This body of literature, albeit small, draws attention to significant and interrelated patterns of online representation that function to pathologise natural genital variation and promote and normalise a homogenous, surgically altered ideal vulva to Western women.
Analysis yielded five significant and interrelated patterns of representation, each functioning to promote and normalise the practice of FGCS: pathologisation of genital diversity; female genital appearance as important to wellbeing; characteristics of women's genitals are important for sex life; female body as degenerative and improvable through surgery; and FGCS as safe, easy, and effective.
In this study we have shown that several members of the Prdm gene family (Prdm6, 8, 12, 13 and 16) have interrelated expression patterns during mouse CNS neurogenesis, which suggest roles in neuronal class specification and differentiation.
We speculate that pain, memory, and emotions are interrelated in triangular pattern, and each of these three is related to other two in bidirectional pattern, i.e., stimulation of one of these will stimulate other symptoms/networks and vice versa (triangular theory for chronic pain).
These all indirectly hint that both emotions and pain are interrelated in bidirectional pattern.
Many reasons may be interrelated to the pattern found in the family and medical scales.
These four networks are of great relevance, since they are correlated by similar expression patterns, have interrelated functional attributes and are candidate non-cancer associated genes.
Under natural conditions, most of the environmental variables that influence dormancy are interrelated in a cyclical pattern that is either circadian or annual.
Chronic pain, memory, and emotions could be interrelated in a bidirectional pattern.
All four health indicators were found to be interrelated and these indicators also showed similar patterns by socio-demographic characteristics.
Principal Component Analysis (PCA), a powerful multivariate exploratory tool is used to identify patterns in specifically high (P) dimension, interrelated data sets and express the data sets by highlighting their similarities and differences.
