Previous investigations have shown that the SUPPRESSORS OF MAX2 1-LIKE6, 7 and 8 (SMXL6, 7 and 8) proteins redundantly repress strigolactone (SL) signaling in plant growth and development. Recently, a growing body of evidence indicated that SLs positively regulate plant drought resistance through functional analyses of genes involved in SL biosynthesis and positive regulation of SL signaling. However, the functions of the SL-signaling negative regulators SMXL6, 7 and 8 in drought resistance and the associated mechanisms remain elusive. To reveal the functions of these SMXL proteins, we analyzed the drought-resistant phenotype of the triple smxl6,7,8 mutant plants and studied several drought resistance-related traits. Our results showed that the smxl6,7,8 mutant plants were more resistant to drought than wild-type plants. Physiological investigations indicated that the smxl6,7,8 mutant plants exhibited higher leaf surface temperature, reduced cuticle permeability, as well as decreases in drought-induced water loss and cell membrane damage in comparison with wild-type plants. Additionally, smxl6,7,8 mutant plants displayed an increase in anthocyanin biosynthesis during drought, enhanced detoxification capacity and increased sensitivity to abscisic acid in cotyledon opening and growth inhibition assays. A good correlation between the expression levels of some relevant genes and the examined physiological and biochemical traits was observed. Our findings together indicate that the SMXL6, 7 and 8 act as negative regulators of drought resistance, and that disruption of these SMXL genes in crops may provide a novel way to improve their drought resistance.

As we all know, waste pollution is one of the most serious environmental issues in the world. Efficient detection of solid waste (SW) in aerial images can improve subsequent waste classification and automatic sorting on the ground. However, traditional methods have some problems, such as poor generalization and limited detection performance. This article presents an anchor-based object detector for solid waste in aerial images (SWDet). Specifically, we construct asymmetric deep aggregation (ADA) network with structurally reparameterized asymmetric blocks to extract waste features with inconspicuous appearance. Besides, considering the waste with blurred boundaries caused by the resolution of aerial images, this article constructs efficient attention fusion pyramid network (EAFPN) to obtain contextual information and multiscale geospatial information via attention fusion. And the model can capture the scattering features of irregular shape waste. In addition, we construct the dataset for solid waste aerial detection (SWAD) by collecting aerial images of SW in Henan Province, China, to validate the effectiveness of our method. Experimental results show that SWDet outperforms most of existing methods for SW detection in aerial images.

Recent years have seen rapid progress in target-detection missions, whereas small targets, dense target distribution, and shadow occlusion continue to hinder progress in the detection of small targets, such as cars, in remote sensing images. To address this shortcoming, we propose herein a backbone feature-extraction network called “RepDarkNet” that adds several convolutional layers to CSPDarkNet53. RepDarkNet considerably improves the overall network accuracy with almost no increase in inference time. In addition, we propose a multi-scale cross-layer detector that significantly improves the capability of the network to detect small targets. Finally, a feature fusion network is proposed to further improve the performance of the algorithm in the AP@0.75 case. Experiments show that the proposed method dramatically improves detection accuracy, achieving AP = 75.53% for the Dior-vehicle dataset and mAP = 84.3% for the Dior dataset, both of which exceed the state-of-the-art level. Finally, we present a series of improvement strategies that justifies our improvement measures.

Although metabolomics is widely used to assess the detrimental effects of antibiotics and characterize stress response, the relationships between metabolites and biological endpoints following antibiotics remain unknown. In our study, we exposed ryegrass seeds to sulfamethoxazole for five days. The results showed that sulfamethoxazole inhibited plant growth (by 12.90–85.83%). It also decreased chlorophyll content (by 35.40–93.32%), carotenoid content (by 32.76–90.18%), and root cell permeability (by 98.43–99.29%), but increased root reactive oxygen species (ROS) concentration (increasing rate: 11.32- to 137.36-times). Moreover, high sulfamethoxazole concentrations increased superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities. To elucidate the association between metabolites and biological endpoints, we conducted an orthogonal partial least squares analysis. The results showed that sulfamethoxazole significantly altered six metabolic pathways. Among the metabolites modulated by sulfamethoxazole, amino acids mainly affected root growth and ROS concentration, whereas carbohydrates were substantially associated with the effect of sulfamethoxazole on cell permeability. Many metabolites had contrasting effects. For example, some metabolites increased root fresh weight and improved cell permeability by decreasing ROS levels and SOD, POD, and CAT activities. By contrast, some metabolites negatively affected root fresh weight and cell permeability by increasing ROS levels and SOD, POD, and CAT activities. These observations bring new insights into ryegrass responses to sulfamethoxazole-induced stress.

Ship targets in ORSIs (Optical Remote Sensing Images) have the characteristics of various scales, and most of them are medium and small-scale targets. When the existing target detection algorithms are applied to ship target detection in ORSIs, the detection accuracy is low. There are two main reasons for the above problems, one is the mismatch of the receptive fields, and the other is the lack of feature information. For resolving the problem that multi-scale ship targets are difficult to detect, this paper proposes a ship target detection algorithm based on feature enhancement. Firstly, EIRM (Elastic Inception Residual Module) is proposed for feature enhancement, which can capture feature information of different dimensions and provide receptive fields of different scales for mid- and low-level feature maps. Secondly, the SandGlass-L block is proposed by replacing the ReLu6 activation function of the SandGlass block with the Leaky ReLu activation function. Leaky ReLu solves the problem of 0 output when ReLu6 has negative input, so the SandGlass-L block can retain more feature information. Finally, based on SandGlass-L, SGLPANet (SandGlass-L Path Aggregation Network) is proposed to alleviate the problem of information loss caused by dimension transformation and retain more feature information. The backbone network of the algorithm in this paper is CSPDarknet53, and the SPP module and EIRM act after the backbone network. The neck network is SGLPANet. Experiments on the NWPU VHR-10 dataset show that the algorithm in this paper can well solve the problem of low detection accuracy caused by mismatched receptive fields and missing feature information. It not only improves the accuracy of ship target detection, but also achieves good results when extended to other categories. At the same time, the extended experiments on the LEVIR dataset show that the algorithm also has certain applicability on different datasets.

Metabolomics is a commonly used method to study the responses of organisms to environmental changes. However, the relationships between metabolites and biological endpoints still need further discussion. In this study, we exposed wheat seeds to sulfamethoxazole (0, 1, 10, 100 mg/L) for 5 days. The results show that sulfamethoxazole (SMX) had an inhibitory effect on wheat growth. It reduced shoot length, root length, shoot fresh weight, root fresh weight, chlorophyll content, and carotenoid content. At the same time, it increased the concentration of reactive oxygen species, the activity of superoxide dismutase, the activity of peroxidase, and the activity of catalase in the root. An orthogonal partial least squares analysis and correlation analysis were performed. SMX transformed five key metabolic pathways. Notably, certain metabolic alterations exhibited negative correlations with reactive oxygen species (ROS) accumulation and antioxidant enzyme activities (including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT)), while showing positive associations with root growth parameters (fresh weight and length). Conversely, other metabolic changes appeared to promote ROS generation and enhance antioxidant enzyme activities, consequently inhibiting root growth. These findings offer novel perspectives on the metabolic regulation of wheat’s stress response to SMX exposure.