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The impact of missing data was investigated using the last value carried forward (LVCF) imputation method, assuming that a participant's scores remained constant until the next response was observed.
The population structure in the data was investigated using putative population 'K' values ranging from 2-10 (10 replicates per K value) with a STRUCTURE model including admixture and correlated allele frequencies.
Sensitivity to missing data was investigated using a multiple imputation analysis.
The false discovery rate (FDR) for the microarray data was investigated using permutation analysis.
The normal distribution of the data was investigated using the Kolmogorov-Smirnov test.
Evidence of phenotypic differences in all parameters between lines and across generations (where the co-selection data were analysed separately from the cross-infection study data) was investigated using a general linear modelling procedure (GLM procedure) applied with the SAS Software v.8 (SAS Inst. 1999).
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Distributions of data were investigated using histograms and one-sample Kolmogorov-Smirnov test against normal distribution (Kinnear and Gray, 2000).
Patient data were investigated using list mode acquisition to obtain comparable 2-, 3-, and 4-min frames.
Distributions of data were investigated using histograms and the one-sample Kolmogorov-Smirnov test against normal distribution (Kinnear and Gray, 2000).
Categorical data were investigated using the chi-square test.
The qualitative data were investigated using thematic analysis.
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