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Effect of alginate and chitosan addition in the fabrication of polyvinyl alcohol-based matrix for tissue engineering applications

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AIP Conference Proceedings 2024年第1期3215卷

作者： Retno Wahyu Nurhayati Fatimah Azzahra Nuzli Fahdia Mazfufah Siti Fauziyah Rahman Wildan Mubarok Department of Chemical Engineering Faculty of Engineering Universitas Indonesia 16464 Depok Indonesia Research Center for Biomedical Engineering Faculty of Engineering Universitas Indonesia 16464 Depok Indonesia Stem Cells & Tissue Engineering Cluster Indonesian Medical Education and Research Institute Faculty of Medicine Universitas Indonesia Jakarta 10430 Indonesia Doctoral Program in Biomedical Sciences Faculty of Medicine Universitas Indonesia Jakarta 10430 Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Indonesia 16464 Depok Indonesia Division of Chemical Engineering Department of Materials Engineering Science Graduate School of Engineering Science Osaka University 560-0031 Toyonaka Japan

Recently, there has been a growing interest in artificial skin on hydrogels as a potential alternative for animal testing to evaluate the efficacy and safety of new drugs or cosmetics. Synthetic polymers have been widely used due to their superior physicochemical properties. However, synthetic polymers such as polyvinyl alcohol (PVA) are limited by their poor biocompatibility and cell adhesion. Combining synthetic and natural polymers can improve these properties. Therefore, in this study, we aimed to investigate the effect of alginate and chitosan addition to PVA-based hydrogels on protein adsorption, cell viability and cell attachment. The addition of chitosan decreased protein adsorption, which was caused by the changes in electrostatic charge by PVA-chitosan interaction and changes in surface roughness. Primary human fibroblast cells cultured on PVA, PVA-alginate and PVA-chitosan had >80% viability which indicates the cytocompatibility of the hydrogels. More importantly, alginate and chitosan also improved the cell attachment on the PVA-based hydrogels. Taken together, the addition of both alginate and chitosan has the potential to improve PVA-based hydrogels for artificial skin application.

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A MILP Battery Degradation Logarithmic Model with Real-Time Cycle Counting Calculation

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IEEE Transactions on Industry Applications 2025年

作者： Nebuloni, Riccardo Ilea, Valentin Berizzi, Alberto Bovo, Cristian Meraldi, Lorenzo Moretti, Luca Raboni, Pietro Politecnico di Milano Department of Energy Milano Italy Universita di Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy NHOA Energy Milano Italy Atlante Milano Italy DIgSILENT-STREL Cremona Italy

This study proposes a novel Mixed Integer Linear programming (MILP) model for optimal management of Battery Energy Storage System (BESS) plants integrated with a realtime cycle counting algorithm exploiting an innovative logarithmic formulation for battery degradation. By incorporating this logarithmic approach, the model effectively includes various stress factors like State of Energy, C-Rate, Depth of Discharge, and Temperature, along with Solid Electrolyte Interphase film formation, into a tractable linear model. Moreover, the limits of the Rainflow Counting Algorithm (RCA) are overcome. The comparison with the results obtained by applying the RCA method to the full physical model are positive: the count faithfully reflects that obtained through RCA, and degradation estimation is close to the real value, with relative error below 0.1%. © 1972-2012 IEEE.

关键词： Integer programming

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Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images

arXiv

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arXiv 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Roshanzamir, Mohamad Joloudari, Javad Hassannataj Shoeibi, Afshin Jafari, Mahboobeh Hussain, Sadiq Sani, Zahra Alizadeh Hasanzadeh, Fereshteh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Panahiazar, Maryam Zare, Assef Shariful Islam, Sheikh Mohammed Rajendra, U. Acharya Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid hospital Iran University of Medical Sciences Tehran Iran Institute for Computational Health Sciences University of California San Francisco United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filtering (CNN-SVM+Sobel) achieved the highest classification accuracy of 99.02% in accurate detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. Copyright © 2021, The Authors. All rights reserved.

关键词： Data mining

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Transverse acoustic spin and torque from pure spinning of objects

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Physical Review B 2021年第6期104卷 L060104-L060104页

作者： M. Farhat P.-Y. Chen M. Amin A. Alù Y. Wu Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Electrical and Computer Engineering University of Illinois at Chicago Chicago Illinois 60607 USA College of Engineering Taibah University Madinah 42353 Saudi Arabia Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Acoustic spin has been recently explored for many applications. In particular, transverse acoustic spin was demonstrated for inhomogeneous acoustic fields. In this Letter, we show the emergence of acoustic spin and torque in rotating acoustic objects of the same physical properties as the surrounding, to single out the effects purely due to rotation. The spinning of a cylindrical column of air or water in the same medium possesses intrinsic spin angular momentum, and we study the torque and force it experiences in evanescent acoustic fields. The resulting discontinuity can thus scatter sound in unusual ways, including a negative radiation force, although it has no imaginary part in its parameters.

关键词： Acoustic interactions Acoustic metamaterials Acoustic wave phenomena Linear acoustics

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Ptychographic lens-less polarization microscopy

arXiv

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arXiv 2022年

作者： Kim, Jeongsoo Song, Seungri Kim, Bora Park, Mirae Oh, Seung Jae Kim, Daesuk Cense, Barry Huh, Yong-Min Lee, Joo Yong Joo, Chulmin Department of Mechanical Engineering Yonsei University Seoul03722 Korea Republic of Department of Ophthalmology Asan Medical Center University of Ulsan College of Medicine Seoul05505 Korea Republic of Department of Radiology College of Medicine Yonsei University Seoul03722 Korea Republic of YUHS-KRIBB Medical Convergence Research Institute Seoul03722 Korea Republic of Department of Mechanical System Engineering Jeonbuk National University Jeonju54896 Korea Republic of Optical and Biomedical Engineering Laboratory Department of Electrical Electronic & Computer Engineering The University of Western Australia PerthWA6009 Australia Department of Biochemistry & Molecular Biology College of Medicine Yonsei University Seoul03722 Korea Republic of

Birefringence, an inherent characteristic of optically anisotropic materials, is widely utilized in various imaging applications ranging from material characterizations to clinical diagnosis. Polarized light microscopy enables high-resolution, high-contrast imaging of optically anisotropic specimens, but it is associated with mechanical rotations of polarizer/analyzer and relatively complex optical designs. Here, we present a novel form of polarization-sensitive microscopy capable of birefringence imaging of transparent objects without an optical lens and any moving parts. Our method exploits an optical mask-modulated polarization image sensor and single-input-state LED illumination design to obtain complex and birefringence images of the object via ptychographic phase retrieval. Using a camera with a pixel resolution of 3.45 µm, the method achieves birefringence imaging with a half-pitch resolution of 2.46 µm over a 59.74 mm2 field-of-view, which corresponds to a space-bandwidth product of 9.9 megapixels. We demonstrate the high-resolution, large-area birefringence imaging capability of our method by presenting the birefringence images of various anisotropic objects, including a birefringent resolution target, liquid crystal polymer depolarizer, monosodium urate crystal, and excised mouse eye and heart tissues. © 2022, CC BY.

关键词： Birefringence

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Retraction Note: An adaptive QoS computation for medical data processing in intelligent healthcare applications

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Neural Computing and Applications 2024年第17期36卷 10381-10381页

作者： Sodhro, Ali Hassan Malokani, Abdul Sattar Sodhro, Gul Hassan Muzammall, Muhammad Zongwei, Luo Electrical Engineering Department Sukkur IBA University Sukkur Pakistan IDA-Computer and Information Science Department Linkoping University Linköping Sweden Department of Biomedical Engineering Kyunghee University South Korea Suwon Korea Department of Physics Shah Abdul Latif University Khairpur Mirs Pakistan Department of Computer Science Bahria University Islamabad Pakistan Shenzhen Key Laboratory of Computational Intelligence Department of the Computer Science and Engineering Southern University of Science and Technology Shenzhen China

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Maximum A-Posteriori Probability (MAP) Terahertz Parameter Extraction for Pancreatic Ductal Adenocarcinoma

Maximum A-Posteriori Probability (MAP) Terahertz Parameter E...

引用

International Conference on Infrared and Millimeter Waves

作者： Debamitra Chakraborty Bradley N. Mills Jing Cheng Scott A. Gerber Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics University of Rochester Rochester New York USA Department of Surgery University of Rochester Medical Center Rochester New York USA Department of Electrical and Computer Engineering University of Rochester Rochester New York USA

ISBN: (数字)9781728194271

ISBN: (纸本)9781728194288

We developed a maximum a-posteriori probability-based terahertz parameter extraction method to obtain tissue characteristics of paraffin-embedded murine pancreatic ductal adenocarcinoma samples. This estimation algorithm can produce conservative estimates of tissue parameters without resorting to frequency-domain analysis. We report well-resolved and statistically significant differences in tumor tissue regions as compared to their healthy counterparts using our parameters extraction algorithm. We propose to use the index of refraction and the absorption as imaging biomarker parameters for biological tissue characterization.

关键词： Maximum a posteriori estimation Absorption Frequency-domain analysis Biological tissues Refractive index Estimation Probability

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Crowdsourcing Skin Demarcations of Chronic Graft-Versus-Host Disease in Patient Photographs: Training Versus Performance Study

JMIR Dermatology

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JMIR Dermatology 2023年第1期6卷 e48589页

作者： McNeil, Andrew J. Parks, Kelsey Liu, Xiaoqi Jiang, Bohan Coco, Joseph McCool, Kira Fabbri, Daniel Duhaime, Erik P. Dawant, Benoit M. Tkaczyk, Eric R. Dermatology Service and Research Service Department of Veterans Affairs Tennessee Valley Healthcare System Nashville TN United States Department of Dermatology Vanderbilt University Medical Center Nashville TN United States Department of Electrical and Computer Engineering Vanderbilt University Nashville TN United States Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN United States Centaur Labs Boston MA United States

Background: Chronic graft-versus-host disease (cGVHD) is a significant cause of long-term morbidity and mortality in patients after allogeneic hematopoietic cell transplantation. Skin is the most commonly affected organ, and visual assessment of cGVHD can have low reliability. Crowdsourcing data from nonexpert participants has been used for numerous medical applications, including image labeling and segmentation tasks. Objective: This study aimed to assess the ability of crowds of nonexpert raters-individuals without any prior training for identifying or marking cGHVD-to demarcate photos of cGVHD-affected skin. We also studied the effect of training and feedback on crowd performance. Methods: Using a Canfield Vectra H1 3D camera, 360 photographs of the skin of 36 patients with cGVHD were taken. Ground truth demarcations were provided in 3D by a trained expert and reviewed by a board-certified dermatologist. In total, 3000 2D images (projections from various angles) were created for crowd demarcation through the DiagnosUs mobile app. Raters were split into high and low feedback groups. The performances of 4 different crowds of nonexperts were analyzed, including 17 raters per image for the low and high feedback groups, 32-35 raters per image for the low feedback group, and the top 5 performers for each image from the low feedback group. Results: Across 8 demarcation competitions, 130 raters were recruited to the high feedback group and 161 to the low feedback group. This resulted in a total of 54,887 individual demarcations from the high feedback group and 78,967 from the low feedback group. The nonexpert crowds achieved good overall performance for segmenting cGVHD-affected skin with minimal training, achieving a median surface area error of less than 12% of skin pixels for all crowds in both the high and low feedback groups. The low feedback crowds performed slightly poorer than the high feedback crowd, even when a larger crowd was used. Tracking the 5 most reliabl

关键词： artificial intelligence cGVHD crowdsourcing dermatology feasibility graft-versus-host disease imaging labeling medical image segmentation skin

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Design of a Hybrid SMA-Pneumatic based Wearable Upper Limb Exoskeleton 25

Design of a Hybrid SMA-Pneumatic based Wearable Upper Limb E...

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25th ACM International Symposium on Wearable computers, ISWC 2021

作者： Golgouneh, Alireza Beaudette, Eric Woelfle, Heidi Li, Bai Subash, Niharikha Redhouse, Amanda Jones, Mark Martin, Tom Lobo, Michele Holschuh, Brad Dunne, Lucy Wearable Technology Lab University of Minnesota-Twin Cities United States Department of Physical Therapy and Biomechanics and Movement Science Program University of Delaware United States Department of Electrical and Computer Engineering Virginia Tech University United States

ISBN: (纸本)9781450384629

Upper limb mobility impairments affect individuals at all life stages. Exoskeletons can assist in rehabilitation as well as performing Activities of Daily Living (ADL). Most commercial assistive devices still rely on rigid robotics with constrained biomechanical degrees of freedom that may even increase user exertion. Therefore, this paper discusses the iterative design and development of a novel hybrid pneumatic actuation and Shape Memory Alloy (SMA) based wearable soft exoskeleton to assist in shoulder abduction and horizontal flexion/extension movements, with integrated soft strain sensing to track shoulder joint motion. The garment development was done in two stages which involved creating (1) SMA actuators integrated with soft sensing, and (2) integrating pneumatic actuation. The final soft exoskeleton design was developed based on the insights gained from two prior prototypes in terms of wearability, usability, comfort, and functional specifications (i.e., placement and number) of the sensors and actuators. The final exoskeleton is a modular, multilayer garment which uses a hybrid and customizable actuation strategy (SMA and inflatable pneumatic bladder). © 2021 Owner/Author.

关键词： Wearable technology

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Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studies

arXiv

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arXiv 2022年

作者： Wang, Rongguang Chaudhari, Pratik Davatzikos, Christos Department of Electrical and Systems Engineering University of Pennsylvania United States Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Radiology Perelman School of Medicine University of Pennsylvania United States

Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols. In the current study, and in the context of three brain diseases, we provide evidence which suggests that when properly trained, machine learning models can generalize well across diverse conditions and do not necessarily suffer from bias. Specifically, by using multi-study magnetic resonance imaging consortia for diagnosing Alzheimer's disease, schizophrenia, and autism spectrum disorder, we find that well-trained models have a high area-under-the-curve (AUC) on subjects across different subgroups pertaining to attributes such as gender, age, racial groups, and different clinical studies and are unbiased under multiple fairness metrics such as demographic parity difference, equalized odds difference, equal opportunity difference etc. We find that models that incorporate multi-source data from demographic, clinical, genetic factors and cognitive scores are also unbiased. These models have better predictive AUC across subgroups than those trained only with imaging features but there are also situations when these additional features do not help. © 2022, CC BY.

关键词： Magnetic resonance imaging

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