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arXiv 2021年

作者： Andjelkovic, Stefan Miskov-Zivanov, Natasa Computational and Systems Biology Joint Carnegie Mellon University University of Pittsburgh Computational Biology PhD Program University of Pittsburgh 4420 Bayard St PittsburghPA15213 United States Electrical and Computer Engineering Bioengineering Computational and Systems Biology University of Pittsburgh 4420 Bayard St PittsburghPA15213 United States

Simulation on directed graphs is an important method for understanding the dynamics in the systems where connectivity graphs contain cycles. Discrete Stochastic Heterogeneous Simulator (DiSH) is one of the simulation tools with wide application, which uses regulator values to calculate state updates of regulated elements. Here we present a new simulation approach DiSH-trend which also takes into account the trends in regulating elements. We demonstrate the features of trend-based regulation, as well as hybrid regulation, which is a combination of the trend- and level-based approaches. The modeling capabilities are demonstrated on a small toy model, showcasing different functionalities. Real-world capabilities are demonstrated on a larger network model of food insecurity in the Ethiopian region Oromia. Adding trend-based regulation to models results in increased modeling flexibility, and hybrid regulation improves qualitative dynamic behavior prediction. With appropriate data, DiSH-trend becomes a powerful tool for exploring intervention strategies. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest X-ray image

arXiv

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arXiv 2021年

作者： Hu, Zongsheng Yang, Zhenyu Lafata, Kyle J. Yin, Fang-Fang Wang, Chunhao Medical Physics Graduate Program Duke Kunshan University Jiangsu Kunshan215316 China Department of Radiation Oncology Duke University DurhamNC27710 United States Department of Radiology Duke University DurhamNC27710 United States Department of Electrical and Computer Engineering Duke University DurhamNC27710 United States

Purpose: To develop a deep-learning model that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray image. Methods: Based on a pre-trained VGG-16 architecture, the deep-learning design consisted of a 5-Dense layer neural network with varying sizes, while the last three convolutional layers were set as free parameters for training. In radiomics analysis, a 2D sliding kernel was implemented to map the impulse response of radiomic features throughout the entire X-ray image;thus, each feature is rendered as a 2D map in the same dimension as X-ray image. For comparison purpose, a pilot model was trained using X-ray image only. Subsequently, 2 radiomic feature maps (RFMs) were selected based on cross-correlation analysis in reference to the pilot model saliency map results. The radiomics-boosted model was then trained based on the same deep-learning design using X-ray image plus the selected RFMs as input. Both models were developed using 812 chest X-ray images with 262/288/262 COVID-19/Non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned as training-validation/independent test sets. For each model, 50 versions were trained with random assignments of training/validation cases following 7:1 ratio in the training-validation set. Sensitivity, specificity, accuracy, and ROC curves together with Area-Under-the-Curve (AUC) were compared between the two models. Results: The performance of detecting COVID-19 vs Non-COVID-19 pneumonia in the pilot model using X-ray image only were:1) sensitivity 0.90±0.07 vs 0.78±0.09, 2) specificity 0.94±0.04 vs 0.94±0.04, 3) accuracy 0.93±0.03 vs 0.89±0.03, and 4) ROC AUC 0.96±0.02 vs 0.92±0.04. Entropy and Short-Run-Emphasize were selected based on their high correlations with pilot model saliency map results. In the radiomics-boosted model, the corresponding results demonstrated significant improvements (p © 2021, CC BY-NC-ND.

关键词： Impulse response

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Structural and performance comparison between SU-8 microfabricated and 3D-printed microneedle electrodes

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Journal of 3D Printing in Medicine 2020年第1期4卷 29-44页

作者： Soltanzadeh, Ramin Afsharipour, Elnaz Anssari, Neda Mansouri, Behzad Shafai, Cyrus Biomedical Graduate Studies Program University of Manitoba Winnipeg R3T 5V6 MB Canada Electrical and Computer Engineering Department University of Manitoba Winnipeg R3T 5V6 MB Canada Department of Internal Medicine (Neurology) University of Manitoba Winnipeg R3A 1R9 MB Canada

Aim: This study is aimed to compare the structure and performance of 3D-printed and microfabricated microneedle arrays (MNAs). Materials & methods: MNAs were produced using either stereolithography printing or were micro-fabricated using SU-8 polymer. The structure of both MNA types were tested and compared through scanning electron microscopy. MNA performance was also evaluated through electrical impedence spectroscopy and nerve conduction velocity testing. Results & conclusion: The 3D-printed MNAs were shown to have higher gain than microfabricated MNAs at high frequencies. However, the selectivity of the microfabricated MNAs is more than 3D-printed MNAs. Scanning electron microscopy image comparison also showed significant differences between the output of the 3D-printed MNAs and the microfabricated MNAs. It was understood that the microfabricated MNAs' geometries are closer to the primary design than the 3D-printed MNAs. © 2020 Future Medicine Ltd.

关键词： 3D printing microfabrication microneedle array performance difference strucutral difference

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O-246 A cutting-edge artificial intelligence system tracking embryo development and pinpointing the optimal day for blastocyst utilization

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Human Reproduction 2024年第SUPPLEMENT_1期39卷

作者： Del Gallego, R Zhylko, D Pardo, S Nogueira, D Lawrenz, B Selim, S Bayram, A Elkhatib, I Shamout, F E Fatemi, H M ART Fertility Clinics Clinical Data Management Abu Dhabi United Arab Emirates New York University Abu Dhabi Electrical and Computer Engineering Program- Engineering Division Abu Dhabi United Arab Emirates ART Fertility Clinics IVF Department Abu Dhabi United Arab Emirates ART Fertility Clinics Medical Department Abu Dhabi United Arab Emirates

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Review of Graphene-based thermal polymer nanocomposites: Current state of the art and future prospects

arXiv

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arXiv 2020年

作者： Lewis, Jacob S. Perrier, Timothy Barani, Zahra Kargar, Fariborz Balandin, Alexander A. Center University of California Riverside CA 92521 USA Materials Science and Engineering Program Bourns College of Engineering University of California Riverside CA 92521 US Department of Electrical and Computer Engineering Bourns College of Engineering University of California Riverside CA 92521 USA

We review the current state-of-the-art of graphene-enhanced thermal interface materials for the management of heat the next generation of electronics. Increased integration densities, speed, and power of electronic and optoelectronic devices require thermal interface materials with substantially higher thermal conductivity, improved reliability, and lower cost. Graphene has emerged as a promising filler material that can meet the demands of future high-speed and high-powered electronics. This review describes the use of graphene as a filler in curing and non-curing polymer matrices. Special attention is given to strategies for achieving the thermal percolation threshold with its corresponding characteristic increase in the overall thermal conductivity. Many applications require high thermal conductivity of the composites while simultaneously preserving electrical insulation. A hybrid filler graphene and boron nitride approach is presented as possible technology for independent control of electrical and thermal conduction. Reliability and lifespan performance of thermal interface materials is an important consideration towards the determination of appropriate practical applications. The present review addresses these issues in detail, demonstrating the promise of the graphene-enhanced thermal interface materials as compared to alternative technologies. Copyright © 2020, The Authors. All rights reserved.

关键词： III-V semiconductors

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Deep-Strong Coupling between Cavity Photons and Terahertz TO Phonons in PbTe

Deep-Strong Coupling between Cavity Photons and Terahertz TO...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Baydin, Andrey Manjappa, Manukumara Mishra, Sobhan Subhra Xu, Hongjing Tay, Fuyang Kim, Dasom Hernandez, Felix G.G. Rappl, Paulo H.O. Abramof, Eduardo Singh, Ranjan Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Instrumentation and Applied Physics Department Indian Institute of Science Bangalore560012 India Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University 639798 Singapore Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Instituto de Física Universidade de Sao Paulo SP Sao Paulo05508-090 Brazil Laboratorio Associado de Sensores e Materiais Instituto Nacional de Pesquisas Espaciais SP Sao Jose dos Campos12201-970 Brazil Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

We have investigated the resonant coupling of photons with TO phonons in lead telluride in small-mode-volume terahertz cavities, observing a giant vacuum Rabi splitting on the order of the bare cavity-phonon frequency... 详细信息

ISBN: (纸本)9781957171258

关键词： Photons

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Blueshift: Rebalancing engineering engagement, integrity, and learning outcomes across an electronically-enabled remediation hierarchy

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computers in Education Journal 2019年第1期10卷 1-12页

作者： DeMara, Ronald F. Sheikhfaal, Shadi Wilder, Paul J. Chen, Baiyun Hartshorne, Richard Department of Electrical Engineering and Computer Science University of Central Florida OrlandoFL32816 United States Department of Engineering Computer Program and Technology Valencia College OrlandoFL32811 United States Center for Distributed Learning University of Central Florida OrlandoFL32816 United States Division Dean for Learning Sciences Educational Research University of Central Florida OrlandoFL32816 United States

With a growing number of enrollments in engineering programs, more time and energy for tutoring and grading exams/quizzes is necessary. The BLUESHIFT framework utilizes a synergistic combination of faculty, Graduate Teaching Assistants, and educational technologies to provide reliable approaches to not only meet the increased time, cost, and energy efficiency needed for increasing numbers of engineering students, but also to focus on improving the quality of education, skills, and employability of our graduates in computing-related fields. An Evaluation and Proficiency Center (EPC) has been piloted at a large state university, where key elements of the BLUESHIFT learning flow have been integrated into selected undergraduate engineering courses. The EPC integrates computer-based evaluation with a close-knit review and learning cycle based on directed and open tutoring. This approach has received overwhelmingly positive responses from students regarding the effectiveness of pedagogical approaches (i.e., Exemplar Vignettes, content tutoring), assessment models (i.e., electronically delivered quizzes, flexible scheduling, use of testing center), and tutoring strategies (i.e., self-paced, exam results review). Survey results have been positive with respect to student perceptions across a variety of perspectives compared to paper-based delivery. In this manuscript, the BLUESHIFT framework and process, as well as results collected from student performance and perceptions of the initial implementation, will be addressed. © 2019 American Society for engineering Education. All rights reserved.

关键词： Students

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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

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Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

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Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time 21

Deep inference of latent dynamics with spatio-temporal super...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Feng Zhu Andrew R. Sedler Harrison A. Grier Nauman Ahad Mark A. Davenport Matthew T. Kaufman Andrea Giovannucci Chethan Pandarinath Neuroscience Graduate Program Emory University Center for Machine Learning Georgia Tech Computational Neuroscience Graduate Program The University of Chicago School of Electrical and Computer Engineering Georgia Tech Dept. of Organismal Biology and Anatomy The University of Chicago and Neuroscience Institute The University of Chicago Joint Dept. of Biomedical Engineering UNC Chapel Hill and NC State University and UNC Chapel Hill Neuroscience Center and NC State/UNC Closed-Loop Engineering for Advanced Rehabilitation (CLEAR) Coulter Dept. of Biomedical Engineering Emory University and Georgia Tech and Dept. of Neurosurgery Emory University

ISBN: (纸本)9781713845393

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of latent dynamics from data in which the set of observed variables changes at each time step. The resulting models are able to infer activity for missing samples by combining observations with learned latent dynamics. We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data. In electrophysiology, SBTT enables accurate inference of neuronal population dynamics with lower interface bandwidths, providing an avenue to significant power savings for implanted neuroelectronic interfaces. In applications to two-photon calcium imaging, SBTT accurately uncovers high-frequency temporal structure underlying neural population activity, substantially outperforming the current state-of-the-art. Finally, we demonstrate that performance could be further improved by using limited, high-bandwidth sampling to pretrain dynamics models, and then using SBTT to adapt these models for sparsely-sampled data.

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Rayleigh scattering-based distributed sensing in multicore optical fibers for shape reconstruction in multiplanar disturbance

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Measurement 2025年 256卷

作者： Leandro Macedo Anselmo Frizera Jan Nedoma Radek Martinek Carlos Marques Arnaldo Leal-junior Postgraduate Program in Electrical Engineering of the Federal University of Espírito Santo Av. Fernando Ferrari 514 - Goiabeiras Vitória 29075-910 Espírito Santo Brazil Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB—Technical University of Ostrava 17. listopadu 15 70833 Ostrava Czech Republic Department of Cybernetics and Biomedical Engineering VSB—Technical University of Ostrava 17. listopadu 15 70833 Ostrava Czech Republic CICECO University of Aveiro Campus Universitário de Santiago 3810-193 Aveiro Portugal

In recent years, advancements in smart cities and multifunctional monitoring have driven new demands for shape reconstruction devices. Multicore fibers (MCFs) are increasingly used due to their parallel data transmission capabilities and multiparameter sensing potential. This paper presents a Rayleigh-scattering-based distributed sensing approach with MCFs for shape reconstruction under multiplanar disturbances. Optical Frequency-Domain Reflectometry (OFDR) is utilized to analyze four cores in a seven-core fiber, enabling shape reconstruction through cross-correlation of spectral responses relative to an unstrained reference. Two configurations are compared: Configuration 1, a spatially separated core arrangement enabling independent strain independent strain measurements to be analyzed with a Frenet-Serret frame modeling and Random Forest (RF) algorithms, achieving a high accuracy with a maximum error of 8.72 × 10 −3 cm; and Configuration 2, a simplified approach analyzing all cores in the same OFDR channel, yielding a higher error of 0.27 cm when a RF algorithm was fed with features derived from the Rayleigh backscattered signal (loss amplitude, strain, spectral shift, and s- and p-polarization) measured by an optical backscatter reflectometer. Three protocols—pure bending, pure torsion, and combined bending/torsion—validate these configurations. The results emphasize this multifunctional MCF-based sensor system’s potential for flexible, integrated shape reconstruction solutions.

关键词： Distributed optical fiber sensors Multicore optical fibers Rayleigh backscattering Shape reconstruction

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