Our model explains the role of multiple phosphorylation sites, compared to a single site, in the regulation of protein degradation.
The multiple hit model explains how antibodies neutralize viruses, and recent data on the stoichiometry of antibody neutralization suggest that the organization of viral surface proteins on viruses, in addition to virus size, influences the level of antibody occupancy required for neutralization.
The allostatic load model explains the additive effects of multiple biological processes that accelerate pathophysiology related to stress, particularly in the central nervous system.
This multiple regression model explains ∼10% of the total variance in gene transferability and all predictors except protein function remain significant after the Bonferroni correction for multiple tests (table 1).
We have generated a simple model in an attempt to explain multiple genetic events and complex physiological pathways and recognise the limitations of attempting to do this.
This evolutionary model has been used to explain multiple series of complex aggregates of sexual diploid species and asexual polyploid hybrids which are perpetuated through gametophytic apomixis [ 44- 48].
Body mass, body height, 2 shock absorber solid rear axle suspension, and working on a sheep farm created the strongest multiple regression model explaining 16% of variance in VDVZ.
Addition of stoichiometric measures and other soil attributes (e.g. soil C N, C P, δ15N) in a multiple regression model explained more of the variation than a single factor plus the land use effect (though soil order still explained a small, but significant amount of variance for measures of microbial biomass).
A multiple regression model explained 79% of the variance in placebo variability (F = 59.7; p < 0.0001).
Multiple linear regression model explained 43.3% of the variance in the associations between HOMA and hsCRP, WC, and PA.
