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Deep convolutional networks often encounter information bottlenecks when extracting land object features, resulting in critical geometric information loss, which impedes semantic segmentation capabilities in complex geospatial backgrounds. We developed LULC-SegNet, a semantic segmentation network for land use and land cover (LULC), which integrates features from the denoising diffusion probabilistic model (DDPM). This network enhances the clarity of the edge segmentation, detail resolution, and the visualization and accuracy of the contours by delving into the spatial details of the remote sensing images. The LULC-SegNet incorporates DDPM decoder features into the LULC segmentation task, utilizing machine learning clustering algorithms and spatial attention to extract continuous DDPM semantic features. The network addresses the potential loss of spatial details during feature extraction in convolutional neural network (CNN), and the integration of the DDPM features with the CNN feature extraction network improves the accuracy of the segmentation boundaries of the geographical features. Ablation and comparison experiments conducted on the Circum-Tarim Basin Region LULC Dataset demonstrate that the LULC-SegNet improved the LULC semantic segmentation. The LULC-SegNet excels in multiple key performance indicators compared to existing advanced semantic segmentation methods. Specifically, the network achieved remarkable scores of 80.25% in the mean intersection over union (MIOU) and 93.92% in the F1 score, surpassing current technologies. The LULC-SegNet demonstrated an IOU score of 73.67%, particularly in segmenting the small-sample river class. Our method adapts to the complex geophysical characteristics of remote sensing datasets, enhancing the performance of automatic semantic segmentation tasks for land use and land cover changes and making critical advancements. [ABSTRACT FROM AUTHOR]

With increasing research on geological hazards and the development of geographic information technology, slope units play an increasingly important role in landslide susceptibility assessment and prevention work. The scientific and reasonable division of slope units directly impacts the accuracy and practicality of analysis results. Despite the significant progress in slope unit division techniques, most existing methods still have certain limitations, such as a strong dependence on manually set thresholds during the division process, resulting in low levels of automation and efficiency. To address this issue, a new parameter-free slope unit extraction algorithm that integrates terrain factors, called Terrain Factor Parameter-Free Slope Unit Division (TFPF-SU), is introduced. This eliminates the issue of manually setting parameter thresholds during the slope unit division process. This algorithm fully utilizes the terrain information provided by digital elevation models (DEMs) to accurately calculate the curvature, slope, and aspect data for each point. On the basis of the inherent consistency principles among slope, aspect, and curvature, object-oriented image segmentation technology is used to achieve slope unit division. We select Dongchuan District in Yunnan Province, China, as a test area to verify the TFPF-SU algorithm and conduct a detailed comparative analysis and validation of the results with those obtained via traditional hydrological analysis methods from both qualitative and quantitative perspectives. In the quantitative analysis, we utilize the size and shape of the slope units. The results indicate the following: ① the slope units obtained with the TFPF-SU method are more uniform in size, avoiding issues with oversized or irregularly shaped units; ② the slope unit shapes obtained with the TFPF-SU method are more reasonable, with about 70% of the units falling within a reasonable shape index range, compared to only about 32% with the hydrological method; and ③ the slope units produced by the TFPF-SU method align more closely with terrain authenticity, exhibiting a higher degree of topographical conformity. [ABSTRACT FROM AUTHOR]

Land use/cover change (LUCC) can impact the provision of ecosystem service values (ESVs), particularly in wetland regions that are subject to frequent and unsustainable land conversions. Exploring the past and future trajectory of LUCC and its effects on ESV has a great significance for wetland management and habitat stability. This study tried to reveal the patterns and magnitude of LUCC on ESV under varying land development scenarios in the Yellow River Delta region, which is a typical region undergoing serious degradation in China. In this study, a combined approach utilizing equivalent coefficients of ecosystem services was employed to determine the ESV of the wetland in relation to the major land use types (LUTs). The Markov–FLUS model was then used to simulate LUTs across multiple scenarios in 2030 and to clarify the relationship of ESV between wetland and other LUTs. The results indicated that the wetland was severely degraded, with a loss in area of 6679.89 ha between 2000 and 2020. Cropland and water body were the main sources of diversion and turnover for the wetland, respectively. Despite the multiple scenario projections revealed, the wetland area exhibited a similar growth rate and a homogeneity in ESV under the natural development (ND), urban construction and development (UCD), and the ecological development (ED) scenarios. The ED scenario was deemed the optimal development strategy for the wetland ecosystem. Our research will improve the comprehension of land development decisions and promote sustainable development in estuarine wetland areas. [ABSTRACT FROM AUTHOR]

This study examined the impact of temporary air quality control measures on reducing pollutants during the 2022 Winter Olympics in China, utilizing real-time monitoring data from 2017 and 2022 to assess spatial and temporal variations in critical air pollutant concentrations. The results showed that concentrations of PM2.5, PM10, CO, SO2, and NO2 in the Beijing–Tianjin–Hebei region during the Olympic Games showed a marked decrease compared to the historical period, with reductions of 36.59%, 20.35%, 33.95%, 28.90%, and 22.70%, respectively. Significant north–south spatial differences were observed in Beijing, Tianjin, and Hebei Province during the Olympic period. The cities of Zhangjiakou, Chengde, Qinhuangdao, Beijing, and Tangshan showed the most significant pollution reduction. Based on assessments conducted during the Olympic period, it was noted that more than 95% of the daily average concentrations of pollutants are below the maximum values set by the World Health Organization for the interim target. Our research shows that provisional regulations effectively control the emission of air pollutants, providing a solid reference and basis for ensuring air quality during major international events. [ABSTRACT FROM AUTHOR]

Background and Purpose: Liver fibrosis is a serious cause of morbidity and mortality worldwide characterized by accumulation of extracellular matrix produced by hepatic stellate cells (HSCs). The protein kinase CK2 is a pro‐survival kinase overexpressed in human tumours. However, the biological role of CK2 in liver fibrosis is largely unknown. We aimed to investigate the mechanism by which CK2 promotes liver fibrosis. Experimental Approach: In vitro, LX‐2 cells were stimulated with transforming growth factor‐β (TGF‐β). HSCs were also isolated for research. In vivo, the adeno‐associated virus AAV‐sh‐csnk2a1 was used to knockdown CK2α specifically in HSCs, and CX‐4945 was used to pharmacologically inhibit the enzymatic activity of CK2 in murine models of fibrosis induced by carbon tetrachloride (CCl4) and a 3,5‐diethoxycarbonyl‐1,4‐dihydrocollidine (DDC) diet. Histological and biochemical analyses were performed to study the involvement of CK2 in regulation of fibrogenic and fibrolytic factors as well as activation properties of HSCs. Key Results: HSC‐specific genetic invalidation of CK2α or pharmacological inhibition of CK2 protected mice treated with CCl4 or fed a DDC diet against liver fibrosis and HSC accumulation. Mechanistically, CK2α, which bound to Smoothened (SMO), was a positive regulator of the Hedgehog signal transduction pathway. CK2 prevented ubiquitination and proteasomal degradation of SMO, which was abolished by knockdown of CK2α or pharmacological inhibition of CK2. Conclusions and Implications: CK2 activation is critical to sustain the activated and fibrogenic phenotype of HSCs via SMO stabilization. Therefore, inactivation of CK2 by CX‐4945 may be of therapeutic interest for liver fibrotic diseases. [ABSTRACT FROM AUTHOR]

Studies of diabetic glomerular injury have raised the possibility of developing useful early biomarkers and therapeutic approaches for the treatment of type 2 diabetic nephropathy (T2DN). In this study, we found that FGF13 expression is induced in glomerular endothelial cells (GECs) during T2DN progression. Endothelial-specific deletion of Fgf13 potentially alleviates T2DN damage, while Fgf13 overexpression has the opposite effect. Mechanistically, Fgf13 deficiency results in improved mitochondrial homeostasis and endothelial barrier integrity in T2DN. Moreover, FGF13-sensitive alteration of Parkin safeguards mitochondrial homeostasis in endothelium of T2DN through promotion of mitophagy and inhibition of apoptosis. Additionally, it is confirmed that the beneficial effects of Fgf13 deficiency on T2DN are abolished by endothelial-specific double deletion of Fgf13 and Prkn. The effects of Fgf13 deficiency on mitophagy and apoptosis through Parkin-dependent regulation may be distinct and separable events under diabetic conditions. These data show that the bifunctional role of Fgf13 deficiency in promoting mitophagy and inhibiting apoptosis through Parkin can shape mitochondrial homeostasis regulation in GECs and T2DN progression. As a potential therapeutic target for prevention and control of T2DN, a mechanistic understanding of the biofunction of FGF13 may also be relevant to the pathogenesis of other FGF13- and Parkin-associated diseases. [ABSTRACT FROM AUTHOR]