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The importance of histone PTMs is undoubtedly complex, and while many functional correlations are well-established further studies are required to establish in detail how patterns of PTMs differ between actively transcribed and transcriptionally repressed genes and how these patterns change upon activation of a gene or during differentiation [ 6- 8].
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It is also worth pointing out that for many functional modules correlation indexes below 50 were observed.
RSFC-Snowballing and RSFC-Boundary Mapping largely complement one another, but also provide unique parcellation information; together, the methods identify independent entities with distinct functional correlations across many cortical and subcortical locations in the brain.
However, many functional traits have negative genetic correlations with milk yield, and reductions in genetic merit for health and fitness have been observed.
Surface-based cortical registration methods that are driven by geometrical features, such as folding, provide sub-optimal alignment of many functional areas due to variable correlation between cortical folding patterns and function.
More interesting is the finding that many functional modules with high internal correlation had also high standard deviations.
Consequently, their correlation with cryo-ET has great potential in identifying structural correlates of many functional states.
As a consequence of negative genetic correlations between milk yield and fitness traits, a decline in many functional traits has been observed (e.g. Jones et al., 1994; Lucy, 2001).
An attempt to identify the functional correlations between Ra and light regulation led us to monitor the expression of many genes in response to different fluences of early BL.
*p-values found doing Spearman's rho test for correlation The discrepancy with the X chromosome can be explained by the X chromosomes' many functional differences from the autosomes [ 24].
Not surprisingly, many functional similarities exist.
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