检索结果-内蒙古大学图书馆

Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Remedios, Lucas W. Bao, Shunxing Remedios, Samuel W. Lee, Ho Hin Cai, Leon Y. Li, Thomas Deng, Ruining Newlin, Nancy R. Saunders, Adam M. Cui, Can Li, Jia Liu, Qi Lau, Ken S. Roland, Joseph T. Washington, Mary K. Coburn, Lori A. Wilson, Keith T. Huo, Yuankai Landman, Bennett A. Vanderbilt University Department of Computer Science Nashville United States Vanderbilt University Department of Electrical and Computer Engineering Nashville United States Johns Hopkins University Department of Computer Science Baltimore United States National Institutes of Health Department of Radiology and Imaging Sciences Bethesda United States Vanderbilt University Department of Biomedical Engineering Nashville United States Vanderbilt University Medical Center Department of Biostatistics Nashville United States Vanderbilt University Medical Center Center for Quantitative Sciences Nashville United States Vanderbilt University Medical Center Epithelial Biology Center Nashville United States Vanderbilt University School of Medicine Department of Cell and Developmental Biology Nashville United States Vanderbilt University Medical Center Division of Gastroenterology Hepatology and Nutrition Department of Medicine Nashville United States Vanderbilt University Medical Center Vanderbilt Center for Mucosal Inflammation and Cancer Nashville United States Vanderbilt University School of Medicine Program in Cancer Biology Nashville United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Medical Center Department of Pathology Microbiology and Immunology NashvilleTN United States

Purpose: Cells are building blocks for human physiology;consequently, understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions in both health and disease. Hematoxylin and eosin (H&E) is the standard stain used in histological analysis of tissues. Microscope slides of H&E-stained tissues are widely available in both clinical and research settings. While H&E is ubiquitous and reveals tissue microanatomy, the classification and tracking of cell subtypes often requires expert knowledge and the use of specialized stains, such as immunofluorescence staining. To reduce both the manual annotation burden and reliance on specialized staining technologies, artificial intelligence has been proposed for the automatic classification of cell types on H&E slides. For example, the recent Colon Nucleus Identification and Classification (CoNIC) Challenge focused on labeling 6 cell types on imaging of H&E stains from the human colon. However, this is a very small fraction of the number of potential cell subtypes within the intestines. Specifically, the CoNIC Challenge was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). To approach this problem, we propose to use inter-modality learning to label previously un-labelable cell types on H&E. Approach: We take advantage of the cell classification information inherent in whole slide images (WSIs) of multiplexed immunofluorescence (MxIF) histology to create cell level annotations for 14 subclasses. We performed style transfer on the same MxIF tissues to synthesize realistic virtual H&E which we paired with the MxIF-derived cell subclassification labels. We evaluated the efficacy of using a supervised learning scheme where the input was realistic-quality virtual H&E and the labels were MxIF-derived cell subclasses. We assessed our model on a testing

关键词： Tissue

Topological Surface State Evolution in Bi2Se3 via Surface Etching

学校读者我要写书评

暂无评论

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

Reducing system dimensionality with long-range collective dipole-dipole interactions

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Boddeti, Ashwin K. Wang, Yi Juarez, Xitlali G. Boltasseva, Alexandra Odom, Teri W. Shalaev, Vladimir Alaeian, Hadiseh Jacob, Zubin Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States School of Physics and Astronomy Purdue University West LafayetteIN47907 United States

Dimensionality plays a crucial role in long-range dipole-dipole interactions (DDIs). We demonstrate that a resonant nanophotonic structure modifies the apparent dimensionality in an interacting ensemble of emitters, as revealed by population decay dynamics. Our measurements on a dense ensemble of interacting quantum emitters in a resonant nanophotonic structure with long-range DDIs reveal an effective dimensionality reduction to d¯ = 2.20(12), despite the emitters being distributed in 3D. This contrasts the homogeneous environment, where the apparent dimension is d¯ = 3.00. Our work presents a promising avenue to manipulate dimensionality in an ensemble of interacting emitters. © 2023, CC0.

关键词： Dipole moment

Nucleus subtype classification using inter-modality learning

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Remedios, Lucas W. Bao, Shunxing Remedios, Samuel W. Lee, Ho Hin Cai, Leon Y. Li, Thomas Deng, Ruining Cui, Can Li, Jia Liu, Qi Lau, Ken S. Roland, Joseph T. Washington, Mary K. Coburn, Lori A. Wilson, Keith T. Huo, Yuankai Landman, Bennett A. Vanderbilt University Department of Computer Science Nashville United States Vanderbilt University Department of Electrical and Computer Engineering Nashville United States Johns Hopkins University Department of Computer Science Baltimore United States National Institutes of Health Department of Radiology and Imaging Sciences Bethesda United States Vanderbilt University Department of Biomedical Engineering Nashville United States Vanderbilt University Medical Center Department of Biostatistics Nashville United States Vanderbilt University Medical Center Center for Quantitative Sciences Nashville United States Vanderbilt University Medical Center Epithelial Biology Center Nashville United States Vanderbilt University School of Medicine Department of Cell and Developmental Biology Nashville United States Vanderbilt University Medical Center Division of Gastroenterology Hepatology and Nutrition Department of Medicine Nashville United States Vanderbilt University Medical Center Vanderbilt Center for Mucosal Inflammation and Cancer Nashville United States Vanderbilt University School of Medicine Program in Cancer Biology Nashville United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Medical Center Department of Pathology Microbiology and Immunology NashvilleTN United States

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available both for clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge has recently innovated on robust artificial intelligence labeling of six cell types on H&E stains of the colon. However, this is a very small fraction of the number of potential cell classification types. Specifically, the CoNIC Challenge is unable to classify epithelial subtypes (progenitor, endocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), or connective subtypes (fibroblasts, stromal). In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E. We leveraged multiplexed immunofluorescence (MxIF) histology imaging to identify 14 subclasses of cell types. We performed style transfer to synthesize virtual H&E from MxIF and transferred the higher density labels from MxIF to these virtual H&E images. We then evaluated the efficacy of learning in this approach. We identified helper T and progenitor nuclei with positive predictive values of 0.34 ± 0.15 (prevalence 0.03 ± 0.01) and 0.47 ± 0.1 (prevalence 0.07 ± 0.02) respectively on virtual H&E. This approach represents a promising step towards automating annotation in digital pathology. © 2024, CC BY.

关键词： Cell culture

Data Processing of Functional Optical Microscopy for Neuroscience

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Benisty, Hadas Song, Alexander Mishne, Gal Charles, Adam S. Yale Neuroscience New HavenCT06510 United States Max Planck Institute for Intelligent Systems Stuttgart Germany Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. In this review, we cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges. © 2022, CC BY-NC-ND.

关键词： Fluorescence microscopy

On the Selection of Neural Network Architecture for Supervised Motor Unit Identification from High-Density Surface EMG*

学校读者我要写书评

暂无评论

On the Selection of Neural Network Architecture for Supervis...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Filip Urh Damjan Strnad Alex Clarke Dario Farina Aleš Holobar Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Bioengineering Imperial College London London United Kingdom

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

In the last decade, accurate identification of motor unit (MU) firings received a lot of research interest. Different decomposition methods have been developed, each with its advantages and disadvantages. In this study, we evaluated the capability of three different types of neural networks (NNs), namely dense NN, long short-term memory (LSTM) NN and convolutional NN, to identify MU firings from high-density surface electromyograms (HDsEMG). Each type of NN was evaluated on simulated HDsEMG signals with a known MU firing pattern and high variety of MU characteristics. Compared to dense NN, LSTM and convolutional NN yielded significantly higher precision and significantly lower miss rate of MU identification. LSTM NN demonstrated higher sensitivity to noise than convolutional *** Relevance-MU identification from HDsEMG signals offers valuable insight into neurophysiology of motor system but requires relatively high level of expert knowledge. This study assesses the capability of self-learning artificial neural networks to cope with this problem.

关键词： Artificial neural networks Firing Convolution Training Electromyography Muscles Signal to noise ratio

Multifunctional metaoptics based on bilayer metasurfaces

学校读者我要写书评

暂无评论

Light(science & Applications) 2019年第1期8卷 466-474页

作者： You Zhou Ivan I.Kravchenko Hao Wang Hanyu Zheng Gong Gu Jason Valentine Interdisciplinary Materials Science Program Vanderbilt UniversityNashvilleTN 37212USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Min H.Kao Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA Department of Electric Engineering and Computer Science Vanderbilt UniversityNashvilleTN 37212USA Department of Mechanical Engineering Vanderbilt UniversityNashvilleTN 37212USA

Optical metasurfaces have become versatile platforms for manipulating the phase,amplitude,and polarization of light.A platform for achieving independent control over each of these properties,however,remains elusive due to the limited engineering space available when using a single-layer *** instance,multiwavelength metasurfaces suffer from performance limitations due to space filling constraints,while control over phase and amplitude can be achieved,but only for a single ***,we explore bilayer dielectric metasurfaces to expand the design space for *** ability to independently control the geometry and function of each layer enables the development of multifunctional metaoptics in which two or more optical properties are independently *** a proof of concept,we demonstrate multiwavelength holograms,multiwavelength waveplates,and polarization-insensitive 3D holograms based on phase and amplitude *** proposed architecture opens a new avenue for designing complex flat optics with a wide variety of functionalities.

关键词： surface. optics independently

Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

学校读者我要写书评

暂无评论

NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

Unique material modifications of Ga2O3 enabled by ultrafast laser irradiation 11

学校读者我要写书评

暂无评论

Unique material modifications of Ga2O3 enabled by ultrafast ...

Oxide-based Materials and Devices XI 2020

作者： Ahn, Minhyung Sarracino, Alex Ansari, Abdul Torralva, Ben Yalisove, Steven Phillips, Jamie Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Climate and Space Sciences and Engineering University of Michigan Ann ArborMI48109 United States Materials Science and Engineering Department University of Michigan Ann ArborMI48109 United States

ISBN: (纸本)9781510633254

Ultrafast (femtosecond pulse) laser irradiation provides unique laser-material interactions associated with high instantaneous electric fields and short timescales that lead to a nonequilibrium state between excited electrons and phonons. These interactions result in material modifications that differ from conventional chemical, physical, and thermal processing at much longer timescales, and an opportunity to address material and device processing challenges associated with wide-bandgap materials such as Ga2O3. In this work, we explore ultrafast laser irradiation (Ti:sapphire, 150 fs pulse width) of bulk (010) Sn-doped β-Ga2O3 under two different wavelengths, fundamental (780 nm) and frequency-doubled (390 nm), and a range of laser fluences. We identified a regime for laser-induced damage threshold resulting in material ablation, thermally-induced straight crack formation, and recrystallization. Rastering on a β-Ga2O3 substrate created surface nanostructures including laser-induced periodic surface structures at a high spatial frequency (period ∼250 nm). These highly aligned periodic structures can be controlled by laser polarization and wavelength, presenting a means for direct writing of surface nanostructures. Enhanced atomic movement associated with a transient metallic state can provide a means for intentional generation of point defects via the laser irradiation. These point defects may offer a means of electrical modification, which was demonstrated as more than five orders of the magnitude enhancement of lateral conductance on rastered β-Ga2O3. Moreover, a hydrophobic surface of β-Ga2O3was achieved by ultrafast laser irradiation. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Hydrophobicity