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Quantitative data were obtained implementing the Census software tool [37].
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The time values presented below are obtained implementing the designing and testing stages of the MLP-based approach in Matlab 2007a and using a standard personal computer with a 2.4 GHz Intel Core2 Duo CPU, 4 GB of DDR2 PC2-5300 RAM and running Linux.
The results presented in this paper were obtained by implementing the genetic algorithm in the Matlab environment.
Optimal fatty ester composition and potential FAMEs profile were obtained by implementing an Evolutionary Genetic Algorithms (EGA).
The design parameters to determine the size of the collector were obtained by implementing a simulation model for double flow air solar collectors.
The positions of these peptides were obtained by implementing scripts written in Python programming language.
Initial growth parameters were obtained by implementing a latent class growth analysis (assuming no within-class variance) fitted to the data.
Neighbor-joining trees were derived from the trimmed alignments using scoredist distances as implemented in BioNJ [ 45] and maximum likelihood trees were obtained as implemented in PhyML using the NJ tree as a starting point [ 46].
The alignment of choice based on congruence optimization with unambiguously aligned data was that obtained implementing the Q-INS-i algorithm for all nuclear genes, except for the LSU-A fragment in the case of the ingroup dataset.
The confidence intervals (95%) for all EC50 values were obtained with the implemented function "ED" of the "drc" package using the delta method and the t distribution.
DEGs were obtained using the implemented top-table function and user-specific thresholds (i.e., 2-fold change; p-value ≤ 0.05) as recommended in [ 47].
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