Brassinosteroids (BRs) induce plant tolerance to a wide spectrum of stresses. To study how BR induces stress tolerance, we manipulated the BR levels in cucumber (Cucumis sativus) through a chemical genetics approach and found that BR levels were positively correlated with the tolerance to photo-oxidative and cold stresses and resistance to Cucumber mosaic virus. We also showed that BR treatment enhanced NADPH oxidase activity and elevated H 2 O 2 levels in apoplast. H 2 O 2 levels were elevated as early as 3 h and returned to basal levels 3 d after BR treatment. BR-induced H 2 O 2 accumulation was accompanied by increased tolerance to oxidative stress. Inhibition of NADPH oxidase and chemical scavenging of H 2 O 2 reduced BR-induced oxidative and cold tolerance and defense gene expression. BR treatment induced expression of both regulatory genes, such as RBOH, MAPK1, and MAPK3, and genes involved in defense and antioxidant responses. These results strongly suggest that elevated H 2 O 2 levels resulting from enhanced NADPH oxidase activity are involved in the BR-induced stress tolerance.

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Urban agriculture is making an increasing contribution to food security in large cities around the world. The potential contribution of biodiversity to ecological intensification in urban agricultural systems has not been investigated. We present monitoring data collected from rice fields in 34 community farms in mega-urban Shanghai, China, from 2001 to 2015, and show that the presence of a border crop of soybeans and neighboring crops (maize, eggplant and Chinese cabbage), both without weed control, increased invertebrate predator abundance, decreased the abundance of pests and dependence on insecticides, and increased grain yield and economic profits. Two 2 year randomized experiments with the low and high diversity practices in the same locations confirmed these results. Our study shows that diversifying farming practices can make an important contribution to ecological intensification and the sustainable use of associated ecosystem services in an urban ecosystem.

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Plastic mulching on farmland has been increasing worldwide for decades due to its superior advantages for improving crop yields. Monitoring Plastic-Mulched Land-cover (PML) can provide essential information for making agricultural management decisions and reducing PML’s eco-environmental impacts. However, mapping PML with remote sensing data is still challenging and problematic due to its complicated and mixed characteristics. In this study, a new Object-Based Image Analysis (OBIA) approach has been proposed to investigate the potential for combined use of Sentinel-1 (S1) SAR and Sentinel-2 (S2) Multi-spectral data to extract PML. Based on the ESP2 tool (Estimation of Scale Parameter 2) and ED2 index (Euclidean Distance 2), the optimal Multi-Resolution Segmentation (MRS) result is chosen as the basis of following object-based classification. Spectral and backscattering features, index features and texture features from S1 and S2 are adopted in classifying PML and other land-cover types. Three machine-learning classifiers known as the—Classification and Regression Tree (CART), the Random Forest (RF) and the Support Vector Machine (SVM) are carried out and compared in this study. The best classification result with an overall accuracy of 94.34% is achieved by using spectral, backscattering, index and textural information from integrated S1 and S2 data with the SVM classifier. Texture information is demonstrated to contribute positively to PML classifications with SVM and RF classifiers. PML mapping using SAR information alone has been greatly improved by the object-based approach to an overall accuracy of 87.72%. By adding SAR data into optical data, the accuracy of object-based PML classifications has also been improved by 1–3%.

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This paper proposes a complete and effective smart over-voltage monitoring and identification system. In recent years, smart grids are of the greatest interest in power system research. One of the main features of smart grid is their self-healing, which can continuously carry out online self-evaluation, discover existing faults, and correct them immediately. The over-voltage smart monitoring-identification-suppression systems play a key role in the construction of self-healing grids. In this paper, eight kinds of common over-voltage are discussed and analyzed. The S-transform algorithm is used to extract features of over-voltage. Aiming at the main features of each kind of over-voltage, six different characteristic quantities are proposed. A well designed fuzzy expert system and a support vector machine are employed as the classifiers to build a two-step identification model. The accuracy of the identification system is verified by field records. Results show that this system is feasible and promising for real applications.