No abstract

Cytochrome P450 (P450)-dependent metabolites of arachidonic acid, the epoxyeicosatrienoic acids (EETs), are proposed to be endothelium-derived hyperpolarizing factors (EDHF) that affect vascular tone; however, the effects of EDHF on endothelial-derived nitric oxide biosynthesis remain unknown. We examined the regulation of endothelial nitric-oxide synthase (eNOS) by EDHF and investigated the relevant signaling pathways involved. The P450 epoxygenases CYP102 F87V mutant, CYP2C11-CYPOR, and CYP2J2 were transfected into cultured bovine aortic endothelial cells, and the effects of endogenously formed or exogenously applied EETs on eNOS expression and activity were assessed. Transfection with the P450 epoxygenases led to increased eNOS protein expression, an effect that was attenuated by cotreatment with the P450 inhibitor 17-ODYA. Northern analysis demonstrated that P450 transfection led to increased eNOS mRNA levels consistent with an effect at the pretranslational level. P450 epoxygenase transfection resulted in increased eNOS activity as measured by the conversion of L-arginine to L-citrulline. Addition of synthetic EETs (50 -200 nM) to the culture media also increased eNOS expression and activity. Treatment with mitogen-activated protein kinase (MAPK), MAPK kinase, and protein kinase C inhibitors apigenin, 2Ј-amino-3Ј-methoxyflavone (PD98059), and 1-(5-isoquinolinesulfonyl)-2-methylpiperazine (H-7), respectively, significantly inhibited the effects of P450 transfection on eNOS expression. Overexpression of P450 epoxygenases or addition of synthetic EETs increased Thr495 phosphorylation of eNOS, an effect that was inhibited by both apigenin and PD98059. Overexpression of P450 epoxygenases in rats resulted in increased aortic eNOS expression, providing direct evidence that EDHF can influence vascular eNOS levels in vivo. Based on this data, we conclude that EDHF up-regulates eNOS via activation of MAPK and protein kinase C signaling pathways.Vascular endothelial cells control vascular tone and modulate blood flow to organs by synthesizing and releasing the vasoactive autocoids endothelium-derived relaxing factor (EDRF), which is synonymous with nitric oxide (NO), and prostacyclin (PGI 2 ) (Furchgott and Zawadzki, 1980;Palmer et al., 1987). Among these, NO probably plays a more important role. In vascular endothelium, NO is produced by a constitutively expressed enzyme known as endothelial nitricoxide synthase (eNOS), which converts L-arginine to L-citrulline (Vallance et al., 1989;Moncada and Higgs, 1993). In addition to endothelium-dependent vasodilatation, NO also has a number of other critical functions in the vascular system, including inhibition of platelet aggregation, inhibition of endothelial cell adhesion molecule expression, preven-

This review/research paper summarizes data on development of the external genitalia of the spotted hyena, a fascinating mammal noted for extreme masculinization of the female external genitalia. The female spotted hyena is the only extant mammal that mates and gives birth through a pendulous penis-like clitoris. Our studies indicate that early formation of the phallus in both males and females is independent of androgens; indeed the phallus forms before the fetal testes or ovaries are capable of synthesizing androgens. Likewise, pre- and postnatal growth in length of the penis and clitoris is minimally affected by “androgen status”. Nonetheless, several internal morphologies, as well as external surface features of the phallus, are androgen-dependent and thus account for dimorphism between the penis and clitoris. Finally, estrogens play a critical role in penile and clitoral development, specifying the position of the urethral orifice, determining elasticity of the urethral meatus, and facilitating epithelial-epithelial fusion events required for proper formation of the distal urethra/urogenital sinus and prepuce. Accordingly, prenatal inhibition of estrogen synthesis via administration of letrozole (an aromatase inhibitor) leads to malformations of the glans as well as the prepuce (hypospadias). The effects of prenatal androgens, anti-androgens and impaired estrogen synthesis correlated with the tissue expression of androgen and estrogen receptors.

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.

Some interesting recent studies have shown that neural network models are useful alternatives in modeling survival data when the assumptions of a classical parametric or semiparametric survival model such as the Cox (1972) model are seriously violated. However, to the best of our knowledge, the plausibility of adapting the emerging extreme learning machine (ELM) algorithm for single-hidden-layer feedforward neural networks to survival analysis has not been explored. In this paper, we present a kernel ELM Cox model regularized by an L0-based broken adaptive ridge (BAR) penalization method. Then, we demonstrate that the resulting method, referred to as ELMCoxBAR, can outperform some other state-of-art survival prediction methods such as L1-or L2-regularized Cox regression, random survival forest with various splitting rules, and boosted Cox model, in terms of its predictive performance using both simulated and real world datasets. In addition to its good predictive performance, we illustrate that the proposed method has a key computational advantage over the above competing methods in terms of computation time efficiency using an a real-world ultra-high-dimensional survival data.