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With the development of multispectral imaging technology, retrieving soil heavy metal content using multispectral remote sensing images has become possible. However, factors such as soil pH and spectral resolution affect the accuracy of model inversion, leading to low precision. In this study, 242 soil samples were collected from a typical area of the Pearl River Delta, and the Cu content in the soil was detected in the laboratory. Simultaneously, Sentinel-2 remote sensing image data were collected, and two-dimensional and three-dimensional spectral indices were established. Constructing independent decision trees based on pH values, using the Successive Projections Algorithm (SPA) combined with the Boruta algorithm to select the characteristic bands for soil Cu content, and this was combined with Optuna automatic hyperparameter optimization for ensemble learning models to establish a model for estimating Cu content in soil. The research results indicated that in the SPA combined with the Boruta feature selection algorithm, the characteristic spectral indices were mainly concentrated in the spectral transformation forms of TBI2 and TBI4. Full-sample modeling lacked predictive ability, but after classifying the samples based on soil pH value, the R2 of the RF and XGBoost models constructed with the samples with pH values between 5.85 and 7.75 was 0.54 and 0.76, respectively, with corresponding RMSE values of 22.48 and 16.12 and RPD values of 1.51 and 2.11. This study shows that the inversion of soil Cu content under different pH conditions exhibits significant differences, and determining the optimal pH range can effectively improve inversion accuracy. This research provides a reference for further achieving the efficient and accurate remote sensing of heavy metal pollution in agricultural soil.

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.

Objective: This study aimed to investigate methodologies for the extraction and purification of polysaccharides from Rosa roxburghii Tratt fruits and their impact on various cellular processes in prostate cancer DU145 cells, including survival rate, migration, invasion, cell cycle, and apoptosis. Results: Compared to the control group, the polysaccharide exhibited a significant reduction in the viability, migration, and invasion rates of DU145 cells in a time- and dose-dependent manner within the polysaccharide-treated groups. Additionally, it effectively arrested the cell cycle of DU145 cells at the G0/G1 phase by downregulating the expressions of CDK-4, CDK-6, and Cyclin D1. Furthermore, it induced apoptosis by upregulating the expressions of Caspase 3, Caspase 8, Caspase 9, and BAX. Methods: Polysaccharides were extracted from Rosa roxburghii Tratt sourced from Yunnan, China. Extraction and decolorization methods were optimized using response surface methodology, based on a single-factor experiment. Polysaccharide purification was carried out using DEAE-52 cellulose and Sephadex G-100 column chromatography. The optimal dosage of R. roxburghii Tratt polysaccharide affecting DU145 cells was determined using the CCK-8 assay. Cell migration and invasion were assessed using transwell and scratch assays. Flow cytometry was employed to analyze the effects on the cell cycle and apoptosis. Western blotting and Quantitative real-time PCR were utilized to examine protein and mRNA expressions in DU145 cells, respectively. Conclusions: Rosa roxburghii Tratt polysaccharides, consisting of D-mannose, L-rhamnose, N-acetyl-D-glucosamine, D-galacturonic acid, D-glucose, D-galactcose, D-xylose, L-arabinose, and L-fucose, possess the ability to hinder DU145 cell proliferation, migration, and invasion while inducing apoptosis through the modulation of relevant protein and gene expressions.

The production of municipal sludge is steadily increasing in line with the production of sewage. A wealth of organic contaminants, including nutrients and energy, are present in municipal sludge. Anaerobic fermentation can be used to extract useful resources from sludge, producing hydrogen, methane, short-chain fatty acids, and, via further chain elongation, medium-chain fatty acids. By comparing the economic and use values of these retrieved resources, it is concluded that a high-value resource transformation of municipal sludge can be achieved via the production of medium-chain fatty acids using anaerobic fermentation, which is a hotspot for future research. In this study, the selection of the pretreatment method, the method of producing medium-chain fatty acids, the influence of the electron donor, and the technique used to enhance product synthesis in the anaerobic fermentation process are introduced in detail. The study outlines potential future research directions for medium-chain fatty acid production using municipal sludge. These acids could serve as a starting point for investigating other uses for municipal sludge.

The sequestration of carbon in forests plays a crucial role in mitigating global climate change and achieving carbon neutrality goals. Carbon use efficiency (CUE) is an essential metric used to evaluate the carbon sequestration capacity and efficiency of Vegetation. Previous studies have emphasized the importance of assessing CUE at specific regions and times to better understand its spatiotemporal variations. The southeastern region of Tibet in the Qinghai-Tibet Plateau is recognized as one of the most biodiverse areas in China and globally, characterized by diverse vegetation types ranging from subtropical to temperate. In this study, we focused on Nyingchi, which is the largest virgin forest area in southeast Tibet, to explore the spatial-temporal dynamic characteristics of regional CUE based on MODIS remote sensing products. The following results were obtained: (1) On a monthly scale, regional CUE exhibits significant seasonal variations, with varying patterns among different vegetation types. Specifically, the fluctuation of CUE is the lowest in high-altitude forest areas and the greatest in grasslands and barrens. On an annual scale, forests exhibit higher fluctuations than areas with sparse vegetation and the overall volatility of CUE increased over the past 11 years. (2) There are regional differences in the trend of CUE changes, with a substantial downward trend in the Himalayan region and a significant upward trend in the residual branches of the Gangdise Mountains. More than 75% of the regions exhibit no persistent trend in CUE changes. (3) Vegetation type is the main determinant of the range and characteristics of vegetation CUE changes, while the geographical location and climatic conditions affect the variation pattern. CUE in the southern and northern regions of Nyingchi at 28.5°N exhibits different responses to temperature and precipitation changes, with temperature having a more significant impact on CUE.

From the perspective of treating wastes with wastes, bamboo sawdust was integrated with a hydroxyapatite (HAP) precursor to create engineered nano-HAP/micro-biochar composites (HBCs) by optimizing the co-precipitated precursor contents and co-pyrolysis temperature (300, 450, 600 °C). The physicochemical properties of HBCs, including morphologies, porosities, component ratios, crystalline structures, surface elemental chemical states, surface functional groups, and zeta potentials as a function of carbonization temperatures and components of precursors, were studied. Biochar matrix as an efficient carrier with enhanced specific surface area to prevent HAP from aggregation was desired. The sorption behavior of heavy metal (Cu(II), Cd(II), and Pb(II)) and pharmaceuticals (carbamazepine and tetracycline) on HBCs were analyzed given various geochemical conditions, including contact time, pH value, ionic strength, inferencing cations and anions, coexisting humic acid, and ambient temperature. HBCs could capture these pollutants efficiently from both simulated wastewaters and real waters. Combined with spectroscopic techniques, proper multiple dominant sorption mechanisms for each sorbate were elucidated separately. HBCs presented excellent reusability for the removal of these pollutants through six recycles, except for tetracycline. The results of this study provide meaningful insight into the proper integration of biochar–mineral composites for the management of aquatic heavy metals and pharmaceuticals.