Describing Ordinal Odds Ratios for Stratified r × c Tables

For an r × ctable with ordinal responses, odds ratios are commonly used to describe the relationship between the row and column variables. This article shows two types of ordinal odds ratios where local‐global odds ratios are used to compare several groups on a c‐category ordinal response and a global odds ratio is used to measure the global association between a pair of ordinal responses. When there is a stratification factor, we consider Mantel‐Haenszel (MH) type estimators of these odds ratios to summarize the association from several strata. Like the ordinary MH estimator of the common odds ratio for several 2 × 2 contingency tables, the estimators are used when the association is not expected to vary drastically among the strata. Also, the estimators are consistent under the ordinary asymptotic framework in which the number of strata is fixed and also under sparse asymptotics in which the number of strata grows with the sample size. Compared to the maximum likelihood estimators, simulations find that the MH type estimators perform better especially when each stratum has few observations. This article provides variances and covariances formulae for the local‐global odds ratios estimators and applies the bootstrap method to obtain a standard error for the global odds ratio estimator. At the end, we discuss possible ways of testing the homogeneity assumption.     

Killing of Escherichia coli cells modulated by components of the stability system ParD of plasmid R1

Summary The proteins P10 and P12 have been shown to be gene products of a new stability system, ParD, of plasmid R1. It is now shown that an R1 miniplasmid, pAB112, carrying a trans-complementable amber mutation in the gene of the P10 protein, is lethal for the host in the absence of suppression. This lethal effect is suppressed in a supF background and also by deletions in pAB112 that affect the gene of the P12 protein. These data indicate that the P12 protein has a lethal effect on the host and that this effect is neutralized by the P10 protein. The possibility that the stabilization conferred by the ParD system could be due to a counterselection, mediated by P12, of cells that lose the plasmid at cell division, is discussed.     

Micelle formation of diacylglycerophosphocholines in organic solvents I. effects of the solvents on krafft points

Krafft points of diacylglycerophosphocholines (PC) were measured in alkanes-cyclohexane solutions by differential scanning calorimetry, and it was found that they were regularly increased following the increase in alkane content in the solutions and the chain length of the alkanes. From these results it was deduced that the mixing of PC with alkanes occurred in the gel state of the PC, but not in micelles at higher temperatures above the Krafft points. where micellar solutions are provided. The penetration of alkanes into gel state PC was found to be dominated by Langmuir type interaction, and the affinity of alkanes increases with increasing in chain lengths. Above the Krafft points, the micelle formation was confirmed by using the fluorescence probe technique.     

RELATIONSHIP BETWEEN CELL CYCLE AND LIGHT‐LIMITED GROWTH RATE IN OCEANIC PROCHLOROCOCCUS (MIT9312) AND SYNECHOCOCCUS (WH8103) (CYANOBACTERIA)1

The relationships between growth rate, cell‐cycle parameters, and cell size were examined in two unicellular cyanobacteria representative of open‐ocean environments: Prochlorococcus (strain MIT9312) and Synechococcus (strain WH8103). Chromosome replication time, C, was constrained to a fairly narrow range of values (～4–6 h) in both species and did not appear to vary with growth rate. In contrast, the pre‐ and post‐DNA replication periods, B and D, respectively, decreased with increasing growth rate from maxima of ～30 and 10–20 h to minima of ～4–6 and 2–3 h, respectively. The combined duration of the chromosome replication and postreplication periods (C+D), a quantity often used in the estimation of Prochlorococcus in situ growth rates, varied ～2.4‐fold over the range of growth rates examined. This finding suggests that assumptions of invariant C+D may adversely influence Prochlorococcus growth rate estimates. In both strains, cell mass was the greatest in slowly growing cells and decreased 2‐ to 3‐fold over the range of growth rates examined here. Estimated cell mass at the start of replication appeared to decrease with increasing growth rate, indicating that the initiation of chromosome replication in Prochlorococcus and Synechococcus is not a simple function of cell biomass, as suggested previously. Taken together, our results reflect a notable degree of similarity between oceanic Synechococcus and Prochlorococcus strains with respect to their growth‐rate‐specific cell‐cycle characteristics.     

Signs of stabilisation and stable coexistence

Many empirical studies motivated by an interest in stable coexistence have quantified negative density dependence, negative frequency dependence, or negative plant–soil feedback, but the links between these empirical results and ecological theory are not straightforward. Here, we relate these analyses to theoretical conditions for stabilisation and stable coexistence in classical competition models. By stabilisation, we mean an excess of intraspecific competition relative to interspecific competition that inherently slows or even prevents competitive exclusion. We show that most, though not all, tests demonstrating negative density dependence, negative frequency dependence, and negative plant–soil feedback constitute sufficient conditions for stabilisation of two‐species interactions if applied to data for per capita population growth rates of pairs of species, but none are necessary or sufficient conditions for stable coexistence of two species. Potential inferences are even more limited when communities involve more than two species, and when performance is measured at a single life stage or vital rate. We then discuss two approaches that enable stronger tests for stable coexistence‐invasibility experiments and model parameterisation. The model parameterisation approach can be applied to typical density‐dependence, frequency‐dependence, and plant–soil feedback data sets, and generally enables better links with mechanisms and greater insights, as demonstrated by recent studies.