Data in brief
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The presented dataset provides a normative high-resolution three-dimensional (3D) macromolecular proton fraction (MPF) map of the healthy rat brain in vivo and source images used for its reconstruction. The images were acquired using the protocol described elsewhere (Naumova, et al. High-resolution three-dimensional macromolecular proton fraction mapping for quantitative neuroanatomical imaging of the rodent brain in ultra-high magnetic fields. ⋯ Source images were acquired from a living animal on an 11.7 T small animal MRI scanner with isotropic spatial resolution of 170 µm3 and total acquisition time about 1.5 h. The 3D dataset can be used for multiple purposes including interactive viewing of rat brain anatomy, measurements of reference MPF values in various brain structures, and development of image processing techniques for the rodent brain segmentation. It also can serve as a gold standard image for implementation and optimization of rodent brain MRI protocols.
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The data in this paper are related to the research article entitled "Filamentation of metabolic enzymes in Saccharomyces cerevisiae" Q. J. Shen et al. (2016) [1]. ⋯ Here we selected cytoophidia formed by 6 filament-forming proteins in the budding yeast S. cerevisiae, and performed living-cell imaging of cells expressing the proteins fused with GFP. The dynamic features of the six types of cytoophidia were analyzed. In the data, both raw movies and analysed results of the dynamics of cytoophidia are presented.
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The data show respiratory variables in 108 critically ill patients with acute respiratory failure placed on proportional assist ventilation with load adjustable gain factors (PAV+) after at least 36 h on passive mechanical ventilation. PAV+ was continued for 48 h until the patients met pre-defined criteria either for switching to controlled modes or for breathing without ventilator assistance. Data during passive mechanical ventilation and during PAV+ are reported. ⋯ The reported variables are tidal volume, driving pressure (ΔP, the difference between static end-inspiratory plateau pressure and positive end-expiratory airway pressure), respiratory system compliance and resistance, and arterial blood gasses. The data are supplemental to our original research article, which described individual ΔP in these patients and examined how it related to ΔP when the same patients were ventilated with passive mechanical ventilation using the currently accepted lung-protective strategy "Driving pressure during assisted mechanical ventilation. Is it controlled by patient brain?" [1].
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Gastric carcinoma MKN45 cells stably transfected with the full-length ST3GAL4 gene were characterised by glycomic and sialoproteomic analysis. Complementary strategies were applied to assess the glycomic alterations induced by ST3GAL4 overexpression. The N- and O-glycome data were generated in two parallel structural analyzes, based on PGC-ESI-MS/MS. ⋯ The sialoproteomic analysis based on titanium-dioxide enrichment of sialopeptides with subsequent LC-MS/MS identification was performed. This analysis identified 47 proteins with significantly increased sialylation. The data in this article is associated with the research article published in Biochim Biophys Acta "Glycomic analysis of gastric carcinoma cells discloses glycans as modulators of RON receptor tyrosine kinase activation in cancer" [1].
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This article provides NODDI diffusion metrics in the brains of 52 healthy participants and computer simulation data to support compatibility of hybrid diffusion imaging (HYDI), "Hybrid diffusion imaging"[1] acquisition scheme in fitting neurite orientation dispersion and density imaging (NODDI) model, "NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain"[2]. HYDI is an extremely versatile diffusion magnetic resonance imaging (dMRI) technique that enables various analyzes methods using a single diffusion dataset. One of the diffusion data analysis methods is the NODDI computation, which models the brain tissue with three compartments: fast isotropic diffusion (e.g., cerebrospinal fluid), anisotropic hindered diffusion (e.g., extracellular space), and anisotropic restricted diffusion (e.g., intracellular space). ⋯ The second dataset provided here is the computer simulation with initial conditions guided by the first dataset as inputs and gold standard for model fitting. The computer simulation data provide a direct comparison of NODDI indices computed from the HYDI acquisition [1] to the NODDI indices computed from the originally proposed acquisition [2]. These data are related to the accompanying research article "Age Effects and Sex Differences in Human Brain White Matter of Young to Middle-Aged Adults: A DTI, NODDI, and q-Space Study" [3].