohistochemical HER2 stainin...

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Conference on Lasers and Electro-Optics (CLEO)

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 Wenjie Dong Morgan Angus Darrow Elham Kamangar Han Sung Lee Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of California Bioengineering Department University of California California NanoSystems Institute (CNSI) University of California Computer Science Department University of California Physics and Astronomy Department University of California Translational Pathology Core Laboratory Statistics Department University of California Los Angeles CA USA Department of Pathology and Laboratory Medicine University of California at Davis Sacramento CA USA Department of Surgery University of California Los Angeles CA USA

We present deep learning-based virtual immunohistochemical (IHC) HER2 staining of label-free breast tissue sections, matching the standard IHC HER2 staining performed by histotechnologists.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

We present deep learning-based virtual immunohistochemical (IHC) HER2 staining of label-free breast tissue sections, matching the standard IHC HER2 staining performed by histotechnologists.

关键词： Breast tissue Lasers and electrooptics Electrooptical waveguides Standards

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Physiological assessment of brain, cardiovascular, and respiratory changes in multimodal motor imagery brain-computer interface training

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Brain-computer Interfaces 2025年第1期12卷

作者： Evangelos Georgaras Athanasios Vourvopoulos a Electrical and Computer Engineering Department National Technical University of Athens Athens Greece b Bioengineering Department Institute for Systems and Robotics - Lisboa Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal

ABSTRACTBrain-computer Interfaces (BCIs) can provide a non-muscular communication channel for individuals with motor impairments. When integrated with virtual reality (VR) and haptic feedback, motor imagery (MI)-based BCIs can augment the rehabilitation outcome for patients with severe motor impairments. However, the physiological impact of these protocols beyond brain-related signals, that reflect autonomic nervous system (ANS) activity, remains underexplored. This study aims to investigate variations in a broader range of physiological signals besides electroencephalography (EEG) – including electrocardiography (ECG), photoplethysmography (PPG), and respiration – across different experimental conditions and to identify the factors driving these changes. 19 healthy subjects underwent MI training across five combinations of feedback conditions: abstract vs. realistic feedback, head-mounted display (HMD) vs. monitor, and the presence or absence of haptic feedback, compared with motor execution data. PPG results were compared with ECG results to assess the reliability of the finger-clip PPG sensor regarding its ability to replace ECG in cases where ease of use and unobtrusiveness in heart monitoring are required. Current findings show that VR-based MI with haptic feedback, results in increased modulation of Beta and Gamma bands, while all conditions may impose a greater mental burden than motor execution, as indicated by the increased respiration rate and decreased heart-rate variability.

关键词： Brain-computer interfaces autonomic nervous system motor imagery virtual reality haptic feedback

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Phase Recovery and Holographic Imaging using Recurrent Neural Networks (RNNs)

Phase Recovery and Holographic Imaging using Recurrent Neura...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Luzhe Huang Tairan Liu Xilin Yang Yi Luo Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of California Los Angeles California USA Bioengineering Department University of California Los Angeles California USA California Nano Systems Institute (CNSI) University of California Los Angeles California USA David Geffen School of Medicine University of California Los Angeles California USA

We demonstrate a recurrent neural network (RNN) enabled holographic imaging method that simultaneously performs autofocusing and phase recovery, achieving faster reconstruction speed and extended depth-of-field.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Recurrent neural networks Imaging Lasers and electrooptics Electrooptical waveguides Image reconstruction

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Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification

arXiv

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arXiv 2024年

作者： Menon, Karthik Zanoni, Andrea Khan, Owais Geraci, Gianluca Nieman, Koen Schiavazzi, Daniele E. Marsden, Alison L. Stanford School of Medicine StanfordCA United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Division of Cardiovascular Medicine Stanford School of Medicine StanfordCA United States Department of Radiology Stanford School of Medicine StanfordCA United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN United States Department of Bioengineering Stanford University StanfordCA United States

Background: Non-invasive simulations of coronary hemodynamics have improved clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree, which ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline. Objective: We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating vessel-specific coronary flows as well as cardiac function;and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data. Methods: We assimilate patient-specific measurements of myocardial blood flow from clinical CT myocardial perfusion imaging to estimate branch-specific coronary artery flows. Simulated noise in the clinical data is used to estimate the joint posterior distributions of the model parameters using adaptive Markov Chain Monte Carlo sampling. Additionally, the posterior predictive distribution for the relevant quantities of interest is determined using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. This leads to improved correlations between high- and low-fidelity model outputs. Results: Our framework accurately recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement noise uncertainty. We observe substantial reductions in confidence intervals for estimated quantities of interest compared to single-fidelity Monte Carlo estimation and state-of-the-art multi-fidelity Monte Carlo methods. This holds especially true for quantities of interest that showed limited correlation between the low- and high-fidelity model

关键词： Monte Carlo methods

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Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling

arXiv

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arXiv 2024年

作者： Selcuk, Sahan Yoruc Yang, Xilin Bai, Bijie Zhang, Yijie Li, Yuzhu Aydin, Musa Unal, Aras Firat Gomatam, Aditya Guo, Zhen Angus, Darrow Morgan Kolodney, Goren Atlan, Karine Haran, Tal Keidar Pillar, Nir Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA United States Bioengineering Department University of California Los AngelesCA United States California NanoSystems Institute University of California Los AngelesCA United States Department of Pathology and Laboratory Medicine University of California at Davis SacramentoCA United States Bnai-Zion Medical Center Haifa Israel Hadassah Hebrew University Medical Center Jerusalem Israel David Geffen School of Medicine University of California Los AngelesCA United States

Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Accurate assessment of immunohistochemically (IHC) stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in IHC-stained BC tissue images. Our approach analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details. This method addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view, leading to a blind testing classification accuracy of 84.70%, on a dataset of 523 core images from tissue microarrays. Our automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might significantly impact cancer treatment planning. Copyright © 2024, The Authors. All rights reserved.

关键词： Tissue

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High-throughput optofluidic nanoplasmonic biosensor array for monitoring single-cell secretion in real-time

High-throughput optofluidic nanoplasmonic biosensor array fo...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Yen-Cheng Liu Saeid Ansaryan Xiaokang Li Eduardo R. Arvelo Hatice Altug Institute of Bioengineering École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Ludwig Institute for Cancer Research University of Lausanne Lausanne Switzerland Department of Oncology Centre hospitalier universitaire vaudois (CHUV) Lausanne Switzerland Department of Electrical and Computer Engineering University of Wisconsin-Madison Madison WI USA

We report a high-throughput biosensing microarray platform integrating ultrasensitive nanoplasmonic substrate and microwell compartments for label-free and real-time secretome monitoring. Interleukin-2 from hundreds o... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Statistical analysis Measurement by laser beam Lasers and electrooptics Real-time systems Biosensors Electrooptical waveguides Monitoring

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eXplainable AI for data driven control: an inverse optimal control approach

arXiv

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arXiv 2025年

作者： Porcari, Federico Materassi, Donatello Formentin, Simone Department of Electronics Information and Bioengineering Politecnico di Milano via G. Ponzio 34/5 Milano20133 Italy Department of Electrical and Computer Engineering University of Minnesota Twin Cities United States

Understanding the behavior of black-box data-driven controllers is a key challenge in modern control design. In this work, we propose an eXplainable AI (XAI) methodology based on Inverse Optimal Control (IOC) to obtain local explanations for the behavior of a controller operating around a given region. Specifically, we extract the weights assigned to tracking errors and control effort in the implicit cost function that a black-box controller is optimizing, offering a more transparent and interpretable representation of the controller’s underlying objectives. This approach presents connections with well-established XAI techniques, such as Local Interpretable Model-agnostic Explanations (LIME) since it is still based on a local approximation of the control policy. However, rather being limited to a standard sensitivity analysis, the explanation provided by our method relies on the solution of an inverse Linear Quadratic (LQ) problem, offering a structured and more control-relevant perspective. Numerical examples demonstrate that the inferred cost function consistently provides a deeper understanding of the controller’s decision-making process, shedding light on otherwise counterintuitive or unexpected phenomena. © 2025, CC BY.

关键词： Cost functions

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Directing Cholangiocyte Morphogenesis in Natural Biomaterial Scaffolds (vol 9, 2102698, 2022)

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ADVANCED SCIENCE 2022年第14期9卷 e2201951页

作者： Smith, Quinton Bays, Jennifer Li, Linqing Shareef, Haniyah Chen, Christopher S. Bhatia, Sangeeta N. Howard Hughes Medical Institute Chevy Chase MD 20815 USA David H. Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA 02142 USA Department of Bioengineering Boston University Boston MA 02215 USA The Wyss Institute for Biologically Inspired Engineering Harvard University Boston MA 02215 USA Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02142 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02142 USA

Patients with Alagille syndrome carry monogenic mutations in the Notch signaling pathway and face complications such as jaundice and cholestasis. Given the presence of intrahepatic ductopenia in these patients, Notch2 receptor signaling is implicated in driving normal biliary development and downstream branching morphogenesis. As a result, in vitro model systems of liver epithelium are needed to further mechanistic insight of biliary tissue assembly. Here, primary human intrahepatic cholangiocytes as a candidate population for such a platform are systematically evaluated, and conditions that direct their branching morphogenesis are described. It is found that extracellular matrix presentation, coupled with mitogen stimulation, promotes biliary branching in a Notch-dependent manner. These results demonstrate the utility of using 3D scaffolds for mechanistic investigation of cholangiocyte branching and provide a gateway to integrate biliary architecture in additional in vitro models of liver tissue.

关键词： biomaterials, cholangiocyte morphogenesis liver microfluidics morphogenesis

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Bio-FlatScope: A flat, lensless microscope for fluorescence imaging

Bio-FlatScope: A flat, lensless microscope for fluorescence ...

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Optics and the Brain, BRAIN 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

作者： Wu, Jimin Yan, Dong Boominathan, Vivek Adams, Jesse K. Veeraraghavan, Ashok Robinson, Jacob T. Department of Bioengineering Rice University 6100 Main Street HoustonTX77005 United States Applied Physics Program Rice University 6100 Main Street HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Department of Neuroscience Baylor College of Medicine One Baylor Plaza HoustonTX77030 United States Department of Computer Science Rice University 6100 Main Street HoustonTX77005 United States

We developed a lensless microscope with small, inexpensive form factor, large field-of-view (FOV) and cellular resolution for fluorescence imaging. Proof-of-principle measurements with "Bio-FlatScope" are pr... 详细信息

ISBN: (纸本)9781557528209

关键词： Fluorescence imaging

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