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The Buzhaoba open-pit mine is an important lignite production base in Yunnan Province, China. As mining activities have continued to progress, varying degrees of deformation have occurred in different areas of the Buzhaoba open-pit mine, threatening normal coal production and mine safety. To comprehensively investigate the characteristics of surface deformation and its influencing factors at the Buzhaoba open-pit mine, this study employed the following methods: first, the SBAS-InSAR technique was used to process 86 Sentinel-1A ascending and descending orbit remote sensing images from 2020 to 2023, obtaining LOS surface deformation information for the mining area; second, leveling observation data were used to validate the accuracy of the SBAS-InSAR results, and based on the principle of two-dimensional deformation decomposition, the east–west and vertical surface deformation information of the mining area was obtained; finally, the temporal variation characteristics and influencing factors of the Buzhaoba open-pit mine were analyzed. The study results indicate that (1) the maximum LOS surface deformation rates in the ascending and descending orbits of the mining area were −42.1 mm/a and −114.0 mm/a, respectively; (2) the correlation coefficient between the SBAS-InSAR monitoring results and the leveling observation results was 0.938, confirming the reliability of the SBAS-InSAR monitoring results; (3) the maximum east–west and vertical deformation rates obtained from the two-dimensional deformation decomposition were −103.4 mm/a and −189.2 mm/a, respectively, with the surface deformation in the east–west direction being more pronounced; (4) internal factors such as stratigraphic lithology and geological structures, as well as atmospheric rainfall, have a certain degree of influence on the surface deformation of the Buzhaoba open-pit mine. Therefore, the research results of this study can provide important data support and theoretical references for safety management and disaster prevention in the mining area. [ABSTRACT FROM AUTHOR]

In this study, a deep learning method combining knowledge graph and diffusion Transformer has been proposed for cucumber disease detection. By incorporating the diffusion attention mechanism and diffusion loss function, the research aims to enhance the model's ability to recognize complex agricultural disease features and to address the issue of sample imbalance efficiently. Experimental results demonstrate that the proposed method outperforms existing deep learning models in cucumber disease detection tasks. Specifically, the method achieved a precision of 93%, a recall of 89%, an accuracy of 92%, and a mean average precision (mAP) of 91%, with a frame rate of 57 frames per second (FPS). Additionally, the study successfully implemented model lightweighting, enabling effective operation on mobile devices, which supports rapid on-site diagnosis of cucumber diseases. The research not only optimizes the performance of cucumber disease detection, but also opens new possibilities for the application of deep learning in the field of agricultural disease detection. [ABSTRACT FROM AUTHOR]

In this study, a deep learning-based system for apple defect detection and quality grading was developed, integrating various advanced image-processing technologies and machine learning algorithms to enhance the automation and accuracy of apple quality monitoring. Experimental validation demonstrated the superior performance of the proposed model in handling complex image tasks. In the defect-segmentation experiments, the method achieved a precision of 93%, a recall of 90%, an accuracy of 91% and a mean Intersection over Union (mIoU) of 92%, significantly surpassing traditional deep learning models such as U-Net, SegNet, PSPNet, UNet++, DeepLabv3+ and HRNet. Similarly, in the quality-grading experiments, the method exhibited high efficiency with a precision of 91%, and both recall and accuracy reaching 90%. Additionally, ablation experiments with different loss functions confirmed the significant advantages of the Jump Loss in enhancing model performance, particularly in addressing class imbalance and improving feature learning. These results not only validate the effectiveness and reliability of the system in practical applications but also highlight its potential in automating the detection and grading processes in the apple industry. This integration of advanced technologies provides a new automated solution for quality control of agricultural products like apples, facilitating the modernization of agricultural production. [ABSTRACT FROM AUTHOR]

In this study, an advanced method for apricot tree disease detection is proposed that integrates deep learning technologies with various data augmentation strategies to significantly enhance the accuracy and efficiency of disease detection. A comprehensive framework based on the adaptive sampling latent variable network (ASLVN) and the spatial state attention mechanism was developed with the aim of enhancing the model's capability to capture characteristics of apricot tree diseases while ensuring its applicability on edge devices through model lightweighting techniques. Experimental results demonstrated significant improvements in precision, recall, accuracy, and mean average precision (mAP). Specifically, precision was 0.92 , recall was 0.89 , accuracy was 0.90 , and mAP was 0.91 , surpassing traditional models such as YOLOv5, YOLOv8, RetinaNet, EfficientDet, and DEtection TRansformer (DETR). Furthermore, through ablation studies, the critical roles of ASLVN and the spatial state attention mechanism in enhancing detection performance were validated. These experiments not only showcased the contributions of each component for improving model performance but also highlighted the method's capability to address the challenges of apricot tree disease detection in complex environments. Eight types of apricot tree diseases were detected, including Powdery Mildew and Brown Rot, representing a technological breakthrough. The findings provide robust technical support for disease management in actual agricultural production and offer broad application prospects. [ABSTRACT FROM AUTHOR]

Obesity is caused by an imbalance between energy intake and energy expenditure. This study aimed to determine the effects and mechanisms of 2′,4′‐dihydroxy‐6′‐methoxy‐3′,5′‐dimethylchalcone (DMC) on exercise tolerance in high‐fat diet (HFD)‐fed mice. Male C57BL/6J mice were randomly divided into two categories (7 groups [n = 8]): sedentary (control [CON], HFD, 200 mg/kg DMC, and 500 mg/kg DMC) and swimming (HFD, 200 mg/kg DMC, and 500 mg/kg DMC). Except the CON group, all other groups were fed HFD with or without DMC intervention for 33 days. The swimming groups were subjected to exhaustive swimming (three sessions/week). Changes in swimming time, glucolipid metabolism, body composition, biochemical indicators, histopathology, inflammation, metabolic mediators, and protein expression were assessed. DMC combined with regular exercise improved endurance performance, body composition, glucose and insulin tolerance, lipid profile, and the inflammatory state in a dose‐dependent manner. Further, DMC alone or combined with exercise could restore normal tissue morphology, reduce fatigue‐associated markers, and boost whole‐body metabolism and the protein expression of phospho‐AMP‐activated protein kinase alpha/total‐AMP‐activated protein kinase alpha (AMPK), sirtuin‐1 (SIRT1), peroxisome‐proliferator‐activated receptor gamma coactivator 1alpha (PGC‐1α), and peroxisome proliferator‐activated receptor alpha in the muscle and adipose tissues of HFD‐fed mice. DMC exhibits antifatigue effects by regulating glucolipid catabolism, inflammation, and energy homeostasis. Furthermore, DMC exerts a synergistic exercise‐related metabolic effect via the AMPK‐SIRT1‐PGC‐1α signaling pathway, suggesting that DMC is a potential natural sports supplement with mimicked or augmented exercise effects for obesity prevention. [ABSTRACT FROM AUTHOR]

Summary: During plant‐pathogenic fungi and host plants interactions, numerous pathogen‐derived proteins are secreted resulting in the activation of the unfolded protein response (UPR) pathway. For efficient trafficking of secretory proteins, including those important in disease progression, the cytoplasmic coat protein complex II (COPII) exhibits a multifunctional role whose elucidation remains limited.Here, we discovered that the COPII cargo receptor MoErv29 functions as a target of MoHac1, a previously identified transcription factor of the UPR pathway. In Magnaporthe oryzae, deletion of MoERV29 severely affected the vegetative growth, conidiation and biotrophic invasion of the fungus in susceptible rice hosts.We demonstrated that MoErv29 is required for the delivery of secreted proteins through recognition and binding of the amino‐terminal tripeptide motifs following the signal peptide. By using bioinformatics analysis, we predicted a cargo spectrum of MoErv29 and found that MoErv29 is required for the secretion of many proteins, including extracellular laccases and apoplastic effectors. This secretion is mediated through the conventional endoplasmic reticulum–Golgi secretion pathway and is important for conferring host recognition and disease resistance.Taken together, our results revealed how MoErv29 operates on effector secretion, and our findings provided a critical link between COPII vesicle trafficking and the UPR pathway. [ABSTRACT FROM AUTHOR]

E-waste dismantling activities have caused serious heavy metals pollution in some parts of China. In this study, the contents of 15 metals (Ag, As, Cd, Co, Cr, Cu, Li, Mn, Ni, Pb, Rb, Sb, Sn, Tl, and Zn) in soil, sediment, and water hyacinth derived from the e-waste dismantling activities were investigated. Based on these data, the Geo-accumulation index (Igeo) and the potential ecological risk index (RI) were used to assess potential ecological risk. The results showed that the concentration of As, Cd, Cu, Sb, Sn, and Zn in soil for 6.4–131, 0.09–11.2, 25.5–5900, 3.7–443, 5.7–272, and 39–2040 mg kg−1, which the average content exceeded the natural background levels for 5.6, 57, 87, 142.7, 16.7 and 12.7 times, respectively. The concentration of Cu, Sb, Sn, and Zn in sediment for 41.2–468.3, 2.59–33.83, 10.3–92.9, and 148–502.7 mg kg−1, respectively, which the average content exceeded the natural background levels for 14.7, 20.9, 7.1, and 5.7 times, respectively. The Igeo indicated that soil and sediment at sites where e-waste was still heavily contaminated with a variety of heavy metals (such as Cd, Cu, Pb, Sb, Sn, and Zn), which were obvious accumulation effects. The RI in the surface of sediment indicated the average ecological risk of As, Cd, Co, and Cu was high risk and the total risk reached a very high risk at each sampling site. Water hyacinth has strong ability to enrich heavy metals. We suggest that the heavy metals in soil and sediment should be periodically monitored, especially Cd, Cu, Pb, Sb, Sn, and Zn. Water hyacinth can be used to monitor these heavy metals contaminated in river. The cleaning up of the soil and sediment should be a priority for any future remediation program in the study area. [ABSTRACT FROM AUTHOR]