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Patterned graphene oxide via one-step thermal annealing for controlling collective cell migration

Journal of Materials Chemistry B 2024年第35期12卷 8733-8745页

作者： Huang, Chien-Yu Chen, Chong-You Wei, Chia-Hung Yang, Jia-Wei Lin, Yu-Chien Kao, Chih-Fei Chung, Johnson H. Y. Chen, Guan-Yu Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Biological Science and Technology College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Intelligent Polymer Research Institute Institute for Innovative Materials University of Wollongong NSW2500 Australia National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Graphene oxide (GO) is a two-dimensional metastable nanomaterial. Interestingly, GO formed oxygen clusterings in addition to oxidized and graphitic phases during the low-temperature thermal annealing process, which could be further used for biomolecule bonding. By harnessing this property of GO, we created a bio-interface with patterned structures with a common laboratory hot plate that could tune cellular behavior by physical contact. Due to the regional distribution of oxygen clustering at the interface, we refer to it as patterned annealed graphene oxide (paGO). In addition, since the paGO was a heterogeneous interface and bonded biomolecules to varying degrees, arginine-glycine-aspartic acid (RGD) was modified on it and successfully regulated cellular-directed growth and migration. Finally, we investigated the FRET phenomenon of this heterogeneous interface and found that it has potential as a biosensor. The paGO interface has the advantages of easy regulation and fabrication, and the one-step thermal reduction method is suitable for biological applications. We believe that this low-temperature thermal annealing method would make GO interfaces more accessible, especially for the development of nano-interfacial modifications for biological applications, revealing its potential for biomedical applications. © 2024 The Royal Society of Chemistry.

关键词： Biomolecules

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Time-Multiplexed Miniaturized Two-Photon Microscopy

Time-Multiplexed Miniaturized Two-Photon Microscopy

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Optics and the Brain, BRAIN 2024 - Part of Optica Biophotonics Congress: biomedical Optics

作者： Liu, Shing-Jiuan Zhang, Zixiao Mattison, Ben Yang, Weijian Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Biomedical Engineering University of California Davis DavisCA95616 United States Biomedical Engineering Graduate Group University of California Davis DavisCA95616 United States

We propose a time-multiplexed miniaturized two-photon microscope (TM-MINI2P), enabling a two-fold increase in imaging speed while maintaining a high spatial resolution. Using TM-MINI2P, we conducted high-speed in-vivo... 详细信息

ISBN: (纸本)9781957171340

关键词： Two photon processes

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Optimal Resource Allocation for NOMA Wireless Networks

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computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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Environmental Disinfection Based on Mobile Intelligent Networking Highly Efficient Ultraviolet Light Machines

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Aerosol and Air Quality Research 2024年第2期24卷 1-10页

作者： Chung, Chun-Yen Sung, Wen-Tsai Wang, Lin-Chi Siami, Lailatus Santos, John Paul Computer and Game Development Program Kun Shan University Tainan710303 Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung411030 Taiwan Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung81157 Taiwan Department of Environmental Engineering Chung Yuan Christian University Taoyuan32023 Taiwan Department of Civil Engineering Chung Yuan Christian University Taoyuan32023 Taiwan

Since 2019, the world has suffered from the outbreak of the "Coronavirus Disease (COVID)-19 pandemic. This study used the principle of Ultraviolet (UV)-C disinfection to produce a mobile high-power UV-C sterilizer. It focused on combining intelligent control systems, human sensors, and Internet of Things (IoT) systems to carry out disinfection tasks in unmanned indoor spaces. Because of its mobile nature, the sterilizer could be moved at any time in various learning, research, and activity rooms for disinfection. In addition to manually controlling the sterilizer, a Wi-Fi or Narrowband (NB)-IoT-enabled remote control was provided to start the disinfection process. In order to prevent ultraviolet light from harming the human body, sensors were employed self-acting to detect the presence of people and turn off the ultraviolet light when people were detected in the area. At the same time, a camera on the sterilizer would capture an image and notify the relevant personnel through a LINE notification, to prevent the human body from being exposed to ultraviolet light, causing harm to the teachers and students on campus. During operation, voice broadcast reminders and a status indicator could provide information about the operation status. After disinfection, users could view the disinfection process through a mobile device application or a webpage, which would automatically notify relevant personnel of the disinfection status through a LINE notification. In this way, the disinfection progress could be updated at anytime, anywhere and the current disinfection status in the classroom could be displayed in real-time, allowing teachers and students on campus to carry out teaching and learning in a safe and secure environment. © The Author(s).

关键词： Internet of things

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Assisting Upper Limb Prosthesis with a computer Vision System for Material Detection 46

Assisting Upper Limb Prosthesis with a Computer Vision Syste...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Roper-Bryant, Dezmun Berman, Joseph Huang, He North Carolina State University Department of Electrical and Computer Engineering United States North Carolina State University University of North Carolina at Chapel Hill Joint Department of Biomedical Engineering United States

ISBN: (纸本)9798350371499

The concept of utilizing computer vision to aid in the control of prosthetic limbs has been explored in recent studies. However, many computer vision systems are challenged by noisy and cluttered backgrounds in realistic scenarios. In this study, we investigated the feasibility of using a state-of-the-art object detection model (YOLOv8) to identify objects grasped by a myoelectric prosthetic hand. A custom model was trained to recognize various types of cups and was integrated with an EMG-based prosthesis controller. The classification accuracy of the model during an object grasping task with a human subject was approximately 93%. Predictions of grasped objects will be used in future work to adjust the parameters of a grip force controller for a prosthetic hand based on the detected object's properties. © 2024 IEEE.

关键词： Artificial limbs

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A mathematical approach to mitigate the impact of compression waves in shear wave elastography 12

A mathematical approach to mitigate the impact of compressio...

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Optical Elastography and Tissue Biomechanics XII 2025

作者： Asemani, Hamidreza Rolland, Jannick P. Parker, Kevin J. The Institute of Optics University of Rochester RochesterNY United States Department of Electrical and Computer Engineering University of Rochester RochesterNY United States Department of Biomedical Engineering University of Rochester RochesterNY United States

ISBN: (纸本)9781510683907

Shear wave elastography (SWE) has emerged as a valuable imaging technique across several modalities such as ultrasound, magnetic resonance imaging, and optical coherence tomography. Its capacity to assess tissue stiffness and identify lesions positions it as a promising clinical tool for diagnosing a wide range of diseases. The primary aim of SWE is to measure shear wave speed (SWS);however, unwanted compression waves and bulk tissue motion can complicate this process. Conventional methods, in many cases, face challenges in separating shear and compression waves, resulting in inaccurate SWS measurements. This study introduces a novel estimator, called integrated difference autocorrelation (IDA), designed to specifically estimate reverberant shear wave speed in the presence of compression waves and noise. Unlike conventional methods, the IDA estimator computes the velocity differences between adjacent particles, effectively mitigating the impact of long-wavelength compression waves and other wide-area movements like those induced by respiration. The effectiveness of the IDA estimator was assessed through several approaches: elastography simulation of a y-shaped cylinder in a soft background, magnetic resonance elastography (MRE) on a brain phantom with two lesions, ultrasound elastography on a breast phantom with a lesion, and ultrasound elastography in the human liver-kidney region. Our findings indicate that the IDA estimator provides accurate SWS estimates (within 19% of reference) even in the presence of strong compression waves that can cause earlier estimators to completely fail. Moreover, the IDA estimator demonstrates consistent performance across different modalities and excitation scenarios, underscoring its robustness and potential clinical applicability. © 2025 SPIE.

关键词： Shear waves

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A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations 63

A Model-Based Approach for Improving Reinforcement Learning ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ozcan, Erhan Can Giammarino, Vittorio Queeney, James Paschalidis, Ioannis Ch. Boston University Division of Systems Engineering BostonMA02215 United States CambridgeMA02139 United States Boston University Division of Systems Engineering Department of Electrical and Computer Engineering Department of Biomedical Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented policy loss combining a maximum entropy reinforcement learning objective with a behavioral cloning loss that leverages a forward dynamics model. Then, we propose an algorithm that automatically adjusts the weights of each component in the augmented loss function. Experiments on a variety of continuous control tasks demonstrate that the proposed algorithm outperforms various benchmarks by effectively utilizing available expert observations. © 2024 IEEE.

关键词： Deep reinforcement learning

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Cochlear Decomposition: A Novel Bio-Inspired Multiscale Analysis Framework 48

Cochlear Decomposition: A Novel Bio-Inspired Multiscale Anal...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Alfalahi, Hessa Khandoker, Ahsan Alhussein, Ghada Hadjileontiadis, Leontios Khalifa University of Science and Technology Department of Biomedical Engineering Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece

ISBN: (纸本)9781728163277

Signal multiscale decomposition (SMD) is an effective analysis for the identification of modal information in time-domain signals. So far, various SMD approaches, such as the Multiresolution Wavelet Transform (MWT), the Empirical Mode Decomposition (EMD), and the Variational Mode Decomosition (VMD) have been proposed. However, issues, such as mode mixing for signals with closelyspaced modes, have been identified. To confront such problems, we propose here a novel spatial auditory decomposition framework for non-stationary signals, namely the Cochlear Decomposition (CD). CD is inspired by the biological rules of the spiral human cochlea and it is built upon the concept of 'place-pitch' or the tonotopic organization of the spiral cochlea. The new insight here is to formulate a set of basis functions, namely cochlear wavelets, whose dilation factors are determined by their angular position on the cochlear spiral (i.e., coupled rotation and dilation). Under proper parameterization, iterative application of spatial filters eventually results in signal's mono-components, each arising from specific angular positions along the spiral cochlea, with high time and frequency localization. The performance of the proposed CD is validated via synthetic acoustic signals and real non-stationary speech signals analysis. The analysis results show that CD outperforms the performance of MWT, EMD, and VMD, exhibiting, at the same time, high noise robustness. We also show that the CD disentangles lowfrequency temporal modulations of sounds, supporting perceptual phenomena in both speech and music. Clearly, CD paves the way for higher-level speech perception deep learning models and also for efficient cochlear implants design. Future work to incorporate the nonlinearity of the cochlea into a binaural hearing framework is underway. © 2023 IEEE.

关键词： Audition

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Building Robust Fraud Detection Model for Insurance Claims Using SMOTE 3rd

Building Robust Fraud Detection Model for Insurance Claims U...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Malathi, M. Armash, S. M. Mohammed Kavitha, S. Chinthamani, B. Sinthia, P. Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India UG Scholar Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electrical and Electronics Engineering Saveetha Engineering College Chennai India Department of Electronics and Instrumentation Engineering Easwari Engineering College Chennai India Department of Biomedical Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9789819738090

The insurance fraud claim detection is a critical task in the insurance industry. Various methodologies were proposed in terms of detecting fraud claims but the main thing is handling the proportionality between the fraud and non-fraud cases in the dataset before it was given to the model, in that case class imbalance may occur. Class imbalance where the number of fraudulent claims is significantly lower than legitimate claims which pose a challenge for accurate fraud detection. In this paper, we employed [Elreedy and Atiya in Inf Sci 505:32–64, 2019] Synthetic Minority Over-sampling Technique (SMOTE) algorithm to address the imbalance problem and enhance the performance of the fraud claim detection model. SMOTE is a popular technique for oversampling the minority class by creating synthetic samples that are similar to the existing minority class instances. By generating synthetic examples, SMOTE helps in balancing the class distribution and allows the model to learn from a more representative dataset. This technique is particularly effective when the available data is limited and insufficient to capture the complexities of the limited class. This process results in a larger and more balanced dataset, enabling the model to learn from a diverse range of fraudulent claim patterns. By applying SMOTE to our dataset, we are able to overcome the class imbalance issue and improve the performance of the fraud claim detection model. The resampled dataset provides a more accurate representation of the underlying distribution, leading to enhanced detection of fraudulent claims. We evaluate the performance of the model by measuring various metrics such as accuracy, precision, recall and F1-score. Our findings demonstrate the effectiveness of SMOTE in addressing class imbalance with improved random forest and LightGBM model for fraud claim detection process. The utilization of SMOTE contributes to better identification of fraudulent insurance claims, reducing potential losses an

关键词： Crime

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On a Sparse Shortcut Topology of Artificial Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第4期3卷 595-608页

作者： Fan, Feng-Lei Wang, Dayang Guo, Hengtao Zhu, Qikui Yan, Pingkun Wang, Ge Yu, Hengyong Rensselaer Polytechnic Institute Department of Biomedical Engineering TroyNY12180 United States University of Massachusetts Department of Electrical and Computer Engineering LowellMA01854 United States

In established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effectiveness of shortcuts, there remain open questions on the mechanism and characteristics. For example, why are shortcuts powerful? Why do shortcuts generalize well? In this article, we investigate the expressivity and generalizability of a novel sparse shortcut topology. First, we demonstrate that this topology can empower a one-neuron-wide deep network to approximate any univariate continuous function. Then, we present a novel width-bounded universal approximator in contrast to depth-bounded universal approximators and extend the approximation result to a family of equally competent networks. Furthermore, with generalization bound theory, we show that the proposed shortcut topology enjoys excellent generalizability. Finally, we corroborate our theoretical analyses by comparing the proposed topology with popular architectures, including ResNet and DenseNet, on well-known benchmarks and perform a saliency map analysis to interpret the proposed topology. Our work helps understand the role of shortcuts and suggests further opportunities to innovate neural architectures. © 2020 IEEE.

关键词： Topology

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