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  • 1
    Online Resource
    Online Resource
    Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2023
    Oxford University Press (OUP) ; 2023
    In:  Nucleic Acids Research Vol. 51, No. D1 ( 2023-01-06), p. D18-D28
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 51, No. D1 ( 2023-01-06), p. D18-D28
    Abstract: The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global academic and industrial communities. With the explosive accumulation of multi-omics data generated at an unprecedented rate, CNCB-NGDC constantly expands and updates core database resources by big data archive, integrative analysis and value-added curation. In the past year, efforts have been devoted to integrating multiple omics data, synthesizing the growing knowledge, developing new resources and upgrading a set of major resources. Particularly, several database resources are newly developed for infectious diseases and microbiology (MPoxVR, KGCoV, ProPan), cancer-trait association (ASCancer Atlas, TWAS Atlas, Brain Catalog, CCAS) as well as tropical plants (TCOD). Importantly, given the global health threat caused by monkeypox virus and SARS-CoV-2, CNCB-NGDC has newly constructed the monkeypox virus resource, along with frequent updates of SARS-CoV-2 genome sequences, variants as well as haplotypes. All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkac1073
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1472175-2
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Identification and genomic analysis of antifungal property of a tomato root endophyte Pseudomonas sp. p21
    Springer Science and Business Media LLC ; 2017
    In:  Antonie van Leeuwenhoek Vol. 110, No. 3 ( 2017-3), p. 387-397
    Details
    In: Antonie van Leeuwenhoek, Springer Science and Business Media LLC, Vol. 110, No. 3 ( 2017-3), p. 387-397
    Type of Medium: Online Resource
    ISSN: 0003-6072 , 1572-9699
    URL: Article
    DOI: 10.1007/s10482-016-0811-5
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 1478112-8
    SSG: 12
    Location Call Number Limitation Availability
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  • 3
    Online Resource
    Online Resource
    Super-resolution ultrasound localization microscopy based on a high frame-rate clinical ultrasound scanner: an in-human feasibility study
    IOP Publishing ; 2021
    In:  Physics in Medicine & Biology Vol. 66, No. 8 ( 2021-04-21), p. 08NT01-
    Details
    In: Physics in Medicine & Biology, IOP Publishing, Vol. 66, No. 8 ( 2021-04-21), p. 08NT01-
    Abstract: Non-invasive detection of microvascular alterations in deep tissues in vivo provides critical information for clinical diagnosis and evaluation of a broad-spectrum of pathologies. Recently, the emergence of super-resolution ultrasound localization microscopy (ULM) offers new possibilities for clinical imaging of microvasculature at capillary level. Currently, the clinical utility of ULM on clinical ultrasound scanners is hindered by the technical limitations, such as long data acquisition time, high microbubble (MB) concentration, and compromised tracking performance associated with low imaging frame-rate. Here we present a robust in-human ULM on a high frame-rate (HFR) clinical ultrasound scanner to achieve super-resolution microvessel imaging using a short acquisition time ( 〈 10 s). Ultrasound MB data were acquired from different human tissues, including a healthy liver and a diseased liver with acute-on-chronic liver failure, a kidney, a pancreatic tumor, and a breast mass using an HFR clinical scanner. By leveraging the HFR and advanced processing techniques including sub-pixel motion registration, MB signal separation, and Kalman filter-based tracking, MBs can be robustly localized and tracked for ULM under the circumstances of relatively high MB concentration associated with standard clinical MB administration and limited data acquisition time in humans. Subtle morphological and hemodynamic information in microvasculature were shown based on data acquired with single breath-hold and free-hand scanning. Compared with contrast-enhanced power Doppler generated based on the same MB dataset, ULM showed a 5.7-fold resolution improvement in a vessel based on a linear transducer, and provided a wide-range blood flow speed measurement that is Doppler angle-independent. Microvasculatures with complex hemodynamics can be well-differentiated at super-resolution in both normal and pathological tissues. This preliminary study implemented the ultrafast in-human ULM in various human tissues based on a clinical scanner that supports HFR imaging, indicating the potentials of the technique for various clinical applications. However, rigorous validation of the technique in imaging human microvasculature (especially for those tiny vessel structure), preferably with a gold standard, is still required.
    Type of Medium: Online Resource
    ISSN: 0031-9155 , 1361-6560
    URL: Article
    DOI: 10.1088/1361-6560/abef45
    RVK:
    WA 15000
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 1473501-5
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022
    Oxford University Press (OUP) ; 2022
    In:  Nucleic Acids Research Vol. 50, No. D1 ( 2022-01-07), p. D27-D38
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 50, No. D1 ( 2022-01-07), p. D27-D38
    Abstract: The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkab951
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1472175-2
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 5
    Online Resource
    Online Resource
    Fast super-resolution ultrasound microvessel imaging using spatiotemporal data with deep fully convolutional neural network
    IOP Publishing ; 2021
    In:  Physics in Medicine & Biology Vol. 66, No. 7 ( 2021-04-07), p. 075005-
    Details
    In: Physics in Medicine & Biology, IOP Publishing, Vol. 66, No. 7 ( 2021-04-07), p. 075005-
    Abstract: Ultrasound localization microscopy (ULM) has been proposed to image microvasculature beyond the ultrasound diffraction limit. Although ULM can attain microvascular images with a sub-diffraction resolution, long data acquisition time and processing time are the critical limitations. Deep learning-based ULM (deep-ULM) has been proposed to mitigate these limitations. However, microbubble (MB) localization used in deep-ULMs is currently based on spatial information without the use of temporal information. The highly spatiotemporally coherent MB signals provide a strong feature that can be used to differentiate MB signals from background artifacts. In this study, a deep neural network was employed and trained with spatiotemporal ultrasound datasets to better identify the MB signals by leveraging both the spatial and temporal information of the MB signals. Training, validation and testing datasets were acquired from MB suspension to mimic the realistic intensity-varying and moving MB signals. The performance of the proposed network was first demonstrated in the chicken embryo chorioallantoic membrane dataset with an optical microscopic image as the reference standard. Substantial improvement in spatial resolution was shown for the reconstructed super-resolved images compared with power Doppler images. The full-width-half-maximum (FWHM) of a microvessel was improved from 133 μ m to 35 μ m, which is smaller than the ultrasound wavelength (73 μ m). The proposed method was further tested in an in vivo human liver data. Results showed the reconstructed super-resolved images could resolve a microvessel of nearly 170 μ m (FWHM). Adjacent microvessels with a distance of 670 μ m, which cannot be resolved with power Doppler imaging, can be well-separated with the proposed method. Improved contrast ratios using the proposed method were shown compared with that of the conventional deep-ULM method. Additionally, the processing time to reconstruct a high-resolution ultrasound frame with an image size of 1024 × 512 pixels was around 16 ms, comparable to state-of-the-art deep-ULMs.
    Type of Medium: Online Resource
    ISSN: 0031-9155 , 1361-6560
    URL: Article
    DOI: 10.1088/1361-6560/abeb31
    RVK:
    WA 15000
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 1473501-5
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
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