RESUMO
Plant glutamate receptor homologs (GLRs), which function as key calcium channels, play pivotal roles in various developmental processes as well as stress responses. The moss Physcomitrium patens, a representative of the earliest land plant lineage, possess multiple pathways of hormone signaling for coordinating growth and adaptation responses. However, it is not clear whether GLRs are connected to hormone-mediated growth control in the moss. In this study, we report that one of the two GLRs in P. patens, PpGLR1, involves in abscisic acid (ABA)-mediated growth regulation. ABA represses the growth of wild-type moss, and intriguingly, the PpGLR1 transcript levels are significantly increased in response to ABA treatment, based on both gene expression and the PpGLR1pro::GUS reporter results. Furthermore, the growth of Ppglr1 knockout moss mutants is hypersensitive to ABA treatment. These results suggest that PpGLR1 plays a critical role in ABA-mediated growth regulation, which provide useful information for our further investigation of the regulatory mechanism between Ca2+ signal and ABA in moss growth control.
Assuntos
Ácido Abscísico , Bryopsida , Ácido Abscísico/farmacologia , Ácido Abscísico/metabolismo , Bryopsida/genética , Bryopsida/metabolismo , Regulação da Expressão Gênica de Plantas/genética , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Receptores de Glutamato/genética , Receptores de Glutamato/metabolismo , Hormônios/metabolismoRESUMO
A novel image processing model Grayscale Surface Direction Angle Model (GSDAM) is presented and the algorithm based on GSDAM is developed to segment setae from Chaetoceros microscopic images. The proposed model combines the setae characteristics of the microscopic images with the spatial analysis of image grayscale surface to detect and segment the direction thin and long setae from the low contrast background as well as noise which may make the commonly used segmentation methods invalid. The experimental results show that our algorithm based on GSDAM outperforms the boundary-based and region-based segmentation methods Canny edge detector, iterative threshold selection, Otsu's thresholding, minimum error thresholding, K-means clustering, and marker-controlled watershed on the setae segmentation more accurately and completely.
