ABSTRACT. Olfaction is crucial for insects' survival because it enables them to recognize various environmental information. It is primarily mediated by a large family of chemoreceptors, including olfactory receptors (ORs), gustatory receptors (GRs), and ionotropic receptors (IRs). Here, we assembled the transcriptome of the economically important pest of palms, Rhynchophorus ferrugineus, to reveal its chemoreceptor gene repertoire. About 8.08 Gbp data were generated using a HiSeq platform and their assembly led to a total of 24,439 unigenes. Among the transcripts, 12,523 (51.24%) showed significant similarity (E-value <10 -5 ) to known proteins in the National Center for Biotechnology Information Nr database. From these sequences, 18 candidate genes of ORs were identified. Nine putative transcripts were homologous to GR genes, while 9 were similar to IR genes. The expression profiles of all identified chemoreceptor genes were determined by quantitative real-time PCR in antenna, head, thorax, abdomen, and legs of both sexes. Most chemoreceptor genes were antenna-enriched. This study demonstrated a successful application of a transcriptome for discovering a large number of divergent chemoreceptor genes of a non-model organism. The findings provide a valuable sequence resource and gene tissue distribution information for systematic functional analysis of molecular mechanisms underlying chemoreception in this pest.

Supervised learning methods (such as partial least squares regression-discriminant analysis, SIMCA, etc) are widely used in explosives recognition. The correct classification rate may be lowered if a sample or substrate is not included in the training dataset. Unsupervised learning methods (such as hierarchical clustering analysis, K-means, etc) have the potential to solve this problem. In this paper we analyzed results of using as input variables the intensities of seven lines and then five intensity ratios of the seven lines. It was demonstrated that unsupervised learning methods had the ability to achieve a better classification result.

ABSTRACT. The coconut leaf beetle, Brontispa longissima, is a destructive pest of palm plants. Although its ecological and biological characteristics are well understood, its genetic information remains largely unknown. To advance our understanding of its molecular basis of biology and ecology, we sequenced and analyzed its whole transcriptome by using high-throughput Illumina paired-end sequencing technology. Approximately 8.08 Gb of clean reads were generated in a single run, which were assembled by using Trinity into 41,652 unigenes with an average length of 932 bp. By sequence similarity searches for known proteins, 23,077 (55.4%) unigenes were annotated by BLASTx searches against the NCBI non-redundant protein database. Of the unigenes assembled, 18,153 and 13,733 were assigned to Gene Ontology and Clusters of Orthologous Groups of proteins, respectively. In addition, 10,415 unigenes were mapped onto 247 pathways using the W. Yan et al. 8360©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (3): 8359-8365 (2015) Kyoto Encyclopedia of Genes and Genomes Pathway database. These transcriptomic resources will facilitate gene identification and elucidate the molecular mechanisms of biological and ecological aspects underlying this palm pest, in order to design a new control strategy.