Identification of Potential Pathways of Morella cerifera Seedlings in Response to Alkali Stress via Transcriptomic Analysis.

Alkali stress, a type of abiotic stress, severely inhibits plant growth. Only a few studies have investigated the mechanism underlying the transcriptional-level response of Morella cerifera to saline-alkali stress. Based on RNA-seq technology, gene expression differences in the fibrous roots of M. cerifera seedlings exposed to low- and high-concentration alkali stress (LAS and HAS, respectively) were investigated, and the corresponding 1312 and 1532 alkali stress-responsive genes were identified, respectively. According to gene set enrichment analysis, 65 gene sets were significantly enriched. Of these, 24 gene sets were shared by both treatment groups. LAS and HAS treatment groups exhibited 9 (all downregulated) and 32 (23 downregulated) unique gene sets, respectively. The differential gene sets mainly included those involved in trehalose biosynthesis and metabolism, phospholipid translocation, and lignin catabolism. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that M. cerifera seedlings were specifically enriched in stilbenoid, diarylheptanoid, and gingerol biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; and sesquiterpenoid and triterpenoid biosynthesis. Moreover, the related genes involved in hormone signaling pathways and transcription factors were determined through a localization analysis of core abiotic stress pathways. These genes and their molecular mechanisms will be the focus of future research.

[Identification of Pummelo Cultivars Based on Hyperspectral Imaging Technology].

Existing methods for the identification of pummelo cultivars are usually time-consuming and costly, and are therefore inconvenient to be used in cases that a rapid identification is needed. This research was aimed at identifying different pummelo cultivars by hyperspectral imaging technology which can achieve a rapid and highly sensitive measurement. A total of 240 leaf samples, 60 for each of the four cultivars were investigated. Samples were divided into two groups such as calibration set (48 samples of each cultivar) and validation set (12 samples of each cultivar) by a Kennard-Stone-based algorithm. Hyperspectral images of both adaxial and abaxial surfaces of each leaf were obtained, and were segmented into a region of interest (ROI) using a simple threshold. Spectra of leaf samples were extracted from ROI. To remove the absolute noises of the spectra, only the date of spectral range 400~1000 nm was used for analysis. Multiplicative scatter correction (MSC) and standard normal variable (SNV) were utilized for data preprocessing. Principal component analysis (PCA) was used to extract the best principal components, and successive projections algorithm (SPA) was used to extract the effective wavelengths. Least squares support vector machine (LS-SVM) was used to obtain the discrimination model of the four different pummelo cultivars. To find out the optimal values of σ2 and γ which were important parameters in LS-SVM modeling, Grid-search technique and Cross-Validation were applied. The first 10 and 11 principal components were extracted by PCA for the hyperspectral data of adaxial surface and abaxial surface, respectively. There were 31 and 21 effective wavelengths selected by SPA based on the hyperspectral data of adaxial surface and abaxial surface, respectively. The best principal components and the effective wavelengths were used as inputs of LS-SVM models, and then the PCA-LS-SVM model and the SPA-LS-SVM model were built. The results showed that 99.46% and 98.44% of identification accuracy was achieved in the calibration set for the PCA-LS-SVM model and the SPA-LS-SVM model, respectively, and a 95.83% of identification accuracy was achieved in the validation set for both the PCA-LS-SVM and the SPA- LS-SVM models, which were built based on the hyperspectral data of adaxial surface. Comparatively, the results of the PCA-LS-SVM and the SPA-LS-SVM models built based on the hyperspectral data of abaxial surface both achieved identification accuracies of 100% for both calibration set and validation set. The overall results demonstrated that use of hyperspectral data of adaxial and abaxial leaf surfaces coupled with the use of PCA-LS-SVM and the SPA-LS-SVM could achieve an accurate identification of pummelo cultivars. It was feasible to use hyperspectral imaging technology to identify different pummelo cultivars, and hyperspectral imaging technology provided an alternate way of rapid identification of pummelo cultivars. Moreover, the results in this paper demonstrated that the data from the abaxial surface of leaf was more sensitive in identifying pummelo cultivars. This study provided a new method for to the fast discrimination of pummelo cultivars.

[Determination of photosynthetic pigments in citrus leaves based on hyperspectral images datas].

The effective region was segmented from the hyperspectral image of citrus leaf by threshold method with the average spectrum extracted and used to describe the corresponding leaf. Based on the different spectral pre-processing methods, the prediction models of three photosynthetic pigments (i. e., chlorophyll a, chlorophyll b, and carotenoid) were calibrated by partial least squares (PLS), BP neural network (BPNN) and least square support vector machine (LS-SVM). The LS-SVM model for chlorophyll a was established based on multiplicative scatter correction (MSC), and the correlation coefficient (Rp) and the root mean square error of prediction (RMSEP) were 0.898 3 and 0.140 4, respectively. The LS-SVM model for chlorophyll b with Rp = 0.912 3 and RMSEP = 0.042 6, was established based on standard normal variable (SNV). The PLS model for carotenoid was established with Rp = 0.712 8 and RMSEP = 0.062 4 based on moving average smoothing (MAS), but the result was no better than the other two. The results illustrated that these three photosynthetic pigments could be nondestructively and real time estimated by hyperspectral image.

[Identification of different Citrus sinensis (L.) Osbeck trees varieties using Fourier transform infrared spectroscopy and hierarchical cluster analysis].

Researched on diversity of the spring leaf samples of seven different Citrus sinensis (L.) Osbeck varieties by Fourier transform infrared (FTIR) spectroscopy technology, the results showed that the Fourier transform infrared spectra of seven varieties leaves was composited by the absorption band of cellulose and polysaccharide mainly, the wave number of characteristics absorption peaks were similar at their FTIR spectra. However, there were some differences in shape of peaks and relatively absorption intensity. The conspicuous difference was presented at the region between 1 500 and 700 cm(-1) by second derivative spectra. Through the hierarchical cluster analysis (HCA) of second derivative spectra between 1 500 and 700 cm(-1), the results showed that the clustering of the different varieties of Citrus sinensis (L.) Osbeck varieties was classification according to genetic relationship. The results showed that FTIR spectroscopy combined with hierarchical cluster analysis could be used to identify and classify of citrus varieties rapidly, it was an extension method to study on early leaves of varieties orange seedlings.

[Study on synchronous correlation between fruit characteristic spectrum and the parameter of internal quality for Hamlin sweet orange fruit].

The purpose of our study is to study the relationship between the reflective spectrum of fruit and the internal quality of the orange fruit and find the suitable mark spectrum that can synchronously measure the several fruit quality index at same time to lay a foundation for the development of a rapid and nondestructive field fruit quality analysis technique by analyzing the visible-near infrared spectrum. Mature Hamlin orange (Citrus sinensis (L)cv. Hamlin sweet orange) fruits were individually mensurated for their reflective spectrum by using FieldSpec-HH spectrometer and for their contents of total soluble solid (TSS), citric acid and vitamin C (Vc) by chemistry analysis. The experiment results showed that the fruit reflectivity values (x) at 988 nm was significantly correlated to both TSS (y)(r = 0.387* *, y = 13. 957x + 5.405), TSS/acid ratio(y)(r = 0. 440* *, y = 75.120x + 37.256), and Vc(r = 0.309*). Both of TSS and Vc contends were positively correlated with the second derivatives of the reflective spectrum at 943 nm, with correlation coefficients of 0.339* and 0.355*. TSS/acid ratio was positively correlated only with the reciprocal log values of the reflective spectrum at 944 nm (r = 0.304*). The results in this study indicated that fruit quality indexes TSS, Vc and TSS/acid can be synchronously, rapidly and nondestructively field measured at the same time by the 988 or 429 nm reflective spectrum test and special regress equation operation.

[Study on the correlation of spectral characteristic and nitrogen content of the soil in citrus orchard of three Gorges Reservoir Area].

The relationship between the spectrum characteristics and nitrogen content of soils in citrus orchard of the Three Gorges Reservoir Area was studied by analyzing the visible-near infrared spectrum. The results showed that the soil reflectivity in creased lineally as the wavelength increases across the visible spectrum and reached a stable plateau in the short wavelength near-infrared region (780-1750 nm)without much fluctuation. In the long wavelength near-infrared region (1750-2400 nm) the reflec tivity of the soils was higher with higher fluctuation. There were three strong absorbance peaks around 1416, 1913 and 2209 nm, respectively, in the long wavelength infrared region. Soil available nitrogen content and total nitrogen content were positively correlated with soil light reflectivity but negatively correlated with catoptric-spectrum values reciprocal logarithm. At 541 nn of visible light region, a high positive correlation was found between the available nitrogen content and the first derivative of the soil reflective spectrum with a correlation coefficient of + 0.605** and the best fitting equation was y = 2E + 09x(2) - 3E + 06x + 890.49, where R2 = 0.5, and x is the first derivative of the soil reflective spectrum. At 1909 nm of the near-infrared long wavelength region, the correlation between the total nitrogen content and the reciprocal-log values of the reflective spectrum of the soils was the best with a correlation coefficient of -0.612**, and the best fitting equation was y = 1.3721x(2) - 2.1075x + 0.8592, where R2 = 0.4, and x is the reciprocal values of the log reflective spectrum of the soils.