Glucocorticoid receptors were studied in cultured human lymphocytes from normal donors after transformation with the Epstein-Barr Virus (EBV) and compared to those of circulating human mononuclear leukocytes. Both whole cell and cytosol fractions were examined for [3H]-dexamethasone binding. The concentration and absolute number of glucocorticoid binding sites were increased five-fold in the trans-formed cells when compared to the non-transformed human mononuclear leukocytes. However, the affinity (Kd) of the glucocorticoid receptor for dexamethasone was the same in both types of cells. The denatured glucocorticoid receptor, covalently labelled with [3H]-dexamethasone-21-mesylate, was identified by SDS polyacrylamide gel electrophoresis as a protein moiety with Mr ≅ 92,000, similar to that obtained from human non-transformed mononuclear leukocytes. The pattern of the activation of the hormone-receptor complexes, analyzed by phosphocellulose chromatography, was similar in both types of cells, and also the time-courses of loss of specific binding during thermal activation were similar. These results suggest that viral transformation is associated with increases in the concentration and the absolute number of glucocorticoid receptors whereas other qualitative receptor characteristics remain similar. Thus, transformation of cells with EB virus can provide a constant source of glucocorticoid receptors for study.
Principal Components Analysis (PCA) provides a simple approach to visualize the complicated dynamic response of a multi-degree-of-freedom system in terms of its principal modal responses. PCA is a technique used in statistics for the analysis of multivariate data. PCA was first developed by Pearson and was later independently developed by Hotelling. To illustrate the use of PCA, the eight-story moment-resistant frame was subjected to the Corralitos record of the 1989 Loma Prieta Earthquake. The chapter investigates similarities between PCA mode shapes and the elastic mode shapes and quantities derived from these shapes as a function of response intensity and structural system type. PCA is valuable for understanding higher mode interactions, the relevance of first-mode approximations of various response quantities, and for identifying important behavior during dynamic response, such as the development of weak story mechanisms.