The generation of H-1-NMR-detectable mobile lipid in stimulated lymphocytes: Relationship ts cellular activation, the cell cycle, and phosphatidylcholine-specific phospholipase C

Mobile Lipids detected using H-1-NMR in stimulated lymphocytes were correlated with cell cycle phase, expression of the interleukin-2 receptor alpha and proliferation to assess the activation status of the lymphocytes. Mobile lipid levels, IL-2R alpha expression and proliferation increased after treatment with PMA and ionomycin. PMA or ionomycin stimulation alone induced increased IL-2R alpha expressiom but not proliferation, PMA- but not ionomycin-stimulation generated mobile lipid, Treatment with anti-CD3 antibody did not increase IL-2R alpha expression or proliferation but did generate increased amounts of mobile lipid, The cell cycle status of thymocytes treated with anti-CD3, PMA or ionomycin alone indicated an. accumulation of the cells in the G(1) phase of the cell cycle, The generation of mobile lipid was abrogated in anti-CD3 antibody-stimulated thymic lymphocytes but not in splenic lymphocytes, using a phosphatidylcholine-specific phospholipase C (PC-PLC) inhibitor which blocked cells in the G(1)/S phase of the cell cycle, This suggests that the H-1-NMR-detectable mobile Lipid may be generated in anti-CD3 antibody-stimulated thymic lymphocytes by the action of PC-PLC activity via the catabolism of PC, in the absence of classical signs of activation. (C) 1997 Academic Press.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.