WRKY transcription factors constitute one of the largest gene families in plants and are involved in many biological processes, including growth and development, physiological metabolism, and the stress response. In earlier studies, the WRKY gene family of proteins has been extensively studied and analyzed in many plant species. However, information on WRKY transcription factors in Acer truncatum has not been reported. In this study, we conducted genome-wide identification and analysis of the WRKY gene family in A. truncatum, 54 WRKY genes were unevenly located on all 13 chromosomes of A. truncatum, the highest number was found in chromosomes 5. Phylogenetic relationships, gene structure, and conserved motif identification were constructed, and the results affirmed 54 AtruWRKY genes were divided into nine subgroup groups. Tissue species analysis of AtruWRKY genes revealed which were differently exhibited upregulation in flower, leaf, root, seed and stem, and the upregulation number were 23, 14, 34, 18, and 8, respectively. In addition, the WRKY genes expression in leaf under cold stress showed that more genes were significantly expressed under 0, 6 and 12 h cold stress. The results of this study provide a new insight the regulatory function of WRKY genes under abiotic and biotic stresses.

Creating attractive urban green spaces in severely cold and harsh climates is significant for promoting peoples’ health and perceived restoration. However, there is little evidence regarding the urban green spaces in wintery and cold climates and its restorative benefits. This study utilized a pixel grid approach to quantify winter landscape characteristics and a self-reporting method to assess the restorative benefits of audiovisual interactions. The results show the following: (1) Different types of roads in urban parks have significant differences in their level of restorativeness, and the restorativeness benefits of the primary path in winter parks are the strongest. (2) The presence of snowy elements in winter landscapes can enhance park users’ potential to experience restorative characteristics in relation to “being away”. Moreover, there exists a noteworthy positive correlation between deciduous trees and their restoration benefits. (3) People’s perceptions of the tranquility of the soundscape and the duration of environmental exposure are critical mediators in the impact of the restorative path effect. (4) Compared with women, men have a higher restorative level in both the landscape and soundscape. This elucidates the restorative role of white space landscapes and soundscapes in public psychological perception when proposing appropriate forest-based healthcare strategies. It also provides theoretical guidance and optimization schemes for the overall planning, health planning, and design of white spaces shaped by cold urban green spaces.

To estimate the key water quality parameters on a large scale, based on Pearson’s correlation analysis and band ratio, this study first obtains multiple sensitive band combinations (R ≥ 0.30, p < 0.01) for three key water quality parameters: dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Then, principal component analysis is used to reduce the dimensions and analyze multiple optimal combinations, and the first three principal components (PCs) of the optimal combinations are selected to analyze the water quality parameters. Finally, the water quality parameter models of DO, TN, and TP are proposed and compared based on spectral analysis and field measured water quality data respectively using Gaussian process regression and PCs for each parameter. Through model verification and by comparing the performance of the three models, it is found that the TP model performed well (R = 0.9824, p < 0.01), and TP grade accuracy rate is up to 94.97%. Through the error analysis of TN and DO, it is found that 93.0% of error samples occurs when TP < 0.1 mg/L in the water quality. These results would provide a scientific basis for water quality monitoring and water environment management in the study area and could also be used as a reference for water quality monitoring in other basins.

The papain-like cysteine proteases (PLCPs) is a subfamily of cysteine proteases that plays an important role in leaf senescence, and some of its members are involved in the regulation of plant growth and development under stress. In this study, we cloned a new gene, ZmSAG39, from maize. Expression profile analysis showed that ZmSAG39 was induced by darkness and drought treatments. In addition, the ZmSAG39 overexpression in maize accelerated the senescence of maize leaves under darkness and drought treatments. However, the knockout of ZmSAG39 in maize enhanced the resistance of maize to darkness and drought stresses and reduced the degree of senescence of maize leaves. Under drought stress, compared with WT plants, the knockout lines had a higher seed germination rate, seedling survival rate and chlorophyll content, and lower reactive oxygen species (ROS) level and malondialdehyde (MDA) content. In addition, quantitative real-time PCR (qRT-PCR) analysis showed that ZmSAG39 negatively regulated some stress-related genes but positively regulated senescence-related genes under darkness and drought stress conditions. To summarize, these results indicate that ZmSAG39 is a senescence-related gene and plays a negative role in response to darkness and drought stresses. This study laid a theoretical foundation for the innovation of maize germplasm resources with high quality, high yield and strong stress resistance.

Under the background of global warming, the frequent occurrence and long-term persistence of drought events have substantial negative effects on agricultural production. As the main maize production area in midwestern Jilin Province, frequent drought and a shortage of irrigation water pose substantial threats to the production of maize. We analyzed the balance of water supply and demand in each growth period and the degree of maize yield affected by drought. The results indicate that the FIO-ESM climate model can effectively simulate the changes in temperature and precipitation, and was highly applicable to the study area. From 1980 to 2020, the drought risk indices for the sowing to jointing, jointing to tasseling, tasseling to milk-ripe, and milk-ripe to maturity stages were 0.62, 0.52, 0.48, and 0.60, respectively. In the future, the chances of a RCP8.5 scenario drought risk and an enhanced RCP4.5 scenario have eased. Spatially, the high-risk areas shift in a “west−central−southwest” pattern. Effective precipitation will decrease in the future, while the increasing water requirement of maize increases the dependence on irrigation water. The irrigation requirement index is more than 70% for all periods, particularly in the milk-ripe to maturity stage. The relative meteorological yields were positively correlated with the CWDI of the whole growth period, with the rate of reduction in maize yield and the yield reduction coefficient of variation at a high level of risk between 1980 and 2020. In the future, the negative impact of drought risk on the yield of maize lessened with no obvious trend in production. In particular, the rate of reduction and reduction coefficient of variation for the RCP8.5 scenario were 1.24 and 1.09, respectively.

In order to address the limited scale and insufficient diversity of research datasets for maize leaf diseases, this study proposes a maize disease image generation algorithm based on the cycle generative adversarial network (CycleGAN). With the disease image transfer method, healthy maize images can be transformed into diseased crop images. To improve the accuracy of the generated data, the category activation mapping attention mechanism is integrated into the original CycleGAN generator and discriminator, and a feature recombination loss function is constructed in the discriminator. In addition, the minimum absolute error is used to calculate the differences between the hidden layer feature representations, and backpropagation is employed to enhance the contour information of the generated images. To demonstrate the effectiveness of this method, the improved CycleGAN algorithm is used to transform healthy maize leaf images. Evaluation metrics, such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Fréchet inception distance (FID), and grayscale histogram can prove that the obtained maize leaf disease images perform better in terms of background and detail preservation. Furthermore, using this method, the original CycleGAN method, and the Pix2Pix method, the dataset is expanded, and a recognition network is used to perform classification tasks on different datasets. The dataset generated by this method achieves the best performance in the classification tasks, with an average accuracy rate of over $91\%$. These experiments indicate the feasibility of this model in generating high-quality maize disease leaf images. It not only addresses the limitation of existing maize disease datasets but also improves the accuracy of maize disease recognition in small-sample maize leaf disease classification tasks.