Improving the performance of rice () under drought stress has the potential to significantly affect rice productivity. Here, we report that the ERF family transcription factor OsLG3 positively regulates drought tolerance in rice. In our previous work, we found that has a positive effect on rice grain length without affecting grain quality. In this study, we found that was more strongly expressed in upland rice than in lowland rice under drought stress conditions. By performing candidate gene association analysis, we found that natural variation in the promoter of is associated with tolerance to osmotic stress in germinating rice seeds. Overexpression of significantly improved the tolerance of rice plants to simulated drought, whereas suppression of resulted in greater susceptibility. Phylogenetic analysis indicated that the tolerant allele of may improve drought tolerance in cultivated rice. Introgression lines and complementation transgenic lines containing the elite allele of showed increased drought tolerance, demonstrating that natural variation in contributes to drought tolerance in rice. Further investigation suggested that plays a positive role in drought stress tolerance in rice by inducing reactive oxygen species scavenging. Collectively, our findings reveal that natural variation in contributes to rice drought tolerance and that the elite allele of is a promising genetic resource for the development of drought-tolerant rice varieties.
Unemployment remains a major cause for both developed and developing nations, due to which they lose their financial and economic impact as a whole. Unemployment rate prediction achieved researcher attention from a fast few years. The intention of doing our research is to examine the impact of the coronavirus on the unemployment rate. Accurately predicting the unemployment rate is a stimulating job for policymakers, which plays an imperative role in a country's financial and financial development planning. Classical time series models such as ARIMA models and advanced non‐linear time series methods be previously hired for unemployment rate prediction. It is known to us that mostly these data sets are non‐linear as well as non‐stationary. Consequently, a random error can be produced by a distinct time series prediction model. Our research considers hybrid prediction approaches supported by linear and non‐linear models to preserve forecast the unemployment rates much precisely. These hybrid approaches of the unemployment rate can advance their estimates by reproducing the unemployment ratio irregularity. These models' appliance is exposed to six unemployment rate statistics sets from Europe's selected countries, specifically France, Spain, Belgium, Turkey, Italy and Germany. Among these hybrid models, the hybrid ARIMA‐ARNN forecasting model performed well for France, Belgium, Turkey and Germany, whereas hybrid ARIMA‐SVM performed outclass for Spain and Italy. Furthermore, these models are used for the best future prediction. Results show that the unemployment rate will be higher in the coming years, which is the consequence of the coronavirus, and it will take at least 5 years to overcome the impact of COVID‐19 in these countries.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.