Segmentation of the SWR1 3D Map We segmented the SWR1 3D map using the Eraser tool in Chimera.
For "focus" refinement, we segmented the final reconstruction map using the "Segmentation" tool in UCSF Chimera (Pettersen et al., 2004).
Based on these image stacks, we segmented the VT cavity using IPTOOLS, a segmentation software which has been successfully used in segmentation of tubular cavities as described in Poznyakovskiy et al. (2008) and Poznyakovskiy et al. (2011).
Before extracting features, we segmented and registered images using a previously described probabilistic segmentation approach based on statistical shape models (Mace et al., 2010).
We segmented the log-ratio data using the Circular Binary Segmentation (CBS) algorithm, distributed through the Bioconductor DNAcopy package (version 1.28) [ 10].
To rule out possible variations in ES due to segmentation differences in data reconstructed with different filter kernels, we segmented the lungs in the image reconstructed with the standard kernel, and used this segmentation for both reconstructions.
To determine the percentage of white and grey matter voxels, we segmented the MNI template into grey and white matter using the segmentation routine in SPM 8.
Then, for each test strain, we analyzed each CNV region as follows: We segmented the data from the region using the MATLAB Bioinformatics Toolbox implementation of Circular Binary Segmentation ([35], http://www.mathworks.com/access/helpdesk/help/toolbox/bioinfo/ref/cghcbs.html), with the default parameters.
Finally, we segmented the wave transformation image using intuitionistic fuzzy theory.
In this study, we segmented the speech files into frames of 20 ms (L = 20) (or 160 samples (M = 160)).
We segmented the RdRP dimers from all the tetramer dataset and performed 2D classification (Supplementary Fig. S3) and 3D refinement.
