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Additional file 4: Table S2. The cis-acting elements of PvHsfs.

Additional file 3: Table S1. Identification of PvHsf members in P. vulgaris.

Additional file 6: Table S4. The database and software websites.

Additional file 7: Table S5. QRT-PCR primer of PvHsfs designed by Primer premier 5.0 software.

Duddingtonia flagrans is a nematode-trapping fungus that is widely used to control parasitic nematodes in livestock. To investigate the features and advantages of D. flagrans for nematode control at the molecular level, the genome and transcriptome of D. flagrans were sequenced and studied through a comparative analysis. The genome of D. flagrans had a size of 36.3 Mb and contained a higher ratio of repetitive sequences, which revealed its slow evolution. Comparative genomics analysis revealed the genome of D. flagrans to be relatively conserved compared to eight sequenced fungal genomes. Genes related to nematode trapping, virulence and pathogenicity were expanded in the D. flagrans genome, such as PHI genes, cytochrome P450 genes and various secretory protein genes. Remarkably, D. flagrans possessed a significant variety and abundance of putative proteases, while carbohydrate degradation-related genes were rare. Based on transcriptional profiling of different nematode trapping stages (0 h, 12 h and 48 h), the genes for some crucial secreted enzymes were up-regulated during nematode trapping process, including Zn(2)-C6-type transcription factor, chitinases, serine proteases and lectin-coding genes. The draft genome and transcriptome of D. flagrans provide evidence for the high pathogenicity and weaker saprophytic capability of this organism and constitute a theoretical foundation for the clinical use of D. flagrans as a biocontrol agent against parasitic nematodes.

Joint sparsity has attracted considerable attention in recent years in many fields including sparse signal recovery in compressed sensing (CS), statistics, and machine learning. Traditional convex models suffer from the suboptimal performance though enjoying tractable computation. In this paper, we propose a new non-convex joint sparsity model, and develop a corresponding multi-stage adaptive convex relaxation algorithm. This method extends the idea of iterative support detection (ISD) from the single vector estimation to the multi-vector estimation by considering the joint sparsity prior. We provide some preliminary theoretical analysis including convergence analysis and a sufficient recovery condition. Numerical experiments from both compressive sensing and feature learning show the better performance of the proposed method in comparison with several state-of-the-art alternatives. Moreover, we demonstrate that the extension of ISD from the single vector to multi-vector estimation is not trivial. In particular, while ISD does not work well for reconstructing the signal channel sparse Bernoulli signal, it does achieve significantly improved performance when recovering the multi-channel sparse Bernoulli signal thanks to its ability of natural incorporation of the joint sparsity structure., Comment: arXiv admin note: text overlap with arXiv:1008.4348 by other authors