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This document is an editorial from the journal "Remote Sensing" titled "Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities." It discusses the importance of monitoring environmental changes caused by human activities, such as urban expansion, agriculture, deforestation, and mining. The editorial highlights the value of satellite and airborne remote sensing, as well as geospatial approaches, in studying the impact of human activities on the environment. The document provides an overview of 12 published manuscripts that cover various ecological scenarios and human activities, offering new theories, methods, and findings in the field. The editorial concludes by suggesting the need for further research on different types of ecological environments and human activities, as well as improving the accuracy of analyzing the combined effects of natural disturbances and human alterations on the environment. [Extracted from the article]

Automatic extraction of tailing ponds from Very High-Resolution (VHR) remotely sensed images is vital for mineral resource management. This study proposes a Pseudo-Siamese Visual Geometry Group Encoder-Decoder network (PSVED) to achieve high accuracy tailing ponds extraction from VHR images. First, handcrafted feature (HCF) images are calculated from VHR images based on the index calculation algorithm, highlighting the tailing ponds' signals. Second, considering the information gap between VHR images and HCF images, the Pseudo-Siamese Visual Geometry Group (Pseudo-Siamese VGG) is utilized to extract independent and representative deep semantic features from VHR images and HCF images, respectively. Third, the deep supervision mechanism is attached to handle the optimization problem of gradients vanishing or exploding. A self-made tailing ponds extraction dataset (TPSet) produced with the Gaofen-6 images of part of Hebei province, China, was employed to conduct experiments. The results show that the proposed method achieves the best visual performance and accuracy for tailing ponds extraction in all the tested methods, whereas the running time of the proposed method maintains at the same level as other methods. This study has practical significance in automatically extracting tailing ponds from VHR images which is beneficial to tailing ponds management and monitoring. [ABSTRACT FROM AUTHOR]

In response to the critical need for accurate pavement rutting prediction in pavement design, this study introduces a novel deep-learning model, Circle LSTM, designed for the estimation of rutting depths in asphalt pavements. Leveraging data from a full-scale field accelerated road testing facility in Tongzhou, Beijing, which spans 2.038 kilometres and features 19 distinct asphalt pavements, the Circle LSTM model utilizes variables such as temperature and load axis for its predictions. Methodologically, the Circle LSTM outperforms traditional machine learning approaches by cycling through different LSTM models to enhance short-term dependence modeling, showcasing its strength in addressing nonlinear problems inherent in road engineering. Evaluation of the model on the test dataset yielded promising results, with error metrics showing an average RMSE of 2.4170, an average MAE of 2.9454, an average MAPE of 2.61%, and an average coefficient of determination (average R 2) of 0.8253 for 19 different asphalt pavements. These findings underscore the Circle LSTM model's potential not only for fast and reliable rutting depth prediction but also for broader applications in multivariate time-series forecasting tasks in road engineering and beyond. • A linkage method is proposed to improve the short-term dependency modeling ability. • A double-linked neural network stacking mechanism is developed. • The method achieves good results on predicting rutting depth of 19 asphalt pavements. [ABSTRACT FROM AUTHOR]

Cancer stem cells have been shown to be important in tumorigenesis processes, such as tumor growth, metastasis, and recurrence. As such, many three-dimensional models have been developed to establish an ex vivo microenvironment that cancer stem cells experience under in vivo conditions. Cancer stem cells propagating in three-dimensional culture systems show physiologically related signaling pathway profiles, gene expression, cell–matrix and cell–cell interactions, and drug resistance that reflect at least some of the tumor properties seen in vivo. Herein, we discussed the presently available Cancer stem cell three-dimensional culture models that use biomaterials and engineering tools and the biological implications of these models compared to the conventional ones. [ABSTRACT FROM AUTHOR]

Background: Tenascin-C (TNC) is an extracellular matrix protein that is widely expressed in the stromal fibroblasts of various cancers. However, the roles of TNC in colorectal cancer (CRC) cells remain unclear. Methods: The expression of TNC, cancer stem cell-like (CSC) and cell cycle markers, and Hedgehog (HH) signaling pathway genes were assessed in 100 paraffin embedded clinical CRC patient tissues using immunohistochemistry. The interaction between TNC and CSC marker or HH related genes in CRC cells were detected by immunofluorescence. Cell cycle distribution was measured by flow cytometry. Migration and invasion were evaluated by transwell assays. The expressions of TNC, CSC marker, and HH related proteins were analyzed by western blot. Results: TNC expression was markedly upregulated in CRC tissues, and was associated with worse clinical outcomes. TNC overexpression was positively associated with CSC marker LSD1, cell cycle markers CDK4 and p16, and HH signaling pathway related genes SMO and GLI1 in clinical CRC tissue samples. TNC silencing downregulated the expression of the CSC marker LSD1, and the proliferation, migration, and invasion of CRC cells. Interestingly, the GLI1 inhibitor GANT61 strongly inhibited the expression of TNC in CRC cells. Conclusions: TNC may drive tumor progression and is involved in CSC properties via the HH signaling pathway. TNC has potential value in the evaluation of poor prognosis in CRC. [ABSTRACT FROM AUTHOR]

Cancer stem cells have been shown to be important in tumorigenesis processes, such as tumor growth, metastasis, and recurrence. As such, many three-dimensional models have been developed to establish an ex vivo microenvironment that cancer stem cells experience under in vivo conditions. Cancer stem cells propagating in three-dimensional culture systems show physiologically related signaling pathway profiles, gene expression, cell–matrix and cell–cell interactions, and drug resistance that reflect at least some of the tumor properties seen in vivo. Herein, we discussed the presently available Cancer stem cell three-dimensional culture models that use biomaterials and engineering tools and the biological implications of these models compared to the conventional ones. [ABSTRACT FROM AUTHOR]

Purpose: Bioactive glass-ceramic (BGC) coatings have been extensively studied and clinically used as bone substitute materials because of their osteogenesis, osteoinduction, and osteoconduction characteristics. Although the Hedgehog (Hh) signaling pathway plays an important role in skeletal development, the relationship between BGC coatings and the Hh signaling pathway is unknown.Methods: In this study, a BGC coating is fabricated by furnace sintering, and its surface is investigated by a scanning electron microscope (SEM) and a transmission electron microscope (TEM). Furthermore, the expression of Ki67 is evaluated using immunofluorescence, and osteogenesis-related factors and Hh signaling pathway molecules on the BGC coating are examined by real-time reverse transcription polymerase chain reaction (real-time RT-PCR) and Western blotting in bone marrow mesenchymal stem cells (BMSCs).Results: The SEM and the TEM show that the BGC coating surface is smooth, without cracks, and composed of particles with mesoporous structure. The expression of Ki67 positive BMSCs of the BGC group is higher than that of the control group. Real-time RT-PCR and Western blotting assay reveal that the expression levels of osteoblast-related genes (BMP2, Osteocalcin, ALP, Runx2) and Hh signaling pathway molecules (Gli1, Smo) are much higher for the BGC coating group than those for the control group. Furthermore, after treating with Smo inhibitor cyclopamine, the Smo and Gli1 expressions in BMSCs are dramatically down-regulation for the BGC coating compared to those for the control group. Both mRNA and protein expression levels of osteogenesis-related factors was downregulated after treating Smo inhibitor cyclopamine in BMSCs with the BGC coating.Conclusions: The BGC coatings promote osteogenesis probably via the Hh signaling pathway, which provides a theory reference for future clinical application of bone formation. [ABSTRACT FROM AUTHOR]

This study analysed the changing pattern of the spectral features of copper-stressed leaves for several vegetation types, and explored the mechanism of the Copper Stress Vegetation Index (CSVI). First, the change of seven key spectral features (Green Peak, Red Valley, Red Shoulder, NIR (Near Infrared) Reflectance Platform, Blue-Edge, Yellow-Edge, and Red-Edge) with copper stress level from low to high, were presented and analysed. Second, the chlorophyll contents in leaves were investigated to explain the spectral characteristics at the visible band. Third, the leaf structure and absorption related to copper were analysed to explore the reason of changing pattern at NIR band. The results showed that there are significant changing trends at Blue-Edge, Green Peak, and Red-Edge while the changing pattern at NIR band depends on the vegetation type. The analysis on chlorophyll content, leaf structure, and absorption related to copper, gave an overall mechanism explanation for the spectral characteristics of copper-stressed vegetation and the wavelengths used in CSVI. The results and conclusions in this paper, contribute new knowledge of copper-stressed vegetation reflectance and the CSVI, and provide mechanism basement for the remote sensing of copper-stressed vegetation. [ABSTRACT FROM AUTHOR]

Abstract The role of Hedgehog (HH)/ glioma-associated oncogene homolog 1 (GLI1) pathway has been implicated in a variety of cancer entities, and the targeted pathway inhibition mediated by GLI1 is of therapeutic relevance. However, its oncogenicity and cross-talks with other cancer pathways including PI3K/Akt/NFκB, which modulates the HH/GLI1 signal strength, have rarely been explored in colorectal adenocarcinoma. We assessed the expression of GLI1 and its relationship with other cancer stemness genes, cell cycle markers, epithelial-mesenchymal transition (EMT), PI3K/Akt/NFκB signaling pathway genes, and HIF1α in 100 paraffin-embedded colorectal adenocarcinoma tissue samples using immunohistochemistry. We further addressed the effect of GLI1 on EMT, cell cycle, and its putative interaction with the PI3K/Akt/NFκB cascade in colorectal adenocarcinoma cell lines. The expression of GLI1 in colorectal adenocarcinoma tissues was found to correlate with the clinical stages, and distant metastasis. Moreover, GLI1 was found to be an independent predictor of poor overall survival and disease-free survival in colorectal adenocarcinoma. GLI1-expressing cancer cells also expressed their representative cancer stem-like cell (CSC) markers (SOX9 and CD133), as well as HIF1α. GLI1 expression was also strongly linked to EMT-related and PI3K/Akt/NFκB signaling genes. Downregulation of GLI1 by inhibitor treatment in colorectal adenocarcinoma cell lines resulted in reduced expression of CSC markers, cell clonogenicity, S-phase subpopulations, as well as the migration and invasion ability. Importantly, Akt inhibitor Perifosine significantly inhibited the expression of pAkt and GLI1 in colorectal adenocarcinoma cells. Combination of GLI1 inhibitor GANT61 and NFκB p65 inhibitor QZN exhibited much higher inhibition compared to using any of them individually on colorectal adenocarcinoma cells. We suggested that GLI1 may be a novel stem cell marker, and cancer stemness was activated via PI3K/Akt/NFκB pathway. In addition, co-targeting GLI1 and PI3K/Akt/NFκB signaling simultaneously might provide an alternative therapeutic strategy for colorectal adenocarcinoma patients. Highlights • GLI1 is a prognostic determinant in colorectal adenocarcinoma. • GLI1 may be a potential cancer stem-like cells marker in colorectal adenocarcinoma. • GLI1 activated cancer stemness probably via the PI3K/Akt/NFκB signaling. • Co-targeting GLI1 and PI3K/Akt/NFκB signaling simultaneously might provide an alternative therapeutic strategy for colorectal adenocarcinoma patients. [ABSTRACT FROM AUTHOR]

• The spatio-temporal changes of FVC at the scale of dumping sites were analyzed. • The actions and characteristics of destruction and restoration were identified. • Restoration effect of mine was evaluated with reference to FVC before destruction. • The contributions of climate, human activities, and topography were quantified. • The approach of the paper is important for optimizing the restoring strategy. Ecological restoration in open-pit coal mines faces significant challenges, particularly regarding vegetation restoration in dumping sites, which often experience unstable vegetation status after restoration. Hence, monitoring the vegetation restoration process and evaluating restoration effect in dumping sites are crucial. In this study, the restoration process in dumping sites was rebuilt based on the Fractional Vegetation Cover (FVC), and the restoration effect was evaluated with reference to the FVC before destruction. Considering the topographic factors in dumping sites, the relative contributions of climate change and human activities was quantified. The future restoration potential of dumping sites was explored using the constructed FVC Restoration Potential Index (FRPI). The results show that: (1) This study can accurately identify the actions and characteristics of vegetation destruction and restoration of open-pit coal mines' dumping sites based on FVC time-series curves. (2) The vegetation restoration effect in dumping sites can be accurately evaluated based on the constructed "difference-value" indicator. Additionally, the FRPI enabled the assessment of dumping sites' future FVC restoration potential, providing valuable guidance for future restoration projects. (3) The correlation between topographic factors and FVC in dumping sites was slightly weak, with climate change and human activities serving as the main driving forces. Climate change was key in outer dumping sites, whereas human activities had a greater influence in Inner dumping site. This study provided a valuable approach for assessing the restoration effect, potential, and attribution, facilitating the optimization of future restoration projects. [ABSTRACT FROM AUTHOR]

The clarification of the impact of human activities on vegetation in mining areas contributes to the harmonization of mining and environmental protection. This study utilized Geographically and Temporally Weighted Regression (GTWR) to establish a quantitative relationship among the Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and Digital Elevation Model (DEM). Furthermore, residual analysis was performed to remove the impact of natural factors and separately assess the impact of human activities on vegetation restoration. The experiment was carried out in Shangwan Mine, China, and following results were obtained: (1) During the period of 2000 to 2020, intensified huan activities corresponded to positive vegetation changes (NDVI-HA) that exhibited an upward trend over time. (2) The spatial heterogeneity of vegetation restoration was attributed to the DEM. It is negatively correlated with NDVI in natural conditions, while under the environment of mining activities, there is a positive correlation between NDVI-HA and DEM. (3) The contribution of human activities to vegetation restoration in mining areas has been steadily increasing, surpassing the influences of temperature and precipitation since 2010. The results of this study can provide important references for the assessment of vegetation restoration to some extent in mining areas. [ABSTRACT FROM AUTHOR]

Abstract Although Tenascin-C (TNC) as an extracellular matrix protein involved in various cancers, the mechanisms by which TNC leads to decreased survival time remain to be clarified in CRC. We assessed the expression of TNC and its relationship with cancer associated fibroblasts (CAFs) markers, epithelial-to-mesenchymal transition (EMT) and cell cycle markers in 100 paraffin-embedded CRC tissue samples using immunohistochemistry. TNC expression was higher in CRC tissue samples than in adjacent non-tumor-tissues (P <.001). In addition, TNC was involved in clinical stage (P =.030), pT stage (P =.049), distant metastasis (P =.004), tumor recurrence (P =.007), and tumor budding (P <.001). TNC play crucial roles in regulating the poor 5-year CRC survival rate by Kaplan-Meier analysis, and was an independent predictor of poor overall survival (P =.007) and disease-free survival (P =.004) in CRC. Moreover, it was postively correlated with CAF (SMA (P <.001) and FSP1 (P =.005)) and cell cycle marker p27 (P =.013) along with EMT (E-cadherin, P =.599; Snail, P <.001; vimentin, P =.012). TNC may promote EMT-like change and proliferation, which lead to poor prognosis for patients with CRC. Highlights • TNC, prognostic determinant in CRC. • TNC, potential CAFs marker in CRC. • TNC is crucial for proliferation and EMT in CRC cells. [ABSTRACT FROM AUTHOR]

Cr(iii) catalysts supported by a series of diphosphinothiophene ligands have been developed, all of which, upon activation with MMAO-3A, are active for ethylene tri-/tetramerization. The effect of ligand substitution on the catalytic performance has been examined. The Cr precatalyst supported by the diphosphinothiophene ligand containing one trimethylsilyl group at the C2 position achieved a high activity of up to 686 kg (g Cr h−1)−1 with a total selectivity of up to 69% toward 1-hexene (29.5%) and 1-octene (39.5%). Two representative Cr complexes bearing the diphosphinothiophene ligand were synthesized and structurally characterized by single-crystal X-ray diffraction. Comparison of the coordination structure data of the two Cr complexes with those of an analogous diphosphine Cr complex reveals a direct correlation between the bond length of two adjacent bridging carbon atoms and catalytic activity: a shorter bond length and hence smaller ligand bite angle lead to higher catalytic activity. These diphosphinothiophene ligands featuring a fine-tuned backbone provide a model for the precise understanding of the impact of ligand variations on the catalytic performance. [ABSTRACT FROM AUTHOR]

This study proposed a novel method to extract endmembers from hyperspectral image based on discrete firefly algorithm (EE-DFA). Endmembers are the input of many spectral unmixing algorithms. Hence, in this paper, endmember extraction from hyperspectral image is regarded as a combinational optimization problem to get best spectral unmixing results, which can be solved by the discrete firefly algorithm. Two series of experiments were conducted on the synthetic hyperspectral datasets with different SNR and the AVIRIS Cuprite dataset, respectively. The experimental results were compared with the endmembers extracted by four popular methods: the sequential maximum angle convex cone (SMACC), N-FINDR, Vertex Component Analysis (VCA), and Minimum Volume Constrained Nonnegative Matrix Factorization (MVC-NMF). What’s more, the effect of the parameters in the proposed method was tested on both synthetic hyperspectral datasets and AVIRIS Cuprite dataset, and the recommended parameters setting was proposed. The results in this study demonstrated that the proposed EE-DFA method showed better performance than the existing popular methods. Moreover, EE-DFA is robust under different SNR conditions. [ABSTRACT FROM AUTHOR]

The monitoring and evaluation of environmental and ecological status in mining area is critical for effective mineral management guidance, and remote sensing is a cost-effective solution for covering a wide spatial area with high temporal frequency. However, the diverse landscape in mining areas presents a challenge for finding a suitable monitoring method. To address this challenge, this study proposes a remote sensing-based comprehensive mining ecological index (CMEI), which integrates vegetation greenness, soil wetness, urban heat, air quality and water quality indicators obtained from Landsat images and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The integration is achieved through a principal component analysis (PCA) to encapsulate various aspects of the environment in opencast mining areas. The proposed CMEI was then applied to assess the performance of an ecological restoration project carried out in the Xilinhot coalfield in Inner Mongolia, China, over the past two decades. Our findings show that the overall ecological environment in the dumping sites and backfilling sites of Xilinhot coalfield has improved from a score of 0.15 in 2005 to 0.33 in 2020, according to the CMEI. Nevertheless, our study also highlights that some newly established dumping sites require further strengthening of management and maintenance measures. The CMEI presents a novel and effective approach for monitoring and evaluating the ecological environment in mining areas, and it can potentially be applied to assess the ecological environment of opencast mining areas globally. [ABSTRACT FROM AUTHOR]

This study reveals the common surface mineral characteristics over coalbed methane (CBM) enrichment regions from spaceborne Hyperion images and the possibility of hyperspectral remote sensing for prospecting coalbed methane exploration target region with an inexpensive, large-scale, rapid and convenient way. For this study, a typical scheme including bands removal, atmospheric correction, SNR estimation, Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and n-dimensional visualization, was applied on Hyperion image in Qinshui Basin, China, to accurately extract mineral endmembers. The endmember spectra of carbonate and clay mineral were identified based on the diagnostic absorption feature, while no endmember of ferrous iron mineral was found. The absorption feature parameters of carbonate (absorption position and especially asymmetric factor) and clay mineral (absorption position, left and right shoulder positions) were quantitatively estimated, which showed the endmember spectra of carbonate and clay mineral from hyperspectral images in this paper were correct. In addition, this study proposed an interesting research direction: measuring the asymmetric absorption factor to facilitate the identification of carbonates. Furthermore, Spectral Angle Mapper (SAM) and Spectral Feature Fitting (SFF) were used to map carbonates and clay minerals. Two assessment methods, visual interpretation based on field spectra and X-ray diffraction (XRD) analysis, were utilized to evaluate the mapping accuracy, which showed total agreements of 84.03% and 72.58%, respectively. The results in this paper suggested that both carbonates and clay minerals were rich while there was hardly any ferrous iron mineral, on the surface of CBM enrichment regions. In addition, another similar experiment was carried out in Ordos Basin and also showed similar surface mineral characteristics on CBM enrichment region. [ABSTRACT FROM AUTHOR]

This study analyzed changes in nighttime light during the 2019 Spring Festival using Luojia 1-01 nighttime images in six western cities of China (Chengdu, Panzhihua, Kunming, Yuxi, Lhasa, and Jinchang). First, the radiance of the nighttime images was calculated. Second, the light area (LA) and average light intensity (ALI) were estimated for both Spring Festival and non-festival dates. Third, the differences in LA and ALI between the Spring Festival and non-festival were analyzed for all six cities. Migration population data from Baidu Inc. were used to examine the relationship between the changes of nighttime light and the population migration. The results show that, during the non-festival to Spring Festival period, the decrease in LA values coincided with negative net immigration. During the Spring Festival to non-festival period, the LA values increased, which coincided with positive net immigration. The F-test shows that the positive linear relationship between the normalized change in LA and the normalized net immigration is significant at the 0.05 level. This strongly indicates that population migration causes changes in LA. Moreover, while the population is considerably less in these cities during the Spring Festival, the ALI is noticeably higher, which suggests that urban activities are intensified during this period. This study demonstrates the applicability of using Luojia 1-01 nighttime images to detect the nighttime light changes for the Spring Festival in western cities, China, which can then be used to evaluate population migration and urban activities in the Spring Festival. Considering the higher spatial resolution of Luojia 1-01 than NPP (National Polar-orbiting Partnership) / VIIRS (Visible infrared Imaging Radiometer), this study may inspire more applications of Luojia 1-01 to track the activities in a variety of festival-cultures and cities. [ABSTRACT FROM AUTHOR]

This study proposes a deep quadruplet network (DQN) for hyperspectral image classification given the limitation of having a small number of samples. A quadruplet network is designed, which makes use of a new quadruplet loss function in order to learn a feature space where the distances between samples from the same class are shortened, while those from a different class are enlarged. A deep 3-D convolutional neural network (CNN) with characteristics of both dense convolution and dilated convolution is then employed and embedded in the quadruplet network to extract spatial-spectral features. Finally, the nearest neighbor (NN) classifier is used to accomplish the classification in the learned feature space. The results show that the proposed network can learn a feature space and is able to undertake hyperspectral image classification using only a limited number of samples. The main highlights of the study include: (1) The proposed approach was found to have high overall accuracy and can be classified as state-of-the-art; (2) Results of the ablation study suggest that all the modules of the proposed approach are effective in improving accuracy and that the proposed quadruplet loss contributes the most; (3) Time-analysis shows the proposed methodology has a similar level of time consumption as compared with existing methods. [ABSTRACT FROM AUTHOR]

Mining has caused considerable damage to vegetation coverage, especially in grasslands. It is of great significance to investigate the specific contributions of various factors to vegetation cover change. In this study, fractional vegetation coverage (FVC) is used as a proxy indicator for vegetation coverage. We constructed 50 sets of geographically weighted artificial neural network models for FVC and its driving factors in the Shengli Coalfield. Based on the idea of differentiation, we proposed the geographically weighted differential factors-artificial neural network (GWDF-ANN) to quantify the contributions of different driving factors on FVC changes in mining areas. The highlights of the study are as follows: (1) For the 50 models, the average RMSE was 0.052. The lowest RMSE was 0.007, and the highest was 0.112. For the MRE, the average value was 0.007, the lowest was 0.001, and the highest was 0.023. The GWDF-ANN model is suitable for quantifying FVC changes in mining areas. (2) Precipitation and temperature were the main driving factors for FVC change. The contributions were 32.45% for precipitation, 24.80% for temperature, 22.44% for mining, 14.44% for urban expansion, and 5.87% for topography. (3) Over time, the contributions of precipitation and temperature exhibited downward trends, while mining and urban expansion showed positive trajectories. For topography, its contribution remains generally unchanged. (4) As the distance from the mining area increases, the contribution of mining gradually decreases. At 200 m away, the contribution of mining was 26.69%; at 2000 m away, the value drops to 17.8%. (5) Mining has a cumulative effect on vegetation coverage both interannually and spatially. This study provides important support for understanding the mechanism of vegetation coverage change in mining areas. [ABSTRACT FROM AUTHOR]

Although tenascin-C (TNC), an extracellular matrix protein, has been shown to be widely expressed in stromal fibroblasts in various cancers, the role of its expression in esophageal squamous cell carcinoma (ESCC) cells remains unclear. Using immunohistochemistry, we investigated the expression of cancer stem-like cell (CSC) markers, epithelial-to-mesenchymal transition (EMT)-related genes, and the Akt/hypoxia-inducible factor-1α (HIF1α) signal pathway in ESCC tissue specimens from 154 patients. We further addressed the effects of TNC on the Akt/HIF1α axis and its putative association with cancer stemness in several ESCC cell lines by immunofluorescence imaging and western blot analysis. Our data suggest that TNC expression was positively correlated with the expression of the CSC marker SOX2 (p =.002), and TNC-expressing cancer cells expressed SOX2 in ESCC tissues. Moreover, TNC expression was strongly associated with EMT-related gene Snail (p =.022) and positively correlated with pAkt-Ser473 (p =.004) and HIF1α (p =.003). Furthermore, TNC-silencing down-regulated the expression of CSC marker SOX2 (p <.001) and EMT-related marker Snail (p <.001). The Akt inhibitor Perifosine inhibited the protein expression of pAkt-Ser473, Akt, HIF1α, and TNC in TE10 (an ESCC cell line) cells. Short-term exposure of TE10 cells to cobalt chloride caused an increase in protein expression of HIF1α, TNC, and SOX2 in a time-dependent manner. Taken together, these results suggest that TNC may enhance the cancer stem-like properties and promote EMT-like changes via the Akt/HIF1α axis. [ABSTRACT FROM AUTHOR]

Measuring trajectory similarity is a fundamental algorithm in trajectory data mining, playing a key role in trajectory clustering, pattern mining, and classification, for instance. However, existing trajectory similarity measures based on vector representation have challenges in achieving both fast and accurate similarity measurements. On one hand, most existing methods have a high computational complexity of O(n × m), resulting in low efficiency. On the other hand, many of them are sensitive to trajectory sampling rates and lack of accuracy. This article proposes QuadGridSIM, a quadrilateral grid‐based method for trajectory similarity analysis, which enables high‐performance trajectory similarity measure without the cost of low effectiveness. Specifically, we first realize the multiscale coding representation of trajectory data based on quadrilateral discrete grids. Then, a novel trajectory similarity measure is defined to reduce the computational complexity of O(n). Several effectiveness properties of QuadGridSIM are further optimized, including the spatial overlap, directionality, symmetry, and robustness to sampling rate variations. Experimental results based on real‐world and simulated taxi trajectory data indicate that QuadGridSIM outperforms most of the other tested algorithms developed previously in terms of effectiveness, particularly in its robustness regarding trajectory sampling rates. Furthermore, QuadGridSIM exhibits superior performance and is approximately one order of magnitude faster than previous methods in the literature. QuadGridSIM provides a solution to the low‐efficiency problem of massive trajectory similarity analysis and can be applied in many application scenarios, such as route recommendation and suspect detection. [ABSTRACT FROM AUTHOR]

Automatic change detection based on remote sensing is playing an increasingly important role in the national economy construction. To address the problem of limited change detection accuracy in existing single-level difference networks, this study proposes the Multi-level Difference Network (MDNet) for automatic change detection of ground targets from very high-resolution (VHR) remote sensing images. An early-difference network and a late-difference network are combined by MDNet to extract multi-level change features. The early-difference network can focus on change information throughout to reduce the spurious changes in the change detection results, and the late-difference network can provide deep features of a single image for reducing rough boundaries and scattered holes in the change detection results, thus improving the accuracy. However, not all high-level features extracted by MDNet contribute to the recognition of image differences, and the multi-level change features suffer from cross-channel heterogeneity. Stacking them directly on channels does not make effective use of change information, thus limiting the performance of MDNet. Therefore, the Multi-level Change Features Fusion Module (MCFFM) is proposed in this study for the effective fusion of multi-level change features. In the experiments, the publicly available open-pit mine change detection (OMCD) dataset was used first to achieve a change detection of open-pit mines over a large area, with an F1-score of 89.2%, increasing by 1.3% to 5.9% compared to the benchmark methods. Then, a self-made OMCD dataset was used to achieve an F1-score of 92.8% for the localized and fine-scale change detection in open-pit mines, which is an improvement of 0.7% to 5.4% compared to the benchmark methods. Finally, the Season-varying Change Detection Dataset is used to verify that the MDNet proposed can detect changes in other scenarios very well. The experimental results show that the proposed MDNet has significantly improved the performance of change detection on the three datasets compared with six advanced deep learning models, which will contribute to the development of change detection with VHR remote sensing images. [ABSTRACT FROM AUTHOR]

We have developed novel Cr(III) catalysts supported by asymmetic N , N -diphospholylamine ligands bearing a phenoxaphosphine group. Upon activation with MMAO-3A, the Cr(III) catalysts supported by the PNP ligands are highly active for ethylene tri-/tetramerization with considerable selectivity. The ligand substitution and oligomerization conditions are found to be essential to achieve high activity and controllable selectivity. The catalytic system with asymmetric diphospholylamine ligands exhibited higher activity than that supported by symmetric ligands. Asymmetric diphospholylamine ligand with a N -cyclohexyl group achieved the highest activity of 282.2 kg/(g Cr/h) with a high total selectivity of 83.2% toward valuable 1-hexene (28.7%) and 1-octent (54.5%) at 35 bar under 80 °C. [ABSTRACT FROM AUTHOR]

This study proposed a new narrow band index to characterize the Cu (Copper) stress degree on vegetation (Copper Stress Vegetation Index, CSVI). Firstly, the spectral reflectance and biochemical data of wheat, pea, locust and ash were analysed using Pearson correlation coefficient (r) to select wavelengths sensitive to Cu stress. The calculated Pearson correlation coefficients suggested that the reflectance near 550 nm and 700 nm correlated positively with Cu contents in leaves and solutions, and negative correlation was present in the range of 800–900 nm. Secondly, the selected wavelengths of 550 nm, 700 nm, and 850 nm were used to establish CSVI, and it was compared with existing popular vegetation indices (VIs) related to heavy metal stress (Normalized Difference Vegetation Index (NDVI), Red-Edge Position (REP), Difference Vegetation Index (DVI), Photochemical Reflectance Index (PRI)) by calculating Pearson correlation coefficient between VIs and Cu contents in leaves and solutions. Thirdly, verifications of CSVI on other vegetations were conducted, and the performance of CSVI was also compared with that of NDVI, REP, DVI, and PRI. The results suggested that CSVI showed significant correlation with Cu stress degree, and the correlation of CSVI was much stronger than that of other VIs for all the tested vegetations. The proposed CSVI characterizes the Cu stress degree on vegetation with advantages of better effectiveness, straightforward calculation, and robustness for different vegetations. This study focused on the spectral reflectance at the leaf scale, so it is expected that future work extends it to canopy scale and mixed-pixel scale. [ABSTRACT FROM PUBLISHER]

The integration of radiative cooling and thermochromic technology methods, as a passive-regulating and environmental-friendly thermal management strategy, is highly desirable in reshaping the global energy landscape. However, the multispectral modulation based on the integration methods is challenging due to their low luminous transmissive and emissivity. Herein, we propose a transmittance passive-regulating radiative cooling thermochromic(T-PRCT) smart window using nano-storm technology based on phase change material, capable of adjusting its thermal management performance according to external temperature changes. With achieved luminous transmittance of 43.11% and solar modulation efficiency of 10.64% (from 50.90% to 40.26%) respectively, along with an emissivity positive modulation(∆ε) of 0.23 (from 0.71 to 0.94) in atmospheric windows, the cooling efficiency can be up to 56.52% improvement. The design is applicable in varying climates and holds the capacity to curtail the building's annual energy-saving by over 14.74%. Consequently, the simple and scalable approach-integrated phase change material may provide a promising way to expanding the multispectral manipulation ability of optical systems. Scheme 1. Illustrations of working principle of T-PRCT window in hot (up) and cool day (down). The pink arrows indicate the solar, the orange arrows indicate the Tsol and the red arrows represent heat radiation. On hot days, the NIR is blocked and luminous transmits through the window with thermal radiation enhanced. On cool days, the solar enter the room. Heat radiation is suppressed to minimize heat loss. [Display omitted] • A temperature-adaptive smart windows with passive transmittance and radiative cooling regulation without metal layer. • The emissivity was reversed due to the multiple light scattering effect at metal state of the W-VO 2. • High luminous transmittance(43.11%) and emissivity(0.94/90 °C) can be achieved through a W-doped VO 2 @SiO 2 core-shell structure. • The temperature-adaptive smart window has the ability to curtail the building's annual energy-saving more than low-E glass. [ABSTRACT FROM AUTHOR]

Automatic extraction of land use and land cover changes in mining areas is crucial for the supervision of mineral resources. This study proposes an automatic open-pit mine (OM) change detection method via a novel deep learning model, DA-UNet++ (Deformable-Attention-UNet++), and an object-based approach from high resolution remote sensing images. First, by embedding a deformable convolution module and attention mechanism module into UNet++ network, and constructing a skip sampling strategy, DA-UNet++ is proposed to combat the high heterogeneity of shape and scale of ground objects in OM environment and improve the change detection accuracy. Second, an object-based post-processing (OBPP) approach is presented for enhancing raw change detection results of DA-UNet++, which includes object-based expectation maximisation optimisation and morphological close operation. In the experiments, Season-varying Change Detection Dataset is first employed to evaluate the effectiveness of the proposed DA-UNet++ by comparing with other benchmark models. Then, self-made Ordos OM Change Detection Dataset including 38 OMs is utilised to verify the efficacy of the proposed method, and comprehensive metric F 1 − s c o r e of 85.6% is achieved. Evaluation results prove that the proposed method provides superior change detection performance compared with several benchmark methods, and has practical significance for OM management and monitoring. [ABSTRACT FROM AUTHOR]

Existing spatiotemporal similarity analysis methods for trajectories have the problems of spatiotemporal unsynchronization and low efficiency in processing large‐scale datasets, which cannot satisfy the increasingly urgent requirements of real‐time or quasi‐real‐time applications. To address these problems, this article proposes a grid‐based and synchronized spatiotemporal similarity analysis method based on the spatiotemporal grid model called gsstSIM. First, a low‐dimensional and multi‐scale trajectory coding representation is implemented based on the spatiotemporal grid model. Second, a synchronized spatiotemporal similarity measure is proposed based on trajectory codes. It transforms the similarity analysis from complex geometric calculations to simple algebraic operations of code sets, which reduces the computational complexity. In addition, the trajectory encoding representation with space‐time collinearity enables gsstSIM to measure the synchronized spatiotemporal similarity. Third, the efficient Multi‐scale grid index, called MSGrid, is established to realize fast query of top‐K similar trajectories for large‐scale datasets. Experimental results demonstrate that gsstSIM is more robust to noise positioning points and various sampling rates than the state‐of‐the‐art algorithms STLCSS, TWS and SWS. It can achieve a second‐level response of spatiotemporal similarity query in processing large‐scale datasets, which is much faster than existing algorithms. The proposed method has promising to support the applications with high time‐efficiency requirements such as epidemic tracking and traffic condition calculation. [ABSTRACT FROM AUTHOR]

Parcel‐level evaluation of urban land use efficiency is necessary for city administrations to understand land use status and problems, and to further optimize land use pattern and policy. However, existing research on urban land use efficiency is mainly carried out at city level or block level, and it is difficult to meet the demand for rapid access to fine‐grained land use efficiency across the entire city. In view of this problem, this article proposes a parcel‐level evaluation method of urban land use efficiency based on multisource spatiotemporal data, including an evaluation index system for parcel‐level land use efficiency, quantitative representation of evaluation indexes based on multisource spatiotemporal data, and a land use efficiency calculation model. The proposed method is then applied to China's Ningbo City, and the evaluation accuracy is 78% compared with the officially reported data and on‐site investigation. Experimental results also show that urban land use efficiency is strongly spatially autocorrelated in Ningbo City, and reveal the impact patterns of factors such as planning policy, facilities, traffic conditions and environmental conditions on the utilization efficiency of different types of land. This research has proved able to accurately evaluate the utilization efficiency of all land parcels across a city in a rapid manner, and to obtain their distribution characteristics and correlation factors. It can further provide data and technical support for formulating targeted urban planning policies and implementing scientific land development. [ABSTRACT FROM AUTHOR]

• The cause of negative luminous modulation been identified and discussed. • Flipped absorption difference solves negative modulation by utilizing the LSPR effect • The experimental findings unequivocally demonstrated that the Δ T lum can reach 1.86 %. The T lum can be maintained at 57.18 %. The Δ T sol can also be increased at 8.35 %. • A rectangular and circular nanoparticles array based on the localized surface plasmon resonance theory is designed on glass ultrathin-film. Vanadium dioxide (VO 2)-based thermochromic smart window show excellent promise for energy-saving and have been extensively researched. However, the performance of smart windows is restrained by the VO 2 film negative modulation with luminous transmittance. Unfortunately, there has been no breakthrough in exploring negative modulation mechanism for VO 2 thin nanofilm until now. In this work, we propose a novel structure design consisting of densely packed VO 2 nanostructure over glass based on rectangular and circular nanoparticles array structure. The preparation process involves the magnetron sputtering and low-pressure annealing technique, which is well-suited for the glass substrate. According to optical measurements, the luminous transmittance modulation (Δ T lum = 1.86 %) was significantly increased compared to the continuous vanadium oxide film. Simultaneously, leading to the solar modulation (Δ T sol = 8.35 %) and luminous transmittance (T lum = 57.18 %@30 °C) also were improved, which can be ascribed to the absorption between lager and small nanoparticles in the luminous when VO 2 is in rutile state. The mechanisms are investigated by localized surface plasmon resonance theoretical analyses and numerical simulations. Our novel approach offers a promising solution for constructing energy-efficient VO 2 -based smart windows. [ABSTRACT FROM AUTHOR]

B7-H4 is a unique negative regulator of T cells that is typically significantly overexpressed in various carcinomas and is associated with poor prognosis. However, the effects of B7-H4 expression on epithelial–mesenchymal transition (EMT) and cancer stemness of colorectal cancer (CRC) are not entirely clear. In the present study, we used tissue samples from 98 patients with CRC and CRC cell lines to determine the clinicopathological significance of B7-H4 in CRC and its effects on CRC stemness. We performed immunohistochemical staining; immunofluorescence imaging; western blotting; and tumor sphere formation, wound healing, transwell migration, and in vivo tumorigenesis assays. B7-H4 expression was upregulated in CRC tissues and was associated with lymph node metastasis, distant metastasis, clinical stage, a shorter overall survival rate, and disease-free survival rate. Cox regression analyses indicated that B7-H4 is an independent poor prognostic factor for CRC. In addition, B7-H4 expression was correlated with the expression of EMT-related proteins and cancer stemness-related proteins. Moreover, immunohistochemical and immunofluorescence analyses revealed that B7-H4 was correlated with CD133 and CD44 expression levels in both CRC tissues and HT29 and HCT116 cell lines. Conversely, B7-H4 knockdown downregulated the expression of EMT- and cancer stemness-related proteins, while inhibiting tumor spheroid formation, cell migration, and invasion of CRC cell lines. These results indicate that B7-H4 can promote EMT and may be a novel stem cell marker, suggesting its potential as a prognostic biomarker for CRC. [ABSTRACT FROM AUTHOR]

The spatio-temporal pattern of vegetation disturbance by open-pit mining activities such as the disturbance area and magnitude are important for coordinated development of surface mining and environmental protection. This paper proposes an automatic method (Auto-VDR) for identifying vegetation destruction and restoration of various open-pit mines. The proposed method consists of three steps: construction of the sample dataset, identification of disturbance types, and extraction of disturbance time and magnitude. First, a sample dataset with three types was constructed according to the interannual trends of Normalized Difference Vegetation Index (NDVI) in mining areas: "Restoration after destruction (RAD)", "Destruction without restoration (DWR)" and "No destruction (ND)". Second, in order to accommodate to various open-pit mines with different background conditions (original vegetation cover prior to mining, loss rate and magnitude of vegetation due to mining, etc.), the "Shape-based distance" was used to match the NDVI curves of each pixel to the three disturbance types. Third, the disturbance time and magnitude in the destroyed regions were extracted based on the vegetated/bare ground threshold (α). The Shendong coal base in China was selected as the study area consisting of 133 open-pit coal mines. Annual NDVI was generated using Landsat time-series data. The maps of vegetation destruction and restoration caused by surface mining during 1990–2021 were drawn with accuracies of 0.82 and 0.87, respectively. The identification accuracy of the Auto-VDR is higher than Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) and Continuous Change Detection and Classification (CCDC). The results show that from 1990 to 2021, a total of 400.08 km2 of vegetation has been destroyed, accounting for 35.93% of the study area. Furthermore, restoration activities mainly began in 2010, lagging mining activities by approximately three years. The accumulated area of restoration was 177.91 km2 and accounts for 15.98% of the study area. The vegetation destruction at the Shendong coal base has gone through four stages, with 2008, 2015 and 2018 as splits, corresponding to the developing stage of the China coal industry. In particular, the area of vegetation destruction per year reached the maximum of 61.16 km2 in 2008, which was 1.38 times the total area mined over the past 17 years. There were 14% of the 133 open-pit mines in Shendong coal base with a restoration rate of 0.8 and 59% of the mines with a restoration rate of 0.5. This method has been demonstrated to be able to extract vegetation destruction and restoration processes accurately and rapidly for various open-pit mines at a scale of coal base, which provides an important reference for supervising the impact of mining on the environment and assessing the effectiveness of ecological restoration. [Display omitted] • An automated method for identifying vegetation disturbance of open-pit mines. • The method is applicable to various open-pit mines at a large scale of coal base. • Higher accuracy (overall accuracy = 0.82 and 0.87) compared to other algorithms. • Maps of vegetation disturbance caused by surface mining were drawn. • A total of 400.08 km2 of vegetation has been destroyed in Shendong coal base. [ABSTRACT FROM AUTHOR]

Petroleum exploration using natural source electromagnetic (EM) methods has recently increased due to the economic demand for obtaining high-resolution information about electrical parameter variations related to reservoirs. However, there are challenges present in using traditional EM methods. Regional geo-electrical structures inverted from induced EM signals are insufficient to determine the characteristics of reservoirs. In this study, we propose a natural source prospecting method to directly assess the depths and distributions of oil layers. This is accomplished by deriving electromagnetic radiation (EMR) signals caused by oil flow and reservoir fractures. The method has been validated by theoretical analyses and field measurements in Chepaizi Uplift, China. The modeling results show that Super-Low Frequency (SLF) magnetic amplitudes reveal stationary trends for various types of oil trap models. EMR anomalies, (distinguished from actual SLF magnetic signals) are noise-suppressed and enhanced using the ensemble empirical mode decomposition (EEMD) and the “DB4” wavelet transform. An empirical Frequency-Depth transformation model is developed to directly implement the depth sounding. EMR configurations at the corresponding depths of oil layers are recognized as the target of discovery i.e., reservoirs. The estimated depths of oil layers in producing wells are verified by the actual production, and the depth deviations of reservoirs are generally within a ±10% margin of error. This method is intended to be a viable tool in the accurate identification, delineation and dynamic monitoring of petroleum reservoirs. [ABSTRACT FROM AUTHOR]

Abstract: Dynamic monitoring of coalbed methane (CBM) reservoirs plays an important role in reservoir evaluation, production estimation, exploitation and development planning in order to efficiently operate producing wells. This paper proposes a passive Super-Low Frequency (SLF) electromagnetic prospecting and monitoring method, which helps us derive electromagnetic radiation (EMR) anomalies from reservoirs to directly identify and dynamically analyze CBM reservoirs. The modeling study shows that the SLF magnetic responses are sensitive to high resistivity layers. These responses turn out to be approximately stationary and can be seen as simply a component of the background field. This stationary background field can be clearly distinguished and then dynamic anomaly extraction would be completed. In order to suppress cultural noise and high frequency (HF) random noise, the methods of empirical mode decomposition (EMD) and wavelet transform are used in data processing. The reconstructed curves are employed to identify EMR anomalies at corresponding depths of reservoirs, and subsequently help directly interpret and dynamically monitor reservoirs. The SLF prospecting method is validated using the field data observed from CBM wells in the years from 2007 to 2013 in Qinshui Basin, China. The results present that the high EMR “wave packets” contribute conceivably to the CBM reservoir identification. Compared with audio-magnetotelluric (AMT) inversion results, the SLF identification resolution is greatly improved. The dynamic characteristics of producing reservoirs are revealed using EMR anomalies, and agree with production histories and other surveys. [Copyright &y& Elsevier]

By effectively observing the land surface and obtaining farmland conditions, satellite remote sensing has played an essential role in agricultural drought monitoring over past decades. Among all remote sensing techniques, optical and thermal remote sensing have the most extended history of being utilized in drought monitoring. The primary goal of this paper is to illustrate how optical and thermal remote sensing have been and will be applied in the monitoring, assessment, and prediction of agricultural drought. We group the methods into four categories: optical, thermal, optical and thermal, and multi-source. For each category, a concise explanation is given to show the inherent mechanisms. We pay special attention to solar-induced chlorophyll fluorescence, which has great potential in early drought detection. Finally, we look at the future directions of agricultural drought monitoring, including (1) early detection; (2) spatio-temporal resolution; (3) organic combination of multi-source data; and (4) smart prediction and assessment based on deep learning and cloud computing. [ABSTRACT FROM AUTHOR]

The natural environment is essential for human survival and development since it provides water resources, land resources, biological resources and climate resources etc. As a developing country, China has witnessed a significant change in the natural environment in recent decades; and therefore, monitoring and mastering the status of the environment is of great significance. Due to the characteristics of large-scale and dynamic observation, remote sensing technology has been an indispensable approach for environmental monitoring. This paper reviews the satellite resources, institutions and policies for environmental monitoring in China, and the advances in research and application of remote sensing from five aspects: ecological index retrieval, environmental monitoring in protected areas, rural areas, urban areas and mining areas. The remote sensing models and methods for various types of environmental monitoring, and the specific applications in China are comprehensively summarized. This paper also points out major challenges existing at the current stage: satellite sensor problems, integrated use challenges of datasets, uncertainty in the retrieval process of ecological variables, scaling effect problems, a low degree of automation, the weak ability of forecasting and comprehensive analysis, and a lack of computational power for massive datasets. Finally, the development trend and future directions are put forward to direct the research and application of environmental monitoring and protection in the new era. [ABSTRACT FROM AUTHOR]

Background: Although the leucine zipper-EF-hand-containing transmembrane protein 1 (LETM1) is one of the mitochondrial inner membrane proteins that is involved in cancer prognosis in various tumors, LETM1 as a biomarker for prognostic evaluation of non-small cell lung carcinoma (NSCLC) has not been well studied.Methods: To address this issue, we used 75 cases NSCLC, 20 cases adjacent normal lung tissues and NSCLC cell lines. We performed immunohistochemistry staining and western blot analysis as well as immunofluorescence imaging.Results: Our studies show that expression of LETM1 is significantly correlated with the lymph node metastasis (p = 0.003) and the clinical stage (p = 0.005) of NSCLC. The Kaplan-Meier survival analysis revealed that NSCLC patients with positive expression of LETM1 exhibits a shorter overall survival (OS) rate (p = 0.005). The univariate and multivariate Cox regression analysis indicated that LETM1 is a independent poor prognostic marker of NSCLC. In addition, the LETM1 expression is correlated with cancer stemness-related gene LGR5 (p < 0.001) and HIF1α expression (p < 0.001), but not with others. Moreover, LETM1 expression was associated with the expression of cyclin D1 (p = 0.003), p27 (p = 0.001), pPI3K(p85) (p = 0.025), and pAkt-Thr308 (p = 0.004). Further, our studies show in LETM1-positive NSCLC tissues the microvessel density was significantly higher than in the negative ones (p = 0.024).Conclusion: These results indicate that LETM1 is a potential prognostic biomarker of NSCLC. [ABSTRACT FROM AUTHOR]