Sentence examples for em data set from inspiring English sources

DOI: http://dx.doi.org/10.7554/eLife.04491.003 10.7554/eLiFigure91 figuregure 1—figure supplement 1. Raw micrographs and initial class averages for WT F2 negative stain EM data set.

The second EM data set focused on secreted amino acid measurements from a separate study of yeast cultured in different ammonium and potassium concentrations [ 33].

The first EM data set compares wild type yeast to the gdh1/GDH2 (glutamate dehydrogenase) strain [ 31], which indicated good agreement between predicted metabolic changes of intracellular metabolite levels and fluxes [ 31, 32].

It was found that, for both imputation approaches, model building with backward elimination applied to the initial EM data set was easier to implement, and produced good results, compared to those from a complete case analysis.

Similar to the negative stain analysis, class averages from the cryo-EM data set revealed that the majority of particles are early intermediates.

Putative 3′-domain classes are enriched in the degraded sample 1, while Group II classes are enriched in the intact sample 2. DOI: http://dx.doi.org/10.7554/eLife.04491.016 10.7554/eLiFigure91.017 Figure 6 figure supplement 2. 3D classification of Group I particles from cryo-EM data set of affinity-purified sample.

We analysed a large number of randomly recorded low-magnification EM data sets of HTLV-1-infected (Figure 1A) and uninfected (Figure 1B) CD4+ T cells.

Alignment and classification of images in both 2D and 3D are key methods for improving SNR and detection and sorting of heterogeneity in EM data sets.

This study was divided into two parts and describes: (i) the reconstruction and validation of an expanded S. cerevisiae metabolic network, iMM904; and (ii) the systematic inference of intracellular metabolic states from two yeast EM data sets using a constraint-based sampling approach.

To analyze these volumes, we used the publicly available software package IMOD, specifically developed for the visualization and analysis of EM data sets in three dimensions (http://bio3d.colorado.edu/imod/) and stereology was performed using a custom plug-in for IMOD.

Identification of homogeneous subsets of images in a macromolecular electron microscopy (EM) image data set is a critical step in single-particle analysis.