Identification of two novel waxy alleles and development of their molecular markers in sorghum.

High amylopectin grains of waxy sorghum have a high economic value in the food and bioenergy industries because of their increased starch digestibility and higher ethanol conversion rate compared with wild-type sorghum grains. Mutation in the granule-bound starch synthase (GBSS) gene contributes to the waxy phenotype. Two classes of waxy alleles, wx(a) and wx(b), have been characterized previously. In the present work, we identified two novel types of waxy mutations in the sorghum GBSS gene, designated as wx(c) and wx(d). The wx(c) allele has a G deletion at the 5' splicing site of the ninth intron, causing a shift of the 5' cleavage site; in turn, a reading frame shift occurred and resulted in an early translation termination. The wx(d) allele contained a mutation at the 3' splicing site of the 10th intron, which led to a splicing site shift and resulted in the deletion of five amino acids (GTGKK) in the predicted translation product. Furthermore, cleaved amplified polymorphic sequence (CAPS) markers were developed to detect the wx(c) and wx(d) alleles. With these markers, classification of waxy alleles was performed in nearly 100 sorghum accessions from our breeding program. Most waxy sorghum cultivars in China were either wx(a) or wx(c), implying that these two mutations are preferentially maintained during domestic selection in glutinous sorghum production.

[Studies on the estimation of soil organic matter content based on hyper-spectrum].

Hyperspectral remote sensing technology can be extensively applied in soil nutrient research due to its three special advantages, high spectral resolution, strong waveband continuity as well as a great deal of spectral information. Based on analyzing the soil organic matter content using hyper-spectral remote sensing technology, soil nutrients status and its dynamic changes can be fully understood, thus providing the scientific basis for guidance of the agricultural production and protection of agricultural ecological environment. The present paper studies the relationship between soil spectrum and soil organic fraction based on spectrum curves (ranging from 350 to 2500 nm) of 34 soil samples, which were collected in Yujiang and Taihe County, Jiangxi Province. First, soil reflection spectrum was mathematically manipulated into first derivative reflectance spectra (FDR) and inverse-log spectra (log(1/R)); second, the relationship between soil spectrum and soil organic fraction was investigated by step-wise multiple linear regression (SMLR) and partial least square regression (PLSR) on the ground of characteristic absorption; third, corresponding estimation model was built and examined. The result conveys that spectral data are compressed by carrying out arithmetic average operation by 10 nm for intervals. The first derivative of the reflectivity is an effective spectrum indicator, in the stepwise multiple linear regression analysis of soil organic matter, for the first derivative transformation, the regression models' precision of establishment and verification increased. The model built by PLSR method based on the characteristic absorption bands precedes that of SMLR. In the PLSR model of soil reflection spectrum and the inverse-log spectra, the test samples' average of relative error is 16% and 17% respectively, the correlation coefficient between retrieval value and measured value is 0.84 and 0.91 respectively, for it's faster to estimate the soil organic fraction.

Assessing Activity Pattern Similarity with Multidimensional Sequence Alignment based on a Multiobjective Optimization Evolutionary Algorithm.

Due to the complexity and multidimensional characteristics of human activities, assessing the similarity of human activity patterns and classifying individuals with similar patterns remains highly challenging. This paper presents a new and unique methodology for evaluating the similarity among individual activity patterns. It conceptualizes multidimensional sequence alignment (MDSA) as a multiobjective optimization problem, and solves this problem with an evolutionary algorithm. The study utilizes sequence alignment to code multiple facets of human activities into multidimensional sequences, and to treat similarity assessment as a multiobjective optimization problem that aims to minimize the alignment cost for all dimensions simultaneously. A multiobjective optimization evolutionary algorithm (MOEA) is used to generate a diverse set of optimal or near-optimal alignment solutions. Evolutionary operators are specifically designed for this problem, and a local search method also is incorporated to improve the search ability of the algorithm. We demonstrate the effectiveness of our method by comparing it with a popular existing method called ClustalG using a set of 50 sequences. The results indicate that our method outperforms the existing method for most of our selected cases. The multiobjective evolutionary algorithm presented in this paper provides an effective approach for assessing activity pattern similarity, and a foundation for identifying distinctive groups of individuals with similar activity patterns.