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The fact that we used sequential samples allowed us to use the linear mixed model on non-independent (paired) samples, which focusses on the response of the variables of interest (bacterial taxa) during the course of the experiment and minimizes background noise of the high variability between individuals.
PFGE analysis of digested samples allowed us to use the unique restriction banding patterns of the strains as a genetic screen to visually determine whether the plasmids were transferred to the recipient strains.
The quality selection of the samples allowed us to use the best-fit parameters from each sample as a point estimate for the primary parameters.
The use of pedigreed samples allowed us to distinguish segregating SNPs from putative paralogous sequence variants resulting from the relatively recent genome duplication of salmonid species.
Because miRNAs are involved in many cell processes [ 3], the use of these patient-matched samples allowed us to remove interpatient variability.
The prospective nature of the parent Omega Study facilitated the exclusion of women with diagnosed pregestational diabetes and renal disease, and the use of early-pregnancy biological samples allowed us to characterize Cd body burden during the critical period of early pregnancy when pathophysiologic changes of GDM are believed to start (Blackburn 2013).
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The fact that PIRLS uses representative random samples allows us to generalize the findings to the population of fourth graders in the respective countries.
Prior to DNA extraction, an appropriate amount of an artificial sequence was spiked into the test samples, allowing us to assess the extraction efficiency used for normalisation.
Using the GEM as a common modeling framework for all samples allows us to study the influence of sample size and sampling method on disease dynamics.
The large number of samples allows us to perform a robustness analysis of derived p-values.
The large number of samples allows us to perform a stability analysis on the significantly regulated genes.
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