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Longitudinal, Multimodal Tracking Reveals Lasting Neurovascular Impact of Individual Microinfarcts

Advanced Science 2025年第0期12卷 e2417003页

作者： Jin, Yifu He, Fei Rathore, Haad Sun, Yingchu Zhang, Jia-ao Li, Xinyu Yin, Rongkang Zhu, Hanlin Xie, Chong Luan, Lan Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Neuroengineering Initiative Rice University HoustonTX77005 United States Applied Physics Program Rice University HoustonTX77005 United States Department of Bioengineering Rice University HoustonTX77005 United States

Microinfarcts, the "invisible lesions", are prevalent in aged and injured brains and associated with cognitive impairments, yet their neurophysiological impact remains largely unknown. Using a multimodal chronic neural platform that combines functional microvasculature imaging with spatially resolved neural recording, the neurovascular effect of a single microinfarct is investigated. Unlike larger strokes, microinfarcts induced only temporary suppression of neural activity with minimal cell death, with recovery paralleling vasculature remodeling at the infarct core. Neural activity is more severely suppressed at the shallower cortical layer despite milder vascular damage compared to deeper layers, and the excitability of fast-spiking interneurons attenuation is accompanied by heightened bursting of regular spiking neurons. Spike phase locking at the low-gamma band is disrupted, indicating a lasting impairment of long-range assembly communication. These results highlight the subtle yet significant neurovascular disruptions of a single microinfarct. © 2025 The Author(s). Advanced Science published by Wiley-VCH GmbH.

关键词： Flexible electronics

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Waveform Development for Neurotransmitter Detection on Novel Boron-Doped Diamond Microelectrodes

Waveform Development for Neurotransmitter Detection on Novel...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Bhavna Gupta Mason L. Perillo Isabelle E. Christensen James R. Siegenthaler Robert Rechenberg Michael F. Becker Wen Li Erin K. Purcell Neuroscience Program at Michigan State University East Lansing MI USA Department of Biomedical Engineering Michigan State University East Lansing MI USA Department of Mechanical Engineering Michigan State University East Lansing MI USA Fraunhofer Center Midwest East Lansing MI USA Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA Department of Biomedical Engineering and the Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

Technologies capable of detecting changes in neurochemical signals can be used to understand the underlying mechanisms of neurological diseases. They are also important components of systems which rely on neurochemical sensing as a source of feedback for closed-loop neuromodulation[1], [2]. Recently, our research team has developed a boron-doped diamond microelectrode (BDDME) fiber for neurotransmitter detection using fast-scan cyclic voltammetry (FSCV). These electrodes have several potential benefits in comparison to more traditional, carbon fiber microelectrodes (CFMEs), including a wider working potential window, customizable fabrication, and increased resistance to etching. Given the unique characteristics of BDDMEs in comparison to CFMEs, there is a need to re-examine and optimize the potential waveforms used for neurotransmitter detection. In this study, we explored waveform parameters (holding potential, scan rate, and application frequency) for serotonin detection on BDDME fibers. We found that frequency attenuation of normalized peak currents was mitigated on BDDMEs in comparison to CFMEs. Effects of varying holding potential and scan rate were qualitatively similar between the two electrode types. Frequency effects suggest that repeating the measurements at BDDME at higher frequency does not decrease the current as drastically as on the CFME. However, both electrodes showed optimal current response at 10 Hz. These studies represent an important step forward in our development of a next-generation, chronically implantable electrochemical sensor for neurotransmitter detection.

关键词： Resistance Electric potential Optical fiber sensors Microelectrodes Neurotransmitters Diamonds Frequency measurement

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Mobile robot position control in environment with static obstacles

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IET Cyber-Systems and Robotics 2020年第2期2卷 78-87页

作者： Konstantinos Giannousakis Anthony Tzes Electrical&Computer Engineering Department University of PatrasRio 26500Greece Electrical&Computer Engineering Program New York University Abu DhabiAbu DhabiP.O.Box 129188United Arab Emirates

This study addresses the problem of efficiently navigating a differential drive mobile robot to a target pose in a region with obstacles,without explicitly generating a *** robot is assumed to be equipped with an omnidirectional range sensor,while the region may or may not be a priori *** the known obstacles in each iteration of the controller,the shortest path connecting the robot and the target point provides a raw desired movement *** the unobstructed area in that direction,the size of the robot and the obstacle contours in its visibility range,the reference direction is ***,respecting the velocity and acceleration constraints of the robot,the angular velocity is properly selected to rotate the robot towards the reference direction,while the linear velocity is chosen to efficiently minimise the distance to the final target,as well as to avoid *** the robot has reached the target,the controller switches to orientation mode in order to fix the *** studies demonstrate the effectiveness of the algorithm.

关键词： robot visibility shortest

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Abnormal skeletal muscle and myocardial vasoreactivity manifests prior to heart failure in a diabetic cardiomyopathy rat model

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Discover medicine 2025年第1期2卷 2页

作者： Sadi Loai Hai-Ling Margaret Cheng Institute of Biomedical Engineering University of Toronto Toronto ON Canada. Translational Biology & Engineering Program Ted Rogers Centre for Heart Research 661 University Avenue Room 1433 Toronto ON M5G 1M1 Canada. The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada.

Background:Microvascular dysfunction (MVD) is a recognized sign of disease in heart failure progression. Intact blood vessels exhibit abnormal vasoreactivity in early stage, subsequently deteriorating to rarefaction and reduced perfusion. In managing heart failure with preserved ejection fraction (HFpEF), earlier diagnosis is key to improving management. In this study, we applied a steady-state blood-pool magnetic resonance imaging (MRI) method to investigate if it can sensitively detect abnormal leg muscle vasoreactivity, a sign of MVD, posited to manifest before structural and functional cardiac changes emerge in a diabetes model of HFpEF. Methods:Male and female Sprague-Dawley rats were maintained on either a high-fat, high-sugar diet or a control diet for 6 months after the induction of diabetes ( = 5 per group). Beginning at month 1 or 2 post-diabetes and every 2 months thereafter, rats underwent steady-state blood-pool MRI to assess vasoreactivity in the heart or skeletal muscle, respectively. A T1-reducing blood-pool agent was administered and the T1 relaxation time dynamically measured as animals breathed in elevated CO levels to modulate vessels. Results:In male rats, the normally unresponsive heart to 10% CO revealed a pro-vasoconstriction response beginning at 5 months post-diabetes. Abnormal leg skeletal muscle vasoreactivity appeared even earlier, at 2 months: the usual vasodilatory response to 5% CO was interrupted with periods of vasoconstriction in diseased rats. In female rats, differences were observed between healthy and diseased animals only in the first 2 months post-diabetes and not later. In the heart, vasodilation to 10% CO seen in healthy females was abolished in diabetic females. In skeletal muscle, 5% CO was suboptimal in inducing reproducible vasoreactivity, but young diabetic females responded by vasodilation only. Conclusions:Abnormal vasoreactivity presented earlier than overt functional changes in both heart and skeletal muscle in dia

关键词： Blood-pool T1 imaging Diabetes Gadofosveset Heart Hypercapnia Microvascular dysfunction Skeletal muscle Vasoreactivity

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Comparison of Coherent and Incoherent Donut Beams for Deep Tissue STED Microscopy

Comparison of Coherent and Incoherent Donut Beams for Deep T...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Thariq Shanavas Robert R. McLeod Mark E. Siemens Juliet T. Gopinath Department of Physics University of Colorado Boulder CO USA Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO USA Department of Physics University of Denver Denver CO Materials Science Engineering Program University of Colorado Boulder CO USA

We numerically compare the null quality for STED microscopy generated by Laguerre-Gaussian beams with orbital angular momentum and donut beams generated by incoherent addition of orthogonal Hermite Gaussian beams when... 详细信息

ISBN: (纸本)9781957171258

关键词： Biological imaging Gaussian beams Microscopy Spatial light modulators Spiral phase Step index fibers

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Deep Learning based Fault Diagnosis in a Grid-Connected Photovoltaic Systems 19

Deep Learning based Fault Diagnosis in a Grid-Connected Phot...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Hichri, Amal Hajji, Mansour Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Higher Institute of Applied Sciences and Technology of Kasserine Kasserine Tunisia Electrical and Computer Engineering Program Texas A&m University at Qatar Doha23874 Qatar Prince Sultan University Department of Mathematics and Sciences Riyadh11586 Saudi Arabia Signal Processing and Image National Engineering School of Monastir Research Laboratory of Automation 5019 Tunisia Chemical Engineering Program Texas A&m University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Photovoltaic (PV) system fault diagnosis is crucial because it helps PV system operators reduce energy and income losses. It also decreases the risk of fire and electric shock from PV system failures. Thus, the implementation of fault diagnosis tool is required. The most well-known techniques are machine learning models. Although these techniques provided well in terms of diagnosis and classification accuracy, they rely on manual feature extraction, which is time consuming, expensive, and requires diagnostic skill. Manual feature extraction is a problem that has to be addressed. This paper therefore presents a PV system fault diagnosis techniques that can automatically extract features from raw data in order to classify PV system faults. The implemented methods include long short-term memory (LSTM) bidirectional long short-term memory (BiLSTM) networks, which are deep learning based algorithms. Finally, the presented fault diagnosis approaches showed a good diagnosis performances when applied to an emulated GCPV system. © 2022 IEEE.

关键词： Failure analysis

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PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising

arXiv

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arXiv 2023年

作者： Jang, Hyemi Park, Junsung Jung, Dahuin Lew, Jaihyun Bae, Ho Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Cyber Security Ewha Womans University Korea Republic of

Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synthesized noise. Since clean-noisy image pairs from the real world are extremely costly to gather, self-supervised learning, which utilizes noisy input itself as a target, has been studied. To prevent a self-supervised denoising model from learning identical mapping, each output pixel should not be influenced by its corresponding input pixel;This requirement is known as J -invariance. Blind-spot networks (BSNs) have been a prevalent choice to ensure J -invariance in self-supervised image denoising. However, constructing variations of BSNs by injecting additional operations such as downsampling can expose blinded information, thereby violating J -invariance. Consequently, convolutions designed specifically for BSNs have been allowed only, limiting architectural flexibility. To overcome this limitation, we propose PUCA, a novel J -invariant U-Net architecture, for self-supervised denoising. PUCA leverages patch-unshuffle/shuffle to dramatically expand receptive fields while maintaining J -invariance and dilated attention blocks (DABs) for global context incorporation. Experimental results demonstrate that PUCA achieves state-of-the-art performance, outperforming existing methods in self-supervised image denoising. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

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Comparison of Algebraic Reconstruction Technique Methods and Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography (MIT)

Comparison of Algebraic Reconstruction Technique Methods and...

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2021 International Conference on Biomedical engineering, ICoBE 2021

作者： Lubis, A.J. Nasir, N.F. Mohd Zakaria, Z. Jusoh, M. Azizan, M.M. Zakaria, M.Z. Kazim, M.N.F.M. Rahman, S. Faculty of Engineering and Computer Science Universitas Harapan Medan Medan Indonesia Computer Engineering Program Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Biomedical Electronic Engineering Program Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Universiti Malaysia Perlis Perlis Malaysia Department of Electrical and Electronic Department Faculty of Engineering and Built Environment Universiti Sains Islam Malaysia Negeri Sembilan Malaysia Faculty of Mechanical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Mechatronic Engineering Program Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia

Magnetic induction tomography (MIT) is a technique used for imaging electromagnetic properties of objects using eddy current effects. The non-linear characteristics had led to more difficulties with its solution especially in dealing with low conductivity imaging materials such as biological tissues. Two methods that could be applied for MIT image processing which is the Generative Adversarial Network (GAN) and the Algebraic Reconstruction Technique (ART). ART is widely used in the industry due to its ability to improve the quality of the reconstructed image at a high scanning speed. GAN is an intelligent method which would be able to carry out the training process. In the GAN method, the MIT principle is used to find the optimum global conductivity distribution and it is described as a training process and later, reconstructed by a generator. The output is an approximate reconstruction of the distribution's internal conductivity image. Then, the results were compared with the previous traditional algorithm, namely the regularization algorithm of BPNN and Tikhonov Regularization method. It turned out that GAN had able to adjust the non-linear relationship between input and output. GAN was also able to solve non-linear problems that cannot be solved in the previous traditional algorithms, namely Back Propagation Neural Network (BPNN) and Tikhonov Regularization method. There are several other intelligent algorithms such as CNN (Convolution Neural Network) and K-NN (K-Nearest Neighbor), but such algorithms have not been able to produce the expected image quality. Thus, further study is still needed for the improvement of the image quality. The expected result in this study is the comparison of these two techniques, namely ART and GAN to get the best results on the image reconstruction using MIT. Thus, it is shown that GAN is a better candidate for this purpose. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Generative adversarial networks

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SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm

SoC-Oriented Implementation of Machine Learning Based Breast...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Abdelrahman Saeed Ayman Tawfik Hassan Mostafa Ahmed Hussein Khalil Electronics and Communications Engineering Department Cairo University Giza Egypt Department of Electrical and Computer Engineering Ajman University United Arab Emirates (UAE) University of Science and Technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology Giza Egypt

Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU).

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Distributed 3D-Map Matching and Merging on Resource-Limited Platforms Using Tomographic Features

Distributed 3D-Map Matching and Merging on Resource-Limited ...

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European Conference on Mobile Robots (ECMR)

作者： Halil Utku Unlu Anthony Tzes Prashanth Krishnamurthy Farshad Khorrami Electrical & Computer Engineering Department New York University Brooklyn NY USA Electrical Engineering Program New York University Abu Dhabi Abu Dhabi UAE Center for Artificial Intelligence and Robotics New York University Abu Dhabi Abu Dhabi UAE

A fast, robust, resource-efficient, and distributed 3D map matching and merging algorithm utilizing extracted tomographic features is studied. Instead of depending on 3D features and descriptors, 2D features are extracted from 2D projections of horizontal sections of gravity-aligned local maps and matched with slices from the other map at different height differences, enabling the estimation of four degrees of freedom. The proposed algorithm is observed to provide order-of-magnitude improvements in memory and time efficiency over state-of-the-art feature extraction and registration pipelines, rendering it useful for near real-time map merging tasks in resource-limited platforms (e.g. UAVs).

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