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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]

As core algorithms of geographic computing, overlay analysis algorithms typically have computation-intensive and data-intensive characteristics. It is highly important to optimize overlay analysis algorithms by parallelizing the vector polygons after reasonable data division. To address the problem of unbalanced data partitioning in the task decomposition process for parallel polygon overlay analysis and calculation, this paper presents a data partitioning method based on shape complexity index optimization, which achieves data equalization among multicore parallel computing tasks. Taking the intersection operator and difference operator of the Vatti algorithm as examples, six polygon shape indexes are selected to construct the shape complexity model, and the vector data are divided in accordance with the calculated shape complexity results. Finally, multicore parallelism is achieved based on OpenMP. The experimental results show that when a data set with a large amount of data is used, the effect of the multicore parallel execution of the Vatti algorithm's intersection operator and difference operator based on shape complexity division is clearly improved. With 16 threads, compared with the serial algorithm, speedups of 29 times and 32 times can be obtained. Compared with the traditional multicore parallel algorithm based on polygon number division, the speed can be improved by 33% and 29%, and the load balancing index is reduced. For a data set with a small amount of data, the acceleration effect of this method is similar to that of traditional methods involving multicore parallelism. [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]

The lack of high-spectral and high-resolution remote sensing data is impeding the differentiation of various fruit tree species that share comparable spectral and spatial features, especially for evergreen broadleaf trees in tropical and subtropical areas. Here, we propose a novel decision tree approach to map the spatial distribution of fruit trees at a 10 m spatial resolution based on the growth stage features extracted from Sentinel-1A (S-1A) time-series synthetic aperture radar (SAR) data. This novel method was applied to map the spatial distribution of fruit trees in Maoming City, which is known for its vast cultivation of fruit trees, such as litchi, citrus, and longan. The results showed that the key to extracting information on the distribution of fruit trees lies in the fact that the fruit ripening and expansion period attenuates the information on the vegetation of fruit trees, a characteristic of the reproductive period. Under VH polarization, different fruit tree growth stage traits were more separable and easier to distinguish. The optimal features, such as H v (high valley value of the 14 May, 26 May, and 7 June SAR data), T b (difference between the 7 June and 14 January SAR data), Cr (high valley value of the 13 July, 25 July, and 6 August SAR data), and L o (high valley value of the 23 September, 17 October, and 11 November SAR data), were constructed based on the optimal window. The thresholds for these features were set to 1, 1, 1.5, and 1, respectively. The classification model can effectively distinguish different fruit trees and extract distribution information with overall accuracy (OA) of 90.34% and a Kappa coefficient of 0.84. The proposed method extracts the spatial distribution information of different fruit trees more accurately and provides a reference for the extraction of more tropical and subtropical species. [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]

The temperature vegetation dryness index (TVDI) is widely used for the monitoring of global or regional drought because of its strong drought-monitoring capabilities and ease of implementation. However, the temporal errors in the land surface temperature (LST) and normalized difference vegetation index (NDVI) can affect warm and cold edges, thus determining the quality of the TVDI, especially in regions affected by climate change, such as Shandong Province. This paper explores this issue in the region in 2011, using daily MODIS MOD09GA and MOD11A1 data products. For each image acquisition time, the warm and cold edges of the NDVI–LST were extracted based on the NDVI, derived from red and near-infrared reflectance data, and the LST, derived from the MOD11A1 dataset. Then, the variations in the warm and cold edges with the LST and NDVI were analyzed. Subsequently, the influence of warm and cold edges, based on the daily values of the temperature, NDVI and precipitation during the observed period, was assessed using a linear regression. The soil moisture (SM) data obtained from the Global Land Data Assimilation System (GLDAS) datasets and the crop water stress index (CWSI) obtained from the MOD16A2 products were used for the assessment. The spatial and temporal variations in drought in Shandong Province from 2011 to 2020 were measured based on Theil–Sen median trend analysis and the Mann–Kendall test. The results show that apparently random variations were evident in the temporal evolution of the slope of the warm edge, indicating that daily data were appropriate to determine the boundary of the warm edge. Daily data were also appropriate to determine the boundary of the cold edge in a similar way. Additionally, the temperature, NDVI and precipitation in this region affected by climate change had a negative correlation with the slope and a positive correlation with the intercept. The validation results show that there was a significant negative correlation between the observed TVDI and GLDAS soil moisture values (R2 > 0.62) in 12 scatter plots. Therefore, we deduced that the monthly or yearly TVDI product produced by the daily MODIS data has a higher precision than that produced by 8-day or monthly data in regions affected by climate change. The spatial and temporal variations show that the trend of slight and moderate droughts first increased and then decreased, and, in particular, some areas presented severe drought from 2011 to 2015. The results obtained in this study are important for the scheduling of irrigation and drought warnings. [ABSTRACT FROM AUTHOR]

The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB) nighttime light data is a powerful remote sensing data source. However, due to stray light pollution, there is a lack of VIIRS data in mid-high latitudes during the summer, resulting in the absence of high-precision spatiotemporal continuous datasets. In this paper, we first select nine-time series interpolation methods to interpolate the missing images. Second, we construct image pixel-level temporal continuity constraints and spatial correlation constraints and remove the pixels that do not meet the constraints, and the eliminated pixels are filled with the focal statistics tool. Finally, the accuracy of the time series interpolation method and the spatiotemporally constrained interpolation method (STCIM) proposed in this paper are evaluated from three aspects: the number of abnormal pixels (NP), the total light brightness value (TDN), and the absolute value of the difference (ADN). The results show that the images simulated by the STCIM are more accurate than the nine selected time series interpolation methods, and the image interpolation accuracy is significantly improved. Relevant research results have improved the quality of the VIIRS dataset, promoted the application research based on the VIIRS DNB long-time series night light remote sensing image, and enriched the night light remote sensing theory and method system. [ABSTRACT FROM AUTHOR]

Building change detection is an important task in the remote sensing field, and the powerful feature extraction ability of the deep neural network model shows strong advantages in this task. However, the datasets used for this study are mostly three-band high-resolution remote sensing images from a single data source, and few spectral features limit the development of building change detection from multisource remote sensing images. To investigate the influence of spectral and texture features on the effect of building change detection based on deep learning, a multisource building change detection dataset (MS-HS BCD dataset) is produced in this paper using GF-1 high-resolution remote sensing images and Sentinel-2B multispectral remote sensing images. According to the different resolutions of each Sentinel-2B band, eight different multisource spectral data combinations are designed, and six advanced network models are selected for the experiments. After adding multisource spectral and texture feature data, the results show that the detection effects of the six networks improve to different degrees. Taking the MSF-Net network as an example, the F1-score and IOU improved by 0.67% and 1.09%, respectively, compared with high-resolution images, and by 7.57% and 6.21% compared with multispectral images. [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]

Road crack segmentation based on high-resolution images is an important task in road service maintenance. The undamaged road surface area is much larger than the damaged area on a highway. This imbalanced situation yields poor road crack segmentation performance for convolutional neural networks. In this paper, we first evaluate the mainstream convolutional neural network structure in the road crack segmentation task. Second, inspired by the second law of thermodynamics, an improved method called a recurrent adaptive network for a pixelwise road crack segmentation task is proposed to solve the extreme imbalance between positive and negative samples. We achieved a flow between precision and recall, similar to the conduction of temperature repetition. During the training process, the recurrent adaptive network (1) dynamically evaluates the degree of imbalance, (2) determines the positive and negative sampling rates, and (3) adjusts the loss weights of positive and negative features. By following these steps, we established a channel between precision and recall and kept them balanced as they flow to each other. A dataset of high-resolution road crack images with annotations (named HRRC) was built from a real road inspection scene. The images in HRRC were collected on a mobile vehicle measurement platform by high-resolution industrial cameras and were carefully labeled at the pixel level. Therefore, this dataset has sufficient data complexity to objectively evaluate the real performance of convolutional neural networks in highway patrol scenes. Our main contribution is a new method of solving the data imbalance problem, and the method of guiding model training by analyzing precision and recall is experimentally demonstrated to be effective. The recurrent adaptive network achieves state-of-the-art performance on this dataset. [ABSTRACT FROM AUTHOR]

There are significant differences in the dominant driving factors of rocky desertification evolution in different historical periods in southwest karst mountainous areas. However, previous studies were mostly conducted in specific periods. In this study, taking Bijie City as an example, the spatial and temporal evolution pattern of rocky desertification in Bijie City in the recent 35 years was analyzed by introducing the feature space model and the gravity center model, and then the dominant driving factors of rocky desertification in the study area in different historical periods were clarified based on GeoDetector. The results were as follows: (1) The point-to-point B (bare land index)-DI (dryness index) feature space model has high applicability for rocky desertification monitoring, and its inversion accuracy was 91.3%. (2) During the past 35 years, the rocky desertification in Bijie belonged to the moderate rocky desertification on the whole, and zones of intensive and severe rocky desertification were mainly distributed in the Weining Yi, Hui, and Miao Autonomous Region. (3) During 1985–2020, the rocky desertification in Bijie City showed an overall weakening trend ('weakening–aggravating–weakening'). (4) From 1985 to 2020, the gravity center of rocky desertification in Bijie City moved westward, indicating that the aggravating degree of rocky desertification in the western region of the study area was higher than that in the eastern region. (5) The dominant factors affecting the evolution of rocky desertification in the past 35 years shifted from natural factor (vegetation coverage) to human activity factor (population density). The research results could provide decision supports for the prevention and control of rocky desertification in Bijie City and even the southwest karst mountainous area. [ABSTRACT FROM AUTHOR]

Background and Purpose: Migration and differentiation of epidermal cells are essential for epidermal regeneration during wound healing. Fibroblast growth factor 21 (FGF21) plays key roles in mediating a variety of biological activities. However, its role in skin wound healing remains unknown. Experimental Approach Fgf21 knockout (Fgf21 KO) mice were used to determine the effect of FGF21 on wound healing. The source of FGF21 and its target cells were determined by immunohistochemistry, immunoblotting, and ELISA assay. Moreover, Sirt1flox/flox and Atg7flox/flox mice were constructed and injected with the epidermal‐specific Cre virus to elucidate the underlying mechanisms. Migration and differentiation of keratinocytes were evaluated in vitro by cell scratch assays, immunofluorescence, and qRT‐RCR. The effects were further assessed when SIRT1, ATG7, ATG5, BECN1, and P53 were silenced. Interactions between SIRT1 and autophagy‐related genes were assessed using immunoprecipitation assays. Key Results: FGF21 was active in fibroblasts and promoted migration and differentiation of keratinocytes following injury. After wounding, SIRT1 expression and autophagosome synthesis were lower in Fgf21 KO mice. Depletion of ATG7 in keratinocytes counteracted the FGF21‐induced increases in migration and differentiation, suggesting that autophagy is required for the FGF21‐mediated pro‐healing effects. Furthermore, epithelial‐specific Sirt1 knockout abolished the FGF21‐mediated improvements of autophagy and wound healing. Silencing of SIRT1 in keratinocytes, which decreased deacetylation of p53 and autophagy‐related proteins, revealed that FGF21‐induced autophagy during wound healing was SIRT1‐dependent. Conclusions and implications: FGF21 is a key regulator of keratinocyte migration and differentiation during wound healing. FGF21 may be a novel therapeutic target to accelerate would healing. [ABSTRACT FROM AUTHOR]

Aiming at the complex nonlinear relationship among factors affecting blasting fragmentation, the input weight and hidden layer threshold of ELM (extreme learning machine) were optimized by gray wolf optimizer (GWO) and the prediction model of GWO-ELM blasting fragmentation was established. Taking No. 2 open-pit coal mine of Dananhu as an example, seven factors including the rock tensile strength, compressive strength, hole spacing, row spacing, minimum resistance line, super depth, and specific charge are selected as the input factors of the prediction model. The average size of blasting fragmentation X50 is selected as the output factor of the prediction model and compared with the results of PSO-ELM and ELM. The results show that MAPE of GWO-ELM, PSO-ELM, and ELM are 1.78%, 5.40%, and 10.90%, respectively; their RMSE are 0.007, 0.022, and 0.045, respectively. The ELM model optimized by the gray wolf optimizer is more accurate and has stronger data fitting ability than PSO-ELM and ELM models, and the prediction accuracy of GWO-ELM is much higher than that of PSO-ELM and ELM. [ABSTRACT FROM AUTHOR]

In order to solve the serious dust pollution problem in mining and loading process of burnt rock open-pit coal mines, a dust suppression technology was proposed to investigate the compound dust suppressant with four functions of wetting, coagulation, moisture absorption, and moisture retention, and improve the hydrophilicity and dust suppression efficiency of burnt rock. Through single-factor experiments, four functional reagents were selected. Determined the best mass concentration ratio by orthogonal test, and the optimum dilution ratio was determined through the contact angle verification test. Fourier infrared spectroscopy (FTIR) analysis of the functional group of the compound dust suppressant showed that the hydrophilic functional group was increased. The scanning electron microscopy (SEM) revealed that the dust surface formed a dense film after spraying the dust suppressant, and the effect of fine dust coagulation was obvious. The results of the wind tunnel simulation test revealed that the inhibitory efficiency of the compound dust suppressant on total dust and respirable dust could reach up to 81.90% and 87.06%, respectively, under a wind speed of 4.00 m/s. The field test data for mining and loading spray dust suppression in open-pit coal mines revealed that the dust suppression efficiency of whole dust and respirable dust was greater than 85.70%, which indicates that the compound dust suppressant can effectively suppress the dust of burnt rock, and effectively improve the working environment quality of mining and loading in open-pit coal mines. Implications: Open-pit coal mines located in arid areas will produce a large amount of dust during the mining and loading process, which will cause serious air pollution. In particular, the burnt rock is highly hydrophobic, and it is difficult to achieve effective removal by water spraying. The open-pit coal mine face advances faster and has strong mobility, There are few researches on dust reduction in this link. Therefore, in this study, in order to improve the dust suppression efficiency in the mining and loading process, a compound dust suppressant to improve the hydrophilicity of the burnt rock was developed through single-factor experiments and orthogonal experiments. Microscopic analysis using FTIR and SEM confirmed the wetting and coagulation effect of the dust suppressant. Then designed a spray dust suppression program for the mining and loading process and achieved good results. [ABSTRACT FROM AUTHOR]

With the rapid development of remote sensing technologies, the frequency of observations of the same location is increasing, and many satellites and sensors produced a large amount of time series images. These images make long-term change detection and dynamic characteristic estimation of ground features possible. However, conventional remote sensing image change detection methods mostly rely on manual visual interpretation and supervised or unsupervised computer-aided classification. Traditional methods always face many bottlenecks when processing big and fast-growing datasets, such as low computational efficiency, low level of automation, and different identification standards and accuracies caused by different operators. With the rapid accumulation of remote sensing data, it has become an important but more challenging task to conduct change detection in a more precise, automated and standardized way. The development of geointelligent computing technologies provides a means of solving these problems and improve the accuracy and efficiency of remote sensing image change detection. In this paper, we presented a novel deep learning model called nest network(NestNet) based on a convolutional neural network to improve the accuracy of the automatic change detection task by using remotely sensed time series images. NestNet extracts the respective features of bi-temporal images using an encoding parallel module and subsequently employs absolute different operations to process the features of two images. Compared with change detection method based on U-Shaped network plus plus (UNet++), the parallel module improves the efficiency of NestNet. Finally, a decoding module is used to generate a predicted change image. This paper compares NestNet to traditional methods and state-of-the-art deep learning models on two datasets. The experimental results demonstrate that the accuracy of NestNet is better than that of state-of-the-art methods. It can be concluded that the NestNet model is a potential approach for change detection using high resolution remote sensing images. [ABSTRACT FROM AUTHOR]

Under the context of global climate change, vegetation on the Tibetan Plateau (TP) has experienced significant changes during the past three decades. In this study, the spatiotemporal changes of growing season vegetation index (GSVI) on the TP were analyzed using various methods from pixel level to ecoregion level. In addition, a relative importance approach was employed to investigate the regulating effect of temperature and precipitation on vegetation. During the period of 1982–2012, vegetation on the TP was generally experiencing a greening trend, but with pronounced fluctuations. The interannual variation of the long-term GSVI was most significant in the Qaidam Basin and southern forest. At ecoregion scale, vegetation in the arid and frigid arid zones showed a browning tendency, with other ecoregions presenting greener trends. Over a large proportion of the TP, there exist change points in the GSVI time series, which were mainly concentered around the year 1996 and 2000. The Hurst exponent identified that a majority (88%) of the vegetation on the plateau would maintain a persistent trend in the future, which would mainly consist of undetermined development and greening trends. TP vegetation during the 1990s experienced more greening than in the 1980s or 2000s according to the interdecadal analysis. The long-term change in growing season vegetation was most positively correlated with the temperature during the same period, followed by the temperature in the preseason and postseason periods. There were more negative relationships of vegetation change with precipitation than with temperature. The relative contribution of the temperature to the vegetation changes exhibited an opposite spatial pattern to that of precipitation. Overall, the findings in this work provide an essential archive of decade-scale vegetation dynamics that may be helpful for projecting the future ecosystem dynamics on the Tibetan Plateau, such as the consistent greening. [ABSTRACT FROM AUTHOR]

In this study, the effects of the disulfide bond cleavage induced by peracetic acid oxidation on the surface properties and surface hydrophobicity of soy proteins isolate (SPI) were investigated. The surface hydrophobicity, foaming capacity and emulsifying capacity of oxidized-SPI increased gradually at the initial stage with the increase of peracetic acid concentration. When the concentration of peracetic acid was increased up to 0.4%, compared with that of native SPI, the surface hydrophobicity, foaming capacity, emulsifying capacity and emulsifying stability of oxidized-SPI increased by 114.0, 81.4, 65.2, 49.8%, respectively, and achieved optimal results. However, excessive oxidation led to a decrease in surface hydrophobicity, foaming capacity and emulsifying stability of SPI, but it had no obvious effect on the foaming stability and emulsifying capacity of SPI. The foaming capacity, emulsifying capacity and emulsifying stability of SPI were positively related to the changes of surface hydrophobicity which caused by disulfide bond cleavage. The results of fluorescence spectroscopy, CD spectroscopy, and particle size analysis showed that the disulfide bond cleavage did cause great changes in the molecular structure of SPI, but there was no clear correlation between the molecular structural change and the surface activity of SPI. These suggested that the improvement of foaming capacity, emulsifying capacity and emulsifying stability of SPI could be achieved by changing its surface hydrophobicity via peracetic acid oxidation. [ABSTRACT FROM AUTHOR]

Remotely sensed measurements of anthropogenic nocturnal lighting have been extensively used for studying human settlements and socioeconomic dynamics. Considerable efforts have been devoted to build the connections between nightlight signals and demographic and economic variables at local to global scales in order to obtain observationally based estimates for human activity. Recently, the first cloud-free composite of global nighttime light data derived from the Suomi National Polar orbiting Partnership (Suomi-NPP) with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor yields an increasingly clearer view of the earth surface on nights. The responses to socioeconomic activity and the potential utility of VIIRS nighttime light data, however, still remain less well understood. In this study, we examined the quantitative relationships between VIIRS nightlight-derived indices and socioeconomic variables at fine and local scales. Our results suggest that the total night radiance has significant positive associations with two airport performance indicators – passenger traffic and aircraft movement. In addition, there is a strong correlation with four urbanization variables – human population, gross domestic product, electric power consumption and paved road area. Our findings suggest that VIIRS nightlight data could be more indicative of socioeconomic dynamics and may provide insights into the potential applications for studying human settlement and urbanization processes based on anthropogenic nocturnal lighting. [ABSTRACT FROM AUTHOR]

• A novel salinization monitoring model based on 3-D feature space had been proposed with the overall accuracy of 93%. • The relative actions of climate change and human activities have been quantitatively distinguished from a new perspective of "' soil solute transport-vegetation response '". • The NDVI ∩ other factors were the dominant factors in the salinization process. • Human activities gradually became the dominant factor in salinization improvement or intensification area. Within the context of global change, the evolution of salinization in the Yellow River Delta has undergone dramatic changes. However, it is not clear how it responds to the combined processes of climate change and human activity（reclamation projects, changes in land-use types, and increases in fertilizer use）Based on the optimal soil salinization monitoring index model, this study retrieved the annual time series data set for salinization from 1984 to 2022 and then analyzed the spatial and temporal evolution patterns of salinization in the modern Yellow River Delta. The mechanism of the response of salinization evolution to climate change and human activities was investigated using Geodetector. The main conclusions were as follows: (1) From 1984 to 2022, the salinization intensity in the northeastern coastal zone of the Yellow River Delta showed an increasing trend, while that in the southwestern inland area showed an improving trend. (2) The high-frequency salinization zones were mainly concentrated near the mainstream and tributaries of the Yellow River, while zones with desalinization, re-salinization, and new salinization zones were mainly distributed in the main stream of the Yellow River. (3) There were obvious differences in the dominant factors in the evolution of salinization in different historical periods. In 1984, soil component factors and vegetation coverage were the dominant factors in the evolution of salinization. In 2022, vegetation coverage and land-use types had the strongest explanatory power for the salinization process. (4) The influence of natural and human activity factors on the salinization process was not independent, but a mutually reinforcing process. The interaction between NDVI and other factors has dominated the evolution of salinization in the Yellow River Delta. From 1984 to 2022, human activities were the dominant factors that promoted the improvement or intensification of salinization in the region. [ABSTRACT FROM AUTHOR]

In this paper, the binding characteristics of aflatoxin B 1 (AFB 1) with the herring sperm deoxyribonucleic acid (DNA) in vitro were investigated through different analytical methods. The ultraviolet-visible spectroscopy (UV‐vis), fluorescence, and circular dichroism (CD) spectra results showed that a new AFB 1 -DNA complex was formed. All the results suggested that AFB 1 interacted with free DNA in vitro in an intercalating binding mode. The results of the DNA melting experiments also showed that the melting temperature of DNA increased by about 12.1 °C due to the addition of AFB 1 , which was supposed to be closely related to the intercalation of AFB 1 into DNA. The agar gel electrophoresis experiments further confirmed that the binding mode of AFB 1 and free DNA in vitro was indeed intercalation. In addition, the fluorescence quenching induced by adding AFB 1 to the ethidium bromide-DNA (EB-DNA) mixture indicated the presence of competitive non-covalent intercalating binding interaction with a competitive binding constant of 5.58 L/mol between AFB 1 , EB, and DNA. The thermodynamic data demonstrated that the main driving forces of the binding reaction were van der Waals forces and hydrogen bond. The resonance light scattering (RLS) assay results showed that the DNA binding saturation values of AFB 1 , EB, psoralen (PSO), and angelicin (ANG) were 2.14, 15.59, 0.74, and 0.74, respectively. These results indicated that the DNA binding capacity of AFB 1 was weaker than that of EB, but stronger than those of PSO and ANG. Unlabelled Image • Interaction is studied by various ways. • Intercalation is confirmed by electrophoresis. • It gives base for a new AFB1 elimination way. [ABSTRACT FROM AUTHOR]

Liver fibrosis, which is characterized by excessive accumulation of extracellular matrix (ECM) primarily produced by hepatic stellate cells (HSCs), can eventually lead to cirrhosis. Fibroblast growth factor 18 (FGF18) mediates various biological activities. However, the precise role of FGF18 in the pathological process of liver fibrosis and the underlying mechanisms have not been elucidated. In this study, we found that FGF18 was markedly upregulated in carbon tetrachloride (CCl 4)-induced fibrotic mouse liver tissues and transforming growth factor β (TGF-β) stimulated LX-2 cells. Furthermore, our studies demonstrated that overexpression of FGF18 in the liver significantly alleviated CCl 4 -induced fibrosis and inhibited the activation of HSCs, while exacerbated by HSC-specific deletion of FGF18. Mechanistically, FGF18 treatment dramatically activated Hippo signaling pathway by suppressing smoothened (SMO) both in vivo and in vitro. Moreover, the interaction between SMO and LATS1 was crucial for the FGF18 induced protective effects. In conclusion, these results indicated that FGF18 attenuates liver fibrosis at least partially via the SMO-LATS1-YAP signaling pathway and therefore may be a potential therapeutic target for liver fibrosis. [Display omitted] • The protective effects of FGF18 on liver fibrosis mainly attributed to alleviating the activation of hepatic stellate cells. • Inhibition of hepatic stellate cells activation depends on Hippo signaling pathway. • FGF18 activated Hippo signaling pathway via SMO/LATS1/YAP pathway. [ABSTRACT FROM AUTHOR]