Hirudo nipponia (known as Shui Zhi in Chinese) is a well-known Chinese medicine with numerous active ingredients in its body, especially in its saliva. This native Chinese blood-sucking leech has been used for therapeutic purposes since before 100 AD. Modern Chinese physicians use it for a wide range of diseases. Genomic data and molecular information about the pharmacologically active substances produced by this medicinal leech are presently unavailable despite this organism’s medicinal importance. In this study, we performed transcriptome profiling of the salivary glands of medicinal leech H. nipponia using the Illumina platform. In total, 84,657,362 clean reads were assembled into 50,535 unigenes. The obtained unigenes were compared to public databases. Furthermore, a unigene sequence similarity search and comparisons with the whole transcriptome of medical leech were performed to identify potential proteins. Finally, more than 21 genes were predicted to be involved in anticoagulatory, antithrombotic, antibacterial, anti-inflammatory and antitumor processes, which might play important roles in the treatment of various diseases. This study is the first analysis of a sialotranscriptome in H. nipponia. The transcriptome profile will shed light on its genetic background and provide a useful tool to deepen our understanding of the medical value of H. nipponia.
To determine the intrinsic relationship between the acoustic emission (AE) phenomenon and the fracture pattern pertaining to the entire fracture process of rock, the present paper proposed a multi-dimensional spectral analysis of the AE signal released during the entire process. Some uniaxial compression AE tests were carried out on the fine sandstone specimens, and the axial compression stress–strain curves and AE signal released during the entire fracture process were obtained. In order to deal with tens of thousands of AE data efficiently, a subroutine was programmed in MATLAB. All AE waveforms of the tests were denoised by wavelet threshold firstly. The fast Fourier transform (FFT) and wavelet packet transform (WPT) were applied to the denoised waveforms to obtain the dominant frequency, amplitude, fractal, and frequency band energy ratio distribution. The results showed that the AE signal in the entire fracture process of fine sandstone had a double dominant frequency band of the low and high-frequency bands, which can be subdivided into low-frequency low-amplitude, high-frequency low-amplitude, high-frequency high-amplitude, and low-frequency high-amplitude signals, according to the magnitude. The low-frequency amplitude relevant fractal dimension and the high-frequency amplitude relevant fractal dimension each had turning points that corresponded to significant decreases in the middle and end stages of loading, respectively. The frequency band energy was mainly concentrated in the range of 0–187.5 kHz, and the energy ratios of some bands had different turning points, which appeared before the complete failure of the rock. It is suggested that the multi-dimensional spectral analysis may understand the failure mechanism of rock better.
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