[Identification and analysis of NAC family in ginger(Zingiber officinale) based on RNA-seq data].

The NAC family is an important transcription factor which regulate plant growth and development， signal transduction， and stress response.In this study， the protein identification， subfamily classification， the determination of physical and chemical properties， protein structure， and expression pattern of NAC family were performed using bioinformatic methods based on the RNA-seq data of ginger. The results showed that a total of 72 NAC transcription factors were identified in 271.1 Mb total nucleotides， and they could be clustered into 13 subfamilies according to the phylogenetic tree.The physical and chemical properties， structure analysis revealed that the amino acid number and isoelectric point were different among 13 NAC subfamilies; the secondary structure of NACs transcription factors mainly consist of random coil， and the tertiary structure is similar.In addition，the expression patterns of genes under different soil moisture and Ralstonia solanacearum infection showed that 23 NACs were differentially expressed， which were mainly distributed in Ⅷ，Ⅶ， and ⅩⅤ subfamilies related to plant senescence， hormone metabolism and cell wall metabolism.The results provide some valuable information for the research and development of NAC transcription factors in ginger.

[Estimating Leaf Area Index of Crops Based on Hyperspectral Compact Airborne Spectrographic Imager (CASI) Data].

The fast estimation of leaf area index (LAI) is significant for learning the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye city, in Heihe River basin, on July 7, 2012, and extracted the spectral reflectance accurately. The potential of broadband and red-edge vegetation index for estimating the LAI of crops was comparatively investigated by combined with the field measured data. On this basis, the sensitive wavebands for estimating the LAI of crops were selected and two new spectral indexes (NDSI and RSI) were constructed, subsequently, the spatial distribution of LAI in study area was analyzed. The result showed that broadband vegetation index NDVI had good effect for estimating the LAI when the vegetation coverage is relatively lower, the R2 and RMSE of estimation model were 0. 52, 0. 45 (p<0. 01) , respectively. For red-edge vegetation index, CIred edge took the different crop types into account fully, thus it gained the same estimation accuracy with NDVI. NDSI(569.00, 654.80) and RSI(597.60, 654.80) were constructed by using waveband combination algorithm, which has superior estimation results than NDVI and CIred edge. The R2 of estimation model used NDSI(569.00, 654.80) was 0. 77(p<0. 000 1), it mainly used the wavebands near the green peak and red valley of vegetation spectrum. The spatial distribution map of LAI was made according to the functional relationship between the NDSI(569.00, 654.80) and LAI. After analyzing this map, the LAI values were lower in the northwest of study area, this indicated that more fertilizer should be increased in this area. This study can provide technical support for the agricultural administrative department to learn the growth of crops quickly and make a suitable fertilization strategy.

[Estimation of organic matter content of north fluvo-aquic soil based on the coupling model of wavelet transform and partial least squares].

For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0.722 and 0.221, respectively. The R2 and RMSE of the best model for detail coefficients are 0.670 and 0.255, respectively.

[Inversion of organic matter content of the north fluvo-aquic soil based on hyperspectral and multi-spectra].

The present study aims to assess the feasibility of multi-spectral data in monitoring soil organic matter content. The data source comes from hyperspectral measured under laboratory condition, and simulated multi-spectral data from the hyperspectral. According to the reflectance response functions of Landsat TM and HJ-CCD (the Environment and Disaster Reduction Small Satellites, HJ), the hyperspectra were resampled for the corresponding bands of multi-spectral sensors. The correlation between hyperspectral, simulated reflectance spectra and organic matter content was calculated, and used to extract the sensitive bands of the organic matter in the north fluvo-aquic soil. The partial least square regression (PLSR) method was used to establish experiential models to estimate soil organic matter content. Both root mean squared error (RMSE) and coefficient of the determination (R2) were introduced to test the precision and stability of the modes. Results demonstrate that compared with the hyperspectral data, the best model established by simulated multi-spectral data gives a good result for organic matter content, with R2=0.586, and RMSE=0.280. Therefore, using multi-spectral data to predict tide soil organic matter content is feasible.

[Assessment of chlorophyll content using a new vegetation index based on multi-angular hyperspectral image data].

The fast estimation of chlorophyll content is significant for understanding the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study gets the hyperspectral imagery data by using a self-developed multi-angular acquisition system during the different maize growth period, the reflectance of maize canopy was extracted accurately from the hyperspectral images under different view angles in the principal plane. The hot-dark-spot index (HDS) of red waveband was calculated through the analysis of simulated values by ACRM model and measured values, then this index was used to modify the vegetation index (TCARI), thus a new vegetation index (HD-TCARI) based on the multi-angular observation was proposed. Finally, the multi-angular hyperspectral imagery data was used to validate the vegetation indexes. The result showed that HD-TCARI could effectively reduce the LAI effects on the assessment of chlorophyll content. When the chlorophyll content was greater than 30 µg x cm(-2), the correlation (R2) between HD-TCARI and LAI was only 26.88%-28.72%. In addition, the HD-TCARI could resist the saturation of vegetation index during the assessment of high chlorophyll content. When the LAI varled from 1 to 6, the linear relation between HD-TCARI and chlorophyll content could be improved by 9% compared with TCARI. The ground validation of HD-TCARI by multi-angular hyperspectral image showed that the linear relation between HD-TCARI and chlorophyll content (R2 = 66.74%) was better than the TCARI (R2 = 39.92%), which indicated that HD-TCARI has good potentials for estimating the chlorophyll content.

Determination of the effects of pre-harvest bagging treatment on kiwifruit appearance and quality via transcriptome and metabolome analyses.

Bagging is an effective cultivation strategy to produce attractive and pollution-free kiwifruit. However, the effect and metabolic regulatory mechanism of bagging treatment on kiwifruit quality remain unclear. In this study, transcriptome and metabolome analyses were conducted to determine the regulatory network of the differential metabolites and genes after bagging. Using outer and inner yellow single-layer fruit bags, we found that bagging treatment improved the appearance of kiwifruit, increased the soluble solid content (SSC) and carotenoid and anthocyanin levels, and decreased the chlorophyll levels. We also identified 41 differentially expressed metabolites and 897 differentially expressed genes (DEGs) between the bagged and control 'Hongyang' fruit. Transcriptome and metabolome analyses revealed that the increase in SSC after bagging treatment was mainly due to the increase in D-glucosamine metabolite levels and eight DEGs involved in amino sugar and nucleotide sugar metabolic pathways. A decrease in glutamyl-tRNA reductase may be the main reason for the decrease in chlorophyll. Downregulation of lycopene epsilon cyclase and 9-cis-epoxycarotenoid dioxygenase increased carotenoid levels. Additionally, an increase in the levels of the taxifolin-3'-O-glucoside metabolite, flavonoid 3'-monooxygenase, and some transcription factors led to the increase in anthocyanin levels. This study provides novel insights into the effects of bagging on the appearance and internal quality of kiwifruit and enriches our theoretical knowledge on the regulation of color pigment synthesis in kiwifruit.

Chloroplast Genome Comparison and Phylogenetic Analysis of the Commercial Variety Actinidia chinensis 'Hongyang'.

Actinidia chinensis 'Hongyang', also known as red yangtao (red heart kiwifruit), is a vine fruit tree native to China possessing significant nutritional and economic value. However, information on its genetic diversity and phylogeny is still very limited. The first chloroplast (cp) genome of A. chinensis 'Hongyang' cultivated in China was sequenced using de novo technology in this study. A. chinensis 'Hongyang' possesses a cp genome that spans 156,267 base pairs (bp), exhibiting an overall GC content of 37.20%. There were 132 genes that were annotated, with 85 of them being protein-coding genes, 39 transfer RNA (tRNA) genes, and 8 ribosomal RNA (rRNA) genes. A total of 49 microsatellite sequences (SSRs) were detected, mainly single nucleotide repeats, mostly consisting of A or T base repeats. Compared with 14 other species, the cp genomes of A. chinensis 'Hongyang' were biased towards the use of codons containing A/U, and the non-protein coding regions in the A. chinensis 'Hongyang' cpDNA showed greater variation than the coding regions. The nucleotide polymorphism analysis (Pi) yielded nine highly variable region hotspots, most in the large single copy (LSC) region. The cp genome boundary analysis revealed a conservative order of gene arrangement in the inverted repeats (IRs) region of the cp genomes of 15 Actinidia plants, with small expansions and contractions of the boundaries. Furthermore, phylogenetic tree indicated that A. chinensis 'Hongyang' was the closest relative to A. indochinensis. This research provides a useful basis for future genetic and evolutionary studies of A. chinensis 'Hongyang', and enriches the biological information of Actinidia species.

Estimating leaf SPAD values of freeze-damaged winter wheat using continuous wavelet analysis.

Freeze injury, one of the most destructive agricultural disasters caused by climate, has a significant impact on the growth and production of winter wheat. Chlorophyll content is an important indicator of a plant's growth status. In this study, we analyzed the hyperspectral reflectance of normal and freeze-stressed leaves of winter wheat using a spectro-radiometer in a laboratory. The response of the chlorophyll spectra of plants under freeze stress was analyzed to predict the severity of freeze injury. A continuous wavelet transform (CWT) was conducted in conjunction with a correlation analysis, which generated a correlation scalogram that summarized the correlation between the chlorophyll content (SPAD value) and wavelet power at different wavelengths and decomposition scales. A linear regression model was established to relate the SPAD values and wavelet power coefficients. The results indicated that the most sensitive wavelet feature (region E: 553 nm, scale 5, R(2) = 0.8332) was located near the strong pigment absorption bands, and the model based on this feature could estimate the SPAD value with a high coefficient of determination (R(2) = 0.7444, RMSE = 7.359). The data revealed that the chlorophyll content of leaves under different low temperatures treatments could be accurately estimated using CWT. Also, this emerging spectral analytical approach can be applied to other complex datasets, including a broad range of species, and may be adapted to estimate basic leaf biochemical elements, such as nitrogen, cellulose, and lignin.