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Machine Learning Approach to Enhance the Performance of MNP?Labeled Lateral Flow Immunoassay

Nano-Micro Letters 2019年第1期11卷 132-146页

作者： Wenqiang Yan Kan Wang Hao Xu Xuyang Huo Qinghui Jin Daxiang Cui Department of Instrument Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment InstrumentKey Laboratory of Thin Film and Microfabrication (Ministry of Education) Shanghai Jiao Tong University School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Department of Biomedical Engineering JiLin Medical University State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo University

The use of magnetic nanoparticle(MNP)-labeled immunochromatography test strips(ICTSs) is very important for point-ofcare testing(POCT). However, common diagnostic methods cannot accurately analyze the weak magnetic signal from ICTSs, limiting the applications of POCT. In this study, an ultrasensitive multiplex biosensor was designed to overcome the limitations of capturing and normalization of the weak magnetic signal from MNPs on ICTSs. A machine learning model for sandwich assays was constructed and used to classify weakly positive and negative samples, which significantly enhanced the specificity and sensitivity. The potential clinical application was evaluated by detecting 50 human chorionic gonadotropin(HCG) samples and 59 myocardial infarction serum samples. The quantitative range for HCG was 1–1000 mIU mL^(-1) and the ideal detection limit was 0.014 mIU mL^(-1), which was well below the clinical threshold. Quantitative detection results of multiplex cardiac markers showed good linear correlations with standard values. The proposed multiplex assay can be readily adapted for identifying other biomolecules and also be used in other applications such as environmental monitoring, food analysis, and national security.

关键词： Point-of-care testing Immunochromatography test strips Magnetic nanoparticles Machine learning Support vector machine

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CONVERGENCE SPEED AND APPROXIMATION ACCURACY OF NUMERICAL MCMC

arXiv

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arXiv 2022年

作者： Cui, Tiangang Dong, Jing Jasra, Ajay Tong, Xin T. School of Mathematics Monash University Australia Graduate School of Business Columbia University United States Applied Mathematics and Computational Science Program Computer Electrical Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Department of Mathematics National University of Singapore Singapore

When implementing Markov Chain Monte Carlo (MCMC) algorithms, perturbation caused by numerical errors is sometimes inevitable. This paper studies how perturbation of MCMC affects the convergence speed and Monte Carlo estimation accuracy. Our results show that when the original Markov chain converges to stationarity fast enough and the perturbed transition kernel is a good approximation to the original transition kernel, the corresponding perturbed sampler has similar convergence speed and high approximation accuracy as well. We discuss two different analysis frameworks: ergodicity and spectral gap, both are widely used in the literature. Our results can be easily extended to obtain non-asymptotic error bounds for MCMC estimators. We also demonstrate how to apply our convergence and approximation results to the analysis of specific sampling algorithms, including Random walk Metropolis and Metropolis adjusted Langevin algorithm with perturbed target densities, and parallel tempering Monte Carlo with perturbed densities. Finally we present some simple numerical examples to verify our theoretical claims. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse problems

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Self-Interference Fluorescent Emission Microscopy for Biological Imaging

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Microscopy and Microanalysis 2020年第S2期7卷 36-37页

作者： A.K. Swan Y. Tong L. Moiseev M. S. Ünlü B.B. Goldberg Electrical and Computer Engineering Boston University MA 02215Department of Physics Boston University Boston MA 02215Department of Biomedical Engineering Boston MA 02215

We present a new method of fluorescence imaging, which yields an unprecedented nm scale vertical resolution. The method uses the unique spectral signature of the fluorescent emission intensity above a reflecting surface to determine vertical position unambiguously. Emission from several heights could be resolved by deconvoluting the spectrum, so that three dimensional imaging is possible. The related technique of standing wave microscopy uses the spatial intensity variation for increased resolution. Applications of this technique include intracellular imaging and screening for specific bacteria, virus or proteins, where discrimination between raised fluorescently labeled specifically bound markers from non-specific binding is *** emission spectrum of fluorescent markers is modified in the presence of a reflecting surface. Within the wavelength range of the fluorescent emission, the selfinterference of the emitted photons from the direct and reflected path results in enhanced or suppressed emission (constructive or destructive interference) depending on the height and wavelength.

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Robust optimal transport with applications in generative modeling and domain adaptation 20

Robust optimal transport with applications in generative mod...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Yogesh Balaji Rama Chellappa Soheil Feizi Department of Computer Science University of Maryland College Park MD Electrical and Computer Engineering and Biomedical Engineering Departments Johns Hopkins University Baltimore MD

ISBN: (纸本)9781713829546

Optimal Transport (OT) distances such as Wasserstein have been used in several areas such as GANs and domain adaptation. OT, however, is very sensitive to outliers (samples with large noise) in the data since in its objective function, every sample, including outliers, is weighed similarly due to the marginal constraints. To remedy this issue, robust formulations of OT with unbalanced marginal constraints have previously been proposed. However, employing these methods in deep learning problems such as GANs and domain adaptation is challenging due to the instability of their dual optimization solvers. In this paper, we resolve these issues by deriving a computationally-efficient dual form of the robust OT optimization that is amenable to modern deep learning applications. We demonstrate the effectiveness of our formulation in two applications of GANs and domain adaptation. Our approach can train state-of-the-art GAN models on noisy datasets corrupted with outlier distributions. In particular, the proposed optimization method computes weights for training samples reflecting how difficult it is for those samples to be generated in the model. In domain adaptation, our robust OT formulation leads to improved accuracy compared to the standard adversarial adaptation methods.

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Cascade RCNN for MIDOG challenge

arXiv

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arXiv 2021年

作者： Razavi, Salar Dambandkhameneh, Fariba Androutsos, Dimitri Done, Susan Khademi, April Electrical Computer and Biomedical Engineering Ryerson University TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada

Mitotic counts are one of the key indicators of breast cancer prognosis. However, accurate mitotic cell counting is still a difficult problem and is labourious. Automated methods have been proposed for this task, but are usually dependent on the training images and show poor performance on unseen domains. In this work, we present a multi-stage mitosis detection method based on a Cascade RCNN developed to be sequentially more selective against false positives. On the preliminary test set, the algorithm scores an F1 score of 0.7492. © 2021, CC BY.

关键词： Medical imaging

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Planar laser activated neuronal scanning (PLANS) system for in vivo flow cytometry

Planar laser activated neuronal scanning (PLANS) system for ...

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2020 Frontiers in Optics Conference, FiO 2020

作者： Watson, Nicholas Mondal, Sudip DuPlissis, Andrew Kim, Ki Hyun Ben-Yakar, Adela Department of Biomedical Engineering University of Texas at Austin 107 W Dean Keeton St. AustinTX78712 United States Department of Mechanical Engineering University of Texas at Austin 204 E Dean Keeton St. AustinTX78712 United States Department of Electrical and Computer Engineering University of Texas at Austin 2501 Speedway AustinTX78712 United States

ISBN: (纸本)9781943580804

关键词： Flow cytometry

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A Cloud connected NO2 and ozone sensor system for personalized pediatric asthma research and management

arXiv

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arXiv 2021年

作者： Dong, Quan Li, Baichen Downen, R. Scott Tran, Nam Chorvinsky, Elizabeth Pillai, Dinesh K. Zaghloul, Mona E. Li, Zhenyu the Department of Biomedical Engineering The George Washington University WashingtonDC20052 United States Division of Pulmonary Medicine Children’s National Hospital WashingtonDC20010 United States the Division of Pulmonary Medicine Children’s National Hospital WashingtonDC20010 United States Department of Electrical Engineering and Computer Engineering George Washington University WashingtonDC20052 United States the Department of Biomedical Engineering The George Washington University WashingtonDC20052 United States

This paper presents a cloud-connected indoor air quality sensor system that can be deployed to patients’ homes to study personal microenvironmental exposure for asthma research and management. The system consists of multiple compact sensor units that can measure residential NO2, ozone, humidity, and temperature at one-minute resolution and a cloud-based informatic system that acquires, stores, and visualizes the microenvironmental data in real-time. The sensor hardware can measure NO2 as low as 10 ppb and ozone at 15 ppb. The cloud informatic system is implemented using open-source software on Amazon Web Service for easy deployment and scalability. This system was successfully deployed to pediatric asthma patients’ homes in a pilot study. In this study, we discovered that some families can have short-term NO2 exposure higher than EPA’s one-hour exposure limit (100 ppb), and NO2 micro-pollution episodes often arise from natural gas appliance usage such as gas stove burning during cooking. By combining the personalized air pollutant exposure measurements with the physiological responses from a patient diary and medical record, this system can enable novel asthma research and personalized asthma management. © 2021, CC BY-NC-SA.

关键词： Ozone

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Bio-FlatScope: A flat, lensless microscope for fluorescence imaging

Bio-FlatScope: A flat, lensless microscope for fluorescence ...

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Optics and the Brain, BRAIN 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

作者： Wu, Jimin Yan, Dong Boominathan, Vivek Adams, Jesse K. Veeraraghavan, Ashok Robinson, Jacob T. Department of Bioengineering Rice University 6100 Main Street HoustonTX77005 United States Applied Physics Program Rice University 6100 Main Street HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Department of Neuroscience Baylor College of Medicine One Baylor Plaza HoustonTX77030 United States Department of Computer Science Rice University 6100 Main Street HoustonTX77005 United States

We developed a lensless microscope with small, inexpensive form factor, large field-of-view (FOV) and cellular resolution for fluorescence imaging. Proof-of-principle measurements with "Bio-FlatScope" are pr... 详细信息

ISBN: (纸本)9781557528209

关键词： Fluorescence imaging

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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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Conference on Lasers and Electro-Optics (CLEO)

作者： Yijie Zhang Tairan Liu Manmohan Singh Ege Çetintaş Yilin Luo Yair Rivenson Kirill V. Larin Aydogan Ozcan Electrical and Computer Engineering Department University of California Los Angeles CA USA Bioengineering Department University of California Los Angeles CA USA California NanoSystems Institute (CNSI) University of California Los Angeles CA USA Department of Biomedical Engineering University of Houston Houston TX USA Department of Molecular Physiology and Biophysics Baylor College of Medicine University of Houston Houston TX USA Department of Surgery David Geffen School of Medicine University of California Los Angeles CA USA

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: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Optical coherence tomography Neural networks Optical computing Coherence Optical fiber networks Lasers and electrooptics Optical imaging

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Adaptive thresholding skin lesion segmentation with gabor filters and principal component analysis 4th

Adaptive thresholding skin lesion segmentation with gabor fi...

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4th International Conference on Research in Intelligent and Computing in engineering, RICE 2019

作者： Thanh, Dang N. H. Hien, Nguyen Ngoc Surya Prasath, V.B. Erkan, Uğur Khamparia, Aditya Department of Information Technology Hue College of Industry Hue Viet Nam Center of Occupational Skills Development Dong Thap University Cao Lanh Viet Nam Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Cincinnati United States Department of Pediatrics University of Cincinnati CincinnatiOH United States Department of Biomedical Informatics College of Medicine University of Cincinnati Cincinnati United States Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati United States Computer Engineering Faculty of Engineering Karamanoğlu Mehmetbey University Karaman Turkey School of Computer Science and Engineering Lovely Professional University PhagwaraPunjab India

ISBN: (纸本)9789811527791

In this article, we study and propose an adaptive thresholding segmentation method for dermoscopic images with Gabor filters and Principal Component Analysis. The Gabor filters is used for extracting statistical features of image and the Principal Component Analysis is applied for transforming features to various bases. In experiments, we implement tests with the ISIC dataset. Segmentation results are assessed by the Dice and the Jaccard similarities. We also compare the proposed method to other similar methods to prove its own effectiveness. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Gabor filters

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