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The issue of low number of patients is particularly true for the analysis of the subgroups of patients who underwent normo- or hypo-caloric ketogenic dietetic regimens.
This is especially true for the analysis of educational attainment, as many respondents completed their education many years before the personality traits were assessed and there is also evidence that personality can be shaped by the educational system (Dahmann and Anger 2014) and tertiary education (Kassenboehmer et al. 2018).
The same holds true for the analysis of IL7RA expression.
This restriction holds particularly true for the analysis of established organs such as rosette leaves.
In particular, this is true for the analysis of gene expression time courses, which are mostly derived from noisy microarray measurements at few unevenly sampled time points.
This is especially true for the analysis of the high-volume, high-dimensional and multiplexed data sets that are increasingly important for identifying immunotherapeutic targets and for discovering immune correlates of vaccine efficacy or disease outcome in cancer patients.
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While this is true for the NPV analysis, the Cox-regression analysis also shows that the 95% confidence intervals (CI) are overlapping.
For 3-locus models COR is slightly more strongly correlated with detection than EDM in the MDR analysis, while the opposite is true for the UCS analysis.
This is also true for the inverse analysis of a structural system where measured structural responses are used as input to assess the structural conditions.
As stated in the motivation (cf. Subsection 1.2), the goal is to overcome poor fitting results in cases of non-stationary shape activities [31, 32] and non-discriminative texture information, which is particularly true for the locomotion analysis scenario presented in this paper.
This also holds true for the current analysis at the subvolume level and is consistent with observations reported in the literature [ 30, 41, 42].
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