Effect of cerium （Ce^3＋） on the growth, photosynthesis and antioxidant enzyme system in rape seedlings （Brassica juncea L.） exposed to two levels of UV-B radiation （T1： 0.15 W/m^2 and T2：0.35 W/m^2） was studi...Effect of cerium （Ce^3＋） on the growth, photosynthesis and antioxidant enzyme system in rape seedlings （Brassica juncea L.） exposed to two levels of UV-B radiation （T1： 0.15 W/m^2 and T2：0.35 W/m^2） was studied by hydroponics under laboratory conditions. After 5 d of UV-B treatment, the aboveground growth indices were obviously decreased by 13.2%-44. 1%（T1） and 21.4%-49.3% （T2）, compared to CK, and except active absorption area of roots, the belowground indices by 14.1%-35.6%（T1） and 20.3%-42.6% （T2）. For Ce＋UV-B treatments, the aboveground and belowground growth indices were decreased respectively by 4.1%-23.6%, 5.2% -23.3%（Ce＋T1） and 10.8%-28.4%, 7.0%-27.8%（Ce＋T2）, lower than those of UV-B treatments. The decrease of growth indices appeared to be the result of changes of physiological processes. Two levels of UV-B radiation induced the decrease in chlorophyll content, net photosynthesis rate, transpiration rate, stomatal conductance and water use efficiency by 11.2%-25.9%（T1） and 20.9%- 56.9%（T2）, whereas increase in membrane permeability and activities of antioxidant enzymes including superoxide dismutase（SOD）, catalase （CAT） and peroxidase （POD） by 6.9%, 22.8%, 21.5%, 9.5%（T1） and 36.6%, 122.3%, 103.5%, 208.9%（T2）, respectively. The reduction of the photosynthetic parameters in Ce＋UV-B treatments was lessened to 3.2%-13.8%（Ce＋T1） and 4.9%-27.6%（Ce＋T2）, and the increase of membrane permeability and activities of antioxidant enzymes except POD in the same treatments were lessened to 2.4%, 8.4%, 6.6%（Ce＋T1） and 30.1%, 116.7%, 75.4%（Ce＋T2）. These results indicate that the regulative effect of Ce on photosynthesis and antioxidant enzymatic function is the ecophysiological basis of alleviating the suppression of UV-B radiation on growth of seedlings. Furthermore, the protective effect of Ce on seedlings exposed to TI level of UV-B radiation is superior to T2 level.展开更多
The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place i...The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.展开更多
基金The National Natural Science Foundation of China （No. 20471030 30570323） and the Foundation of State Planning Committee（No. GFZ040628）
文摘Effect of cerium （Ce^3＋） on the growth, photosynthesis and antioxidant enzyme system in rape seedlings （Brassica juncea L.） exposed to two levels of UV-B radiation （T1： 0.15 W/m^2 and T2：0.35 W/m^2） was studied by hydroponics under laboratory conditions. After 5 d of UV-B treatment, the aboveground growth indices were obviously decreased by 13.2%-44. 1%（T1） and 21.4%-49.3% （T2）, compared to CK, and except active absorption area of roots, the belowground indices by 14.1%-35.6%（T1） and 20.3%-42.6% （T2）. For Ce＋UV-B treatments, the aboveground and belowground growth indices were decreased respectively by 4.1%-23.6%, 5.2% -23.3%（Ce＋T1） and 10.8%-28.4%, 7.0%-27.8%（Ce＋T2）, lower than those of UV-B treatments. The decrease of growth indices appeared to be the result of changes of physiological processes. Two levels of UV-B radiation induced the decrease in chlorophyll content, net photosynthesis rate, transpiration rate, stomatal conductance and water use efficiency by 11.2%-25.9%（T1） and 20.9%- 56.9%（T2）, whereas increase in membrane permeability and activities of antioxidant enzymes including superoxide dismutase（SOD）, catalase （CAT） and peroxidase （POD） by 6.9%, 22.8%, 21.5%, 9.5%（T1） and 36.6%, 122.3%, 103.5%, 208.9%（T2）, respectively. The reduction of the photosynthetic parameters in Ce＋UV-B treatments was lessened to 3.2%-13.8%（Ce＋T1） and 4.9%-27.6%（Ce＋T2）, and the increase of membrane permeability and activities of antioxidant enzymes except POD in the same treatments were lessened to 2.4%, 8.4%, 6.6%（Ce＋T1） and 30.1%, 116.7%, 75.4%（Ce＋T2）. These results indicate that the regulative effect of Ce on photosynthesis and antioxidant enzymatic function is the ecophysiological basis of alleviating the suppression of UV-B radiation on growth of seedlings. Furthermore, the protective effect of Ce on seedlings exposed to TI level of UV-B radiation is superior to T2 level.
文摘The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.
