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Frontiers in Optics, FiO_2019

作者： Li, Weijian Naik, Gururaj V. Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Electrical and Computer Engineering Rice University TX77005 United States

We demonstrate a large photo-refractive effect in 1T-TaS2 in the visible at low intensity white light excitation. By using this optical tunability, we demonstrate tunable meta-gratings for nanophotonics applications. ... 详细信息

ISBN: (纸本)9781943580675

关键词： Finite difference time domain Light intensity Nanophotonics Photorefractive materials Thin film optical properties X ray diffraction

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Plenary Speech Ⅰ: Data-Selection in Machine Learning Algorithms

Plenary Speech Ⅰ: Data-Selection in Machine Learning Algori...

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作者： Paulo S.R.Diniz COPPE/Poli Federal University of Rio de Janeiro Program of Electrical Engineering and Department of Electrical and Computer Engineering

The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifies to improve the related statistical inference or if it consists of an outlier or a noninnovative entry. In this presentation, we discuss some adaptive filtering and machine learning algorithms enabling data selection which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data or some simple estimation based on the available data. Test results show the effectiveness of the proposed algorithms for selecting the innovative data without sacrificing the estimation accuracy, while reducing the computational cost. In neural networks applications the strategy leads to reduced training errors and improved test results.

关键词： Data-Selection in Machine Learning Algorithms Plenary Speech

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PAAQ: Paired Alternating AcQuisitions for virtual high frame rate multichannel cardiac fluorescence microscopy

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Biological imaging 2023年 3卷 e20页

作者： François Marelli Alexander Ernst Nadia Mercader Michael Liebling Computational Bioimaging Idiap Research Institute Martigny Switzerland. Electrical Engineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland. Institute of Anatomy University of Bern Bern Switzerland. Cardiovascular Regeneration Program Centro Nacional de Investigaciones Cardiovasculares Madrid Spain. Electrical & Computer Engineering University of California Santa Barbara CA USA.

In vivo fluorescence microscopy is a powerful tool to image the beating heart in its early development stages. A high acquisition frame rate is necessary to study its fast contractions, but the limited fluorescence intensity requires sensitive cameras that are often too slow. Moreover, the problem is even more complex when imaging distinct tissues in the same sample using different fluorophores. We present Paired Alternating AcQuisitions, a method to image cyclic processes in multiple channels, which requires only a single (possibly slow) camera. We generate variable temporal illumination patterns in each frame, alternating between channel-specific illuminations (fluorescence) in odd frames and a motion-encoding brightfield pattern as a common reference in even frames. Starting from the image pairs, we find the position of each reference frame in the cardiac cycle through a combination of image-based sorting and regularized curve fitting. Thanks to these estimated reference positions, we assemble multichannel videos whose frame rate is virtually increased. We characterize our method on synthetic and experimental images collected in zebrafish embryos, showing quantitative and visual improvements in the reconstructed videos over existing nongated sorting-based alternatives. Using a 15 Hz camera, we showcase a reconstructed video containing two fluorescence channels at 100 fps.

关键词： bioimaging cardiac imaging fluorescence microscopy frame rate enchancement image processing

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Machine Learning based Reduced Kernel PCA for Nonlinear Process Monitoring

Machine Learning based Reduced Kernel PCA for Nonlinear Proc...

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2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Dhibi, Khaled Fezai, Radhia Mansouri, Majdi Kouadri, Abdelmalek Bouzrara, Kais Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar

ISBN: (纸本)9781728151847

Process monitoring is an essential part of industrial systems. It requires higher product quality and safety operations. Therefore, a nonlinear data-driven approach based reduced KPCA (RKPCA) for statistical monitoring of industrial processes is developed. RKPCA is a novel machine learning tool which merges dimensionality reduction and supervised learning. The use of classical KPCA for modeling and monitoring purposes can impose a high computational load when a large number of measurements are recorded. The main idea of the proposed RKPCA approach is to reduce the number of observations (samples) in the data matrix using the Euclidean distance between samples as dissimilarity metric so that only one observation is kept in case of redundancy. The Tennessee Eastman Process (TEP) is used to evaluate the fault detection abilities of the proposed RKPCA technique. The performance of the proposed method is evaluated and compared to the classical KPCA in terms of false alarms rates (FAR), missed detection rates (MDR) and computation times (CT). © 2019 IEEE.

关键词： Fault detection

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AI-driven 3D Spatial Transcriptomics

arXiv

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arXiv 2025年

作者： Almagro-Pérez, Cristina Song, Andrew H. Weishaupt, Luca Kim, Ahrong Jaume, Guillaume Williamson, Drew F.K. Hemker, Konstantin Lu, Ming Y. Singh, Kritika Chen, Bowen Le, Long Phi Baras, Alexander S. Jiang, Sizun Bashashati, Ali Liu, Jonathan T.C. Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Rutgers Robert Wood Johnson Medical School New BrunswickNJ United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Broad Institute of Harvard and MIT CambridgeMA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology Dana Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications1. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue2–4. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability5–8. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), an AI framework that leverages 3D tissue morphology and minimal 2D ST to predict volumetric 3D ST. By pretraining on diverse 3D morphology–transcriptomic pairs from heterogeneous tissue samples and then fine-tuning on minimal 2D ST data from a specific volume of interest, VORTEX learns both generic tissue-related and sample-specific morphological correlates of gene expression. This approach enables dense, high-throughput, and fast 3D ST, scaling seamlessly to large tissue volumes far beyond the reach of existing 3D ST techniques. By offering a cost-effective and minimally destructive route to obtaining volumetric molecular insights, we anticipate that VORTEX will accelerate biomarker discovery and our understanding of morphomolecular associations and cell states in complex tissues. Interactive 3D ST volumes can be viewed at https://***/. © 2025, CC BY-NC-ND.

关键词： Vortex flow

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Predicting 1-year mortality of acute kidney injury: A risk model using electronic health records

Predicting 1-year mortality of acute kidney injury: A risk m...

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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Huang, Chao-Jung Wu, Brian Li, Xiaodong Han, Yongxia Zhang, Yaqi Hao, Shiying Ling, Xuefeng B. National Taiwan University-Stanford Joint Program Office of AI in Biotechnology Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare Taipei Taiwan Clinical Research University of California San Diego San DiegoCA92093 United States School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Electrical Power Engineering South China University of Technology Guangzhou China Department of Surgery Stanford University StanfordCA94305 United States Department of Cardiothoracic Surgery Stanford University StanfordCA94305 United States Clinical and Translational Research Program Betty Irene Moore Children's Heart Center Lucile Packard Children's Hospital Palo AltoCA94305 United States

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Acute kidney injury (AKI) is associated with increased morbidity and mortality in intensive care units (ICU). The sudden episode of kidney failure may lead to end-stage renal disease (ESRD) or deaths, and has been related to significantly increasing costs of ICU admissions and treatments. Early prediction of AKI inpatient mortality will help decision-making, and benefit resource allocation in ICU. Therefore, it is crucial to develop an early warning system for AKI prediction. We aimed to create a more comprehensive predictive model for 1-year AKI mortality. A cohort of 2,247 patients with AKI was assembled, of which the in-hospital mortality was 36.67%. Longitudinal data of each patient were collected from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. An interpretable XGBoost risk model was developed and validated by 10-fold cross validation. Model predictors included 11 routinely collected AKI-related laboratory measurements, 8 complications of AKI, and demographic data. An artificial neural network (ANN) model was also developed in parallel for comparison. The XGBoost model demonstrated an area under the receiver-operating characteristic curve (AUC) of 0.83, which was superior to ANN (AUC = 0.79). Our model was able to predict mortality of AKI in ICU with high accuracy. Our model can predict 1-year AKI mortality. Furthermore, it had great potential for identifying at-risk patients in ICU. These findings indicated that our approach might offer opportunities for better resource utilization and better administration of AKI. © 2021 SPIE.

关键词： Neural networks

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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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Fault Detection of Biological Phenomena Modeled by S-systems ⁎

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IFAC-PapersOnLine 2018年第24期51卷 1305-1310页

作者： Mansouri, Majdi Harkat, Mohamed-Faouzi Nounou, Hazem Nounou, Mohamed Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

In this work we propose a novel fault detection (FD) technique in order to enhance monitoring of biological processes. To do that, a new statistical FD method, that is based on combining the advantages of the double exponentially weighted moving average (EWMA), called Max-DEWMA, with those of the particle filtering (PF), and multiscale representation is developed. The advantages of PF-based multiscale (MS) Max-DEWMA (M-DEWMA) are threefold: (i) the dynamical multiscale representation is proposed to extract accurate deterministic features and decorrelate autocorrelated measurements; (ii) PF is proposed to estimate the states of biological processes; (iii) MS-M-DEWMA chart is able to detect smaller fault shifts in the mean/variances and enhance the monitoring of biological processes. The FD performance is studied using Cad System in E. coli (CSEC) model. PF-based MS-M-DEWMA is used to enhance FD of the CSEC model through monitoring some of the key variables involved in this model such as enzymes, lysine and cadaverine. © 2018

关键词： Fault detection Amino acids computer aided design Escherichia coli Finite difference method Monte Carlo methods Autocorrelated Biological phenomena Biological process E. coli Exponentially weighted moving average Max Double Multiscale representations Particle Filtering

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Dynamic optical contrast imaging for real-time delineation of tumor resection margins using head and neck cancer as a model

arXiv

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arXiv 2022年

作者： Hu, Yong Huang, Shan Han, Albert Y. Moon, Seong Krane, Jeffrey F. Stafsudd, Oscar Grundfest, Warren St. John, Maie A. Los AngelesCA United States Department of Bioengineering UCLA Los AngelesCA United States Department of Materials Science and Engineering UCLA Los AngelesCA United States UCLA Head and Neck Cancer Program UCLA Medical Center Los AngelesCA United States Jonsson Comprehensive Cancer Center UCLA Medical Center Los AngelesCA United States Department of Pathology & Laboratory Medicine UCLA Medical Center Los AngelesCA United States Department of Electrical and Computer Engineering UCLA Los AngelesCA United States

Complete surgical resection of the tumor for Head and neck squamous cell carcinoma (HNSCC) remains challenging, given the devastating side effects of aggressive surgery and the anatomic proximity to vital structures. To address the clinical challenges, we introduce a widefield, label-free imaging tool that can assist surgeons delineate tumor margins real-time. We assume that autofluorescence lifetime is a natural indicator of the health level of tissues, and ratio-metric measurement of the emission-decay state to the emission-peak state of excited fluorophores will enable rapid lifetime mapping of tissues. Here, we describe the principle, instrumentation, characterization of the imager and the intraoperative imaging of resected tissues from 13 patients undergoing head and neck cancer resection. 20 x 20 mm2 imaging takes 2 second/frame with a working distance of 50 mm, and characterization shows that the spatial resolution reached 70 μm and the least distinguishable fluorescence lifetime difference is 0.14 ns. Tissue imaging and Hematoxylin-Eosin stain slides comparison reveals its capability of delineating cancerous boundaries with submillimeter accuracy and a sensitivity of 91.86% and specificity of 84.38%. Copyright © 2022, The Authors. All rights reserved.

关键词： Tumors

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Topological Surface State Evolution in Bi2Se3 via Surface Etching

arXiv

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arXiv 2024年

作者： Yue, Ziqin Huang, Jianwei Wang, Ruohan Li, Jia-Wan Rong, Hongtao Guo, Yucheng Wu, Han Zhang, Yichen Kono, Junichiro Zhou, Xingjiang Hou, Yusheng Wu, Ruqian Yi, Ming Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices Center for Neutron Science and Technology School of Physics Sun Yat-Sen University Guangzhou510275 China Institute of Physics Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Department of Physics and Astronomy University of California Irvine IrvineCA92697 United States

Topological insulators are materials with an insulating bulk interior while maintaining gapless boundary states against back scattering. Bi2Se3 is a prototypical topological insulator with a Dirac-cone surface state around Γ. Here, we present a controlled methodology to gradually remove Se atoms from the surface Se-Bi-Se-Bi-Se quintuple layers, eventually forming bilayer-Bi on top of the quintuple bulk. Our method allows us to track the topological surface state and confirm its robustness throughout the surface modification. Importantly, we report a relocation of the topological Dirac cone in both real space and momentum space, as the top surface layer transitions from quintuple Se-Bi-Se-Bi-Se to bilayer-Bi. Additionally, charge transfer among different surface layers is identified. Our study provides a precise method to manipulate surface configurations, allowing for the fine-tuning of the topological surface states in Bi2Se3, which represents a significant advancement towards nano-engineering of topological states. Copyright © 2024, The Authors. All rights reserved.

关键词： Topological insulators

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