Description of two new species of Avicularia Lamarck 1818 and redescription of Avicularia diversipes (C. L. Koch 1842) (Araneae, Theraphosidae, Aviculariinae)-three possibly threatened Brazilian species

Avicularia diversipes (C. L. Koch 1842) known previously only from its original description is redescribed along with Avicularia sooretama sp. nov. and Avicularia gamba sp. nov. The three species are endemic to Brazilian Atlantic rainforest. With other Avicularia species, they share a procurved anterior eye row, slender embolus and medially folded spermathecae, whereas they have unusual characters, such as a very long and spiraled embolus (A. diversipes) and spermathecae with multilobular apex (A. sooretama sp. nov.). Furthermore, the three species lack a tibial apophysis in males and share a distinctive color pattern ontogeny that is not known in any other Avicularia species. The conservation status of the three species is discussed, especially with respect to endemism, illegal trafficking and habitat destruction. The creation of protected areas in southern State of Bahia, Brazil, is recommended, as well as the inclusion of these species in IUCN and CITES lists. Appendices with figures and species information are presented to facilitate correct specimen identification by custom officers, in order to limit illegal traffic.

Investigation of a new GRASP-based clustering algorithm applied to biological data

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover. it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically. (C) 2009 Elsevier Ltd. All rights reserved.