RESUMO
Wheat leaf rust, caused by Puccinia triticina (Pt), is one of the most severe fungal diseases on wheat globally. Rational utilization of wheat leaf rust resistance (Lr) genes is still the best choice for control this disease. Wheat seedlings carrying Lr19 showed a high resistance phenotype to all Pt races in China. So far, all the cloned seedling Lr genes including Lr1, Lr10 and Lr21, encode protein with NBS-LRR domain. In this study, a wheat gene with NBS-LRR domain from previously established Lr19-resistance-related cDNA library was cloned and designated as TaRGA19. Full length of this gene was amplified by rapid amplification of cDNA ends (RACE). By blast against IWGSC wheat genome database, we have noticed that TaRGA19 was located on chromosome 2DS, which was different from Lr19 located on chromosome 7DL. Compared with susceptible Thatcher line, expression level of TaRGA19 was upregulated in wheat isogenic lines carrying Lr19 (TcLr19) after inoculation of Pt race THTS. By particle bombardment, TaRGA19-GFP fused protein was localized on plasma membrane of epidermal cells. Using virus-induced gene silencing (VIGS), TaRGA19-knockdown plants of TcLr19 showed reduced resistance and few sporulation phenotype upon Pt challenge. Further histological observation indicated that Pt hyphal growth at the infection sites was less suppressed in the TaRGA19-knockdown plants. In conclusion, we speculate this TaRGA19 gene was involved in the Lr19-mediated resistance to wheat leaf rust along with other components.
Assuntos
Basidiomycota/crescimento & desenvolvimento , Resistência à Doença/fisiologia , Genes de Plantas/fisiologia , Proteínas de Membrana , Proteínas de Plantas , Triticum , Proteínas de Membrana/genética , Proteínas de Membrana/metabolismo , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Triticum/genética , Triticum/metabolismo , Triticum/microbiologiaRESUMO
The extraction method of classification feature is primary and core problem in all epileptic EEG detection algorithms, since it can seriously affect the performance of the detection algorithm. In this paper, a novel epileptic EEG feature extraction method based on the statistical parameter of weighted complex network is proposed. The EEG signal is first transformed into weighted network and the weight differences of all the nodes in the network are analyzed. Then the sum of top quintile weight differences is extracted as the classification feature. At last, the extracted feature is applied to classify the epileptic EEG dataset. Experimental results show that the single feature classification based on the extracted feature obtains higher classification accuracy up to 94.75%, which indicates that the extracted feature can distinguish the ictal EEG from interictal EEG and has great potentiality of real-time epileptic seizures detection.
Assuntos
Algoritmos , Eletroencefalografia , Epilepsia/diagnóstico , Bases de Dados Factuais , Entropia , Humanos , Máquina de Vetores de SuporteRESUMO
Objective To observe the effects of epileptiform discharge and its influence on humpwave sleep spindles after sleep deprivation(SD)in children with epilepsy.Methods Monitoring the wakefulness and sleep EEG of samples of 160 children who were diagnosed epilepsy after SD.Results The detection rate of epileptiform discharge(59.3%)in sleep EEG was higher than that in wakefulness EEG after SD(16.3%)(P
