Natural regeneration in abandoned fields following intensive agricultural land use in an Atlantic Forest Island, Brazil

The time required to regrowth a forest in degraded areas depends on how the forest is removed and on the type of land use following removal. Natural regeneration was studied in abandoned old fields after intensive agricultural land use in areas originally covered by Brazilian Atlantic Forests of the Anchieta Island, Brazil in order to understand how plant communities reassemble following human disturbances as well as to determine suitable strategies of forest restoration. The fields were classified into three vegetation types according to the dominant plant species in: 1) Miconia albicans (Sw.) Triana (Melastomataceae) fields, 2) Dicranopteris flexuosa (Schrader) Underw. (Gleicheniaceae) thickets, and 3) Gleichenella pectinata (Willd.) Ching. (Gleicheniaceae) thickets. Both composition and structure of natural regeneration were compared among the three dominant vegetation types by establishing randomly three plots of 1 x 3 m in five sites of the island. A gradient in composition and abundance of species in natural regeneration could be observed along vegetation types from Dicranopteris fern thickets to Miconia fields. The gradient did not accurately follow the pattern of spatial distribution of the three dominant vegetation types in the island regarding their proximity of the remnant forests. A complex association of biotic and abiotic factors seems to be affecting the seedling recruitment and establishment in the study plots. The lowest plant regeneration found in Dicranopteris and Gleichenella thickets suggests that the ferns inhibit the recruitment of woody and herbaceous species. Otherwise, we could not distinguish different patterns of tree regeneration among the three vegetation types. Our results showed that forest recovery following severe anthropogenic disturbances is not direct, predictable or even achievable on its own. Appropriated actions and methods such as fern removal, planting ground covers, and enrichment planting with tree species were suggested in order to restore the natural forest regeneration process in the abandoned old fields.

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

Blood-brain barrier (BBB) permeation is an essential property for drugs that act in the central nervous system (CNS) for the treatment of human diseases, such as epilepsy, depression, Alzheimer's disease, Parkinson disease, schizophrenia, among others. In the present work, quantitative structure-property relationship (QSPR) studies were conducted for the development and validation of in silico models for the prediction of BBB permeation. The data set used has substantial chemical diversity and a relatively wide distribution of property values. The generated QSPR models showed good statistical parameters and were successfully employed for the prediction of a test set containing 48 compounds. The predictive models presented herein are useful in the identification, selection and design of new drug candidates having improved pharmacokinetic properties.

Multiple Island Chains in Primary Resonances.

We analyse the dynamics of a relativistic particle moving in a uniform magnetic field and perturbed by a stationary electrostatic wave. We show that a pulsed wave produces an infinite number of perturbing terms with the same winding number. The perturbation coupling alters the number of island chains as a function of the parameters of the wave. We also observe that the number of chains in is always even if the number of islands in each chain is odd.