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Physically Grounded Deep Learning-Enabled Gold Nanoparticle Localization and Quantification in Photonic Resonator Absorption Microscopy for Digital Resolution Molecular Diagnostics

SSRN

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SSRN 2025年

作者： Lee, Hankeun Li, Siyan Liu, Leyang Wang, Weijing Ayupova, Takhmina Tibbs, Joseph Kim, Chansong Fang, Ying Do, Minh N. Cunningham, Brian T. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL United States Department of Chemistry University of Illinois at Urbana-Champaign UrbanaIL United States Department of Pathobiology College of Veterinary Medicine University of Illinois at Urbana-Champaign UrbanaIL United States Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL United States Nick Holonyak Jr. Micro and Nanotechnology Lab University of Illinois at Urbana-Champaign UrbanaIL United States Center for Genomic Diagnostics Woese Institute for Genomic Biology UrbanaIL United States Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana-Champaign UrbanaIL United States Grainger College of Engineering University of Illinois at Urbana-Champaign UrbanaIL United States Siebel School of Computing and Data Science University of Illinois at Urbana-Champaign UrbanaIL United States Carle Illinois College of Medicine University of Illinois at Urbana-Champaign UrbanaIL United States VinUni-Illinois Smart Health Center VinUniversity Hanoi Viet Nam

Accurate molecular biomarker detection with digital-resolution sensitivity is essential for applications such as disease diagnostics, therapeutic studies, and biomedical research. Here, we present LOCA-PRAM (LOcalization with Context Awareness), a deep learning-based method integrated with a Photonic Resonator Absorption Microscope (PRAM) to achieve digital-resolution detection of biomolecules using gold nanoparticles (AuNPs) as molecular tags. LOCA-PRAM leverages photonic crystal (PC)-AuNP resonant-coupling to enhance signal contrast, facilitating precise quantification of target molecules without partitioning the sample into droplets or enzymatic amplification. Through registration of PRAM images with Scanning Electron Microscopy (SEM) images, we empirically obtain the point spread function (PSF) of AuNP tags, enabling realistic training data generation for the deep learning framework. LOCA-PRAM surpasses conventional image processing method in accuracy and sensitivity, achieving reliable AuNP detection and localization even in high-density conditions, minimizing false-positive and false-negative quantifications and expending the dynamic range of assay. Benchmarking with SEM-derived ground truth confirms LOCA-PRAM’s sub-pixel resolution and ability to accurately quantify AuNPs with overlapping PSF. Overall, the PRAM combined with LOCA-based AuNP digital counting enables real-time, high-precision detection of molecular biomarkers, advancing digital-resolution biosensing for biomedical research and diagnostics. © 2025, The Authors. All rights reserved.

关键词： Gold nanoparticles

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Evolution of Incommensurate Charge Density Waves Quantified with In Situ TEM

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Microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Shen, Jeremy M Sung, Suk Hyun Agarwal, Nishkarsh Stangel, Alex Hovden, Robert Department of Electrical and Computer Engineering University of Michigan Ann Arbor MI USA Rowland Institute at Harvard Harvard University Cambridge MA USA Department of Material Science and Engineering University of Michigan Ann Arbor MI USA Applied Physics Program University of Michigan Ann Arbor MI USA

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Transfer learning approach to modeling multichannel gate-all-around nanosheet field-effect transistors under work function fluctuations

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engineering Applications of Artificial Intelligence 2025年 148卷

作者： Dash, Sagarika Li, Yiming Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Artificial Intelligence Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Microelectronics National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan

Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, leverages knowledge from a pre-trained model to improve the performance of a specific target task. This study aims to apply TL to predict the impact of work function fluctuation (WKF) on the variability of threshold voltage (σVTH) in gate-all-around silicon nanosheet field-effect transistors (GAA Si NS FETs). We used TL to transfer knowledge from models trained on 1-channel (1ch) GAA Si NS FETs to more complex 4-channel (4ch) counterparts. The prediction accuracy is evaluated by using the mean square error(MSE), root mean square error (RMSE), and the mean absolute error(MAE) metrics. Our TL approach achieved an error rate of less than 1%, demonstrating high accuracy in predicting WKF effects on σ VTH of 4ch GAA Si NS FETs using only 500 samples. This study presents a pioneering application of TL using a one-dimensional (1D) convolutional neural network (CNN) long short-term memory (LSTM) topology in semiconductor device technology. The results effectively address the challenge of predicting σ VTH variations in advanced GAA Si NS FETs because of WKF, which eliminates the need for extensive semiconductor device simulations. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Identifying Cellular Neighborhood Phenotypes Differentiating Normal and Quiescent Crohn's Disease via MxIF

Identifying Cellular Neighborhood Phenotypes Differentiating...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Rudravaram, Gaurav Bao, Shunxing Krishnan, Aravind R. Remedios, Lucas W. Kim, Michael E. Saunders, Adam M. Liu, Qi Lau, Ken S. Roland, Joseph T. Washington, Mary K. Coburn, Lori A. Wilson, Keith T. Huo, Yuankai Landman, Bennett A. Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Division of Gastroenterology Hepatology and Nutrition Department of Medicine Vanderbilt University Medical Center NashvilleTN United States Vanderbilt Center for Mucosal Inflammation and Cancer Vanderbilt University Medical Center NashvilleTN United States Program in Cancer Biology Vanderbilt University School of Medicine NashvilleTN United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Department of Pathology Microbiology and Immunology Vanderbilt University Medical Center NashvilleTN United States Center for Quantitative Sciences Vanderbilt University Medical Center NashvilleTN United States Epithelial Biology Center Vanderbilt University Medical Center NashvilleTN United States Department of Cell and Developmental Biology Vanderbilt University School of Medicine NashvilleTN United States Vanderbilt University Department of Biomedical Engineering Nashville United States Department of Biostatistics Vanderbilt University Medical Center Nashville United States

ISBN: (纸本)9781510686045

Crohn's disease (CD) is a chronic inflammatory condition of the gastrointestinal tract characterized by alternating periods of activity and remission. This study investigates whether the cellular environments of quiescent (inactive disease) and normal tissues show similarities or differences, exploring potential residual effects from disease and tissue recovery patterns. While active CD can typically be characterized by high neutrophil density, whereas quiescent tissues present less pronounced inflammatory markers. This study leverages multiplexed immunofluorescence (MxIF) data to examine the distributions and patterns of various nuclei subtypes in quiescent versus normal tissues, aiming to determine how similar or different these tissues are in the absence of active inflammation. In our study, we analyze four tissue samples labeled as normal and five as quiescent from CD patients, as determined by a pathologist. We use rigid and deformable registration of MxIF images to analyze these tissues in a unified space and classify the nuclei into 13 subtypes. Using a KD tree, we identify the 10 nearest neighbors for each nucleus, building a combined distance-weighted neighborhood matrix representing both normal and quiescent tissues. We projected these neighborhoods into 2 dimensions using t-distributed stochastic nearest neighbor(t-SNE), clustered them, and calculated the enrichment for each subtype between normal and quiescent states within each of 20 and 40 clusters. In the 20-cluster analysis, we find monocytes enrich in one cluster and leukocytes enrich in two clusters for normal and quiescent tissues respectively. For the 40-cluster analysis, we observe monocytes, macrophages, and leukocytes enrich in one cluster each in normal tissues, and in the quiescent condition, one cluster enriched with enteroendocrine cells, and two clusters enriched with leukocytes. The results were statistically significant as determined by a chi-squared test (p © 2025 SPIE.

关键词： Tissues White blood cells Diseases and disorders Biological samples Matrices Statistical analysis Image segmentation Image registration Critical dimension metrology Inflammation

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Landau Polaritons in a Full-Dielectric Three-Dimensional Photonic-Crystal Cavity

Landau Polaritons in a Full-Dielectric Three-Dimensional Pho...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Fuyang Tay Ali Mojibpour Shuang Liang Andrey Baydin Arash Ahmadivand Nicolas Marquez Peraca Hongjing Xu Geoff C. Gardner Michael J. Manfra David Hagenmüller Junichiro Kono Department of Electrical and Computer Engineering Rice University Houston Texas USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas USA Department of Physics and Astronomy Purdue University West Lafayette IN USA Birck Nanotechnology Center Purdue University West Lafayette IN USA Smalley-Curl Institute Rice University Houston Texas USA Department of Physics and Astronomy Rice University Houston Texas USA Microsoft Quantum Purdue Purdue University West Lafayette IN USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA School of Materials Engineering Purdue University West Lafayette IN USA ISIS (UMR 7006) Université de Strasbourg and CNRS Strasbourg France Department of Materials Science and Nano Engineering Rice University Houston Texas USA

We fabricated a full-dielectric three-dimensional photonic-crystal cavity containing an ultrahigh-mobility two-dimensional electron gas. By applying a strong perpendicular magnetic field, we created Landau polaritons ... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Couplings HEMTs Laser modes Polaritons Magnetic fields Gas lasers MODFETs

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An Exoskeleton BCI System for Stroke Rehabilitation Using Multi-modality Training Mode

An Exoskeleton BCI System for Stroke Rehabilitation Using Mu...

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International Automatic Control Conference

作者： Kai-Hsiang Su Chiao-Hsin Chen Chia-Hsin Chen Yong-Liang Zhang Li-Wei Ko Institute of Biological Science and Technology National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) College of Biological Science and Technology National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Institute of Molecular Medicine and Bioengineering National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Rehabilitation Department of Kaohsiung Medical University Chung-Ho Memorial Hospital Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Department of Electrical and Computer Engineering Brain Research Center National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan

With the advent of the global aging era, the impact of stroke on humans is continuously increasing. Therefore, new therapeutic approaches to enhance patient recovery are receiving growing attention. Stroke is a severe neurological disorder that causes brain damage, requiring extended periods of rehabilitation for patients to return to normal life. Currently, common clinical methods largely rely on traditional passive limb rehabilitation, primarily targeting peripheral nerve training. Nowadays, an increasing number of people are recognizing the importance of post-stroke neuroplasticity. This study aims to activate patients' central nervous system and proposes a brain-machine interface (BCI) exoskeleton system based on human cognition. By designing three cognitive brainwave training models, including an attention-based standing-up model detecting θ-band power decrease, a relaxation-based sitting-down model detecting α-band power increase, and a walking model detecting MRCP, patients can actively control lower limb exoskeleton movement using *** study provides cross-validation accuracy through both normal subjects and stroke patients, with accuracies of 87.13±9.38 and 80.83±9.68, respectively. Envisioning a rehabilitation system guided by human cognition, our team has developed an intuitive BCI exoskeleton system.

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Automated osteoporosis classification and T-score prediction using hip radiographs via deep learning algorithm

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Therapeutic Advances in Musculoskeletal Disease 2024年 16卷

作者： Chen, Yu-Pin Chan, Wing P. Zhang, Han-Wei Tsai, Zhi-Ren Peng, Hsiao-Ching Huang, Shu-Wei Jang, Yeu-Chai Kuo, Yi-Jie Department of Orthopedics Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Department of Radiology Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Biomedica Corporation New Taipei City Taiwan Program for Aging China Medical University Taichung City Taiwan Institute of Population Health Sciences National Health Research Institutes Miaoli County Taiwan Department of Electrical and Computer Engineering Institute of Electrical Control Engineering National Yang Ming Chiao Tung University Hsinchu County Hsinchu City Taiwan Department of Computer Science and Information Engineering Asia University Taichung City Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung City Taiwan Center for Precision Medicine Research Asia University Taichung City Taiwan Department of Applied Science National Taitung University Taitung County Taitung City Taiwan Department of Orthopedics Wan Fang Hospital Taipei Medical University No. 111 Sec. 3 Xinglong Road Wenshan Taipei 11696 Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

Background: Despite being the gold standard for diagnosing osteoporosis, dual-energy X-ray absorptiometry (DXA) is an underutilized screening tool for osteoporosis. Objectives: This study proposed and validated a controllable feature layer of a convolutional neural network (CNN) model with a preprocessing image algorithm to classify osteoporosis and predict T-score on the proximal hip region via simple hip radiographs. Design: This was a single-center, retrospective study. Methods: An image dataset of 3460 unilateral hip images from 1730 patients (age ⩾50 years) was retrospectively collected with matched DXA assessment for T-score for the targeted proximal hip regions to train (2473 unilateral hip images from 1430 patients) and test (497 unilateral hip images from 300 patients) the proposed CNN model. All images were processed with a fully automated CNN model, X1AI-Osteo. Results: The proposed screening tool illustrated a better performance (sensitivity: 97.2%;specificity: 95.6%;positive predictive value: 95.7%;negative predictive value: 97.1%;area under the curve: 0.96) than the open-sourced CNN models in predicting osteoporosis. Moreover, when combining variables, including age, body mass index, and sex as features in the training metric, there was high consistency in the T-score on the targeted hip regions between the proposed CNN model and the DXA (r = 0.996, p < 0.001). Conclusion: The proposed CNN model may identify osteoporosis and predict T-scores on the targeted hip regions from simple hip radiographs with high accuracy, highlighting the future application for population-based opportunistic osteoporosis screening with low cost and high adaptability for a broader population at risk. Trial registration: TMU-JIRB N201909036. © The Author(s), 2024.

关键词： deep learning neural network osteoporosis radiographs T-score

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Metallic vs. Semiconducting properties of quasi-one-dimensional tantalum selenide van der Waals nanoribbons

arXiv

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arXiv 2021年

作者： Kargar, Fariborz Krayev, Andrey Wurch, Michelle Ghafouri, Yassamin Debnath, Topojit Wickramaratne, Darshana Salguero, Tina T. Lake, Roger Bartels, Ludwig Balandin, Alexander A. Center Department of Electrical and Computer Engineering University of California RiversideCA92521 United States HORIBA Scientific NovatoCA94949 United States Department of Chemistry and Material Science and Engineering Program University of California RiversideCA92521 United States Department of Chemistry University of Georgia AthensGA30602 United States Laboratory for Terahertz and Terascale Electronics Department of Electrical and Computer Engineering University of California RiversideCA92521 United States Center for Computational Materials Science U.S. Naval Research Laboratory WashingtonDC20375 United States

We conducted a tip-enhanced Raman scattering spectroscopy (TERS) and photoluminescence (PL) study of quasi-1D TaSe3-δ nanoribbons exfoliated onto gold substrates. At a selenium deficiency of δ~0.25 (Se/Ta=2.75,), the nanoribbons exhibit a strong, broad PL peak centered around ~920 nm (1.35 eV), suggesting their semiconducting behavior. Such nanoribbons revealed a strong TERS response under 785-nm laser excitation, allowing for their nanoscale spectroscopic imaging. Nanoribbons with a smaller selenium deficiency (Se/Ta=2.85, δ~0.15) did not show any PL or TERS response. The confocal Raman spectra of these samples agree with the previously-reported spectra of metallic TaSe3. The differences in the optical response of the nanoribbons examined in this study suggest that even small variations in Se content can induce changes in electronic structure, causing samples to exhibit either metallic or semiconducting character. The temperature-dependent electrical measurements of devices fabricated with both types of materials corroborate these observations. The density-functional-theory calculations revealed that incorporation of an oxygen atom in a Se vacancy can result in band gap opening and thus enable the transition from a metal to a semiconductor. However, the predicted bandgap is substantially smaller than that derived from PL data. These results indicate that the properties of van der Waals materials can vary significantly depending on stoichiometry, defect types and concentration, and possibly environmental and substrate effects. In view of this finding, local probing of nanoribbon properties with TERS becomes essential to understanding such low-dimensional systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Van der Waals forces

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Rapid TAURUS for relaxation-based color magnetic particle imaging

arXiv

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arXiv 2021年

作者： Arslan, Tunç Alper Özaslan, A. Kurt, Semih Muslu, Yavuz Saritas, Emine Ulku Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Bilkent University Ankara06800 Turkey Neuroscience Program Sabuncu Brain Research Center Bilkent University Ankara06800 Turkey Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI United States Department of Radiology University of Wisconsin-Madison MadisonWI United States Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Science for Life Laboratory Stockholm Sweden

Magnetic particle imaging (MPI) is a rapidly developing medical imaging modality that exploits the non-linear response of magnetic nanoparticles (MNPs). Color MPI widens the functionality of MPI, empowering it with the capability to distinguish different MNPs and/or MNP environments. The system function approach for color MPI relies on extensive calibrations that capture the differences in the harmonic responses of the MNPs. An alternative calibration-free x-space-based method called TAURUS estimates a map of the relaxation time constant, τ, by recovering the underlying mirror symmetry in the MPI signal. However, TAURUS requires a back and forth scanning of a given region, restricting its usage to slow trajectories with constant or piecewise constant focus fields (FFs). In this work, we propose a novel technique to increase the performance of TAURUS and enable τ map estimation for rapid and multi-dimensional trajectories. The proposed technique is based on correcting the distortions on mirror symmetry induced by time-varying FFs. We demonstrate via simulations and experiments in our in-house MPI scanner that the proposed method successfully estimates high-fidelity τ maps for rapid trajectories that provide orders of magnitude reduction in scanning time, while preserving the calibration-free property of TAURUS. Copyright © 2021, The Authors. All rights reserved.

关键词： Trajectories

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Ubiquitous Flat Bands in a Cr-based Kagome Superconductor

arXiv

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arXiv 2024年

作者： Guo, Yucheng Wang, Zehao Xie, Fang Huang, Yuefei Gao, Bin Oh, Ji Seop Wu, Han Liu, Zhaoyu Ren, Zheng Fang, Yuan Biswas, Ananya Zhang, Yichen Yue, Ziqin Hu, Cheng Jozwiak, Chris Bostwick, Aaron Rotenberg, Eli Hashimoto, Makoto Lu, Donghui Kono, Junichiro Chu, Jiun-Haw Yakobson, Boris I. Birgeneau, Robert J. Si, Qimiao Dai, Pengcheng Yi, Ming Department of Physics and Astronomy Rice University HoustonTX77005 United States Advanced Light Source Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Department of Physics University of California BerkeleyCA94720 United States Department of Physics University of Washington Washington98105 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

In the quest for novel quantum states driven by topology and correlation, kagome lattice materials have garnered significant interest due to their distinctive electronic band structures, featuring flat bands (FBs) arising from the quantum destructive interference of the electronic wave function. The tuning of the FBs to the chemical potential would lead to the possibility of liberating electronic instabilities that lead to emergent electronic orders. Despite extensive studies, direct evidence of FBs tuned to the chemical potential and their participation in emergent electronic orders have been lacking in bulk quantum materials. Here using a combination of Angle-Resolved Photoemission Spectroscopy (ARPES) and Density Functional Theory (DFT), we reveal that the low-energy electronic structure of the recently discovered Cr-based kagome metal superconductor CsCr3Sb5 is dominated by a pervasive FB in close proximity to, and below the Fermi level. A comparative analysis with orbital-projected DFT and polarization dependence measurement uncovers that an orbital-selective renormalization mechanism is needed to reconcile the discrepancy with the DFT calculations, which predict the FB to appear 200 meV above the Fermi level. Furthermore, we observe the FB to shift away from the Fermi level by 20 meV in the low-temperature density wave-ordered phase, highlighting the role of the FB in the emergent electronic order. Our results reveal CsCr3Sb5 to stand out as a promising platform for further exploration into the effects of FBs near the Fermi level on kagome lattices, and their role in emergent orders in bulk quantum materials. © 2024, CC BY.

关键词： Density functional theory

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