The growth of seedlings of Townsville sytlo (Stylosanthes humilis H.B.K.) is inhibited by aluminium (Al) ions, their elongation being re-covered with sodium selenate at 1.0 μM. Methyl viologen and hydrogen peroxide, ...The growth of seedlings of Townsville sytlo (Stylosanthes humilis H.B.K.) is inhibited by aluminium (Al) ions, their elongation being re-covered with sodium selenate at 1.0 μM. Methyl viologen and hydrogen peroxide, reactive oxy-gen species (ROS)-generating compounds, also inhibited seedling elongation and again growth was relieved by selenate. Selenate, thus, seemed to be operating as a ROS quencher, since N-acetylcysteine (NAC), an antioxidant com-pound, also stimulated largely the growth of Al-inhibited seedlings. At a higher concentra-tion (0.1 mM), however, selenate inhibited seed-ling growth and elongation was recovered by NAC. Ethylene production by selenate plus NAC-treated seedlings was very higher and thus the gaseous hormone was not responsible for the seedling growth inhibition caused by sele-nate. Hence, it seems that at high levels sele-nate operates as a ROS-generating compound whose effects were counteracted by NAC. It can be deduced that, at low concentration, sele-nates behave as a ROS quencher and at high level as a ROS-promoting species.展开更多
Droughts occur in all climatic regions around the world costing a large expense to global economies. Reasonably accurate prediction of drought events helps water managers proper planning for utilization of limited wat...Droughts occur in all climatic regions around the world costing a large expense to global economies. Reasonably accurate prediction of drought events helps water managers proper planning for utilization of limited water resources and distribution of available waters to different sectors and avoid catastrophic consequences. Therefore, a means to create a simplistic approach for forecasting drought conditions with easily accessible parameters is highly desirable. This study proposes and evaluates newly developed accurate prediction models utilizing various hydrologic, meteorological, and geohydrology parameters along with the use of Artificial Neural Network (ANN) models with various forecast lead times. The present study develops a multitude of forecasting models to predict drought indices such as the Standard Precipitation Index with a lead-time of up to 6 months, and the Soil Moisture Index with a lead-time of 3 months. Furthermore, prediction models with the capability of approximating surface and groundwater storage levels including the Ross River Dam level have been developed with relatively high accuracy with a lead-time of 3 months. The results obtained from these models were compared to current values, revealing that ANN based approach can be used as a simple and effective predictive model that can be utilized for prediction of different aspects of drought scenarios in a typical study area like Townsville, North Queensland, Australia which had suffered severe recent drought conditions for almost six recent years (2014 to early 2019).展开更多
文摘The growth of seedlings of Townsville sytlo (Stylosanthes humilis H.B.K.) is inhibited by aluminium (Al) ions, their elongation being re-covered with sodium selenate at 1.0 μM. Methyl viologen and hydrogen peroxide, reactive oxy-gen species (ROS)-generating compounds, also inhibited seedling elongation and again growth was relieved by selenate. Selenate, thus, seemed to be operating as a ROS quencher, since N-acetylcysteine (NAC), an antioxidant com-pound, also stimulated largely the growth of Al-inhibited seedlings. At a higher concentra-tion (0.1 mM), however, selenate inhibited seed-ling growth and elongation was recovered by NAC. Ethylene production by selenate plus NAC-treated seedlings was very higher and thus the gaseous hormone was not responsible for the seedling growth inhibition caused by sele-nate. Hence, it seems that at high levels sele-nate operates as a ROS-generating compound whose effects were counteracted by NAC. It can be deduced that, at low concentration, sele-nates behave as a ROS quencher and at high level as a ROS-promoting species.
文摘Droughts occur in all climatic regions around the world costing a large expense to global economies. Reasonably accurate prediction of drought events helps water managers proper planning for utilization of limited water resources and distribution of available waters to different sectors and avoid catastrophic consequences. Therefore, a means to create a simplistic approach for forecasting drought conditions with easily accessible parameters is highly desirable. This study proposes and evaluates newly developed accurate prediction models utilizing various hydrologic, meteorological, and geohydrology parameters along with the use of Artificial Neural Network (ANN) models with various forecast lead times. The present study develops a multitude of forecasting models to predict drought indices such as the Standard Precipitation Index with a lead-time of up to 6 months, and the Soil Moisture Index with a lead-time of 3 months. Furthermore, prediction models with the capability of approximating surface and groundwater storage levels including the Ross River Dam level have been developed with relatively high accuracy with a lead-time of 3 months. The results obtained from these models were compared to current values, revealing that ANN based approach can be used as a simple and effective predictive model that can be utilized for prediction of different aspects of drought scenarios in a typical study area like Townsville, North Queensland, Australia which had suffered severe recent drought conditions for almost six recent years (2014 to early 2019).
