Table 5 shows the optimal GARX models including additional exogenous variables about the solar activities and the magnetospheres.
While for the basic model Equations (1 2) the SC size scales linearly with cell output rate, both models including additional feedback mechanism show a reduced change in SZ size.
However, as CFA MetS models including additional features, such as hsCRP (8), uric acid (4), albumin, and liver enzymes, have rarely been studied, it is unknown whether they represent one entity.
The AIC values associated with each model for mortality in all ages are in Table 3. Overall the models including additional heat wave variables (model 2 and 3) gave a better fit to the data compared with model 1 for the same degrees of freedom.
The multivariate models, including additional terms for area of birth, mode of HIV acquisition, and time since first HIV-positive test, did not materially modify the risk estimates even if associations with age and first HIV-positive test were not statistically significant (Table 1).
All first-level statistical models included additional regressors of no interest for each run to model slow signal drifts (constant and polynomial terms), to account for residual head motion (roll, pitch, yaw, and displacement in superior, left and posterior directions) and one regressor to model the 1-second feedback period presented after each decision.
Moderately adjusted models included additional terms for continuous measures of both body mass index (BMI; kilograms per meter squared) and pack-years of smoking.
Full models included additional adjustment for age, menopausal status, tumor size, histological grade, hormone receptor status, HER2 status, surgery type, and overall undertreatment (yes/no) as potential confounders.
Subsequent models included additional adjustment for education, annual household income, health insurance, geographic region, urban/rural residence, systolic blood pressure, antihypertensive medication use, smoking status, alcohol consumption, BMI, atrial fibrillation, LVH, and history of coronary heart disease.
Sensitivity analyses of both covariate- and SES-adjusted models included additional conditioning on quantity (for variety as the independent variable), variety (for quantity as the independent variable), or other concurrent lifestyle factors (total alcohol (continuous); physical activity and energy expenditure (continuous); and smoking status (current, ex- or never)).
Furthermore, Protonate 3D uses a prioritizing branch-and-bound algorithm in combination with a preceding dead-end elimination to handle the state space optimization problem and a force field based energy model including additional terms for tautomerism and ionization effects.
