Catabolism of a (14)C-labelled volatile monoterpene compound (geraniol) to (14)CO(2) was investigated in soils taken from the rhizosphere at distances up to 200 cm from the trunks of three small Populus tremula trees growing at different sites in Slovenia. Emissions of limonene of up to 18 microg m(-2) h(-1) were detected from the soil surface at each site. Evolution of (14)C-labelled CO(2) was measured as a product of catabolism of (14)C-labelled geraniol introduced into the soil samples. Indigenous soil microorganisms degraded the geraniol rapidly. There was a significant difference in relative lag times and rates of catabolism along the gradient from the tree trunks, with relatively longer lag times and lower rates occurring in soil samples from the farthest point from the tree.

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In this study we compared the prediction abilities of the variable connectivity index 1chi(f) (not included in CODESSA) with topological indices available from CODESSA. We selected the boiling points of n = 100 alcohols as the property and examined the pool of 56 topological indices. Prediction capabilities of the developed models were evaluated by classical training/test set approach. RMS errors calculated from the prediction set for the MLR models obtained from CODESSA software with 1, 2, 3, 4, and 5 parameters were 9.06, 5.69, 5.40, 4.9, and 3.37 degrees C, respectively. Using the variable connectivity index with weights x = 0.10 and y = -0.92 for carbon and oxygen atom respectively, we obtain regression BP = 38.12 1chi(f) - 37.56 with the correlation coefficient r = 0.9915, RMS error 4.21 degrees C calculated from the test set, and Fisher ratio F = 5691. Prediction capability of the variable connectivity index was better than for MLR regression model with up to four parameters.

Recently variable molecular connectivity index and variable paths have been tested as molecular descriptors in several structure-property regressions. Here we outline the construction of several variable molecular descriptors, derived from the distance matrix and the "reversed" distance matrix. This includes the variable Balaban J index and the "reversed" Balaban index 1/J as well as a novel index 1/JJ derived from J and 1/J. All the variable descriptors mentioned were constructed by augmenting the distance matrix by replacing the diagonal zeroes with the variables x, y, z,.