[Background] Manganese (Mn) is an essential microelement in cottonseeds, which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures. Therefore a rapid, low-cost, and reagent-free analytical way is demanded to substitute the traditional analytical method.
[Results] The Mn content in cottonseed meal was investigated by near-infrared spectroscopy (NIRS) and chemometrics techniques. Standard normal variate (SNV) combined with first derivatives (FD) was the optimal spectra pre-treatment method. Monte Carlo uninformative variable elimination (MCUVE) and successive projections algorithm method (SPA) were employed to extract the informative variables from the full NIR spectra. The linear and nonlinear calibration models for cottonseed Mn content were developed. Finally, the optimal model for cottonseed Mn content was obtained by MCUVE-SPA-LSSVM, with root mean squares error of prediction (RMSEP) of 1.994 6, coefficient of determination (R2) of 0.949 3, and the residual predictive deviation (RPD) of 4.370 5, respectively.
[Conclusions] The MCUVE-SPA-LSSVM model is accuracy enough to measure the Mn content in cottonseed meal, which can be used as an alternative way to substitute for traditional analytical method.
[Title] Determination of manganese content in cottonseed meal using near-infrared spectrometry and multivariate calibration
[Authors] En YU, Rubing ZHAO, Yunfei CAI, Jieqiong HUANG, Cheng LI, Cong LI, Lei MEI, Lisheng BAO, Jinhong CHEN & Shuijin ZHU
Journal of Cotton Research. 2019, 2: 12
[Background] Cotton (Gossypium hirsutum) provides the largest natural fiber for the textile manufacturing industries, but its production is on the decline due to the effects of salinity. Soil salt-alkalization leads to damage in cotton growth and a decrease in yields. Hyperosmolality-gated calcium-permeable channels (OSCA) have been found to be involved in the detection of extracellular changes which trigger an increase in cytosolic free calcium concentration. Hyperosmolality-induced calcium ion increases have been widely speculated to be playing a role in osmosensing in plants. However, the molecular nature of the corresponding calcium ion channels remains unclearly. In this research work, we describe the OSCAgenes and their putative function in osmosensing in plants by carrying out genome-wide identification, characterization and functional analysis of the significantly up-regulated OSCA gene, GhOSCA1.1 through reverse genetics.
[Results] A total of 35, 21 and 22 OSCA genes were identified in G. hirsutum, G. arboreum, and G. raimondii genomes, respectively, and were classified into four different clades according to their gene structure and phylogenetic relationship. Gene and protein structure analysis indicated that 35 GhOSCA genes contained a conserved RSN1_7TM (PF02714) domain. Moreover, the cis-regulatory element analysis indicated that the OSCA genes were involved in response to abiotic stress. Furthermore, the knockdown of one of the highly up-regulated genes, Gh_OSCA1.1showed that the virus-induced gene silenced (VIGS) plants were highly sensitive to dehydration and salinity stresses compared with the none VIGS plants as evident with higher concentration levels of oxidant enzymes compared with the antioxidant enzymes on the leaves of the stressed plants.
[Conclusions] This study provides the first systematic analysis of the OSCA gene family and will be important for understanding the putative functions of the proteins encoded by the OSCA genes in cotton. These results provide a new insight of defense responses in general and lay the foundation for further investigation of the molecular role played by the OSCA genes, thereby providing suitable approaches to improve crop performance under salinity and drought stress conditions.
[Title] Genome-wide identification of OSCA gene family and their potential function in the regulation of dehydration and salt stress in Gossypium hirsutum
[Authors] Xiu YANG, Yanchao XU, Fangfang YANG, Richard Odongo MAGWANGA, Xiaoyan CAI, Xingxing WANG, Yuhong WANG, Yuqing HOU, Kunbo WANG, Fang LIU & Zhongli ZHOU
Journal of Cotton Research. 2019; 2:11