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Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

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arXiv 2023年

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Manassa, Jason Pietryga, Jacob Qian, Yiwen Cho, Min Gee Rowell, Jonathan L. Zheng, Huihuo Robinson, Richard D. Gu, Junsi Kirilin, Alexey Rozeveld, Steve Ercius, Peter Fessler, Jeffrey A. Xu, Ting Scott, Mary Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Department of Material Science and Engineering Northwestern University EvanstonIL United States Department of Materials Science and Engineering University of California at Berkeley BerkeleyCA United States Department of Chemistry and Chemical Biology Cornell University IthacaNY United States Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States Department of Material Science and Engineering Cornell University IthacaNY United States Kavli Institute at Cornell for Nanoscale Science Cornell University IthacaNY United States Dow Chemical Co. MidlandMI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Physics Program University of Michigan Ann ArborMI United States

Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe3O4 metamaterial, Co3O4 - Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials. © 2023, CC BY.

关键词： Zinc sulfide

Single-shot 4-phase shifting optical coherence microscopy 25

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Single-shot 4-phase shifting optical coherence microscopy

Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV 2021

作者： Zurauskas, Mantas Iyer, Rishyashring R. Boppart, Stephen A. Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL United States Department of Bioengineering University of Illinois at Urbana-Champaign ChampaignIL United States Cancer Center at Illinois University of Illinois at Urbana-Champaign UrbanaIL United States Carle Illinois College of Medicine University of Illinois at Urbana-Champaign ChampaignIL United States

ISBN: (数字)9781510640962

ISBN: (纸本)9781510640955

One limitation of phase shifting optical coherence microscopy (OCM) is that any changes in imaging conditions, caused by either system-level instability or movement of dynamic samples, will inevitably lead to reduction in image quality. Here we present a novel implementation of OCM which permits accurate measurements of phase and intensity, by simultaneously capturing four phase-shifted images on a single camera sensor. Uniquely, our approach enables probing the sample with unpolarized light, producing four phase-shifted interferograms, separated by Ïin two orthogonally polarized detection channel pairs. The proposed set-up is compatible with spatially incoherent illumination sources, enabling high resolution imaging due to its inherently reduced susceptibility to speckle noise. By simultaneously capturing the four phase-shifted images on a single CCD camera chip we demonstrate the capacity to acquire stable phase recordings with a phase sensitivity of 0.1 nm rms error at 500 Hz frame rates, a field-of-view of 250x250 μm, and 1μm lateral resolution. Instantaneous, coherence-gated, phase-sensitive imaging at high frame rates enables observations of the dynamics in cells and tissues, providing instant feedback on metabolic activity and tissue states. We present the novel design and methodology that enables ultrasensitive label-free coherence-gated measurements of intracellular dynamics in cells and cellular networks. © 2021 SPIE.

关键词： CCD cameras

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

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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

Bioelectrical Interfaces Beyond Cellular Excitability: Cancer, Aging, and Gene Expression Reprogramming

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arXiv 2025年

作者： Cadinu, Paolo Burgess, Matthew Jones, Catarina Franco Luciani, Titouan Iarossi, Marzia Schröter, Manuel Nakatsuka, Nako Djamgoz, Mustafa B.A. Gonçalves, Gil Sanjuán-Alberte, Paola Mendes, Paula M. Rawson, Frankie J. Nair, Malavika Levin, Michael Moreddu, Rosalia Program in Cellular and Molecular Medicine Boston Children’s Hospital BostonMA02115 United States Department of Microbiology Blavatnik Institute Harvard Medical School BostonMA02115 United States Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford United Kingdom Department of Bioengineering Institute for Bioengineering and Biosciences Instituto Superior Técnico University of Lisbon Lisbon Portugal Associate Laboratory i4HB Institute for Health and Bioeconomy Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Neuroscience Institut Pasteur Paris75015 France Department of Biomedical Engineering Technion Israel Institute of Technology Haifa Israel ETH Zurich Department of Biosystems Science and Engineering Basel Switzerland Laboratory of Chemical Nanotechnology Neuro-X Institute EPFL Genève1202 Switzerland Department of Life Sciences Imperial College London United Kingdom Department of Mechanical Engineering University of Aveiro Aveiro3810-193 Portugal Guimarães4800-058 Portugal School of Chemical Engineering University of Birmingham Birmingham United Kingdom Bioelectronics Laboratory Division of Regenerative Medicine and Cellular Therapies School of Pharmacy Biodiscovery Institute University of Nottingham Nottingham United Kingdom Allen Discovery Centre Tufts University Tufts University MedfordMA United States Wyss Institute for Bioinspired Engineering Harvard University BostonMA United States School of Electronics and Computer Science University of Southampton Southampton United Kingdom Institute for Life Sciences University of Southampton Southampton United Kingdom

Bioelectrical interfaces represent a significant evolution in the intersection of nanotechnology and biophysics, oRering new strategies for probing and influencing cellular processes. These systems capitalize on the subtle but powerful electric fields within living matter, potentially enabling applications beyond cellular excitability, ranging from targeted cancer therapies to interventions in genetic mechanisms and aging. This perspective article envisions the translation, development and application of next-generation solid-state bioelectrical interfaces and their transformative impact across several critical areas of medical research. © 2025, CC BY.

关键词： Medical nanotechnology

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

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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

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

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

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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

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

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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

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

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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