In order to provide a more comprehensive Gab2 phospho-map, we combined our data with those of previous publications and the curated Phosphosite database (Table 1).
After mapping, we combined the output from both Mosaik runs and the BLAST pipeline to produce a single file for each contig containing the depth of coverage at each base in the genome (using the program ace2dep, from the Marth lab, to convert Mosaik output into depth, and custom Perl scripts to convert BLAST output into depth information).
Because each component time-course has an associated component spatial map, we combined these two pieces of information to recover the R2* data, although now separated into systemic and tumour-specific influences, in a process which is the reverse of the ICA decomposition: where and are the voxel-wise matrices of systemic and tumour-specific R2* time-courses, respectively.
In order to estimate the dense disparity map, we combine our learning-based multilayer feature matching cost with the pixel-based intensity matching cost and hence our data term has the sum of these costs.
In the second step in developing Contribution Mapping, we combine the perspective on research and utilization and the idea of linked actors and key-users with the existing models in the literature, which results in a three-phase process model (see Figure 1).
To generate a mapping reference, we combined information from Gr (D genome) and Ga (A genome) cotton, representative of the diploid ancestral genomes of modern tetraploid cotton.
In terms of MAP estimator, we combine the super-Gaussian with Markov random field to form a hybrid image distribution.
We also explored a multiplicative combination rule whereby we combined the maps in a point-wise multiplicative manner.
To generate a final map of predicted occurrence, we combined encamped and traveling raster layers using Spatial AnalystLocalCombine, which provided an output raster in which a unique output value was assigned to each unique combination of input values.
In this study, we combined forest mapping over 150 years, ethnographic investigations and mental models to investigate the social drivers of rural forests in a French case study.
