After background subtraction, the threshold was automatically adjusted using the 'Li' algorithm [ 63].
Both layers were then adjusted using the Levels function to emphasize the filaments, minimize background, and to make both layers appear approximately equally bright.
Sociodemographic background was adjusted using three variables; father's or other guardian's education (high 17%, middle 16%, low 70%), respondent's family structure (both parents 77%, other 23%), and urbanisation level of residence (capital area 11%, large town 8%, small town 35%, village 31%, sparsely populated rural municipality 16%).
To minimize potential selection bias, patient background characteristics were adjusted using propensity score and/or multivariable regression techniques [ 9, 13, 15, 17].
These parameters were adjusted using prior knowledge about the stiffness in parts of the material, e.g., the background was not irradiated and therefore its stiffness was homogeneous.
Radioactivity values were adjusted using a background radioactivity level value obtained as follows.
Therefore, the clinical backgrounds of patients in each group were adjusted using propensity scoring, to minimize selection bias, and the survival of patients in the chemotherapy and non-chemotherapy groups was compared.
To identify genes that may suggest biologically significant differences due to the mouse background effect, the ANOVA p-values were adjusted using multiple comparison procedures.
Array hybridization intensity signals were adjusted using a global background subtraction and rank-invariant normalization algorithm.
Raw data were background-adjusted using the "normexp" correction method with an offset of 50.
