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  • 1
    Online Resource
    Online Resource
    Super‐Resolution Vibrational Imaging Using Expansion Stimulated Raman Scattering Microscopy
    Wiley ; 2022
    In:  Advanced Science Vol. 9, No. 20 ( 2022-07)
    Details
    In: Advanced Science, Wiley, Vol. 9, No. 20 ( 2022-07)
    Abstract: Stimulated Raman scattering (SRS) microscopy is an emerging technology that provides high chemical specificity for endogenous biomolecules and can circumvent common constraints of fluorescence microscopy including limited capabilities to probe small biomolecules and difficulty resolving many colors simultaneously. However, the resolution of SRS microscopy remains governed by the diffraction limit. To overcome this, a new technique called molecule anchorable gel‐enabled nanoscale Imaging of Fluorescence and stimulated Raman scattering microscopy (MAGNIFIERS) that integrates SRS microscopy with expansion microscopy (ExM) is described. MAGNIFIERS offers chemical‐specific nanoscale imaging with sub‐50 nm resolution and has scalable multiplexity when combined with multiplex Raman probes and fluorescent labels. MAGNIFIERS is used to visualize nanoscale features in a label‐free manner with CH vibration of proteins, lipids, and DNA in a broad range of biological specimens, from mouse brain, liver, and kidney to human lung organoid. In addition, MAGNIFIERS is applied to track nanoscale features of protein synthesis in protein aggregates using metabolic labeling of small metabolites. Finally, MAGNIFIERS is used to demonstrate 8‐color nanoscale imaging in an expanded mouse brain section. Overall, MAGNIFIERS is a valuable platform for super‐resolution label‐free chemical imaging, high‐resolution metabolic imaging, and highly multiplexed nanoscale imaging, thus bringing SRS to nanoscopy.
    Type of Medium: Online Resource
    ISSN: 2198-3844 , 2198-3844
    URL: Issue
    URL: Article
    DOI: 10.1002/advs.v9.20
    DOI: 10.1002/advs.202200315
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2808093-2
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  • 2
    Online Resource
    Online Resource
    Energy‐aware task scheduling with time constraint for heterogeneous cloud datacenters
    Wiley ; 2020
    In:  Concurrency and Computation: Practice and Experience Vol. 32, No. 18 ( 2020-09-25)
    Details
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 32, No. 18 ( 2020-09-25)
    Abstract: Energy optimization with time constraint has become a timely and significant challenge for the datacenters. In this paper, a hardware and software collaborative optimization strategy is implemented to minimize the energy cost while satisfying the time constraint of the datacenters. In the hardware aspect, a DVFS‐capable CPU/GPU/FPGA heterogeneous computing infrastructure is built. This infrastructure can adjust its hardware characteristics dynamically in terms of the software run‐time contexts so that the applications can be executed efficiently with less time and lower energy cost. In the software aspect, a deadline‐aware energy‐efficient task scheduling algorithm based on the Q‐learning approach is investigated. This algorithm can adjust its searching directions smartly in terms of the environment feedback so that it can achieve better optimization performance comparing with the traditional genetic algorithm. However, its convergence time is long due to the large amount of training work, making it inappropriate to be applied in the large‐scale datacenters. To ease this problem, we proposed another new algorithm named Rapid Local Convolution Optimization (RLCO) and combine it with the Q‐learning algorithm. By doing this, the convergence time of the Q‐learning mechanism can be decreased significantly. We conducted both the simulation and real‐world experiments to evaluate the performance of our approaches, and the results proved the proposed algorithm running on the DVFS‐capable heterogeneous hardware architecture could decrease the energy cost of the datacenter significantly even if the datacenter is in large scale.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    URL: Article
    DOI: 10.1002/cpe.v32.18
    DOI: 10.1002/cpe.5437
    RVK:
    SA 4270
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2052606-4
    SSG: 11
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  • 3
    Online Resource
    Online Resource
    MicroMagnify: A Multiplexed Expansion Microscopy Method for Pathogens and Infected Tissues
    Wiley ; 2023
    In:  Advanced Science
    Details
    In: Advanced Science, Wiley
    Abstract: Super‐resolution optical imaging tools are crucial in microbiology to understand the complex structures and behavior of microorganisms such as bacteria, fungi, and viruses. However, the capabilities of these tools, particularly when it comes to imaging pathogens and infected tissues, remain limited. MicroMagnify (µMagnify) is developed, a nanoscale multiplexed imaging method for pathogens and infected tissues that are derived from an expansion microscopy technique with a universal biomolecular anchor. The combination of heat denaturation and enzyme cocktails essential is found for robust cell wall digestion and expansion of microbial cells and infected tissues without distortion. µMagnify efficiently retains biomolecules suitable for high‐plex fluorescence imaging with nanoscale precision. It demonstrates up to eightfold expansion with µMagnify on a broad range of pathogen‐containing specimens, including bacterial and fungal biofilms, infected culture cells, fungus‐infected mouse tone, and formalin‐fixed paraffin‐embedded human cornea infected by various pathogens. Additionally, an associated virtual reality tool is developed to facilitate the visualization and navigation of complex 3D images generated by this method in an immersive environment allowing collaborative exploration among researchers worldwide. µMagnify is a valuable imaging platform for studying how microbes interact with their host systems and enables the development of new diagnosis strategies against infectious diseases.
    Type of Medium: Online Resource
    ISSN: 2198-3844 , 2198-3844
    URL: Article
    DOI: 10.1002/advs.202302249
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2808093-2
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  • 4
    Online Resource
    Online Resource
    Collagen type XVIII alpha 1 chain ( COL18A1 ) variants affect the risk of anti‐tuberculosis drug‐induced hepatotoxicity: A prospective study
    Wiley ; 2021
    In:  Journal of Clinical Laboratory Analysis Vol. 35, No. 2 ( 2021-02)
    Details
    In: Journal of Clinical Laboratory Analysis, Wiley, Vol. 35, No. 2 ( 2021-02)
    Abstract: The role of collagen type XVIII alpha 1 chain ( COL18A1 ) in anti‐tuberculosis drug‐induced hepatotoxicity (ATDH) has not been reported. This study aimed to explore the association between of COL18A1 variants and ATDH susceptibility. Methods A total of 746 patients were enrolled in our study from December 2016 to April 2018, and all subjects in the study signed an informed consent form. The custom‐by‐design 2x48‐Plex SNPscanTM kit was used to genotype all selected 11 SNPs. Categorical variables were compared by chi‐square (χ 2 ) or Fisher's exact test, while continuous variables were compared by Mann‐Whitney's U test. Plink was utilized to analyze allelic and genotypic frequencies, and genetic models. Multivariate logistic regression analyses were used to adjust potential factors. The odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were also calculated. Results Among patients with successfully genotyping, there were 114 cases and 612 controls. The mutant A allele of rs12483377 conferred the decreased risk of ATDH (OR = 0.13, 95%CI: 0.02–0.98, P  = 0.020), and this significance still existed after adjusting age and gender ( P  = 0.024). The mutant homozygote AA genotype of rs12483377 was associated with decreased total protein levels ( P  = 0.018). Conclusion Our study first revealed that the A allele of COL18A1 rs12483377 was associated with the decreased risk of ATDH in the Western Chinese Han population, providing new perspective for the molecular prediction, precise diagnosis, and individual treatment of ATDH.
    Type of Medium: Online Resource
    ISSN: 0887-8013 , 1098-2825
    URL: Issue
    URL: Article
    DOI: 10.1002/jcla.v35.2
    DOI: 10.1002/jcla.23630
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2001635-9
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