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They treated them all as fixed parameters, leading to as many ξ parameters as there are data points, while we modeled them using the standard multivariate meta-analysis model [ 13].
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Venous and capillary plasma concentrations were transformed into their natural logarithms and modeled simultaneously using nonlinear mixed-effects modeling.
Modeled data using computational fluid dynamics produced gradients that were steep along the horizontal, active surface.
We also modeled RSS using order zero and order one Markov chains.
They took the electricity market as a non-linear dynamic system and modeled it using Nash discrete bidding strategies.
Time-dependent changes in plasma concentration were modeled individually, using WinNonlin (Pharsight, St .Louis, Missouri, USA).
In addition we modeled associations using weight-standardized z-scores of AGD as the outcome.
We modeled replacement using CAT and the clock using CIR as in Rota-Stabelli, Daley, et al. (2013).
We also modeled results using all A1C quartiles entered as dummy variables with quartile 1 as the reference group.
Molecular dynamics (MD) simulations were carried out for the modeled systems using the GROMACS 4.5.5 program [ 41].
1KYZ.PDB contains no hydrogens; they were introduced into our modeled TaOMT2 using the H-bonds Module in Sybyl.
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