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The evenness highlighting the complexity of individual microbial population within samples also revealed that no statistical difference between samples in terms of microbial complexity as the values ranged from 0.885 to 0.999.
We also examined the correlation between samples in terms of CpG and CpH methylation levels detected by the wAve.
Comparability of samples was further limited by differences between samples in terms of sociodemographic and postmigration characteristics.
No significant difference was shown to exist between samples in terms of their H. pylori and p53 mutational status.
To identify variations between samples in terms of global SF proteomic profile, we used PCA of the 62 samples across the 342 slice-distinct protein area measurements.
Retention of lignin in the fiber fraction caused minimal variation between samples in terms of the structural components, so that these variations did not overshadow the variations of the less abundant mineral components.
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However, by comparing samples belonging to different generations but of the same size in their study (21 25 samples, their Table 3), one can easily notice the slight increase that occurred between such samples in terms of heterozygosity between the second and third generations (Table 4 in Williams et al. 1995).
Subsequent quantification of the nanostructure by fringe analysis indicates differences between the samples in terms of length, tortuosity, and separation of the graphitic fringes.
Besides the fact that control samples had the highest diversity index (10.267), no significant difference (p > 0.05) between treated samples in terms of diversity index was observed and this revealed that nCeO2 NPs impacted more on the microbial abundance than on the diversity.
The UniFrac distance represents the distance between 2 samples in terms of the microbial community structure.
Although 10 grids were examined for each patient sample, there were noticeable differences between tumor samples in terms of cellularity and patient heterogeneity.
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