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biomedical engineering and Bioengineering (CI-IB&BI), IEEE International Congress of

作者： Andres F. Ruiz-Olaya Emma V. Beltran Edith Pulido Faculty of Mechanical Electronics and Biomedical Engineering Antonio Nariño University Bogota Colombia Bioenginering Program Universidad El Bosque Bogota Colombia

ISBN: (数字)9798331532352

ISBN: (纸本)9798331532369

Steady-State Visually Evoked Potentials (SSVEP) based Brain-Computer Interfaces (BCIs) offer significant potential in assistive and rehabilitation technologies. However, the performance of SSVEP detection is often compromised by artifacts in Electroencephalography (EEG) signals and inter-subject variability, among other factors. This study introduces a subject-specific p reprocessing p ipeline a imed a t enhancing SSVEP signal detection accuracy. The proposed method involves a dual-stage approach, utilizing EEG signals recorded both during a baseline rest state and during tasks known to induce artifacts. By establishing a personalized reference for each subject, the pipeline enables more accurate artifact detection and removal. The pipeline's efficacy w as e valuated u sing Canonical Correlation Analysis (CCA) on an open EEG dataset, with performance assessed across varying time segments (1, 2, 3 y 4 s). Detection accuracy was calculated to quantify performance in SSVEP detection. The performance of the proposed method for the 4-second time segment was superior (96.44%) compared to the standard method (93.78%). The results demonstrate the pipeline's potential to improve the reliability of SSVEP-based BCIs, particularly in highly contaminated signals.

关键词： Electric potential Brain Visualization Accuracy Pipelines Electroencephalography Steady-state Standards Signal detection biomedical engineering

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Plasma-Enabled Graphene Quantum Dot Hydrogels as Smart Anticancer Drug Nanocarriers

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Small 2023年第20期19卷 2206813-2206813页

作者： Kurniawan, Darwin Mathew, Jacob Rahardja, Michael Ryan Pham, Hoang-Phuc Wong, Pei-Chun Rao, Neralla Vijayakameswara Ostrikov, Kostya Chiang, Wei-Hung Department of Chemical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Graduate Institute of Biomedical Optomechatronics College of Biomedical Engineering Taipei Medical University Taipei110 Taiwan BrisbaneQLD4000 Australia

One of the major challenges on the way to low-cost, simple, and effective cancer treatments is the lack of smart anticancer drug delivery materials with the requisite of site-specific and microenvironment-responsive properties. This work reports the development of plasma-engineered smart drug nanocarriers (SDNCs) containing chitosan and nitrogen-doped graphene quantum dots (NGQDs) for drug delivery in a pH-responsive manner. Through a customized microplasma processing, a highly cross-linked SDNC with only 4.5% of NGQD ratio can exhibit enhanced toughness up to threefold higher than the control chitosan group, avoiding the commonly used high temperatures and toxic chemical cross-linking agents. The SDNCs demonstrate improved loading capability for doxorubicin (DOX) via π–π interactions and stable solid-state photoluminescence to monitor the DOX loading and release through the Förster resonance energy transfer (FRET) mechanism. Moreover, the DOX loaded SDNC exhibits anticancer effects against cancer cells during cytotoxicity tests at minimum concentration. Cellular uptake studies confirm that the DOX loaded SDNC can be successfully internalized into the nucleus after 12 h incubation period. This work provides new insights into the development of smart, environmental-friendly, and biocompatible nanographene hydrogels for the next-generation biomedical applications. © 2023 Wiley-VCH GmbH.

关键词： Chitosan

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Super-Fast and Accurate Nonlinear Foot Deformation Prediction Using Graph Neural Networks

SSRN

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SSRN 2024年

作者： Kang, Taehyeon Kim, Jiho Lee, Hyobi Yum, Haeun Kwon, Chani Lee, Sangryun Lee, Taeyong Department of Mechanical and Biomedical Engineering Ewha Womans University Seoul03760 Korea Republic of Department of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul03760 Korea Republic of Human Performance Lab Co. Ltd. #123a 150 Bugahyeon-ro Seodaemun-gu Seoul03759 Korea Republic of Ewha Womans University Korea Republic of

Recently, there has been a significant increase in the number of foot diseases, highlighting the importance of non-surgical treatments. Customized insoles, tailored to an individual's foot morphology, have emerged as a promising solution. However, the traditional design process of the customized insole is both slow and expensive due to the high computational complexity of finite element analysis (FEA) required to predict deformations of the foot. This study explores the applicability of a graph neural network (GNN) based on the MeshGraphNet framework to predict the 3-D shape of the foot under load and test the performance of GNN depending on the number of datasets. A total of 186 3-D undeformed foot CAD geometries are obtained from a series of 2-D foot images with deformations predicted through FEA. This FEA data is then used to train the GNN model, which aims to predict foot displacement with high accuracy and computation speed. After optimization of the weights of the GNN, the model remarkably outperformed FEA simulations in speed, being approximately 100 times faster, while maintaining high accuracy, with R2 values above 95% in predicting foot displacement. This breakthrough suggests that GNN models can greatly improve the efficiency and reduce the cost of manufacturing customized insoles, providing a significant advancement in non-surgical treatment options for foot conditions. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Cofilin-Mediated Filament Softening and Crosslinking Counterbalance to Enhance Actin Network Flexibility

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Physical Review Letters 2024年第21期133卷 218402-218402页

作者： Zachary Gao Sun Michael Murrell Department of Physics Yale University 217 Prospect Street New Haven Connecticut 06511 USA Systems Biology Institute Yale University West Haven Connecticut 06516 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Biomedical Engineering Yale University New Haven Connecticut 06511 USA

Filamentous-actin (F-actin) crosslinking within the cell cytoskeleton mediates the transmission of mechanical forces, enabling changes in cell shape, as occurs during cell division and cell migration. Crosslinking by actin binding proteins (ABPs) generally increases the connectivity of the F-actin network, but also increases network rigidity. As a result, there is a narrow range in the concentration of crosslinker protein at which F-actin networks are both connected and labile. Another ABP, cofilin, severs F-actin filaments at high pH through increasing their bending flexibility and concentrating mechanical stress, inducing fragmentation. By contrast, at lower pH, cofilin increases filament flexibility yet does not sever. Instead, it forms disulfide bonds, which crosslink F-actin into bundles, and bundles into networks. Here, we combine light microscopy and rheology to determine the impact of two potentially opposing effects on the mechanics of F-actin networks—increased flexibility at the filament level, and increased connectivity at the network level. Indeed, by linear rheology, we find that these mechanisms are counterbalanced, such that cofilactin network moduli are only weakly dependent on cofilin concentration over a broad range, in contrast to the dramatic stiffening that occurs with F-actin crosslinking protein. Further, by nonlinear rheology, the network stiffens at a higher stress than crosslinking protein, indicative of a broader range in which the material remains flexible. These results may enable F-actin networks to increase connectivity without heavy penalties to rigidity, and thus provide a new route to modulating active polymer mechanics unseen using traditional F-actin accessory proteins.

关键词： Biomimetic & bio-inspired materials Biomolecular self-assembly Living matter & active matter Persistence length Polymer entanglement Rheological properties Rheology Shear deformation Strain

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Research on YOLOX for small target detection in infrared aerial photography based on NAM channel attention mechanism

Research on YOLOX for small target detection in infrared aer...

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2022 Conference on Infrared, Millimeter, Terahertz Waves and Applications, IMT 2022

作者： Li, Xiangyang Xu, Zhaofei Liu, Qing Xue, Wei Yue, Guanghui Wang, Shulun Graduate School of Yantai Harbin Engineering University Shandong Yantai264000 China Yantai Arrow Photoelectric Technology Co. Ltd. Shandong Yantai264000 China School of Biomedical Engineering Shenzhen University Guangdong Shenzhen518000 China Military Representative Office of Luzhuang in Yantai Shandong Yantai264000 China

ISBN: (纸本)9781510662476

In this paper, we propose an improved target detection algorithm based on YOLOX with attention mechanism for infrared aerial photography, which is used to solve the problem that small targets are easily lost in the environment with dense targets and complex background. First of all, a new channel attention module named SA-NAM is introduced. This makes it possible to utilize the contribution factor of weights to optimize attentional mechanism and SA function to enhance the feature extraction ability in network. Target distribution in real scenarios has long tail distribution, we also adopted Mosaic and Cutmix data enhancement methods and Focal loss function to category imbalance. Moreover, in order to fill the gap of infrared small target data set of aerial views in current target detection field, an infrared aerial small target data set containing human, motor vehicle and non-motor vehicle targets in various scenarios is constructed. Experiments show that the mAP of proposed algorithm on our data set has reached 63.7%, and the detection accuracy is improved by 6.2% compared with baseline YOLOX, which alleviates the miss detection of infrared small target has been ameliorated to a certain extent. © 2023 SPIE.

关键词： Antennas

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Fully Elman Neural Network: A Novel Deep Recurrent Neural Network Optimized by an Improved Harris Hawks Algorithm for Classification of Pulmonary Arterial Wedge Pressure

arXiv

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arXiv 2023年

作者： Fetanat, Masoud Stevens, Michael Jain, Pankaj Hayward, Christopher Meijering, Erik Lovell, Nigel H. The Graduate School of Biomedical Engineering UNSW Sydney Australia Cardiology Department St Vincent’s Hospital Sydney Australia Victor Chang Cardiac Research Institute Sydney Australia School of Medicine UNSW Sydney Australia The Graduate School of Biomedical Engineering School of Computer Science and Engineering UNSW Sydney Australia

Heart failure (HF) is one of the most prevalent life-threatening cardiovascular diseases in which 6.5 million people are suffering in the USA and more than 23 million worldwide. Mechanical circulatory support of HF patients can be achieved by implanting a left ventricular assist device (LVAD) into HF patients as a bridge to transplant, recovery or destination therapy and can be controlled by measurement of normal and abnormal pulmonary arterial wedge pressure (PAWP). While there are no commercial long-term implantable pressure sensors to measure PAWP, real-time non-invasive estimation of abnormal and normal PAWP becomes vital. In this work, first an improved Harris Hawks optimizer algorithm called HHO+ is presented and tested on 24 unimodal and multimodal benchmark functions. Second, a novel fully Elman neural network (FENN) is proposed to improve the classification performance. Finally, four novel 18-layer deep learning methods of convolutional neural networks (CNNs) with multilayer perceptron (CNN-MLP), CNN with Elman neural networks (CNN-ENN), CNN with fully Elman neural networks (CNN-FENN), and CNN with fully Elman neural networks optimized by HHO+ algorithm (CNN-FENN-HHO+) for classification of abnormal and normal PAWP using estimated HVAD pump flow were developed and compared. The estimated pump flow was derived by a non-invasive method embedded into the commercial HVAD controller. The proposed methods are evaluated on an imbalanced clinical dataset using 5-fold cross-validation. The proposed CNN-FENN-HHO+ method outperforms the proposed CNN-MLP, CNN-ENN and CNN-FENN methods and improved the classification performance metrics across 5-fold cross-validation with an average sensitivity of 79%, accuracy of 78% and specificity of 76%. The proposed methods can reduce the likelihood of hazardous events like pulmonary congestion and ventricular suction for HF patients and notify identified abnormal cases to the hospital, clinician and cardiologist for emergency actio

关键词： Convolutional neural networks

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Spectral learning of shared dynamics between generalized-linear processes 24

Spectral learning of shared dynamics between generalized-lin...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lucine L. Oganesian Omid G. Sani Maryam M. Shanechi Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA

ISBN: (纸本)9798331314385

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms.

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Motor Imagery BCI Classification Using Synchrosqueezing Transform

Motor Imagery BCI Classification Using Synchrosqueezing Tran...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Mosab A. A. YOUSIF Mahmut OZTURK Department of Biomedical Engineering Institute of Graduate Studies Istanbul University - Cerrahpasa Istanbul Turkey Department of Electrical and Electronics Engineering Engineering Faculty Istanbul University - Cerrahpasa Istanbul Turkey

Developments in sensors and computer technology have made easier to record and process Electrencephalography (EEG) signals. Obtaining brain-computer interfaces (BCI) with the help of EEG signals is getting more practical and cheaper. Analyzing the meaning of the brain signals by using BCIs is popular and promising research area last years. In this work, we propose a new method to detect and classify the motor imagery (MI) EEG signals. The used dataset contains the trials for the imagination of the movements. While recording the EEG signals, subjects imagined the movements of right fist and left fist several times in each experiments. Our approach employes synchrosqueezing transform (SST) to obtain time-frequency representation matrices of EEG signals for each trial. Principal component analysis has been used for dimension reduction and feature extraction. At the end, classification process has been figured out by using support vector machine (SVM). This algorithm has been applied to each of the selected EEG channels which are directly related to motor movements. Testing and training accuracies of our SST based approach are higher than 99% for each channels separately. Also, we obtained classification results using continuous wavelet transform (CWT) to compare with the SST-based approach. Classification results of each channel have showed the promising performance of SST-based approach obviously.

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Spatially and temporally understanding dynamic solid-electrolyte interfaces in carbon dioxide electroreduction

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Chemical Society Reviews 2023年第15期52卷 5013-5050页

作者： Wang, Jiali Tan, Hui-Ying Qi, Ming-Yu Li, Jing-Yu Tang, Zi-Rong Suen, Nian-Tzu Xu, Yi-Jun Chen, Hao Ming Department of Chemistry National Taiwan University No. 1 Sec. 4 Roosevelt Rd Taiwan Taipei10617 Taiwan College of Chemistry State Key Laboratory of Photocatalysis on Energy and Environment Fuzhou University Fuzhou350116 China College of Chemistry & Chemical Engineering Yangzhou University Yangzhou225002 China National Synchrotron Radiation Research Center Hsinchu30076 Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan

The ubiquity of solid-liquid interfaces in nature and the significant role of their atomic-scale structure in determining interfacial properties have led to intensive research. Particularly in electrocatalysis, however, a molecular-level picture that clearly describes the dynamic interfacial structures and organizations with their correlation to preferred reaction pathways in electrochemical reactions remains poorly understood. In this review, CO2 electroreduction reaction (CO2RR) is spatially and temporally understood as a result of intricate interactions at the interface, in which the interfacial features are highly relevant. We start with the discussion of current understandings and model development associated with the charged electrochemical interface as well as its dynamic landscape. We further highlight the interactive dynamics from the interfacial field, catalyst surface charges and various gradients in electrolyte and interfacial water structures at interfaces under CO2RR working conditions, with emphasis on the interfacial-structure dependence of catalytic reactivity/selectivity. Significantly, a probing energy-dependent "in situ characterization map" for dynamic interfaces based on various complementary in situ/operando techniques is proposed, aiming to present a comprehensive picture of interfacial electrocatalysis and to provide a more unified research framework. Moreover, recent milestones in both experimental and theoretical aspects to establish the correct profile of electrochemical interfaces are stressed. Finally, we present key scientific challenges with related perspectives toward future opportunities for this exciting frontier. © 2023 The Royal Society of Chemistry.

关键词： Electrocatalysis

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Gut lumen-targeted oral delivery system for bioactive agents to regulate gut microbiome

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Journal of Future Foods 2022年第4期2卷 307-325页

作者： Junyu Liu Xiaobin Li Xiaopeng Zhang Runming Wang Sanyang Han Peiwu Qin Xin-Hui Xing Canyang Zhang Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate SchoolShenzhen 518055China Institute of Biochemical Engineering Department of Chemical EngineeringKey Laboratory for Industrial BiocatalysisMinistry of EducationCenter for Synthetic and Systems BiologyTsinghua UniversityBeijing 100084China Institute of Biomedical Health Technology and Engineering Shenzhen Bay LaboratoryShenzhen 518055China

Gut microbiome(GM)is closely related to the overall health status for the human *** dysfunction of microbiome can lead to many diseases such as inflammation,cancer and neurodegenerative ***,it's of great importance to develop effective strategy to regulate *** lumen-targeted oral delivery system(GLT-ODS)has been extensively investigated and widely used in food science and engineering in these years,due to the targeted delivery property,controlled release profile,high biocompatibility and enhanced bioavailability of ***,we comprehensively summarized the current advances in GLT-ODS for bioactive ***,we systematically summarize the GLT-ODS for pre-agents(prebiotics and probiotics)and anti-agents(antibiotics and bacteriophages)according to the type of *** in-depth discussion of representative researches,we refined the limitations of existing research in carrier material and target selection,and drew a blueprint for the future technological research and *** believe that GLT-ODS will become a safe,efficient,simple and precise GM management strategy for improvement of health and shine in the development of precision food and pharmaceutical engineering in future.

关键词： Gut lumen-targeted Oral delivery systems Bioactive agents Gut microenvironment Food engineering

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