[Screening of reference genes based on real-time quantitative PCR analysis in Monochasma savatieri].

Monochasma savatieri belongs to Scrophulariaceae family. It is a facultative parasitic plant distributed in southern China that has been found in Jiangxi, Guangdong, and Fujian provinces. Previous studies on this plant have focused on its chemical constituents and pharmacological effects. The research foundation of molecular biology and functional genomics is still very in-depth, and there is no report on the reference genes. In this study, 6 reference gene(UBQ, GAPDH, AP-2, ACT, TUB and CYP) from transcriptome database, were selected and analyzed in M. savatieri.The experiment mainly involve two variables, soil moisture content and arbuscular mycorrhizal symbiosis(Rhizophagus irregularis). And the different combination of the factors result in a total of 6 treatments.Statistical tools, including GeNorm, NormFinder and Bestkeeper were utilized to assess the suitability of reference genes based on their stability rankings for different treatment. The result showed that the stability of 6 reference genes were significantly different. TUB and GAPDH were the most stable gene, while ACT was the most instable one. The results would provide reliable and optional available reference genes in gene expression analysis of M. savatieri, which has important application value.

Fluorescent sensors for Ca(2+) and Pb(2+) based on binaphthyl derivatives.

Two novel binaphthyl compounds have been synthesized for the selective fluorescent recognition of Ca(2+) or Pb(2+). By introducing different terminal groups to the receptor unit, the fluorescence signals of the receptors are significantly changed: 1 is fluorescence enhancement for Ca(2+), 2 is fluorescence quenching for Pb(2+). The binding properties for metal ions were examined by the absorption and fluorescence spectra. The fluorescence intensity enhancement was ascribed to the complex formation between Ca(2+) and 1 which blocked the photo-induced electron transfer process.

An improved SOM algorithm and its application to color feature extraction.

Reducing the redundancy of dominant color features in an image and meanwhile preserving the diversity and quality of extracted colors is of importance in many applications such as image analysis and compression. This paper presents an improved self-organization map (SOM) algorithm namely MFD-SOM and its application to color feature extraction from images. Different from the winner-take-all competitive principle held by conventional SOM algorithms, MFD-SOM prevents, to a certain degree, features of non-principal components in the training data from being weakened or lost in the learning process, which is conductive to preserving the diversity of extracted features. Besides, MFD-SOM adopts a new way to update weight vectors of neurons, which helps to reduce the redundancy in features extracted from the principal components. In addition, we apply a linear neighborhood function in the proposed algorithm aiming to improve its performance on color feature extraction. Experimental results of feature extraction on artificial datasets and benchmark image datasets demonstrate the characteristics of the MFD-SOM algorithm.