Sex-specific regulatory elements are key components for developing insect genetic sexing systems. The current insect genetic sexing system mainly uses a female-specific modification system whereas little success was reported on male-specific genetic modification. In the silkworm Bombyx mori, a lepidopteran model insect with economic importance, a transgene-based, female-specific lethality system has been established based on sex-specific alternative splicing factors and a female-specific promoter BmVgp (vitellogenin promoter) has been identified. However, no male-specific regulatory elements have yet been identified. Here we report the transgenic identification of two promoters that drive reporter gene expression in a testis-specific manner in B. mori. Putative promoter sequences from the B. mori Radial spoke head 1 gene (BmR1) and beta-tubulin 4 gene (Bmβ4) were introduced using piggybac-based germline transformation. In transgenic silkworms, expression of the reporter gene enhanced green fluorescent protein (EGFP) directed by either BmR1 promoter (BmR1p) or Bmβ4p showed precisely testis-specific manners from the larval to adult stage. Furthermore, EGFP expression of these two transgenic lines showed different localization in the testis, indicating that BmR1p or Bmβ4p might be used as distinct regulatory elements in directing testis-specific gene expression. Identification of these testis-specific promoters not only contributes to a better understanding of testis-specific gene function in insects, but also has potential applications in sterile insect techniques for pest management.

The pitting corrosion characteristics of low carbon steel specimens are studied by acoustic emission (AE) and electrochemical techniques, in a 3.0 wt.% NaCl solution acidified to pH 2.0. The acoustic emission signals generated by pitting corrosion are classified based on multiple acoustic emission parameters using K-means clustering algorithm, then each classified signals are analyzed by acoustic emission parameters correlation plot and distribution with time. Furthermore, each acoustic source characteristics is extracted using Gabor wavelet transform (WT) in the time and frequency domain. An error back propagation (BP) artificial neural network (ANN) is trained according to the classified signals, so as to successfully identify the acoustic emission signals from parallel experiments. Experimental results show that the hydrogen bubble activation, oxidized film rupture and pit growth are typical acoustic emission sources in pitting corrosion process, which can be effectively classified by cluster analysis and recognized by back propagation neural network. The data gathered from laboratory tests combined with the real data from acoustic emission online storage tank floor inspection can help to evaluate the bottom corrosion severity and interpreter the corrosion source, further to make the on-site testing more reliable and reduce the risk.