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To explore expression level of alkaloid and its key synthetase gene in Isatis indigotica upon Plasmodiophora brassicae exposure. The grades of disease severity according to morphology were verified. Moreover, histological observation, physiological and biochemical parameters have been collected together with transcriptomic and metabolomic analysis in Isatis indigotica after infection for 0, 7, 14, 21 d. The results were as follows: (1) After inoculation for 0, 7, 14, 21 d with Plasmodiophora brassicae, Isatis indigotica showed club root grades in 0, 1, 3, and 5 respectively, notably, cortical invasion occurred on 7 d. (2) When Plasmodiophora brassicae exposed lasting 14 d later, the contents of soluble protein and malondialdehyde, along with superoxide dismutase, peroxidase, polyphenol oxidase and catalase activities in Isatis indigotica increased significantly compared to control group at time depended manner. (3) A total of 161 alkaloids were detected in metabolomics, among those alkaloids, indoles were noticed as the most abundant form. There were 16, 17 and 39 discriminating metabolites had been spotted after infected with Plasmodiophora brassicae for 7, 14, 21 d, the most discriminating metabolites enriched at alkaloid and amino acid metabolism pathways. (4) Transcriptome analysis showed that there were 2 439, 256 and 6 437 genes expression alteration for 7, 14, 21 d compared to control, those differentially expressed genes enriched at 11 alkaloids related metabolism pathways. Markedly, expression level of 9 genes (encoding for enzymes thebaine synthase, tyrosine aminotransferase, indole-3-pyruvate monooxygenase and aldehyde dehydrogenase) were increased after infection for 7, 14, 21 d. The results reveal the interaction between Plasmodiophora brassicae and Isatis indigotica, explored the effects of Plasmodiophora brassicae on indole alkaloid synthesis and its key enzyme genes, and lay a foundation for later research on resistance genes and alkaloid secondary metabolic pathways in Isatis indigotica.

Chinese solar greenhouse (CSG) is an artificially constructed agricultural facility that provides a suitable environment for crop growth. However, the imbalanced temperature and humidity phenomenon occurring in greenhouse during summer greatly affects crop yield. This article proposes the application of solar radiation fuzzy adaptive system in greenhouse to address this issue. The design process includes hardware and algorithm design. The hardware part involves designing the travel distance of the curtain machine and the film winding machine, laying out the temperature and humidity sensors, and designing the control cabinet. The algorithm is optimized based on fuzzy control theory, data acquisition module consists of temperature and humidity sensors, executive components include the curtain machine and the film winding machine, and the data transmission, storage and feedback module consists of the control cabinet and the cloud-based database. These form an adaptive system, which will be subjected to a 10-day comparative experiment. The experimental results showed that in region 3, the peak value of temperature reduction in the third layer during the daytime was 26.2 °C, and the humidity increased by a maximum of 31.5 %. In region 2, the temperature increased by a maximum of 4.2 °C during the nighttime in the second layer, and the humidity decreased by 4.1 °C. Introduction of adaptive systems can play an important role in maintaining microclimate temperature and humidity in summer.

With the widespread application of graph structured data in various practical scenarios, the demand for effective modeling and processing is increasing. Graph Transformers (GTs), as a type of model that uses Transformers to process graph data, can effectively alleviate the problems of over smoothing and over squeezing in traditional graph neural network (GNN), and thus can learn better feature representations. Firstly, based on the research on recent GTs related literature, the existing model architectures are divided into two categories: the first category adds graph position and structure information to Transformers through absolute encoding and relative encoding to enhance Transformers' understanding and processing ability of graph structure data; the second type combines GNN with Transformers in different ways (serial, alternating, parallel) to fully utilize their advantages. Secondly, the application of GTs in fields such as information security, drug discovery, and knowledge graphs is introduced, and the advantages and disadvantages of models with different uses are compared and summarized. Finally, the challenges faced by future research on GTs are analyzed from aspects such as scalability, complex graphs, and better integration methods. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation is rapidly developing as a kind of renewable energy that can protect the ecological environment. The establishment of photovoltaic power stations in desertification areas can play a very important role in desert windbreaks and sand fixation as well as improve the ecological environment. The realization of the effective integration of photovoltaics and deserts can have multiple benefits for the economy, society, and ecology. However, the deposition of sand and dust caused by environmental factors in desertification areas can seriously affect the power generation efficiency of PV modules. In this study, the output characteristics of photovoltaic modules were tested under three wind speed conditions (5 m/s, 10 m/s, and 15 m/s), with different sand densities, sand particle sizes, and inclination angles. The experimental study showed that the module's output power gradually decreased with an increase in the density of accumulated sand; the sand accumulation density on the surface of the photovoltaic module increased from 0 to 40 g/m2, and the maximum output power decreased by 32.2%. As the sand particle size increased, the maximum output power of the module rose and was gradually stabilized in the three wind speed groups. As the sand particle size increased, the maximum output power of the module increased and gradually stabilized in the three wind speed groups. When the wind speed was 15 m/s, the component output power of the angle had the largest drop; the tilt angle was 60° when the relative output power rate reached the minimum (i.e., 86.5%); that is, there was electrical energy loss of approximately 13.7%. The sand particle size and accumulated sand density were the control variables related to the module temperature and the transmittance of the proportion of the impact of the module filling factor differences. According to the curve analysis of the filling factor in different sand accumulation densities, the filling factor first increased and then decreased. In this test, the filling factor at the sand accumulation density of 35 g/m2 (module temperature valley) reached the peak value. The change trend for the component filling factor under different sand particle sizes fluctuated. [ABSTRACT FROM AUTHOR]