Ultraviolet Irradiation Induces the Accumulation of Chondroitin Sulfate, but Not Other Glycosaminoglycans, in Human Skin

Ultraviolet (UV) light alters cutaneous structure and function. Prior work has shown loss of dermal hyaluronan after UV-irradiation of human skin, yet UV exposure increases total glycosaminoglycan (GAG) content in mouse models. To more fully describe UV-induced alterations to cutaneous GAG content, we subjected human volunteers to intermediate-term (5 doses/week for 4 weeks) or single-dose UV exposure. Total dermal uronyl-containing GAGs increased substantially with each of these regimens. We found that UV exposure substantially increased dermal content of chondroitin sulfate (CS), but not hyaluronan, heparan sulfate, or dermatan sulfate. UV induced the accumulation of both the 4-sulfated (C4S) and 6-sulfated (C6S) isoforms of CS, but in distinct distributions. Next, we examined several CS proteoglycan core proteins and found a significant accumulation of dermal and endothelial serglycin, but not of decorin or versican, after UV exposure. To examine regulation in vitro, we found that UVB in combination with IL-1α, a cytokine upregulated by UV radiation, induced serglycin mRNA in cultured dermal fibroblasts, but did not induce the chondroitin sulfate synthases. Overall, our data indicate that intermediate-term and single-dose UVB exposure induces specific GAGs and proteoglycan core proteins in human skin in vivo. These molecules have important biologic functions and contribute to the cutaneous response to UV

Nonparametric estimation of general multivariate tail dependence and applications to financial time series

In order to analyse the entire tail dependence structure among random variables in a multidimensional setting, we present and study several nonparametric estimators of general tail dependence functions. These estimators measure tail dependence in different orthants, complementing the commonly studied positive (lower and upper) tail dependence. This approach is in line with the parametric analysis of general tail dependence. Under this unifying approach the different dependencies are analysed using the associated copulas. We generalise estimators of the lower and upper tail dependence coefficient to the general multivariate tail dependence function and study their statistical properties. Tail dependence measures come as a response to the incapability of the correlation coefficient as an extreme dependence measure. We run a Monte Carlo simulation study to assess the performance of the nonparametric estimators. We also employ selected estimators in two empirical applications to detect and measure the general multivariate non-positive tail dependence in financial data, which popular parametric copula models commonly applied in the financial literature fail to capture