Hence, in addition to the prediction of probability of carriage, our model also predicted the expected PPV of a rapid test for a given individual.
Moreover, our model also predicted that some residues at PB1 domain also formed interactions with the ligands that bound to ZZ domain (Fig. 3c).
The model also predicted the autocatalytic kinetics associated with the cleavage of the arylalkyl bond.
This model also predicted well the zones and the level of damages.
The model also predicted a negative response by greater gliders to more intensive harvesting in selectively logged stands.
The improved model also predicted the reduction in maize ET under the condition of soil water deficit.
Importantly, the model also predicted performance deficits in eye movements during baseline VOR in naïve IO-ΔRobo3, which is consistent with our previously published experimental findings (Fig. 8c and Table 5).
The model also predicted well individual contributions to clogging by accumulated inert suspended solids, microbial biomass and plant root materials during the wetland operation.
The model also predicted that most of the particles were received UV dose less than the average value reported by manufacturer of the UV lamps.
In agreement with the experimental data, the model also predicted the decrease of nucleation-sized particles in the postflame region for ethylene flames doped with benzene.
The model also predicted the experimental trend that conversion efficiency increases as the inlet velocity of the initial fuel air mixture increases.
