Separately, we used a pathway-based approach to develop a novel gene expression classifier for discriminating bacterial vs. non-bacterial infection.
Separately, we used a pathway-based approach to develop a novel gene expression classifier for discriminating bacterial vs. non-bacterial LRTI.
While previously randomized screening, automated fear conditioning and remote memory have been used separately, we used these three elements in a novel synergistic combination and were able to identify two of the first genes required specifically for remote memory.
Second, for all specialties combined per residency year, and all residency years combined per specialty separately, we used Pearson's correlation coefficient to quantify the correlations between each composite-scale of teaching qualities and the outcome variable.
Moreover, to test the pro-sociality of each individual separately, we used chi-squared tests with the amount of left and right choices in the control condition as expected values and the amount of left and right choices in the test condition as the observed values.
Third, for a more definitive analysis of the first and second study objectives, for all specialties combined and each specialty separately, we used logistic generalized estimating equations to relate all five composite-scales of teaching qualities to the outcome, adjusted for the abovementioned covariates.
The genes encoding the ArntbHLH (or Arnt basic region with part of Helix 1, as in Arnt-C/EBP) and C/EBP leucine zipper were constructed separately; we used the sequences from human Arnt isoform variant 3 (NCBI NP_848514) and rat liver C/EBP.
Moreover, in contrast to previous reports that relied on the analysis of each locus separately, we used PCA and CA methods that can grasp the different contributions of multiple loci, and therefore allow a true genome-wide comparison of the different populations.
