检索结果-内蒙古大学图书馆

NATURE BIOMEDICAL engineering 2024年第11期8卷 1499-1499页

作者： Wang, Peng Jin, Lin Zhang, Min Wu, Yunsong Duan, Zilei Guo, Yaqiong Wang, Chaoming Guo, Yingqi Chen, Wenwen Liao, Zhiyi Wang, Yaqing Lai, Ren Lee, Luke P. Qin, Jianhua Division of Biotechnology Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China University of Chinese Academy of Sciences Beijing China Beijing Institute for Stem Cell and Regenerative Medicine Chinese Academy of Sciences Beijing China Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences–Key Laboratory of Bioactive Peptides of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming China Core Technology Facility of Kunming Institute of Zoology Chinese Academy of Sciences Kunming China Department of Medicine Brigham and Women’s Hospital Harvard Medical School Boston MA USA Department of Bioengineering Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA USA Institute of Quantum Biophysics Department of Biophysics Sungkyunkwan University Suwon Korea

Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy

学校读者我要写书评

暂无评论

Nanoscale 2021年第15期13卷 7285-7293页

作者： Casasanta, Michael A. Jonaid, G.M. Kaylor, Liam Luqiu, William Y. Solares, Maria J. Schroen, Mariah L. Dearnaley, William J. Wilson, Jarad Dukes, Madeline J. Kelly, Deborah F. Department of Biomedical Engineering Pennsylvania State University University Park 16802 PA United States Materials Research Institute Pennsylvania State University University Park 16802 PA United States Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Department of Electrical and Computer Engineering Duke University Durham 27708 NC United States RayBiotech Life Peachtree Corners 30092 GA United States Applications Science Protochips Inc Morrisville 27560 NC United States

Interest in cryo-Electron Microscopy (EM) imaging has skyrocketed in recent years due to its pristine views of macromolecules and materials. As advances in instrumentation and computing algorithms spurred this progress, there is renewed focus to address specimen-related challenges. Here we contribute a microchip-based toolkit to perform complementary structural and biochemical analysis on low-molecular weight proteins. As a model system, we used the SARS-CoV-2 nucleocapsid (N) protein (48 kDa) due to its stability and important role in therapeutic development. Cryo-EM structures of the N protein monomer revealed a flexible N-terminal "top hat"motif and a helical-rich C-terminal domain. To complement our structural findings, we engineered microchip-based immunoprecipitation assays that led to the discovery of the first antibody binding site on the N protein. The data also facilitated molecular modeling of a variety of pandemic and common cold-related coronavirus proteins. Such insights may guide future pandemic-preparedness protocols through immuno-engineering strategies to mitigate viral outbreaks. This journal is © The Royal Society of Chemistry.

关键词：

TargetNet: Functional microRNA target prediction with deep neural networks

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Min, Seonwoo Lee, Byunghan Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of LG AI Research Seoul07796 Korea Republic of Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Interdisciplinary Program in Artificial Intelligence and Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of

Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions. Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (1) relaxed CTS selection criteria accommodating irregularities in the seed region, (2) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments, and (3) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-theart algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets. © 2021, CC BY-NC-ND.

关键词： RNA

Virtual histological staining of unlabeled autopsy tissue

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Li, Yuzhu Pillar, Nir Li, Jingxi Liu, Tairan Wu, Di Sun, Songyu Ma, Guangdong de Haan, Kevin Huang, Luzhe Hamidi, Sepehr Urisman, Anatoly Haran, Tal Keidar Dean Wallace, William Zuckerman, Jonathan E. Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los Angeles90095 United States University of California Los AngelesCA90095 United States Computer Science Department University of California Los AngelesCA90095 United States School of Physics Xi'an Jiaotong University Shaanxi Xi’an710049 China Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los AngelesCA90095 United States Department of Pathology University of California San FranciscoCA94143 United States Department of Pathology Hadassah Hebrew University Medical Center Jerusalem91120 Israel Department of Pathology Keck School of Medicine University of Southern California Los AngelesCA90033 United States Department of Surgery University of California Los AngelesCA90095 United States

Histological examination is a crucial step in an autopsy;however, the traditional histochemical staining of postmortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining. Copyright © 2023, The Authors. All rights reserved.

关键词： Tissue

Photonic Crystal Enhanced Fluorescence with DNA-based Nano-gripper for Ultrasensitive SARS-CoV-2 Biosensing

学校读者我要写书评

暂无评论

Photonic Crystal Enhanced Fluorescence with DNA-based Nano-g...

IEEE Photonics Conference (IPC)

作者： Yanyu Xiong Lifeng Zhou Laura Cooper Skye Shepherd Tingjie Song Abhisek Dwivedy Lijun Rong Tong Wang Xing Wang Brian T. Cunningham Department of Electrical and Computer Engineering Holonyak Micro and Nanotechnology Laboratory University of Illinois at Urbana−Champaign Urbana Illinois USA Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana−Champaign Urbana Illinois USA Department of Microbiology and Immunology College of Medicine University of Illinois at Chicago Chicago IL USA Department of Bioengineering University of Illinois at Urbana-Champaign Urbana IL USA Department of Chemistry University of Illinois at Urbana-Champaign Urbana IL USA Advanced Science Research Center at Graduate Center City University of New York New York NY USA Cancer Center at Illinois University of Illinois at Urbana-Champaign Urbana IL USA

DNA-origami based nano-grippers, integrated with aptamer-based nanoswitches, generate fluorescent signals when detecting SARS-CoV-2. The integration of Photonic Crystal Enhanced Fluorescence Microscope enables a 10 4 -fold enhancement compared to a single fluorophore reporter on glass substrate, providing a promising tool for ultrasensitive detection and rapid diagnostics.

关键词：

Imaging Atomic-Scale Chemistry from Fused Multi-Modal Electron Microscopy

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Fielitz, Alyssa J. Ha, Don-Hyung Perera, Sanjaya D. Baggari, Ismail El Robinson, Richard D. Fessler, Jeffrey A. Ophus, Colin Rozeveld, Steve 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 Dow Chemical Co. MidlandMI United States Department of Material Science and Engineering Cornell University IthacaNY United States School of Integrative Engineering Chung-Ang University Seoul Korea Republic of Department of Physics Cornell University IthacaNY United States The Rowland Institute at Harvard CambridgeMA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Physics Program University of Michigan Ann ArborMI United States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions. Here, fused multi-modal electron microscopy offers high signal-to-noise ratio (SNR) recovery of material chemistry at nano- and atomic- resolution by coupling correlated information encoded within both elastic scattering (high-angle annular dark field (HAADF)) and inelastic spectroscopic signals (electron energy loss (EELS) or energy-dispersive x-ray (EDX)). By linking these simultaneously acquired signals, or modalities, the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector hardware. In many cases, the dose requirements can be reduced by over one order of magnitude. This high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials. © 2022, CC BY.

关键词： Electron microscopy

Linewidth broadening induced by fundamental thermal fluctuations in metallo-dielectric nanolasers

学校读者我要写书评

暂无评论

Linewidth broadening induced by fundamental thermal fluctuat...

CLEO: Applications and Technology, CLEO_AT 2020

作者： Jiang, Sizhu Pan, Si Hui Deka, Suruj S. Chengyi-Fang Chen, Zijun Fainman, Yeshaiahu El Amili, Abdelkrim Department of Electrical and Computer Engineering University of California San Diego San DiegoCA92093-0407 United States Department of Physics University of California San Diego San DiegoCA92093-0407 United States Material Science and Engineering Program University of California San Diego San DiegoCA92093-0407 United States

We demonstrate that the fundamental thermal fluctuations can induce significant linewidth broadening effect in metallo-dielectric nanolasers through numerical simulation. Broader linewidths are observed with the decre... 详细信息

ISBN: (数字)9781943580767

ISBN: (纸本)9781557528209

关键词： Linewidth

No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases.

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Thomas Z. Xu, Kaiwen Krishnan, Aravind Gao, Riqiang Kammer, Michael N. Antic, Sanja Xiao, David Knight, Michael Martinez, Yency Paez, Rafael Lentz, Robert J. Deppen, Stephen Grogan, Eric L. Lasko, Thomas A. Sandler, Kim L. Maldonado, Fabien Landman, Bennett A. Medical Scientist Training Program Vanderbilt University NashvilleTN37235 United States Biomedical Engineering Vanderbilt University NashvilleTN37235 United States Computer Science Vanderbilt University NashvilleTN37235 United States Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Digital Technology and Innovation Siemens Healthineers PrincetonNJ08540 United States Division of Allergy Pulmonary and Critical Care Medicine Department of Medicine Vanderbilt University Medical Center NashvilleTN37232 United States Department of Thoracic Surgery Vanderbilt University Medical Center NashvilleTN37232 United States Biomedical Informatics Vanderbilt University Medical Center NashvilleTN37232 United States Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN37232 United States

Purpose: Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinical settings in which patients would arguably most benefit are scarce. Materials and Methods: This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged 9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions to assess the area under the receiver operating characteristic curve (AUC) of validated models including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence on chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented each model from their published literature, re-training the models if necessary, and curated each cohort from primary data sources. Results: We observed that model performance varied greatly across clinical use cases. No single predictive model emerged as a clear winner across all cohorts, but certain models excelled in specific clinical contexts. Single timepoint chest CT AI performed well in lung screening, but struggled to generalize to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively promising performance on incidentally-detected nodules. However, when applied to nodules that underwent biopsy, all models underperformed. Conclusion: These results underscore the strengths and limitations of 8 validated predictive models and highlight promising directions towards personalized, noninvasive lung cancer diagnosis. © 2024, CC BY-NC-SA.

关键词： Biopsy

CMR×Recon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wang, Chengyan Lyu, Jun Wang, Shuo Qin, Chen Guo, Kunyuan Zhang, Xinyu Yu, Xiaotong Li, Yan Wang, Fanwen Jin, Jianhua Shi, Zhang Xu, Ziqiang Tian, Yapeng Hua, Sha Chen, Zhensen Liu, Meng Sun, Mengting Kuang, Xutong Wang, Kang Wang, Haoran Li, Hao Chu, Yinghua Yang, Guang Bai, Wenjia Zhuang, Xiahai Wang, He Qin, Jing Qu, Xiaobo Human Phenome Institute Fudan University Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Bioengineering/˜Imperial-X Imperial College London United Kingdom School of Data Science Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Department of Computer Science The University of Texas Dallas United States Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Simens Healthineers Ltd. China Department of Brain Sciences Imperial College London London United Kingdom Department of Computing Imperial College London London United Kingdom

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://***/#!Synapse:syn51471091/wiki/. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

Small Polaron-Induced Ultrafast Ferroelectric Restoration in BiFeO3

学校读者我要写书评

暂无评论

Physical Review X 2025年第2期15卷 021046-021046页

作者： Wenfan Chen Tian Wang Chun-Chieh Yu Yuancheng Jing Xiaosong Li Wei Xiong Materials Science and Engineering Program University of California San Diego La Jolla California 92093 USA Department of Chemistry University of Washington Seattle Washington 98195 USA Department of Chemistry University of California San Diego La Jolla California 92093 USA Department of Electrical and Computer Science Engineering University of California San Diego La Jolla California 92093 USA

In this report, we apply a suite of ultrafast spectroscopic techniques and advanced calculations to reveal the interplay between electronic and lattice degrees of freedom in ferroelectric BiFeO3. Using transient sum frequency generation spectroscopy, which is sensitive to electronic polarizations, we observe a transient electronic dipole reduction upon optical excitation which recovers at 0.5 and 10 ps timescale. The time-dependent density functional theory calculation reveals that both ligand-metal charge transfer and local excitation transition occurred upon photo excitation. To reveal the nature of electronic dipole restoration, we employ transient extreme ultraviolet (EUV) spectroscopy—an element-specific ultrafast technique that follows charge dynamics of Bi, Fe, and O altogether. The transient EUV dynamics observed both ultrafast free charge carrier relaxation to excitons, as well as polaron formation. However, a timescale comparison suggests that only the polaron formation is responsible for the 0.5 ps electronic dipole restoration, whereas the faster electronic relaxation does not contribute to the ferroelectric property changes. Multireference configuration interaction calculation further corroborates this result by showing both Fe and Bi atoms shift from the ground state equilibrium—leading to the polaron formation. Our result disentangles the multidegrees of freedom in ultrafast ferroelectric modulation and identifies the pivotal motion—a local polaron formation—for the fast ferroelectric recovery. It provides crucial insights on the specific lattice distortion that could modulate properties or phase transitions of condensed matter materials.

关键词： Light polarization