Genome-Wide Identification and Characterization of the Potato IQD Family During Development and Stress.

Calmodulin-binding proteins belong to the IQ67 domain (IQD) gene family and play essential roles in plant development and stress responses. However, the role of IQD gene family in potato (Solanum tuberosum L.) is yet to be known. In the present study, 23 StIQDs were identified in the potato genome and named StIQD1 to StIQD23. They were unevenly distributed on 10 of the 12 chromosomes. Phylogenetic analysis divided the IQDs into four subfamilies (IQD I-IV). StIQDs found in three of the four subfamilies. Synteny analysis confirmed that potato and tomato shared a close evolutionary relationship. Besides, RNA-Seq data analysis revealed that the expression of 19 of the 23 StIQDs was detected in at least one of the 12 tissues, and some of which showed a tissue-specific pattern. Quantitative reverse transcriptase-polymerase chain reaction results further confirmed that 14 StIQDs responded differently to various abiotic stresses, including drought, extreme temperature, and CaCl2 treatment, suggesting their significance in stress response. This study presents a comprehensive overview of the potato IQD gene family and lays a foundation for further analysis of the StIQDs functions in plant development and stress response.

Hyperspectral prediction of leaf area index of winter wheat in irrigated and rainfed fields.

The growth status of winter wheat in irrigated field and rainfed field are obviously different and the field types may have an effect on the predictive accuracy of hyperspectral model. The objectives of the present study were to understand the difference of spectral sensitive wavelengths for leaf area index (LAI) in two field types and realize its hyperspectral prediction. In study, a total of 31 and 28 sample sites in irrigated fields and rainfed fields respectively were selected from Wenxi County, and the LAI and canopy spectra were also collected at the main grow stage of winter wheat. The method of successive projections algorithm (SPA) was applied by selecting the important wavelengths, and the multiple linear regression (MLR) and partial least squares regression (PLSR) were used to construct the predictive model based on the important wavelengths and full wavelengths, respectively. Moreover, the parameters of variable importance project (VIP) and B-coefficient derived from PLSR analysis were implemented to validate the evaluated wavelengths using the SPA method. The sensitive wavelengths of LAI for irrigated field and rainfed field were 404, 407, 413, 417, 450, 677, 715, 735, 816, 1127 and 404, 406, 432, 501, 540, 679, 727, 779, 1120, 1290 nm, respectively, and these wavelengths proved to be highly correlated with LAI. Compared with the model performance based on the SPA-MLR and PLSR methods, the method of SPA-MLR was proved to be better (rainfed field: R2 = 0.736, RMSE = 1.169, RPD = 1.6245; irrigated field: R2 = 0.716, RMSE = 1.059, RPD = 1.538). Moreover, the predictive model of LAI in rainfed fields had a better accuracy than the model in irrigated fields. The results from this study indicated that it was necessary to classify the field type while monitoring the winter wheat using the remote sensing technology. This study also demonstrated that the multivariate method of SPA-MLR could accurately evaluate the sensitive wavelengths and construct the predictive model of LAI.