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We demonstrate that metric variability (variance) is strongest in males, with little difference between females and castrates, and that, in our sample, nutrition has little influence on variance in any sex cohort.
PC1 and PC2 in Study 5 account for a larger proportion of metric variability than in Study 1, which may be impacted by the lack of an SOP guiding Study 1.
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Our PE metric includes variability due to human exposure factors such as time location activity patterns of individuals, commuting patterns, and infiltration of ambient pollutants to the indoor environment.
From these studies we know neither how much collinearity exists between hypoglycemia and the chosen metric of glycemic variability, nor how much of the association between glycemic variability and mortality is driven by hypoglycemia.
In order to validate the proposed representation model we relied upon two hypotheses: metric resolution and variability.
The QFD metric attributes the variability in flow ensembles to uncertainty associated with the selection of a model structure and input time series.
Hence, we took a robust metric, the overall variability of gene expression, to guide gene selection.
We chose to use the SD, as this is the most frequently used metric for glycemic variability.
Multi-locus heterozygosity (MLH) values were calculated for each animal as the preferred metric of genetic variability.
Also, we used a time-based metric for glucose variability and we explored multiple cutoff values for hypoglycaemia.
1,5-AG correlated strongly with CGMS-based mean glucose (r = −0.54; p < 0.001), and with a metric of glucose variability (amplitude)—standard deviation (r = 0.60; p < 0.001).
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CEO of Professional Science Editing for Scientists @ prosciediting.com