The mechanisms of diabetes-related gastrointestinal dysmotility remains unclear. This study aimed to investigate the effect and mechanisms of proinflammatory adipokine visfatin (VF) in the contractile dysfunction of diabetic rat colonic smooth muscle. Twenty Sprague-Dawley rats were randomly divided into control and type 2 diabetes mellitus groups. VF levels in the serum and colonic muscle tissues were tested, the time of the bead ejection and contractility of colonic smooth muscle strips were measured, and the expression of ATP-sensitive potassium (KATP) channels in the colonic muscle tissues was analyzed. In vitro, we tested VF’s effects on intracellular reactive oxygen species (ROS) levels, NF-κB’s nuclear transcription, KATP channel expression, intracellular Ca2+ concentrations, and myosin light chain (MLC) phosphorylation in colonic smooth muscle cells (CSMCs). The effects of NAC (ROS inhibitor) and BAY 11-7082 (NF-κB inhibitor) on KATP expression were also tested. Diabetic rats showed elevated VF levels in serum and colonic muscle tissues, a delayed distal colon ejection response time, weakened contractility of colonic smooth muscle strips, and increased KATP channel expression in colonic muscle tissues. VF significantly inhibited the contractility of colonic smooth muscle strips from normal rats. In cultured CSMCs, VF caused ROS overload, increased NF-κB nuclear transcription activity and increased expression of Kir6.1, eventually reducing intracellular Ca2+ levels and MLC phosphorylation. NAC and BAY 11-7082 inhibited the VF–induced Kir6.1 upregulation. In conclusion, VF may cause contractile dysfunction of CSMCs by upregulating the expression of the Kir6.1 subunit of KATP channels via the ROS/NF-κB pathway and interfering with Ca2+ signaling.

To address the issue of low recognition accuracy caused by the high morphological similarity of mangrove plant leaves, this study proposes a rapid identification method for mangrove leaves based on the YOLOv10 model, with corresponding improvements made to the baseline model. First, the open-source tool LabelImg was employed to annotate leaf images and construct a mangrove leaf species dataset. Subsequently, a PSA attention mechanism was introduced to enhance the extraction of leaf detail features, while the SCDown downsampling method was adopted to preserve key characteristics. Furthermore, a BiFPN architecture incorporating SE modules was implemented to dynamically adjust channel weights for multi-scale feature fusion. Finally, the classification and regression tasks are decoupled by separating the detection head, and the final model is named YOLOv10-MSDet. Experimental results demonstrate that the improved model achieves rapid and accurate identification of various mangrove leaf species, with an average recognition accuracy of 92.4%—a 2.84 percentage point improvement over the baseline model, significantly enhancing the precision of mangrove leaf detection.

Mangroves acted as sinks of terrestrial pollutants, but the occurrence and contamination of rare earth elements (REEs) in urban mangroves lacked systematic evaluation. In rapidly developing Shenzhen, China, four typical urban mangroves were selected to determine the REEs in urban mangroves, including Baguang mangrove (BGM) and Futian mangrove (FTM) featured with ecological preserve and central business district, respectively; Xixiang mangrove (XXM) and Shajing mangrove (SJM) both featured with industry district. The mean concentrations of total REEs (TREEs) in sediment (0–25 cm depth, μg g⁻¹) were SJM (465.28) > FTM (411.25) > XXM (342.76) > BGM (118.63), with Ce to be the dominant REEs element. The depositions of REEs in urban mangroves were significantly affected by sediment sulfur accumulation and fine sediment, including silt and clay fractions. The main fractionation pattern of REEs in urban mangroves was the enrichment of light REEs (LREEs) and loss of heavy REEs (HREEs). Geo−accumulation index, modified degree of contamination, pollution load index, and potential ecological risk index showed the moderately contaminated level of REEs in FTM, followed by SJM, and XXM, with uncontaminated level in BGM. This study provided important information on REEs in urban mangroves for pollution prevention and remediation in the future.

Reliable quantitative information regarding sediment sources is essential for target mitigation, particularly in settings with a large number of loose provenances caused by earth disasters. The lakes in the Jiuzhaigou World Natural Heritage Site (WNHS) are facing serious environmental problems of silting and swamping, which threaten the sustainability of the area, especially after the earthquake on 8 August 2017 (the “8.8 earthquake”). Therefore, a field investigation was conducted after the “8.8 earthquake” (June 2020), and the Arrow Bamboo and Rhino Lakes, which were affected by the earthquakes to different degrees, were selected as the research objects. Based on the data of 27 environmental indicators from 31 surface sediment and soil samples in and around the lakes, the spatial distribution characteristics of the lake sediment sources were quantified using composite fingerprint recognition technology. Furthermore, a high protection standard of a WHNS and a process treatment scheme for reducing the siltation of the Jiuzhaigou lakes were proposed. The results showed that the contribution ratio of loose matter sources entering the lake on the road-side of the Arrow Bamboo and Rhino Lakes (16.5% and 21.8%, respectively) was lower than that on the forest-side (83.5% and 78.2%, respectively), indicating that physical barriers such as roads can effectively reduce the sediment input, while the lake forest side contributes a large number of loose matter sources, which has not attracted attention in the past and requires protection. High protection standards for the Jiuzhaigou WHNS are suggested. Accordingly, the entire control scheme of Jiuzhaigou lake sediment reduction including “monitoring–control–interception–buffer–cleaning” is provided. Source erosion monitoring is the first step in blocking the sediment source. Vegetation restoration and surface coverage should be conducted in areas where water and soil losses have occurred. Necessary engineering measures should be implemented to intercept loose material sources at points where geological disasters occur frequently. A buffer zone should be established between the lake and the mountain to intercept the sediment. Sediment caused by geological disasters with low interference must also be cleaned from the lake. The level of nutrients in the lake must be controlled by the regular cleaning of plant debris from the lake and lakeside.

Stand structural configuration dictates ecosystem functional performance. Mangrove ecosystems, located in ecologically sensitive coastal ecotones, require efficient acquisition of stand structure parameters and health assessments based on these parameters for practical applications. Effective assessment of mangrove ecosystem health, crucial for their functional performance in ecologically sensitive coastal ecotones, relies on efficient acquisition of stand structure parameters. This study developed a UAV (Unmanned Aerial Vehicle)-based framework for mangrove health evaluation integrating stand structure parameters, utilizing UAV visible-light imagery, field plot surveys, and computer vision techniques, and applied it to the assessment of a national nature reserve. We obtained the following results: (1) A deep neural network, combining UAV visible-light data with tree height constraints, achieved 88.29% overall accuracy in simultaneously identifying six dominant mangrove species; (2) Stand structure parameters were derived based on individual tree extraction results in seedling zones along forest edges (with canopy individual tree segmentation accuracy ≥ 78.57%), and a stand health evaluation model was constructed; (3) Health assessment revealed that the core zone exhibited significantly superior stand health compared to non-core zones. This method demonstrates high efficiency, significantly reducing the time and effort for monitoring, and offers robust support for future mangrove forest health assessments and adaptive conservation strategies.

Mangrove forests provide many ecological services and are among the most productive intertidal ecosystems on earth. Currently, these forests frequently face significant heavy metal pollution as well as eutrophication. The present study assessed the response of Kandelia obovata Sheue, H.Y. Liu & J. Yong to combined NH4+–N addition and Cd stress based on a three-temperature (3T) model using high-resolution thermal infrared remote sensing. The results show that leaf surface temperature (Tc) and the plant transpiration transfer coefficient (hat) became larger with increasing NH4+–N concentrations in the same Cd treatment, especially under high NH4+–N (50 and 100 mg·L-1) and Cd stress. The thermal bioindicators, growth responses and photosynthetic parameters changed in a consistent fashion, indicating that combined high NH4+–N addition and Cd stress led to stomatal closure, reduced the cooling effect of transpiration, and increased Tc and hat values. Furthermore, appropriate NH4+–N supply reduced stomatal conductance (gs) and the transpiration rate (Tr), which were increased by Cd stress, and then maintained Tc and hat at normal levels. The normalised hat helped to reduce the influence of environmental variation during the diagnosis of mangrove plant health. This indicated that the 3T model with high-resolution thermal infrared remote sensing provides an effective technique for determining the health status of mangrove plants under stress.