A revision of the Neotropical Solenopsidini ant genus Oxyepoecus Santschi, 1926 (Hymenoptera: Formicidae: Myrmicinae): 2. Final. Key for species and revision of the Rastratus species-group

In the first paper of this series (Albuquerque & Brandão, 2004) we revised the Vezenyii species group of the exclusively Neotropical solenopsidine (Myrmicinae) ant genus Oxyepoecus. In this closing paper we update distribution information on the Vezenyii group species and revise the other Oxyepoecus species-group (Rastratus). We describe two species (Oxyepoecus myops n. sp. and O. rosai n. sp.) and redescribe previously known species of the group [O. daguerrei (Santschi, 1933), O. mandibularis (Emery, 1913), O. plaumanni Kempf, 1974, O. rastratus Mayr, 1887, and O. reticulatus Kempf, 1974], adding locality records and comments on the meagre biological data of these species. We also present an identification key to Oxyepoecus species based on workers.

Morphology and reproduction of Mesophyllum erubescens (Foslie) Me. Lemoine (Corallinales, Rhodophyta) from Southern Brazil

The genus Mesophyllum Me. Lemoine includes around 147 species, of which only three have been referred to the Brazilian coast. Mesophyllum erubescens was originally described from Fernando de Noronha Archipelago, Brazil (type locality). Here we present the first detailed description of M. erubescens based on Brazilian material. Samplings were made through scuba diving at the Biological Marine Reserve of Arvoredo Island, Santa Catarina. The relations of M. erubescens with other similar species, especially from the American Atlantic studied by W.R. Taylor are discussed.

Pre-processing for noise detection in gene expression classification data

Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.