In rat hepatocytes grown on gas-permeable membranes (Petzinger et al. In Vitro Cell. Dev. Biol. 24: 491-499, 1988), cellular and canalicular potentials as well as input resistances were measured using two-channel microelectrodes. In HCO3(-)-containing solutions, we found -30.9 +/- 0.4 (SE) (n = 141) and -13.9 +/- 1.4 mV (n = 22) for cell and canalicular membrane potentials, respectively. There was no dependence of these parameters on the age of the primary culture. Canalicular input resistance, however, increased from 13.3 +/- 2.0 M omega (n = 4) at day 1 after seeding to 36.1 +/- 5.0 M omega (n = 9) at day 2 and stabilized thereafter, while cell input resistance continuously decreased from 37.0 +/- 3.3 M omega at 1 h (n = 6) to 5.2 +/- 2.1 M omega (n = 27) at 3 days after preparation. In ion substitution experiments there were no changes in the transference numbers for K+, Na+, or Cl- that could account for this effect. Cable analysis, however, revealed that the decrease in input resistance reflects a time-dependent increase in electrical coupling between cells. We conclude that rat liver cells on gas-permeable membranes are highly suited for the quantitative analysis of cell-to-cell interaction. In addition, cells and canaliculi are readily accessible with two-channel microelectrodes, making this preparation a promising tool for electrophysiological analysis of hepatocellular transport mechanisms.

We study the persistent homology of the data set of syntactic parameters of the world languages. We show that, while homology generators behave erratically over the whole data set, non-trivial persistent homology appears when one restricts to specific language families. Different families exhibit different persistent homology. We focus on the cases of the Indo-European and the Niger-Congo families, for which we compare persistent homology over different cluster filtering values. We investigate the possible significance, in historical linguistic terms, of the presence of persistent generators of the first homology. In particular, we show that the persistent first homology generator we find in the Indo-European family is not due (as one might guess) to the Anglo-Norman bridge in the Indo-European phylogenetic network, but is related to the position of Ancient Greek and the Hellenic branch within the network.

No abstract

This paper develops methodology for regression analysis of ordinal response data subject to interval censoring. This work is motivated by the need to analyze data from multiple studies in toxicological risk assessment. Responses are scored on an ordinal severity scale, but not all responses can be scored completely. For instance, in a mortality study, information on nonfatal but adverse outcomes may be missing. In order to address possible within{study correlations we develop a generalized estimating approach to the problem, with appropriate adjustments to uncertainty statements. We develop expressions relating parameters of the implied marginal model to the parameters of a conditional model with random e ects, and, in a special case, we note an interesting equivalence between conditional and marginal modeling of ordinal responses. We illustrate the methodology in an analysis of a toxicological database .

Exposure-response analysis of acute noncancer risks should consider both concentration (C) and duration (T) of exposure, as well as severity of response. Stratified categorical regression is a form of meta-analysis that addresses these needs by combining studies and analyzing response data expressed as ordinal severity categories. A generalized linear model for ordinal data was used to estimate the probability of response associated with exposure and severity category. Stratification of the regression model addresses systematic differences among studies by allowing one or more model parameters to vary across strata defined, for example, by species and sex. The ability to treat partial information addresses the difficulties in assigning consistent severity scores. Studies containing information on acute effects of tetrachloroethylene in rats, mice, and humans were analyzed. The mouse data were highly uncertain due to lack of data on effects of low concentrations and were excluded from the analysis. A model with species-specific concentration intercept terms for rat and human central nervous system data improved fit to the data compared with the base model (combined species). More complex models with strata defined by sex and species did not improve the fit. The stratified regression model allows human effect levels to be identified more confidently by basing the intercept on human data and the slope parameters on the combined data (on a C x T plot). This analysis provides an exposure-response function for acute exposures to tetrachloroethylene using categorical regression analysis.