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arXiv 2023年

作者： Nambiar, Ananthan Pan, Chao Rana, Vishal Cheraghchi, Mahdi Ribeiro, João Maslov, Sergei Milenkovic, Olgica Department of Bioengineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL United States Center for Artificial Intelligence and Modeling Carl R. Woese Institute for Genomic Biology University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States NOVA LINCS and NOVA School of Science and Technology Caparica Portugal

Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed to screen large populations when the disease prevalence is low. However, it does not fully utilize the quantitative information provided by qPCR methods, nor is it able to accommodate a wide range of pathogen loads. To address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to efficiently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold (Ct) values into multiple bins, leveraging the information from the first stage of screening to improve the detection sensitivity. Dynamic Ct threshold adjustments mitigate dilution effects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by 24% while maintaining a negligible false negative rate. © 2023, CC BY.

关键词： Disease control

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Arrayed Single-Cell Capture and Release Using Localized Oersted Fields

SSRN

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SSRN 2023年

作者： Estrada, Victor Zheng, Ruoda Wang, Yilian Miwa, Hiromi Bogumil, Michael Jackson, Malcolm Wong, Kin Wang, Kang Di Carlo, Dino Carman, Greg P. Sepulveda, Abdon Department of Mechanical and Aerospace Engineering University of California Los AngelesCA90095 United States Department of Bioengineering University of California Los AngelesCA90095 United States Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States University of California – Los Angeles Los AngelesCA90095 United States

The scientific community has traditionally believed that cells of the same type are identical and exhibit homogeneous behavior. However, it is now well understood that the behavior of a single cell may differ significantly from that of its surrounding population. This understanding of single-cell heterogeneity has led to numerous advances in the fields of biology and medicine. Despite these advances, there is still a lack of reliable single-cell analysis platforms that can capture and deterministically release single cells. In this work, we propose a magnetic microfluidic platform that allows users to capture single, magnetically labeled cells at discrete locations in an array configuration and allows for the release of any desired cell or group of cells for further downstream processing. The capture and release processes are accomplished by magnetizing and demagnetizing small, magnetic capture sites using localized Oersted fields produced by electric currents in wires underneath the capture sites. We experimentally demonstrate the capture of superparamagnetic beads and individual magnetically labeled Jurkat cells at discrete locations and their subsequent release. With its advantages over existing devices, our platform has the potential to further facilitate single-cell analyses and support developments in fields such as cancer, stem cell, and neurobiology research. © 2023, The Authors. All rights reserved.

关键词： Microfluidics

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MEMO: Dataset and Methods for Robust Multimodal Retinal Image Registration with Large or Small Vessel Density Differences

arXiv

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arXiv 2023年

作者： Wang, Chiao-Yi Sadrieh, Faranguisse Kakhi Shen, Yi-Ting Chen, Shih-En Kim, Sarah Chen, Victoria Raghavendra, Achyut Wang, Dongyi Saeedi, Osamah Tao, Yang Department of Bioengineering University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Department of Ophthalmology and Visual Sciences University of Maryland School of Medicine BaltimoreMD21201 United States Department of Biological and Agricultural Engineering University of Arkansas FayettevilleAR72701 United States

The measurement of retinal blood flow (RBF) in capillaries can provide a powerful biomarker for the early diagnosis and treatment of ocular diseases. However, no single modality can determine capillary flowrates with high precision. Combining erythrocyte-mediated angiography (EMA) with optical coherence tomography angiography (OCTA) has the potential to achieve this goal, as EMA can measure the absolute RBF of retinal microvasculature and OCTA can provide the structural images of capillaries. However, multimodal retinal image registration between these two modalities remains largely unexplored. To fill this gap, we establish MEMO, the first public multimodal EMA and OCTA retinal image dataset. A unique challenge in multimodal retinal image registration between these modalities is the relatively large difference in vessel density (VD). To address this challenge, we propose a segmentation-based deep-learning framework (VDD-Reg), which provides robust results despite differences in vessel density. VDD-Reg consists of a vessel segmentation module and a registration module. To train the vessel segmentation module, we further designed a two-stage semi-supervised learning framework (LVD-Seg) combining supervised and unsupervised losses. We demonstrate that VDD-Reg outperforms existing methods quantitatively and qualitatively for cases of both small VD differences (using the CF-FA dataset) and large VD differences (using our MEMO dataset). Moreover, VDD-Reg requires as few as three annotated vessel segmentation masks to maintain its accuracy, demonstrating its feasibility. © 2023, CC BY.

关键词： Image registration

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A Novel Vit-Based Multi-Scaled and Rotation-Invariance Approach for Precise Differentiation Between Meningioma and Solitary Fibrous Tumor

A Novel Vit-Based Multi-Scaled and Rotation-Invariance Appro...

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IEEE International Symposium on Biomedical Imaging

作者： Mohamed T. Azam Hossam Magdy Balaha Khadiga M. Ali Nagham E. Mekky Noha A. Hikal Mohammed Ghazal Dibson D. Gondim Akshitkumar Mistry Ayman El-Baz Bioengineering Department University of Louisville Louisville KY USA Department of Pathology Faculty of Medicine Mansoura University Mansoura Egypt Department of Information Technology Faculty of Computers and Information Mansoura University Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE Department of Pathology University of Louisville School of Medicine Louisville KY USA Department of Neurosurgery University of Louisville School of Medicine Louisville KY USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Meningioma (MEN) and Solitary Fibrous Tumor (SFT) pose challenges in diagnosis due to their similar histological characteristics. Common diagnostic methods like immunohistochemistry (IHC) have drawbacks in terms of time, cost, and accessibility. This paper introduces a new approach using Vision Transformer (ViT) on whole-slide images (WSI) for MEN and SFT classification, addressing these challenges. The proposed model employs a rotational and scale invariance approach with a ViT architecture, capturing global and local features at various optical scales in WSIs. To comprehensively an-alyze, pre-trained ViT models (ViT B/16, B/32, L/16, L/32) were applied to different WSI magnification levels. Diagnosis was determined by combining WSI scales using a weighted majority voting scheme. Evaluation on a 92-patient cohort (46 MEN, 46 SFT) showed ViT L/16 model’s robust performance with 90.45% accuracy, 93.03% sensitivity, 87.94% specificity, 88.21% precision, 90.48% balanced accuracy, 90.62% ROC, and 90.56% F1-score. These results affirm the effectiveness of the proposed model, promising innovative tools in histopathology diagnostics.

关键词： computer vision Accuracy Sensitivity Costs Histopathology Transformers Optical imaging

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An Ensemble Approach for Brain Tumor Segmentation and Synthesis

arXiv

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arXiv 2024年

作者： Rivera, Juampablo Heras E. Chopra, Agamdeep S. Ren, Tianyi Oswal, Hitender Pan, Yutong Sordo, Zineb Walters, Sophie Henry, William Mohammadi, Hooman Olson, Riley Rezayaraghi, Fargol Lam, Tyson Jaikanth, Akshay Kancharla, Pavan Ruzevick, Jacob Ushizima, Daniela Kurt, Mehmet Department of Mechanical Engineering University of Washington SeattleWA United States Paul G. Allen School of Computer Science University of Washington SeattleWA United States Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Bioengineering University of Washington SeattleWA United States Department of Electrical and Computer Engineering University of Washington SeattleWA United States Watauga High School BooneNC United States Department of Neurological Surgery University of Washington SeattleWA United States

The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple layers of processing to capture intricate details of complex data, which can then be used on a variety of tasks, including brain tumor classification, segmentation, image synthesis, and registration. Previous research demonstrates high accuracy in tumor segmentation using various model architectures, including nn-UNet and Swin-UNet. U-Mamba, which uses state space modeling, also achieves high accuracy in medical image segmentation. To leverage these models, we propose a deep learning framework that ensembles these state-of-the-art architectures to achieve accurate segmentation and produce finely synthesized images. © 2024, CC BY.

关键词： Image segmentation

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A Neuroimaging Yolov8-Based Cad Framework for Anosmia Grading in Covid-19

A Neuroimaging Yolov8-Based Cad Framework for Anosmia Gradin...

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IEEE International Conference on Image Processing

作者： Hossam Magdy Balaha Mayada Elgendy Ahmed Alksas Mohamed Shehata Norah Saleh Alghamdi Fatma Taher Mohammed Ghazal Mahitab Ghoneim Eslam Hamed Fatma Sherif Ahmed Elgarayhi Mohammed Sallah Mohamed Abdelbadie Salem Elsharawy Kamal Harpal Sandhu Ayman El-Baz Department of Bioengineering J.B. Speed School of Engineering University of Louisville Louisville USA Physics Department Applied Theoretical Physics Research Group Faculty of Science Mansoura University Egypt Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Saudi Arabia The College of Technological Innovation Zayed University Dubai UAE Electrical Computer and Biomedical Engineering Depatrment Abu Dhabi University UAE Department of Radiology Faculty of Medicine Mansoura University Egypt Otolaryngology Department Faculty of Medicine Mansoura University Egypt Department of Physics College of Sciences University of Bisha Saudi Arabia

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

COVID-19, a respiratory illness caused by SARS-CoV-2, has brought attention to a common symptom: loss of smell and taste. Anosmia, a prevalent symptom of COVID-19, varies in severity from mild to severe, necessitating accurate diagnostic tools. The study proposes a novel framework for predicting COVID-19 anosmia severity utilizing YOLOv8 for classification and EigenCAM for interpretability. YOLOv8, optimized for object detection, is adapted for classification tasks using advanced architectural enhancements and mosaic augmentation. EigenCAM provides interpretability by highlighting image regions crucial for predictions, aiding clinical decision-making. Evaluation across multiple YOLOv8 model sizes using DTI and FLAIR modalities reveals robust performance, with the Large model excelling in DTI and the Nano model in FLAIR. Compared to our previous work, the framework significantly enhances accuracy and interpretability in predicting anosmia severity, marking a substantial advancement in medical image analysis. This study underscores the potential of deep learning for precise and interpretable medical diagnostics, offering insights into anosmia severity prediction.

关键词： COVID-19 Deep learning Solid modeling Adaptation models Accuracy Robustness Medical diagnosis

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Work in Progress: Bridging University Technical Innovations to K-12 Classrooms Through Hands-on Activities in Plant Bioelectrics and AI

Work in Progress: Bridging University Technical Innovations ...

引用

IEEE Education engineering (EDUCON)

作者： Jorge Torres Gómez Imen Bekkari Nicolai Spicher Carmen Peláez-Moreno Jan Haase Maurizio Margarini School of Electrical Engineering and Computer Science TU Berlin Berlin Germany Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy University Medical Center Göttingen Göttingen Germany Department of Signal Theory and Communications University Carlos III Madrid (UC3M) Spain NORDAKADEMIE gAG Hochschule der Wirtschaft Elmshorn Germany

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

Technologies developed at universities are direct means to conceive a teaching plan for promoting science, technology, engineering and mathematics (STEM) at the K-12 level of education. This work-in-progress develops this notion: We elaborate a teaching plan for K-12 students based on technologies for recording plant bioelectrical activity. The teaching plan sketches hands-on activities, where pupils can self-assemble the electronic components such as the electro-potential sensor, analog-to-digital (ADC) converter, and Arduino board for processing, as designed by research students at Politecnico di Milano. Using our project as a blueprint, we aim to support other educators at universities to promote further STEM and expose pupils to technical developments at universities.

关键词： Technological innovation Electronic components Recording Pupils engineering education Artificial intelligence STEM

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Wideband Air-Coupled CMUT Arrays for Acoustic Micro-Tapping

Wideband Air-Coupled CMUT Arrays for Acoustic Micro-Tapping

引用

2020 IEEE International Ultrasonics Symposium, IUS 2020

作者： Coutant, Zachary A Adelegan, Oluwafemi Biliroglu, Ali Onder Jeng, Geng-Shi Pitre, John J. Kirby, Mitchell A. Pelivanov, Ivan Yamaner, Feysel Yalcin O'Donnell, Matthew Oralkan, Omer NC State University Department of Electrical and Computer Engineering RaleighNC United States University of Washington Department of Bioengineering SeattleWA United States

ISBN: (纸本)9781728154480

This paper presents a 32-element one-dimensional air-coupled capacitive micromachined ultrasonic transducer (CMUT) array designed to be part of a non-contact elastography system. To achieve the required bandwidth in air, staggered cell sizes are used. The completed devices showed a surface pressure of 950 Pa at the center frequency of 1.45 MHz. The 3-dB, 6-dB, and 10-dB fractional bandwidths are 5%, 10%, and 12.8%. © 2020 IEEE.

关键词： MEMS

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Limits on inferring gene regulatory networks from single-cell measurements of unstable mRNA levels

Limits on inferring gene regulatory networks from single-cel...

引用

IEEE Conference on Decision and Control

作者： Tarun Mahajan Michael Saint-Antoine Roy D. Dar Abhyudai Singh Department of Bioengineering University of Illinois at Urbana-Champaign Urbana IL USA Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA Faculty of Department of Bioengineering University of Illinois at Urbana-Champaign Urbana IL USA Faculty of Electrical and Computer Engineering Biomedical Engineering and Mathematical Sciences University of Delaware Newark DE USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Gene regulatory network inference from single-cell expression data, such as single-cell RNA sequencing, is a popular problem in computational biology. Despite diverse methods spanning information theory, machine learning, and statistics, it is unsolved. This is attributable to measurement errors, lack of perturbation data, or difficulty in causal inference. Yet the role of kinetic parameters of gene expression is unknown. We show how the relative stability of mRNA and protein hampers inference. We use a simple model of gene expression to show that a more stable protein than mRNA deteriorates correlation between the mRNA of a transcription factor and its target gene. This can also happen when mRNA and protein are on the same timescale. Relative difference in timescales affects true interactions more strongly than false positives. Besides correlation, we find that information-theoretic nonlinear measures are also prone to this problem.

关键词： Proteins Sequential analysis Measurement errors Correlation RNA Perturbation methods Machine learning

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Deep learning-based image reconstruction in optical coherence tomography using undersampled spectral data

Deep learning-based image reconstruction in optical coherenc...

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CLEO: Applications and Technology, A and T 2022

作者： Zhang, Yijie Liu, Tairan Singh, Manmohan Çetintaş, Ege Luo, Yilin Rivenson, Yair Larin, Kirill V. Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Department of Biomedical Engineering University of Houston HoustonTX77204 United States Department of Molecular Physiology and Biophysics Baylor College of Medicine University of Houston HoustonTX77204 United States Department of Surgery David Geffen School of Medicine University of California Los AngelesCA90095 United States

We present a deep learning-based image reconstruction method in swept-source optical coherent tomography (OCT) using undersampled spectral data. This method can improve the imaging speed without the need for any hardw... 详细信息

ISBN: (纸本)9781957171050

关键词： Optical tomography

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