RESUMO
Gibberellic acids (GAs) are a group of endogenous phytohormones that play important roles in plant growth and development. SLENDER RICE (SLR) serves as a vital component of the DELLA gene family, which plays an irreplaceable role in regulating plant flowering and height, as well as stress responses. SLR gene has not been reported in mango, and its function is unknown. In present study, two DELLA subfamily genes MiSLR1 and MiSLR2 were identified from mango. MiSLR1 and MiSLR2 were highly expressed in the stems of the juvenile stage, but were expressed at a low level in flower buds and flowers. Gibberellin treatment could up-regulate the expression of MiSLR1 and MiSLR2 genes, but gibberellin biosynthesis inhibitor prohexadione-calcium (Pro-Ca) and paclobutrazol (PAC) treatments significantly down-regulated the expression of MiSLR1, while MiSLR2 was up-regulated. The expression levels of MiSLR1 and MiSLR2 were up-regulated under both salt and drought treatments. Overexpression of MiSLR1 and MiSLR2 genes significantly resulted early flowering in transgenic Arabidopsis and significantly up-regulated the expression levels of endogenous flower-related genes, such as SUPPRESSOR OF CONSTANS1 (SOC1), APETALA1 (AP1), and FRUITFULL (FUL). Interestingly, MiSLR1 significantly reduced the height of transgenic plants, while MiSLR2 gene increased. Overexpression of MiSLR1 and MiSLR2 increased seed germination rate, root length and survival rate of transgenic plants under salt and drought stress. Physiological and biochemical detection showed that the contents of proline (Pro) and superoxide dismutase (SOD) were significantly increased, while the contents of malondialdehyde (MDA) and H2O2 were significantly decreased. Additionally, protein interaction analysis revealed that MiSLR1 and MiSLR2 interacted with several flowering-related and GA-related proteins. The interaction between MiSLR with MiGF14 and MiSOC1 proteins was found for the first time. Taken together, the data showed that MiSLR1 and MiSLR2 in transgenic Arabidopsis both regulated the flowering time and plant height, while also acting as positive regulators of abiotic stress responses.
RESUMO
With the advancement in big data and cloud computing technology, we have witnessed tremendous developments in applying intelligent techniques in network operation and management. However, learning- and data-based solutions for network operation and maintenance cannot effectively adapt to the dynamic security situation or satisfy administrators' expectations alone. Anomaly detection of time-series monitoring indicators has been a major challenge for network administrative personnel. Monitored indicators in network operations are characterized by multiple instances with high dimensions and fluctuating time-series features and rely on system resource deployment and business environment variations. Hence, there is a growing consensus that conducting anomaly detection with machine intelligence under the operation and maintenance personnel's guidance is more effective than solely using learning and modeling. This paper intends to model the anomaly detection task as a Markov Decision Process and adopts the Double Deep Q-Network algorithm to train an anomaly detection agent, in which the multidimensional temporal convolution network is applied as the principal structure of the Q network and the interactive guidance information from the operation and maintenance personnel is introduced into the procedure to facilitate model convergence. Experimental results on the SMD dataset indicate that the proposed modeling and detection method achieves higher precision and recall rates compared to other learning-based methods. Our method achieves model optimization by using human-computer interactions continuously, which guarantees a faster and more consistent model training procedure and convergence.