Gene cloning, heterologous expression and activity identification of latroeggtoxin-Ⅵ / 生物工程学报

One of the distinct characters of Latrodectus tredecimguttatus is that its toxic components exist not only in the venomous glands, but also in the tissues outside the venomous glands and even in the eggs. Investigation on the toxins outside the venomous glands can deepen our understanding of spider toxins and discover new lead molecules with important application prospects. In order to explore the low-abundance proteinaceous toxins in the L. tredecimguttatus eggs, we used bioinformatic strategies to mine a gene sequence encoding a peptide toxin from the transcriptome of L. tredecimguttatus eggs, and then heterologously expressed the gene successfully with a 3'-RACE combined with nest PCR strategy. Biological activity analyses indicated that the expressed peptide toxin, named latroeggtoxin-Ⅵ (LETX-Ⅵ), could inhibit Na⁺ channel currents in ND7/23 cells and promote dopamine release from PC12 cells, without obvious toxicity against Periplaneta americana and bacteria as well as fungi including Staphylococcus aureus and Candida albicans, demonstrating that LETX-Ⅵ is a mammal-specific neurotoxin with a potential application prospect in development of the tool reagents for neurobiological study and the drugs for treating related diseases.

Sparse Fourier single-pixel imaging.

Fourier single-pixel imaging is one of the main single-pixel imaging techniques. To improve the imaging efficiency, some of the recent method typically select the low-frequency and discard the high-frequency information to reduce the number of acquired samples. However, sampling only a small amount of low-frequency components will lead to the loss of object details and will reduce the imaging resolution. At the same time, the ringing effect of the restored image due to frequency truncation is significant. In this paper, a new sparse Fourier single-pixel imaging method is proposed that reduces the number of samples explorations while maintaining increased image quality. The proposed method makes a special use of the characteristics of the Fourier spectrum distribution based on which the power of image information decreases gradually from low to high frequencies in the Fourier space. A variable density random sampling matrix is employed to achieve random sampling with Fourier single-pixel imaging technology, followed by the processing of the sparse Fourier spectra using compressive sensing algorithms to recover the high-quality information of the object. The new algorithm can effectively improve the quality of object restoration comparing with the existing Fourier single-pixel imaging methods which only acquire the low-frequency parts. Additionally, considering that the resolution of the system is diffraction limited, super-resolution imaging can also be achieved. Experimental results demonstrate the mainly correctness but also effectiveness of the proposed method.

Radon single-pixel imaging with projective sampling.

A novel technique for Radon single-pixel imaging with projective sampling, which is based on the theorem of the Radon transform, is proposed. In contrast to current patterns in conventional single-pixel imaging systems, candy-striped patterns called Radon basis patterns, which are produced by projecting the 1D Hadamard functions along different angles, are employed in our proposed technique. Here, the patterns are loaded into a projection system and then illuminated onto an object. The light reflected from the object is detected by a single-pixel detector. An iterative reconstruction method is used to restore the object's 1D projection functions by summing the 1D Hadamard functions and detected intensities. Next, the Radon spectrum of the object is recovered by arranging the 1D projection functions along the projection angle. Finally, the image of the object can be recovered using a filtered back-projection algorithm with the Radon spectrum. Experiments demonstrate that the proposed technique can obtain the information of the Radon spectrum and image of the object. Recognition directly in the Radon spectrum domain, rather than in the image domain, is fast and yields robust and high classification rates. A recognition experiment is performed by detecting the lines in one scene by searching the singular peaks in the Radon spectrum domain. According to the results, the lines in the scene can be easily detected in the Radon spectrum domain. Other shapes can also be detected by the characteristics of those shapes in the Radon spectrum domain.