[Association analysis of phenotypic traits of alkaloid content in Sophora alopecuroides with SSR molecular markers].

To further study and fully exploit the medicinal plant Sophora alopecuroides, the molecular markers related with the phenotypic traits of alkaloid content in S. alopecuroides should be detected. In this study, SSR molecular markers were used to analyze the genetic diversity and genetic structure of 23 S. alopecuroides populations, in combination with the association analysis between molecular markers and the alkaloid contents. The results showed that P, H, I, G_(st) and N_m values were 40.10%, 0.335 3, 0.504 5, 0.433 7 and 0.625 9 respectively, in 23 S. alopecuroides populations. This indicated that there was less gene exchange and higher genetic differentiation among different S. alopecuroides populations. The results of SSR unweighted pair-group method with arithmetic means(UPGMA) cluster showed that the S. alopecuroides populations relationship from Xinjiang was far from the populations of other regions, but the populations of S. alopecuroides from Gansu, Inner Mongolia and Qinghai were closely relevant to those from Ningxia. The 23 populations were further divided into 2 genetic subpopulations by the population structure analysis. Through association analysis, a total of 26 loci in 13 SSR markers were found to be significantly associated(P<0.005)with the content of MA, OMA, SC and OSC, and the rate of explanation on the phenotype variance of related markers ranged from 36.45% to 77.93%. Among the locus, 1 each were related with MA and OSC content at interpretation rate reached as high as 50% with high threshold(P<0.000 1). These results could provide support for the discovery of important genes in the alkaloid biosynthetic and metabolic pathway of S. alopecuroides.

Improved three-dimensional reconstruction algorithm from a multifocus microscopic image sequence based on a nonsubsampled wavelet transform.

In the multifocus microscopic image measurement method, the distortion of the three-dimensional (3D) reconstruction model has always been an important factor affecting the measurement result. In spatial domains, the focus measure algorithm is based on the gradient change of the pixel point to determine the degree of focus of the pixel. So it will be difficult to accurately extract the focus of the pixel in the areas where color difference is not obvious, resulting in 3D model distortion. According to the optical principle, the high-frequency coefficients of the clear image are larger than the high-frequency coefficients of the blurred image. Based on this characteristic, this paper proposes a new multifocus microscopic image 3D reconstruction algorithm using a nonsubsampled wavelet transform (NSWT). The NSWT does not consider the downsampling in wavelet decomposition and has translational invariance. Therefore, the wavelet transform value of each pixel can be calculated in the image, so the high-frequency coefficient of each pixel can be obtained; then the convolution calculation is performed on the high-frequency coefficients of the pixel points in the fixed window as the focus measure value of the pixel point. Compared with the traditional algorithm, the algorithm proposed in this paper can show better unimodal and antinoise performance on the focusing measure curve. In this paper, the reconstruction of the experimental object is Alicona standard block triangular and semicylindrical. The proposed algorithm and the traditional algorithm for comprehensive measure use the root mean square error, peak signal to noise ratio, and correlation coefficient as the measure index. The experimental results and comparative analysis prove the correctness of the proposed algorithm and enable more accurate reconstruction of 3D models based on multifocus microscopic images.