The Ecology of a Keystone Seed Disperser, the Ant Rhytidoponera violacea

Rhytidoponera violacea (Forel) (Hymenoptera: Formicidae) is a keystone seed disperser in Kwongan heathl and habitats of southwestern Australia. Like many myrmecochorous ants, little is known about the basic biology of this species. In this study various aspects of the biology of R. violacea were examined and the researchers evaluated how these characteristics may influence seed dispersal. R. violacea nesting habits (relatively shallow nests), foraging behavior (scramble competitor and lax food selection criteria), and other life history characteristics complement their role as a mutualist that interacts with the seeds of many plant species

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%