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Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity

Light(science & Applications) 2021年第9期10卷 1797-1808页

作者： Neha Goswami Yuchen R.He Yu-Heng Deng Chamteut Oh Nahil Sobh Enrique Valera Rashid Bashir Nahed Ismail Hyunjoon Kong Thanh H.Nguyen Catherine Best-Popescu Gabriel Popescu Department of Bioengineering University of Illinois Urbana-ChampaignUrbanaIllinois61801USA Beckman Institute of Advanced Science and Technology University of Illinois Urbana-ChampaignUrbanaIllinois61801USA Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignUrbanaIllinois61801USA Department of Chemical and Biomolecular Engineering University of Illinois at Urbana-ChampaignUrbanaIL61801USA Department of Civil and Environmental Engineering University of Illinois at Urbana-ChampaignUrbanaIL61801USA NCSA Center for Artificial Intelligence Innovation University of Illinois at Urbana-ChampaignUrbanaIL61801USA Holonyak Micro and Nanotechnology Laboratory University of Illinois at Urbana-ChampaignUrbanaIllinois61801USA Biomedical Research Center Carle Foundation Hospital509W University Ave.UrbanaIllinois61801USA Carle Illinois College of Medicine 807 South Wright St.UrbanaIllinois61801USA Mayo-Illinois Alliance for Technology Based Healthcare UrbanaIllinois61801USA Department of Pathology College of MedicineUniversity of Illinois at ChicagoChicagoILUSA

Efforts to mitigate the COVID-19 crisis revealed that fast,accurate,and scalable testing is crucial for curbing the current impact and that of future *** propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and *** ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length *** these data with fluorescence images for ground truth,we trained semantic segmentation models based on U-Net,a particular type of convolutional neural *** trained network was applied to classify the viruses from the interferometric images only,containing simultaneously SARS-CoV-2,H1N1(influenza-A virus),HAdV(adenovirus),and ZIKV(Zika virus).Remarkably,due to the nanoscale sensitivity in the input data,the neural network was able to identify SARS-CoV-2 *** other viruses with 96%*** inference time for each image is 60 ms,on a common graphic-processing *** approach of directly imaging unlabeled viral particles may provide an extremely fast test,of less than a minute per *** the imaging instrument operates on regular glass slides,we envision this method as potentially testing on patient breath *** necessary high throughput can be achieved by translating concepts from digital pathology,where a microscope can scan hundreds of slides automatically.

关键词： testing neural hundreds

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electrically Reconfigurable Intelligent Optoelectronics in 2-D van der Waals Materials

arXiv

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arXiv 2025年

作者： Wang, Yu Zhang, Dehui Song, Yihao Lee, Jea Jung Tian, Meng Biswas, Souvik Xia, Fengnian Guo, Qiushi Photonics Initiative Advanced Science Research Center City University of New York 85 St Nicholas Terrace New YorkNY10031 United States Physics Program Graduate Center City University of New York 365 5th Ave New YorkNY10016 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley 566 Cory Hall BerkeleyCA94720 United States Department of Electrical Engineering Yale University 15 Prospect St New HavenCT06511 United States E. L. Ginzton Laboratory Stanford University 348 Via Pueblo Mall StanfordCA94305 United States

In optoelectronics, achieving electrical reconfigurability is crucial as it enables the encoding, decoding, manipulating, and processing of information carried by light. In recent years, two-dimensional van der Waals (2-D vdW) materials have emerged as promising platforms for realizing reconfigurable optoelectronic devices. Compared to materials with bulk crystalline lattice, 2-D vdW materials offer superior electrical reconfigurability due to high surface-to-volume ratio, quantum confinement, reduced dielectric screening effect, and strong dipole resonances. Additionally, their unique band structures and associated topology and quantum geometry provide novel tuning capabilities. This review article seeks to establish a connection between the fundamental physics underlying reconfigurable optoelectronics in 2-D materials and their burgeoning applications in intelligent optoelectronics. We first survey various electrically reconfigurable properties of 2-D vdW materials and the underlying tuning mechanisms. Then we highlight the emerging applications of such devices, including dynamic intensity, phase and polarization control, and intelligent sensing. Finally, we discuss the opportunities for future advancements in this field. Copyright © 2025, The Authors. All rights reserved.

关键词： Van der Waals forces

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Ensemble ranking: An aggregation of multiple multicriteria methods and scenarios and its application to power generation planning

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Decision Analytics Journal 2024年 10卷

作者： Alves, Marcos Antonio Oliveira, Bruno Alberto Soares Guimarães, Frederico Gadelha Graduate Program in Electrical Engineering Av. Antônio Carlos 6627 MG Belo Horizonte 31270-901 Brazil FITec - Technological Innovations Av. Cristóvão Colombo 485 Funcionários MG Belo Horizonte 30140-150 Brazil Department of Computer Science Rua Reitor Píres Albuquerque ICEx MG Belo Horizonte 31270-901 Brazil

Evaluating maintenance plans for power generation is a critical task managed by the National System Operator, as it is directly related to criteria such as operational cost, rationing, and availability of natural resources. This study identifies the most suitable maintenance schedule between uncertainties related to solution ranking methods and future scenarios. We investigated the performance of various multicriteria decision-making methods, including the Analytic Hierarchy Process (AHP), TODIM ((an acronym in Portuguese for Interactive Multicriteria decision-making), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR), and Weighted Aggregated Sum Product ASsessment (WASPAS), in prioritizing maintenance plans across three scenarios. These included a scenario derived from an energy generation problem and two others based on variations in hydrology and energy demand parameters. The results analyze and discuss the similarities and differences in the rankings. Due to the lack of consensus among the methods regarding the optimal maintenance plan, we propose an ensemble ranking that considers an important factor for both the method and the scenario. The solution from the ensemble ranking, compared to the lowest-cost solution obtained in the optimization process, showed a slight cost increase of 0.32% and a 9.34% deviation from the base maintenance plan, yet it can deal with different uncertainties. The findings of this study can provide useful and robust information to energy stakeholders and serve as a reference for energy policy. © 2024 The Authors

关键词： Generation maintenance scheduling problems Hydrothermal dispatch Multicriteria decision-making Power generation planning Robust decision-making Uncertainty scenarios

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FaSTrack: A modular framework for real-time motion planning and guaranteed safe tracking

arXiv

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arXiv 2021年

作者： Chen, Mo Herbert, Sylvia L. Hu, Haimin Pu, Ye Fisac, Jaime F. Bansal, Somil Han, SooJean Tomlin, Claire J. School of Computing Science Simon Fraser University Canada Department of Mechanical and Aerospace Engineering UC San Diego United States Department of Electrical Engineering Princeton University United States Department of Electrical and Electronic Engineering University of Melbourne Australia Department of Electrical Engineering and Computer Sciences UC Berkeley United States Control and Dynamical Systems Program California Institute of Technology United States

Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified planning model of the system. The plan is tracked by the system, represented by a more realistic, higher-dimensional tracking model. We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs. Copyright © 2021, The Authors. All rights reserved.

关键词： Trajectories

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Chiral-Dependent Redox Capacitive Biosensor Using Cu-Cys-GSH Nanoparticles for Ultrasensitive H2O2 Detection

Biosensors

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Biosensors 2025年第5期15卷 315页

作者： Duygu Yilmaz Aydin Jie Jayne Wu Jiangang Chen Department of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN 37996 USA. Department of Bioengineering Malatya Turgut Ozal University 44210 Malatya Türkiye. Department of Public Health The University of Tennessee Knoxville TN 37996 USA.

Copper-thiolate nanostructures, formed through the self-assembly of cysteine (Cys) and glutathione (GSH) with copper ions, offer a versatile platform for redox-active applications due to their structural stability and chemical functionality. In this study, Cu-Cys-GSH nanoparticles were synthesized and employed to develop a capacitive biosensor for the ultralow concentration detection of hydrogen peroxide (HO). The detection mechanism leverages a Fenton-like reaction, where HO interacts with Cu-Cys-GSH nanoparticles to generate hydroxyl radicals (·OH) through redox cycling between Cu and Cu ions. These redox processes induce changes in the sensor's surface charge and dielectric properties, enabling highly sensitive capacitive sensing at gold interdigitated electrodes (IDEs). The influence of chirality on sensing performance was investigated by synthesizing nanoparticles with both L- and D-cysteine enantiomers. Comparative analysis revealed that the stereochemistry of cysteine impacts the catalytic activity and sensor response, with Cu-L-Cys-GSH nanoparticles exhibiting superior performance. Specifically, the biosensor achieved a linear detection range from 1.0 fM to 1.0 pM and demonstrated an ultra-sensitive detection limit of 21.8 aM, outperforming many existing methods for HO detection. The sensor's practical performance was further validated using milk and saliva samples, yielding high recovery rates and confirming its robustness and accuracy for real-world applications. This study offers a disposable, low-cost sensing platform compatible with sustainable healthcare practices and facilitates easy integration into point-of-care diagnostic systems.

关键词： AC electrokinetics (ACEK) Cu-Cys-GSH nanoparticles Fenton-like reaction capacitive sensors hydrogen peroxide (H2O2) detection

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Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources

arXiv

引用

arXiv 2023年

作者： Rajendran, Suraj Pan, Weishen Sabuncu, Mert R. Chen, Yong Zhou, Jiayu Wang, Fei Tri-Institutional Computational Biology & Medicine Program Cornell University NY United States Division of Health Informatics Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States School of Electrical and Computer Engineering Cornell University IthacaNY United States Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medical School New YorkNY United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States Department of Computer Science and Engineering Michigan State University East LansingMI United States

Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clinical and cost benefits remain limited due to challenges in data privacy, heterogeneous data sources, and the inability to fully leverage multiple data modalities. In this perspective paper, we introduce "patchwork learning" (PL), a novel paradigm that addresses these limitations by integrating information from disparate datasets composed of different data modalities (e.g., clinical free-text, medical images, omics) and distributed across separate and secure sites. PL allows the simultaneous utilization of complementary data sources while preserving data privacy, enabling the development of more holistic and generalizable ML models. We present the concept of patchwork learning and its current implementations in healthcare, exploring the potential opportunities and applicable data sources for addressing various healthcare challenges. PL leverages bridging modalities or overlapping feature spaces across sites to facilitate information sharing and impute missing data, thereby addressing related prediction tasks. We discuss the challenges associated with PL, many of which are shared by federated and multimodal learning, and provide recommendations for future research in this field. By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models. This paradigm promises to strike a balance between personalization and generalizability, ultimately enhancing patient experiences, improving population health, and optimizing healthcare providers' workflows. © 2023, CC BY.

关键词： Data privacy

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MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images

arXiv

引用

arXiv 2023年

作者： Jiang, Hongxu Imran, Muhammad Muralidharan, Preethika Patel, Anjali Pensa, Jake Liang, Muxuan Benidir, Tarik Grajo, Joseph R. Joseph, Jason P. Terry, Russell DiBianco, John Michael Su, Li-Ming Zhou, Yuyin Brisbane, Wayne G. Shao, Wei Department of Electrical and Computer Engineering University of Florida GainesvilleFL32608 United States Department of Medicine University of Florida GainesvilleFL32608 United States Department of Health Outcomes and Biomedical Informatics University of Florida GainesvilleFL32608 United States College of Medicine University of Florida GainesvilleFL32608 United States Department of Bioengineering University of California Los AngelesCA90095 United States Department of Biostatistics University of Florida GainesvilleFL32608 United States Department of Urology University of Florida GainesvilleFL32608 United States Department of Radiology University of Florida GainesvilleFL32608 United States Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Urology University of California Los AngelesCA90095 United States

Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, potentially enabling low-cost, accurate diagnosis of prostate cancer. Accurate prostate segmentation is crucial for prostate volume measurement, cancer diagnosis, prostate biopsy, and treatment planning. However, prostate segmentation on micro-US is challenging due to artifacts and indistinct borders between the prostate, bladder, and urethra in the midline. This paper presents MicroSegNet, a multi-scale annotation-guided transformer UNet model designed specifically to tackle these challenges. During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations. We achieve this by proposing an annotation-guided binary cross entropy (AG-BCE) loss that assigns a larger weight to prediction errors in hard regions and a lower weight to prediction errors in easy regions. The AG-BCE loss was seamlessly integrated into the training process through the utilization of multi-scale deep supervision, enabling MicroSegNet to capture global contextual dependencies and local information at various scales. We trained our model using micro-US images from 55 patients, followed by evaluation on 20 patients. Our MicroSegNet model achieved a Dice coefficient of 0.939 and a Hausdorff distance of 2.02 mm, outperforming several state-of-the-art segmentation methods, as well as three human annotators with different experience levels. Our code is publicly available at https://***/mirthAI/MicroSegNet and our dataset is publicly available at https://***/records/10475293. © 2023, CC BY-NC-SA.

关键词： Image segmentation

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Valley-polarized Exitonic Mott Insulator in WS2/WSe2 Moiré Superlattice

arXiv

引用

arXiv 2023年

作者： Lian, Zhen Meng, Yuze Ma, Lei Maity, Indrajit Yan, Li Wu, Qiran Huang, Xiong Chen, Dongxue Chen, Xiaotong Chen, Xinyue Blei, Mark Taniguchi, Takashi Watanabe, Kenji Tongay, Sefaattin Lischner, Johannes Cui, Yong-Tao Shi, Su-Fei Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Departments of Materials The Thomas Young Centre for Theory and Simulation of Materials Imperial College London United Kingdom Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Materials Science and Engineering University of California RiversideCA92521 United States Materials Science and Engineering Program School for Engineering of Matter Transport and Energy Arizona State University TempeAZ85287 United States International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Electrical Computer & Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Strongly enhanced electron-electron interaction in semiconducting moiré superlattices formed by transition metal dichalcogenides (TMDCs) heterobilayers has led to a plethora of intriguing fermionic correlated states1–7. Meanwhile, interlayer excitons in a type-II aligned TMDC heterobilayer moiré superlattice, with electrons and holes separated in different layers8,9, inherit this enhanced interaction and strongly interact with each other, promising for realizing tunable correlated bosonic quasiparticles10,11 with valley degree of freedom. We employ photoluminescence spectroscopy to investigate the strong repulsion between interlayer excitons and correlated electrons in a WS2/WSe2 moiré superlattice and combine with theoretical calculations to reveal the spatial extent of interlayer excitons and the band hierarchy of correlated states. We further find that an excitonic Mott insulator state emerges when one interlayer exciton occupies one moiré cell, evidenced by emerging photoluminescence peaks under increased optical excitation power. Double occupancy of excitons in one unit cell requires overcoming the energy cost of exciton-exciton repulsion of about 30-40 meV, depending on the stacking configuration of the WS2/WSe2 heterobilayer. Further, the valley polarization of the excitonic Mott insulator state is enhanced by nearly one order of magnitude. Our study demonstrates the WS2/WSe2 moiré superlattice as a promising platform for engineering and exploring new correlated states of fermion, bosons, and a mixture of both12 © 2023, CC BY.

关键词： Excitons

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Efficient Noise Calculation in Deep Learning-based MRI Reconstructions

arXiv

引用

arXiv 2025年

作者： Dalmaz, Onat Desai, Arjun D. Heckel, Reinhard Çukur, Tolga Chaudhari, Akshay S. Hargreaves, Brian A. Department of Electrical Engineering Stanford University StanfordCA United States Department of Radiology Stanford University StanfordCA United States Department of Computer Engineering Technical University of Munich Munich Germany Department of Electrical and Electronics Engineering Bilkent University Ankara Turkey Department of Biomedical Data Science Stanford University StanfordCA United States Department of Bioengineering Stanford University StanfordCA United States

Accelerated MRI reconstruction involves solving an ill-posed inverse problem where noise in acquired data propagates to the reconstructed images. Noise analyses are central to MRI reconstruction for providing an explicit measure of solution fidelity and for guiding the design and deployment of novel reconstruction methods. However, deep learning (DL)-based reconstruction methods have often overlooked noise propagation due to inherent analytical and computational challenges, despite its critical importance. This work proposes a theoretically grounded, memory-efficient technique to calculate voxel-wise variance for quantifying uncertainty due to acquisition noise in accelerated MRI reconstructions. Our approach approximates noise covariance using the DL network’s Jacobian, which is intractable to calculate. To circumvent this, we derive an unbiased estimator for the diagonal of this covariance matrix—voxel-wise variance—, and introduce a Jacobian sketching technique to efficiently implement it. We evaluate our method on knee and brain MRI datasets for both data- and physics-driven networks trained in supervised and unsupervised manners. Compared to empirical references obtained via Monte-Carlo simulations, our technique achieves near-equivalent performance while reducing computational and memory demands by an order of magnitude or more. Furthermore, our method is robust across varying input noise levels, acceleration factors, and diverse undersampling schemes, highlighting its broad applicability. Our work reintroduces accurate and efficient noise analysis as a central tenet of reconstruction algorithms, holding promise to reshape how we evaluate and deploy DL-based MRI. Our code will be made publicly available upon *** Codes 65C60, 94A08, 68T07 © 2025, CC BY-NC-ND.

关键词： Monte Carlo methods

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Interaction Between a Low-Temperature Plasma and Graphene: An in situ Raman Thermometry Study

引用

Physical Review Applied 2021年第2期15卷 024018-024018页

作者： Carla Berrospe-Rodriguez Joseph Schwan Giorgio Nava Fariborz Kargar Alexander A. Balandin Lorenzo Mangolini Department of Mechanical Engineering University of California Riverside 900 University Ave. Riverside California 92521 USA Department of Electrical and Computer Engineering University of California Riverside 900 University Ave. Riverside California 92521 USA Materials Science and Engineering Program University of California Riverside 900 University Ave. Riverside California 92521 USA

The localized release of energy at the surface of plasma-exposed materials plays a crucial role in many plasma-driven processes. Unfortunately, the interaction between low-temperature plasmas and surfaces is both poorly understood and difficult to characterize. Here, we use Raman thermometry to measure the temperature of graphene during plasma exposure. We observe a significant increase in the graphene temperature even for modest input plasma powers. These results suggest that two-dimensional materials are effective surface probes for the quantitative characterization of plasma-induced heating effects and for further investigation of the plasma-surface interaction.

关键词： Chemical Physics & Physical Chemistry Heat transfer High-frequency & RF discharges in plasmas Plasma-solid interactions Temperature Graphene Low-temperature plasma Raman spectroscopy

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