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Miniaturized Microarray-Format Digital ELISA Enabled by Lithographic Protein Patterning

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SSRN

SSRN 2023年

作者： Stephens, Andrew D. Song, Yujing McClellan, Brandon L. Su, Shiuan-Haur Xu, Sonnet Chen, Kevin Castro, Maria G. Singer, Benjamin H. Kurabayashi, Katsuo Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Neurosurgery University of Michigan Medical School Ann ArborMI48109 United States Department of Cell and Developmental Biology University of Michigan Medical School Ann ArborMI48109 United States Graduate Program in Immunology University of Michigan Medical School Ann ArborMI48109 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Rogel Cancer Center University of Michigan Ann ArborMI48109 United States Department of Internal Medicine Division of Pulmonary and Critical Care Medicine University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI48109 United States

The search for reliable protein biomarker candidates is critical for early disease detection and treatment. However, current immunoassay technologies are failing to meet increasing demands for sensitivity and multiplexing. Here, the authors have created a highly sensitive protein microarray using the principle of single-molecule counting for signal amplification, capable of simultaneously detecting a panel of cancer biomarkers at sub-pg/mL levels. To enable this amplification strategy, the authors introduce a novel method of protein patterning using photolithography to subdivide addressable arrays of capture antibody spots into hundreds of thousands of individual microwells. This allows for the total sensor area to be miniaturized, increasing the total possible multiplex capacity. With the immunoassay realized on a standard 75x25 mm form factor glass substrate, sample volume consumption is minimized to © 2023, The Authors. All rights reserved.

关键词： Biomarkers

Realistic ultrasound image synthesis for improved classification of liver disease

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arXiv 2021年

作者： Che, Hui Ramanathan, Sumana Foran, David J. Nosher, John L. Patel, Vishal M. Hacihaliloglu, Ilker Department of Biomedical Engineering Rutgers University NJ United States Department of Radiology Rutgers Robert Wood Johnson Medical School NJ United States Department of Electrical and Computer Engineering Johns Hopkins University MD United States Rutgers Cancer Institute of New Jersey NJ United States

With the success of deep learning-based methods applied in medical image analysis, convolutional neural networks (CNNs) have been investigated for classifying liver disease from ultrasound (US) data. However, the scarcity of available large-scale labeled US data has hindered the success of CNNs for classifying liver disease from US data. In this work, we propose a novel generative adversarial network (GAN) architecture for realistic diseased and healthy liver US image synthesis. We adopt the concept of stacking to synthesize realistic liver US data. Quantitative and qualitative evaluation is performed on 550 in-vivo B-mode liver US images collected from 55 subjects. We also show that the synthesized images, together with real in vivo data, can be used to significantly improve the performance of traditional CNN architectures for Nonalcoholic fatty liver disease (NAFLD) classification. © 2021, CC BY-NC-ND.

关键词： Generative adversarial networks

Implementation of MPPT technique of solar module with supervised machine learning

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arXiv 2021年

作者： Sharmin, Ruhi Chowdhury, Sayeed Shafayet Abedin, Farihal Rahman, Kazi Mujibur Department of Biomedical Engineering Purdue University 206 S. Martin Jischke Drive West LafayetteIN47907-2032 United States Department of Electrical & Computer Engineering Purdue University 501 Northwestern Ave. West LafayetteIN47907-2035 United States Dhaka Bangladesh

Automated calibration of a maximum power point tracking (MPPT) algorithm and its efficacious implementation for the photo-voltaic (PV) system is pivotal for harnessing maximum possible energy from solar power. However, most existing calibration methods of such an MPPT system are cumbersome and vary greatly with the environmental condition. Hence, an automated pipeline capable of performing suitable adjustments and accurate analysis for solar PV systems is highly desirable. To counter this issue, numerous algorithms have been proposed so far, such as perturb and observe (P&O) method, incremental conductance (IC) method, fractional open circuit voltage (FOCV) method, short circuit current method, fuzzy logic based algorithm etc. While these approaches perform pretty well and produce overall acceptable results, recent surge of data-driven machine learning (ML) approaches hold great promise in this research domain. Since, supervised ML methods are trained directly based on the data, no human-designed heuristics are involved, which makes these techniques highly accurate and robust. As a result, in this paper, we proposed a method using supervised ML in solar PV system for MPPT analysis. For this purpose, an overall schematic diagram of a PV system is designed and simulated to create a dataset in MATLAB/ Simulink. Thus, by analyzing the output characteristics of a solar cell, an improved MPPT algorithm on the basis of neural network (NN) method is put forward to track the maximum power point (MPP) of solar cell modules. To perform the task, Bayesian Regularization method was chosen as the training algorithm as it works best even for smaller data supporting the wide range of the train data set. The theoretical results show that the improved NN MPPT algorithm has higher efficiency compared with the Perturb and Observe method in the same environment, and the PV system can keep working at MPP without oscillation and probability of any kind of misjudgment. So it can not only r

关键词： Maximum power point trackers

Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data

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arXiv 2021年

作者： Che, Hui Radbel, Jared Sunderram, Jag Nosher, John L. Patel, Vishal M. Hacihaliloglu, Ilker The Department of Biomedical Engineering Rutgers University NJ United States The Department of Medicine Rutgers Robert Wood Johnson Medical School NJ United States The Department of Radiology Rutgers Robert Wood Johnson Medical School NJ United States The Department of Electrical and Computer Engineering Johns Hopkins University MD United States The Department of Biomedical Engineering Rutgers University The Department of Radiology Rutgers Robert Wood Johnson Medical School NJ United States

The global pandemic of the novel coronavirus disease 2019 (COVID-19) has put tremendous pressure on the medical system. Imaging plays a complementary role in the management of patients with COVID-19. Computed tomography (CT) and chest X-ray (CXR) are the two dominant screening tools. However, difficulty in eliminating the risk of disease transmission, radiation exposure and not being cost-effective are some of the challenges for CT and CXR imaging. This fact induces the implementation of lung ultrasound (LUS) for evaluating COVID-19 due to its practical advantages of noninvasiveness, repeatability, and sensitive bedside property. In this paper, we utilize a deep learning model to perform the classification of COVID-19 from LUS data, which could produce objective diagnostic information for clinicians. Specifically, all LUS images are processed to obtain their corresponding local phase filtered images and radial symmetry transformed images before fed into the multi-scale residual convolutional neural network (CNN). Secondly, image combination as the input of the network is used to explore rich and reliable features. Feature fusion strategy at different levels is adopted to investigate the relationship between the depth of feature aggregation and the classification accuracy. Our proposed method is evaluated on the point-of-care US (POCUS) dataset together with the Italian COVID-19 Lung US database (ICLUS-DB) and shows promising performance for COVID-19 prediction. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

Kirchhoff's Current Law with Displacement Current

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arXiv 2022年

作者： Eisenberg, Robert Oriols, Xavier Ferry, David K. Department of Physiology and Biophysics Rush University Medical Center ChicagoIL United States Department of Applied Mathematics Illinois Institute of Technology ChicagoIL United States Department of Biomedical Engineering University of Illinois Chicago ChicagoIL United States Departament d’Enginyeria Electrònica Universitat Autònoma de Barcelona Barcelona08193 Spain School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287USA United States

Thompson-Sampling-Based Wireless Transmission for Panoramic Video Streaming

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Thompson-Sampling-Based Wireless Transmission for Panoramic ...

International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Jiangong Chen Bin Li R. Srikant Department of Electrical Computer and Biomedical Engineering at the University of Rhode Island Kingston RI USA Department of Electrical and Computer Engineering Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9783903176294

ISBN: (纸本)9781728193892

Panoramic video streaming has received great attention recently due to its immersive experience. Different from traditional video streaming, it typically consumes 4 ~ 6× larger bandwidth with the same resolution. Fortunately, users can only see a portion (roughly 20%) of 360° scenes at each time and thus it is sufficient to deliver such a portion, namely Field of View (FoV), if we can accurately predict user's motion. In practice, we usually deliver a portion larger than FoV to tolerate inaccurate prediction. Intuitively, the larger the delivered portion, the higher the prediction accuracy. This however leads to a lower transmission success probability. The goal is to select an appropriate delivered portion to maximize system throughput, which can be formulated as a multi-armed bandit problem, where each arm represents the delivered portion. Different from traditional bandit problems with single feedback information, we have two-level feedback information (i.e., both prediction and transmission outcomes) after each decision on the selected portion. As such, we propose a Thompson Sampling algorithm based on two-level feedback information, and demonstrate its superior performance than its traditional counterpart via simulations.

关键词： Prediction algorithms Streaming media Throughput Wireless communication Predictive models Bandwidth Adaptation models

Implantable silicon neural probes with nanophotonic phased arrays for single-lobe beam steering

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arXiv 2024年

作者： Chen, Fu-Der Sharma, Ankita Xue, Tianyuan Jung, Youngho Govdeli, Alperen Mak, Jason C.C. Chameh, Homeira Moradi Movahed, Mandana Brunk, Michael G.K. Luo, Xianshu Chua, Hongyao Lo, Patrick Guo-Qiang Valiante, Taufik A. Sacher, Wesley D. Poon, Joyce K.S. Max Planck Institute of Microstructure Physics Weinberg 2 Halle06120 Germany Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S 3G4 Canada Max Planck-University of Toronto Centre for Neural Science and Technology Canada Krembil Brain Institute University Health Network TorontoON Canada Advanced Micro Foundry Pte. Ltd. 11 Science Park Road Singapore Science Park II 117685 Singapore Division of Neurosurgery Department of Surgery Toronto Western Hospital University of Toronto TorontoON Canada Institute of Biomedical Engineering University of Toronto TorontoON Canada

In brain activity mapping experiments using optogenetics, patterned illumination is crucial for deterministic and localized stimulation of neurons. However, due to optical scattering in brain tissue, light-emitting implantable devices are needed to bring precise patterned illumination to deep brain regions. A promising solution is silicon neural probes with integrated nanophotonic circuits that form tailored beam emission patterns without lenses. Here, we demonstrate neural probes with grating-based light emitters that generate a single steerable light beam across > 60% of the steering range with ≥ 4 dB of background suppression for optogenetic photostimulation. The light emitters, optimized for blue or amber light, combine end-fire optical phased arrays with slab gratings to suppress higher-order sidelobes. In vivo optogenetic photostimulation was demonstrated in Thy1-ChR2-YFP mice. These neural probes, capable of continuous single beam scanning, offer a promising avenue for optogenetic stimulation with cellular-level spatial precision in the deep brain. © 2024, CC BY-NC-ND.

关键词： Nanophotonics

Spatially-varying microscope calibration from unstructured sparse inputs

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Spatially-varying microscope calibration from unstructured s...

Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

作者： Kuo, Grace Monakhova, Kristina Yanny, Kyrollos Ng, Ren Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley BerkeleyCA94720 United States

We propose a method based on blind deconvolution to calibrate the spatially-varying point spread functions of a coded-aperture microscope system. From easy-to-acquire measurements of unstructured fluorescent beads, we... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical transfer function

Faster convergence in deep-predictive-coding networks to learn deeper representations

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arXiv 2021年

作者： Sledge, Isaac J. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States The Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States The Department of Biomedical Engineering University of Florida GainesvilleFL United States

Deep-predictive-coding networks (DPCNs) are hierarchical, generative models. They rely on feed-forward and feed-back connections to modulate latent feature representations of stimuli in a dynamic and context-sensitive manner. A crucial element of DPCNs is a forward-backward inference procedure to uncover sparse, invariant features. However, this inference is a major computational bottleneck. It severely limits the network depth due to learning stagnation. Here, we prove why this bottleneck occurs. We then propose a new forward-inference strategy based on accelerated proximal gradients. This strategy has faster theoretical convergence guarantees than the one used for DPCNs. It overcomes learning stagnation. We also demonstrate that it permits constructing deep and wide predictive-coding networks. Such convolutional networks implement receptive fields that capture well the entire classes of objects on which the networks are trained. This improves the feature representations compared with our lab's previous non-convolutional and convolutional DPCNs. It yields unsupervised object recognition that surpass convolutional autoencoders and are on par with convolutional networks trained in a supervised manner. © 2021, CC BY.

关键词： Convolution