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The relationship of Matrona and Atrocalopteryx (Odonata: Calopterygidae) is still unclear. To better understand the phylogenetic relationship of Matrona and Atrocalopteryx, we sequenced and annotated two complete mitochondrial genomes of Matrona basilaris sampled from two different locations. The length of the two complete mitochondrial genomes of M. basilaris is 16,149 bp and 15,893 bp for the specimens collected in Jinhua, Zhejiang Province and Tianmushan, Zhejiang Province, China, respectively. The two mitochondrial genomes include the typical invertebrate set of 37 genes: 13 protein-coding genes (PGCs), 22 tRNA genes, and 2 rRNA genes. The nucleotide composition of the mitogenome is similar to other odonates with high content of A + T (68.9%) and all PCGs use ATN as the start codon. Tandem repeats were detected in the control regions of the two M. basilaris samples that accounted for the different sequence lengths of the mitochondrial genomes from the two locations. Finally, BI and ML phylogenetic analysis based on the concatenated nucleotide sequences of the 13 PCGs supported the conclusion that M. basilaris is a sister clade to Atrocalopteryx melli.

The complete mitochondrial genome of Xystrocera globosa is 15,706 bp in length, containing 13 protein-coding genes, 22 transfer RNAs, two ribosomal RNAs and the A + T-rich region. The overall base composition is 72.7% AT and 27.3% GC, and the AT content of the control region is 79.3%. In ML and BI phylogenetic trees, X. globosa was a sister clade to X. grayii. The monophyly of Lamiinae and Prioninae were supported in ML analyses, but nevertheless, the monophyly of Cerambycinae was not recovered.

Batch effects are the systematic non-biological differences between batches (groups) of samples in microarray experiments due to various causes such as differences in sample preparation and hybridization protocols. Previous work focused mainly on the development of methods for effective batch effects removal. However, their impact on cross-batch prediction performance, which is one of the most important goals in microarray-based applications, has not been addressed. This paper uses a broad selection of data sets from the Microarray Quality Control Phase II (MAQC-II) effort, generated on three microarray platforms with different causes of batch effects to assess the efficacy of their removal. Two data sets from cross-tissue and cross-platform experiments are also included. Of the 120 cases studied using Support vector machines (SVM) and K nearest neighbors (KNN) as classifiers and Matthews correlation coefficient (MCC) as performance metric, we find that Ratio-G, Ratio-A, EJLR, mean-centering and standardization methods perform better or equivalent to no batch effect removal in 89, 85, 83, 79 and 75% of the cases, respectively, suggesting that the application of these methods is generally advisable and ratio-based methods are preferred. The Pharmacogenomics Journal (2010) 10, 278-291; doi: 10.1038/tpj.2010.57