Influence of salinity and cadmium on capsule strength in Pacific herring eggs

Eggs of Pacific herring (Clupea pallasi) were incubated at 5°C in salinities of 5, 20, and 35‰, and in cadmium concentrations of 0.05, 0.1, 1, 5, and 10 ppm (20‰ S). Bursting pressures of eggs in the eight groups were measured throughout incubation. In general, bursting pressures rose to a primary maximum after fertilization, declined, rose to a secondary maximum, then declined again toward hatching. Rate of attainment of the primary maximum was related to salinity of the incubation medium. Bursting pressures at the primary and secondary maxima reached final values of about 1300 and 700 g, respectively, in incubation salinities at and above 20‰. Corresponding egg volumes were greatest in low salinities (5‰) and near minimum values in salinities of 20‰ or greater (35‰). Cadmium in the incubation medium delayed attainment of primary maximum bursting pressures and primary and secondary maxima were reduced to 200–350 g at Cd concentrations near 1 ppm. Egg volumes also decreased with increased Cd concentration. It appears that Ca⁺⁺/Cd⁺⁺ ratios, depending on salinity and Cd levels in the incubation medium, influence the properties of both the jelly coat and the capsule of herring eggs. Changes in the properties of these layers could make the eggs more susceptible to mechanical damage, particularly at combinations of higher (?1 ppm) Cd concentration and lower (?20 ‰) salinities.     

Use of the score test as a goodness-of-fit measure of the covariance structure in genetic analysis of longitudinal data

Model selection is an essential issue in longitudinal data analysis since many different models have been proposed to fit the covariance structure. The likelihood criterion is commonly used and allows to compare the fit of alternative models. Its value does not reflect, however, the potential improvement that can still be reached in fitting the data unless a reference model with the actual covariance structure is available. The score test approach does not require the knowledge of a reference model, and the score statistic has a meaningful interpretation in itself as a goodness-of-fit measure. The aim of this paper was to show how the score statistic may be separated into the genetic and environmental parts, which is difficult with the likelihood criterion, and how it can be used to check parametric assumptions made on variance and correlation parameters. Selection of models for genetic analysis was applied to a dairy cattle example for milk production.