Sentence examples for removing variability from inspiring English sources

Previous research has generally excluded individuals with QAP or used sine tones to examine pitch naming, in effect removing variability that naturally exists in the population (see Athos et al. 2007).

By removing variability in platelet function associated with the thromboxane pathway, ASA treatment may increase the ability to detect genotype-phenotype association signals related to the remaining platelet activation pathways.

This type of model makes it possible to investigate how the system would react if the samples were physically set to have the same coagulation time, i.e. removing variability due to the effect of starting coagulation time.

Additionally, these cell monolayers are very well aligned to the nanotopographical cues even under varying culture conditions such as cell seeding density or culture time, removing variability in the degree of alignment of the forming monolayer.

While explanatory RCTs will be linked to discovery and corroboration and will aspire to removing variability, pragmatic controlled trials (even including randomization) fit in the area of implementation and embrace variability as the norm [ 74, 75].

EEG-DABS removes variability by using defined parameters of EEG/ECoG for a desired behavior over chronic recordings.

In other words, the results presented here apply the CFPP protocol to the same HWP carbon pool used in the production accounting approach, which is shown in Figure 3. Using the same pool removes variability that would be attributable to applying different conversion factors and allows a more clear comparison of the two accounting systems.

Averaged luciferase values were normalized to the day 1 luciferase value to remove variability on account of transfection efficiency.

Nonetheless, there is a need for an absolute quantification method to measure SMN mRNA in order to remove variability introduced by the use of endogenous controls to normalize data.

Images of contrast estimates were transferred to a group-level random effects analysis with factors Time (six levels, one for each sub-block from Pre1, Pre2, Post; Figure 1A) and Subjects in order to remove variability due to differences in the participants' average responses.

In the second, we removed variability due to building-level factors (which could help target remediation efforts to certain locations).