GRAS transcription factors play important roles in plant abiotic stress response,but their characteristics and functions in cotton have not been fully investigated.A cotton SCL4/7 subgroup gene in the GRAS family,GhSC...GRAS transcription factors play important roles in plant abiotic stress response,but their characteristics and functions in cotton have not been fully investigated.A cotton SCL4/7 subgroup gene in the GRAS family,GhSCL4,was found to be induced by NaCl treatments.Nuclear localization and transactivation activity of GhSCL4 indicate its potential role in transcriptional regulation.Transgenic Arabidopsis thaliana over-expressing GhSCL4 showed enhanced resistance to salt and osmotic stress.What’s more,the transcript levels of salt stress-induced genes(AtNHX1 and AtSOS1)and oxidation-related genes(AtAPX3 and AtCSD2)were more highly induced in the GhSCL4 over-expression lines than in wild type after salt treatment.Furthermore,silencing of GhSCL4 resulted in reduced salt tolerance in cotton caused by reactive oxygen species(ROS)enrichment under salt treatment,and antioxidant enzyme activities were accordingly significantly reduced in GhSLC4-silenced lines.These results indicated that GhSCL4 enhances salt tolerance of cotton by detoxifying ROS.In addition,the transient expression assay confirmed an interactive relationship between GhSCL4 and GhCaM7,which indicated that salt tolerance conferred by GhSCL4 might be associated with salt-induced Ca^(2+)/CaM7-mediated signaling.Taken together,GhSCL4 acts as a positive regulator in cotton during salt stress that is potentially useful for engineering salt-tolerant cotton.展开更多
Digital surveillance systems are ubiquitous and continuously generate massive amounts of data,and manual monitoring is required in order to recognise human activities in public areas.Intelligent surveillance systems t...Digital surveillance systems are ubiquitous and continuously generate massive amounts of data,and manual monitoring is required in order to recognise human activities in public areas.Intelligent surveillance systems that can automatically identify normal and abnormal activities are highly desirable,as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring.This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system.The proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step framework.In the first step,the salient frames are selected from the online video stream using a frame differencing method.A lightweight 3D convolutional neural network(3DCNN)architecture is applied to extract spatio-temporal features from the salient frames in the second step.Finally,the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the camera.The proposed system performs well compared to state-of-theart violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance networks.Comprehensive experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98%with an F1-score of 0.97 on the Hockey Fight dataset,and an accuracy of 99%with an F1-score of 0.98 on the Violent Crowd dataset.展开更多
基金supported by funding from the National Natural Science Foundation of China(Grant No.32101683)the Fundamental Research Funds of Zhejiang Sci-Tech University(Grant No.11612932612116).
文摘GRAS transcription factors play important roles in plant abiotic stress response,but their characteristics and functions in cotton have not been fully investigated.A cotton SCL4/7 subgroup gene in the GRAS family,GhSCL4,was found to be induced by NaCl treatments.Nuclear localization and transactivation activity of GhSCL4 indicate its potential role in transcriptional regulation.Transgenic Arabidopsis thaliana over-expressing GhSCL4 showed enhanced resistance to salt and osmotic stress.What’s more,the transcript levels of salt stress-induced genes(AtNHX1 and AtSOS1)and oxidation-related genes(AtAPX3 and AtCSD2)were more highly induced in the GhSCL4 over-expression lines than in wild type after salt treatment.Furthermore,silencing of GhSCL4 resulted in reduced salt tolerance in cotton caused by reactive oxygen species(ROS)enrichment under salt treatment,and antioxidant enzyme activities were accordingly significantly reduced in GhSLC4-silenced lines.These results indicated that GhSCL4 enhances salt tolerance of cotton by detoxifying ROS.In addition,the transient expression assay confirmed an interactive relationship between GhSCL4 and GhCaM7,which indicated that salt tolerance conferred by GhSCL4 might be associated with salt-induced Ca^(2+)/CaM7-mediated signaling.Taken together,GhSCL4 acts as a positive regulator in cotton during salt stress that is potentially useful for engineering salt-tolerant cotton.
基金Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(2019-0-00136,Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation).
文摘Digital surveillance systems are ubiquitous and continuously generate massive amounts of data,and manual monitoring is required in order to recognise human activities in public areas.Intelligent surveillance systems that can automatically identify normal and abnormal activities are highly desirable,as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring.This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system.The proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step framework.In the first step,the salient frames are selected from the online video stream using a frame differencing method.A lightweight 3D convolutional neural network(3DCNN)architecture is applied to extract spatio-temporal features from the salient frames in the second step.Finally,the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the camera.The proposed system performs well compared to state-of-theart violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance networks.Comprehensive experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98%with an F1-score of 0.97 on the Hockey Fight dataset,and an accuracy of 99%with an F1-score of 0.98 on the Violent Crowd dataset.
