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arXiv 2021年

作者： Jayne, Rachael K. Çagatay Karakan, M. Zhang, Kehan Pierce, Noelle Michas, Christos Bishop, David J. Chen, Christopher S. Ekinci, Kamil L. White, Alice E. Department of Mechanical Engineering Boston University BostonMA02215 United States Photonics Center Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA02115 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Division of Materials Science and Engineering Boston University BostonMA02215 United States Department of Physics Boston University BostonMA02215 United States

We have designed and fabricated a microfluidic-based platform for sensing mechanical forces generated by cardiac microtissues in a highly-controlled microenvironment. Our fabrication approach combines Direct Laser Writing (DLW) lithography with soft lithography. At the center of our platform is a cylindrical volume, divided into two chambers by a cylindrical polydimethylsiloxane (PDMS) shell. Cells are seeded into the inner chamber from a top opening, and the microtissue assembles onto tailor-made attachment sites on the inner walls of the cylindrical shell. The outer chamber is electrically and fluidically isolated from the inner one by the cylindrical shell and is designed for actuation and sensing purposes. Externally applied pressure waves to the outer chamber deform parts of the cylindrical shell and thus allow us to exert time-dependent forces on the microtissue. Oscillatory forces generated by the microtissue similarly deform the cylindrical shell and change the volume of the outer chamber, resulting in measurable electrical conductance changes. We have used this platform to study the response of cardiac microtissues derived from human induced pluripotent stem cells (hiPSC) under prescribed mechanical loading and pacing. Copyright © 2021, The Authors. All rights reserved.

关键词： Shells (structures)

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Channel attention networks for robust MR fingerprinting matching

arXiv

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arXiv 2020年

作者： Soyak, Refik Navruz, Ebru Ersoy, Eda Ozgu Cruz, Gastao Prieto, Claudia King, Andrew P. Unay, Devrim Oksuz, Ilkay Electrical and Electronics Engineering Department Izmir University of Economics Izmir Turkey Faculty of Engineering Izmir Democracy University Izmir Turkey School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Computer Engineering Department Istanbul Technical University Istanbul Turkey

Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varying acquisition parameters pseudo-randomly, so that each tissue generates its unique signal evolution during scanning. Even though MRF provides faster scanning, it has disadvantages such as erroneous and slow generation of the corresponding parametric maps, which needs to be improved. Moreover, there is a need for explainable architectures for understanding the guiding signals to generate accurate parametric maps. In this paper, we addressed both of these shortcomings by proposing a novel neural network architecture consisting of a channel-wise attention module and a fully convolutional network. The proposed approach, evaluated over 3 simulated MRF signals, reduces error in the reconstruction of tissue parameters by 8.88% for T1 and 75.44% for T2 with respect to state-of-the-art methods. Another contribution of this study is a new channel selection method: attention-based channel selection. Furthermore, the effect of patch size and temporal frames of MRF signal on channel reduction are analyzed by employing a channel-wise attention. © 2020, CC BY.

关键词： Magnetic resonance

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Study of position uncertainties of nanowire arrays on silicon waveguide in a polarization rotator device

Study of position uncertainties of nanowire arrays on silico...

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IEEE Nanotechnology Materials and Devices Conference (NMDC)

作者： D. Prete V. Vitali C. Lacava A. Fontana V. Demontis A. Salamon V. Bellani I. Cristiani F. Rossella Istituto Nazionale Fisica Nucleare (INFN) Sezione di Pavia Pavia Italy Dipartimento di Scienze Fisiche Informatiche e Matematiche Università di Modena e Reggio Emilia Modena Italy Department of Electrical Computer and Biomedical Engineering Photonics Research Group University of Pavia Pavia Italy Optoelectronics Research Centre University of Southampton Southampton UK Scuola Normale Superiore Laboratorio NEST Pisa Italy Istituto Nazionale di Fisica Nucleare (INFN) Sezione di Roma Tor Vergata Roma Italy Department of Physics University of Pavia Pavia Italy

Innovative silicon photonic-based polarization converting device can be realized with the integration of semiconducting InP nanowires (NWs) on the silicon photonic platform. We have recently designed a compact polarization converter and demonstrated its full performances (from quasi-TE modes to quasi-TM modes, and vice versa): see talk “Semiconductor NW’s for polarization control in integrated silicon on insulator waveguides” at this conference and [1].

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Personalized automatic sleep staging with single-night data: A pilot study with kl-divergence regularization

arXiv

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arXiv 2020年

作者： Phan, Huy Mikkelsen, Kaare Chén, Oliver Y. Koch, Philipp Mertins, Alfred Kidmose, Preben de Vos, Maarten School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Engineering Aarhus University Denmark Institute of Biomedical Engineering University of Oxford United Kingdom Institute for Signal Processing University of Lübeck Germany Department of Electrical Engineering KU Leuven Belgium

Brain waves vary between people. An obvious way to improve automatic sleep staging for longitudinal sleep monitoring is personalization of algorithms based on individual characteristics extracted from the first night of data. As a single night is a very small amount of data to train a sleep staging model, we propose a Kullback-Leibler (KL) divergence regularized transfer learning approach to address this problem. We employ the pretrained SeqSleepNet (i.e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model. This is done by adding the KL divergence between the output of the subject independent model and the output of the personalized model to the loss function during finetuning. In effect, KL-divergence regularization prevents the personalized model from overfitting to the single-night data and straying too far away from the subject independent model. Experimental results on the Sleep-EDF Expanded database with 75 subjects show that sleep staging personalization with a single-night data is possible with help of the proposed KL-divergence regularization. On average, we achieve a personalized sleep staging accuracy of 79.6%, a Cohen’s kappa of 0.706, a macro F1-score of 73.0%, a sensitivity of 71.8%, and a specificity of 94.2%. We find both that the approach is robust against overfitting and that it improves the accuracy by 4.5 percentage points compared to non-personalization and 2.2 percentage points compared to personalization without regularization. Copyright © 2020, The Authors. All rights reserved.

关键词： Sleep research

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Deep Learning for Brain Age Estimation: A Systematic Review

arXiv

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arXiv 2022年

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Ser, Javier Del Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Assam Silchar788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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A Novel Algorithm for the Design of Ketogenic Meals

A Novel Algorithm for the Design of Ketogenic Meals

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Mediterranean Electrotechnical Conference (MELECON)

作者： Giordano Lanzola Federica Pellegrino Cinzia Ferraris Silvana Quaglini Dept. Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Dept. Public Health Experimental and Forensic Medicine University of Pavia Pavia Italy

ISBN: (数字)9781728152004

ISBN: (纸本)9781728152011

Introduction: The authors previously developed Ketty as a smartphone app for Ketogenic Diet patients. Ketty includes a food intake diary and a parameters monitoring section that allows the clinical staff to remotely oversee patients. In this work we describe a companion tool that has been implemented for empowering those patients in designing tasty meals. Methods: We used the Banca Dati Alimenti (BDA) provided by the Istituto Europeo di Oncologia as the source for the nutrition facts of the food ingredients and The Meal Db which is publicly available on the Internet as a source of recipes. We designed an algorithm for reshaping available recipes making them compliant with the dietary ***: A first standalone prototype written in Java has been implemented importing data from the BDA and interfacing through a Web Service with The Meal DB database. When a target prescription is provided in terms of lipids, carbohydrates and proteins, the algorithm first proposes a set of recipes to work with and then reshapes the selected one solving the planning meal ***: The prototype is be able to create recipes matching very closely the target provided and preserving their tastiness. We are planning to set up a formal validation study for the prototype and to integrate it into Ketty.

关键词： Planning Databases Proteins Lipidomics Remote monitoring biomedical engineering

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Shape Anisotropy-Governed High-Performance Nanomagnetosol for In Vivo Magnetic Particle Imaging of Lungs

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Small 2024年第5期20卷 2305300-2305300页

作者： Nigam, Saumya Mohapatra, Jeotikanta Makela, Ashley V. Hayat, Hanaan Rodriguez, Jessi Mercedes Sun, Aixia Kenyon, Elizabeth Redman, Nathan A. Spence, Dana Jabin, George Gu, Bin Ashry, Mohamed Sempere, Lorenzo F. Mitra, Arijit Li, Jinxing Chen, Jiahui Wei, Guo-Wei Bolin, Steven Etchebarne, Brett Liu, J. Ping Contag, Christopher H. Wang, Ping Precision Health Program Michigan State University East LansingMI48824 United States Department of Radiology College of Human Medicine Michigan State University East LansingMI48824 United States Department of Physics The University of Texas at Arlington ArlingtonTX76019 United States Michigan State University East LansingMI48824 United States Department of Biomedical Engineering College of Engineering Michigan State University East LansingMI48824 United States Human Biology Program College of Natural Science Michigan State University East LansingMI48824 United States Department of Obstetrics Gynecology and Reproductive Sciences College of Human Medicine Michigan State University East LansingMI48824 United States Department of Materials Science and Engineering National Cheng Kung University Tainan City701 Taiwan Department of Mathematics College of Natural Science Michigan State U niversity East LansingMI48824 United States Department of Electrical and Computer Engineering College of Engineering Michigan State University East LansingMI48824 United States Department of Biochemistry and Molecular Biology College of Natural Science Michigan State University East LansingMI48824 United States Department of Pathobiology and Diagnostic Investigation College of Veterinary Medicine Michigan State University East LansingMI48824 United States Osteopathic Medical Specialties College of Osteopathic Medicine Michigan State University East LansingMI48824 United States Department of Microbiology and Molecular Genetics Michigan State University East LansingMI48824 United States

Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has shown extensive lung manifestations in vulnerable individuals, putting lung imaging and monitoring at the forefront of early detection and treatment. Magnetic particle imaging (MPI) is an imaging modality, which can bring excellent contrast, sensitivity, and signal-to-noise ratios to lung imaging for the development of new theranostic approaches for respiratory diseases. Advances in MPI tracers would offer additional improvements and increase the potential for clinical translation of MPI. Here, a high-performance nanotracer based on shape anisotropy of magnetic nanoparticles is developed and its use in MPI imaging of the lung is demonstrated. Shape anisotropy proves to be a critical parameter for increasing signal intensity and resolution and exceeding those properties of conventional spherical nanoparticles. The 0D nanoparticles exhibit a 2-fold increase, while the 1D nanorods have a > 5-fold increase in signal intensity when compared to VivoTrax. Newly designed 1D nanorods displayed high signal intensities and excellent resolution in lung images. A spatiotemporal lung imaging study in mice revealed that this tracer offers new opportunities for monitoring disease and guiding intervention. © 2023 The Authors. Small published by Wiley-VCH GmbH.

关键词： Nanorods

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Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning

arXiv

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arXiv 2022年

作者： Ye, Jiarong Yeh, Yin-Ting Xue, Yuan Wang, Ziyang Zhang, Na Liu, He Zhang, Kunyan Ricker, Ryeanne Yu, Zhuohang Roder, Allison Lopez, Nestor Perea Organtini, Lindsey Greene, Wallace Hafenstein, Susan Lu, Huaguang Ghedin, Elodie Terrones, Mauricio Huang, Shengxi Huang, Sharon Xiaolei College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Department of Physics The Pennsylvania State University University ParkPA United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering The Pennsylvania State University University ParkPA United States Department of Biomedical Engineering George Washington University WashingtonDC United States Systems Genomics Section Laboratory of Parasitic Diseases National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA United States Department of Pathology and Laboratory Medicine Division of Clinical Pathology The Pennsylvania State University College of Medicine HersheyPA United States Department of Veterinary and Biomedical Sciences The Pennsylvania State University University ParkPA United States 413G Eric. J. Barron Innovation Hub 123 S. Burrowes Street State CollegePA16801 United States

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device coupled with label-free Raman Spectroscopy holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such a machine learning approach for analyzing Raman spectra of human and avian viruses. A Convolutional Neural Network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A vs. type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus, IBV) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups-for example, amide, amino acid, carboxylic acid-we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses. © 2022, CC BY.

关键词： Raman spectroscopy

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CNN-based cardiac motion extraction to generate deformable geometric left ventricle myocardial models from Cine MRI

arXiv

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arXiv 2021年

作者： Upendra, Roshan Reddy Wentz, Brian Jamison Simon, Richard Shontz, Suzanne M. Linte, Cristian A. Center for Imaging Science Rochester Institute of Technology RochesterNY United States Biomedical Engineering Rochester Institute of Technology RochesterNY United States Bioengineering Graduate Program University of Kansas LawrenceKS United States Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Information and Telecommunication Technology Center University of Kansas LawrenceKS United States

Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images to reconstruct patient-specific geometric models of the LV myocardium. With the advent of deep learning, accurate segmentation of cardiac chambers from cine cardiac MR images and unsupervised learning for image registration for cardiac motion estimation on a large number of image datasets is attainable. Here, we propose a deep leaning-based framework for the development of patient-specific geometric models of LV myocardium from cine cardiac MR images, using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We use the deformation field estimated from the VoxelMorph-based convolutional neural network (CNN) to propagate the isosurface mesh and volume mesh of the end-diastole (ED) frame to the subsequent frames of the cardiac cycle. We assess the CNN-based propagated models against segmented models at each cardiac phase, as well as models propagated using another traditional nonrigid image registration technique. © 2021, CC BY.

关键词： Image registration

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Extraction of bioactive compounds from Moringa oleifera leaves using different organic solvents

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AIP Conference Proceedings 2023年第1期2601卷

作者： Madeeha Sadia Fazilah Abd Manan Norjihada Izzah Ismail Saravana Kumar Jaganathan Syafiqah Saidin School of Biomedical Engineering and Health Sciences Faculty of Engineering Universiti Teknologi Malaysia 81310 UTM Johor Bahru Johor Malaysia Department of Biomedical Engineering Faculty of Electrical and Computer Engineering NED University of Engineering and Technology Karachi Pakistan Department of Biosciences Faculty of Science Universiti Teknologi Malaysia 81310 UTM Johor Bahru Johor Malaysia Medical devices and Technology Centre Institute of Human Centered Engineering Universiti Teknologi Malaysia 81310 UTM Johor Bahru Johor Malaysia Department of Engineering Faculty of Science and Engineering University of Hull United Kingdom Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Vietnam IJN-UTM Cardiovascular Engineering Centre Institute of Human Centered Engineering Universiti Teknologi Malaysia 81310 UTM Johor Bahru Johor Malaysia

Flavonoid and phenolic contents present in plant leaves play a vital role in different therapeutic applications. They are also known as the most obstinate groups of plant phenolic (bioactive compounds). The leaves of Moringa oleifera (M. oleifera) contain different bioactive constituents that are valuable for remedial purposes. This study aimed to quantitatively identify and assess bioactive compounds in M. oleifera leaves extract that were discovered from different crude solvents using a maceration method. Total phenolic content (TPC) and total flavonoid content (TFC) were quantified by Folin-Ciocalteu and Aluminum chloride methods, respectively. It was noticed that by using the maceration method, 58% percentage yield was recorded in the methanolic leaves extract. The TPC (3.57±0.12 mg GAE/g) and TFC (10.83±2.73 mg QE/g) results showed, the methanolic leaves extract has attained significant maximum concentration of phenolic bioactive compounds (p≤0.05) in comparison to the ethanol and acetone leaves extract. Two important tangible aspects that have influenced the quantified results of bioactive compounds in this study were the selection of its characterization and extraction methods. Therefore, it can be concluded that methanol is a suitable solvent choice for the extraction of bioactive compounds from M. oleifera leaves.

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