Ipsilateral corticocortical projections to the primary and middle temporal visual areas of the primate cerebral cortex: area-specific variations in the morphology of connectionally identified pyramidal cells

We quantified the morphology of over 350 pyramidal neurons with identified ipsilateral corticocortical projections to the primary (V1) and middle temporal (MT) visual areas of the marmoset monkey, following intracellular injection of Lucifer Yellow into retrogradely labelled cells. Paralleling the results of studies in which randomly sampled pyramidal cells were injected, we found that the size of the basal dendritic tree of connectionally identified cells differed between cortical areas, as did the branching complexity and spine density. We found no systematic relationship between dendritic tree structure and axon target or length. Instead, the size of the basal dendritic tree increased roughly in relation to increasing distance from the occipital pole, irrespective of the length of the connection or the cortical layer in which the neurons were located. For example, cells in the second visual area had some of the smallest and least complex dendritic trees irrespective of whether they projected to V1 or MT, while those in the dorsolateral area (DL) were among the largest and most complex. We also observed that systematic differences in spine number were more marked among V1-projecting cells than MT-projecting cells. These data demonstrate that the previously documented systematic differences in pyramidal cell morphology between areas cannot simply be attributed to variable proportions of neurons projecting to different targets, in the various areas. Moreover, they suggest that mechanisms intrinsic to the area in which neurons are located are strong determinants of basal dendritic field structure.

Towards developing reliable models of leaf area on grapevines (Vitis vinifera L.)

A field study in three vineyards in southern Queensland (Australia) was carried out to develop predictive models for individual leaf area and shoot leaf area of two cultivars (Cabernet Sauvignon and Shiraz) of grapevines (Vitis Vinifera L.). Digital image analysis was used to measure leaf vein length and leaf area. Stepwise regressions of untransformed and transformed models consisting of up to six predictor variables for leaf area and three predictor variables for shoot leaf area were carried out to obtain the most efficient models. High correlation coefficients were found for log10 transformed individual leaf and shoot leaf area models. The significance of predictor variables in the models varied across vineyards and cultivars, demonstrating the discontinuous and heterogeneous nature of vineyards. The application of this work in a grapevine modeling environment and in a dynamic vineyard management context are discussed. Sample sizes for quantification of individual leaf areas and areas of leaves on shoots are proposed based on target margins of errors of sampled data.