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Investigating their dynamics, we learn that their dismantling at high-temperature results from collective and individual mechanisms including one-by-one dimer removal.
Fragments from these pairwise alignments are then sorted by their scores, i.e. based on their P-values, and then included one-by-one into a growing consistent set of fragments involving all pairs of sequences – provided they are consistent with the previously included fragments.
To adjust for possible confounders, cofactors were included one-by-one in the age- and gender-adjusted multivariate model.
To test the association of each SNP on age-adjusted serum YKL-40 in the RA group (age 65 and below) and control group, a linear age-adjustment was applied and genotypes were included one-by-one as dependent variables in a multiple linear regression analysis.
Climatic variables identified as the most correlated to dengue incidence were included one by one, because of their strong interrelationship, to test their influence on the model.
Next, any covariates initially excluded based on the cut-off of p ≤ 0.25 were included one by one, and their significance was re-assessed.
The dependent variables are each of our explanatory variables, included one by one.
For all the multivariate filter methods, the top 25 features were incrementally included one by one to develop the decision model of support vector classifier (SVC) using 10-fold cross-validation.
To start with, each risk factor was included one by one.
In Model 1a-e, the scores of trusted others at T1 were included, one by one.
Potential confounders were included if the regression coefficient was changed by more than 10% when they were included one by one in the multivariate models.
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