Cost-effective Bi2WO6 for efficient degradation of rhodamine B and tetracycline.

The morphology-controlled synthesis of nanostructured photocatalysts by an environmentally friendly and low-cost method provides a feasible way to realize practical applications of photocatalysts. Herein, Bi2WO6 (BWO) nanophotocatalysts with mulberry shape, sheet-like, and round-cake morphologies have been successfully synthesized through a highly facile solvothermal process by simply adjusting the solvothermal temperature or utilizing selective addition of ethylene glycol as an orientation agent without using strong acids and bases and/or hazardous chemicals. The ratio of ethylene glycol and glacial acetic acid can affect the morphology and oxygen vacancy content of BWO, thereby influencing the photocatalytic performance. The photocatalytic activity of the as-prepared samples was evaluated by degradation of rhodamine B (RhB) and tetracycline under visible-light irradiation. The results indicated that all the BWO samples exhibited morphology-associated photocatalytic activity, and the sheet-like structure of BWO obtained via solvothermal treatment at 120 °C with ethylene glycol and glacial acetic acid ratio of 1:3 achieved the maximum specific surface area and possessed abundant oxygen vacancies, exhibiting outstanding photocatalytic activity for degradation of RhB and tetracycline. The degradation rate of RhB reached 100% within 20 min. To the best of our knowledge, this value is one of the most remarkable values for pristine BWO photocatalysts. Radical capture experiments demonstrated that hydroxyl radicals (·OH) play major roles compared with electrons (e-) and holes (h+) in the photocatalytic degradation process. A possible mechanism for the photocatalytic degradation of pollutants was proposed to better understand the reaction process. We believe that the more economical, efficient and greener methodology can provide guidance to develop highly efficient photocatalysts with favourable morphology and structure. Supplementary Information: The online version contains supplementary material available at 10.1007/s10854-022-09654-z.

Modeling Seasonal Variations in Vertical GPS Coordinate Time Series Using Independent Component Analysis and Varying Coefficient Regression.

Common seasonal variations in Global Positioning System (GPS) coordinate time series always exist, and the modeling and correction of the seasonal signals are helpful for many geodetic studies using GPS observations. A spatiotemporal model was proposed to model the common seasonal variations in vertical GPS coordinate time series, based on independent component analysis and varying coefficient regression method. In the model, independent component analysis (ICA) is used to separate the common seasonal signals in the vertical GPS coordinate time series. Considering that the periodic signals in GPS coordinate time series change with time, a varying coefficient regression method is used to fit the separated independent components. The spatiotemporal model was then used to fit the vertical GPS coordinate time series of 262 global International GPS Service for Geodynamics (IGS) GPS sites. The results show that compared with least squares regression, the varying coefficient method can achieve a more reliable fitting result for the seasonal variation of the separated independent components. The proposed method can accurately model the common seasonal variations in the vertical GPS coordinate time series, with an average root mean square (RMS) reduction of 41.6% after the model correction.

Phase Difference Measurement of Under-Sampled Sinusoidal Signals for InSAR System Phase Error Calibration.

Phase difference measurement of sinusoidal signals can be used for phase error calibration of the spaceborne single-pass interferometric synthetic aperture radar (InSAR) system. However, there are currently very few papers devoted to the discussion of phase difference measurement of high-frequency internal calibration signals of the InSAR system, especially the discussion of sampling frequency selection and the corresponding measuring method when the high-frequency signals are sampled under the under-sampling condition. To solve this problem, a phase difference measurement method for high-frequency sinusoidal signals is proposed, and the corresponding sampling frequency selection criteria under the under-sampling condition is determined. First, according to the selection criteria, the appropriate under-sampling frequency was chosen to sample the two sinusoidal signals with the same frequency. Then, the sampled signals were filtered by limited recursive average filtering (LRAF) and coherently accumulated in the cycle of the baseband signal. Third, the filtered and accumulated signals were used to calculate the phase difference of the two sinusoidal signals using the discrete Fourier transform (DFT), digital correlation (DC), and Hilbert transform (HT)-based methods. Lastly, the measurement accuracy of the three methods were compared respectively by different simulation experiments. Theoretical analysis and experiments verified the effectiveness of the proposed method for the phase error calibration of the InSAR system.

Mining-Induced Time-Series Deformation Investigation Based on SBAS-InSAR Technique: A Case Study of Drilling Water Solution Rock Salt Mine.

Compared to traditional coal mines, the mining-induced dynamic deformation of drilling solution mining activities may result in even more serious damage to surface buildings and infrastructures due to the different exploitation mode. Therefore, long-term dynamic monitoring and analysis of rock salt mines is extremely important for preventing potential geological damages. In this work, the small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique with Sentinel-1A imagery is utilized to monitor the ground surface deformation of a rock salt mining area. The time-series analysis is carried out to obtain the spatial-temporal characteristics of land subsidence caused by drilling solution mining activities. A typical rock salt mine in Changde, China is selected as the test site. Twenty-four scenes of Sentinel-1A image data acquired from June 2015 to January 2017 are used to obtain the time-series subsidence of the test mine. The temporal-spatial evolution of the derived settlement funnels is revealed. The time-series deformation on typical feature points has been analyzed. Experimental results show that the obtained drilling solution mining-induced subsidence has a spatial characteristic of multiplied peaks along the transversal direction. Temporally, the large-scale surface settlement for the rock salt mine area begins to appear in September 2016, with a time lag of 8 months, and shows an obvious seasonal fluctuation. The maximum cumulative subsidence is detected up to 199 mm. These subsiding characteristics are consistent with the connected groove mining method used in drilling water solution mines. To evaluate the reliability of the results, the SBAS-derived results are compared with the field-leveling measurements. The estimated root mean square error (RMSE) of ±11 mm indicates a high consistency.

Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network.

The water and shadow areas in SAR images contain rich information for various applications, which cannot be extracted automatically and precisely at present. To handle this problem, a new framework called Multi-Resolution Dense Encoder and Decoder (MRDED) network is proposed, which integrates Convolutional Neural Network (CNN), Residual Network (ResNet), Dense Convolutional Network (DenseNet), Global Convolutional Network (GCN), and Convolutional Long Short-Term Memory (ConvLSTM). MRDED contains three parts: the Gray Level Gradient Co-occurrence Matrix (GLGCM), the Encoder network, and the Decoder network. GLGCM is used to extract low-level features, which are further processed by the Encoder. The Encoder network employs ResNet to extract features at different resolutions. There are two components of the Decoder network, namely, the Multi-level Features Extraction and Fusion (MFEF) and Score maps Fusion (SF). We implement two versions of MFEF, named MFEF1 and MFEF2, which generate separate score maps. The difference between them lies in that the Chained Residual Pooling (CRP) module is utilized in MFEF2, while ConvLSTM is adopted in MFEF1 to form the Improved Chained Residual Pooling (ICRP) module as the replacement. The two separate score maps generated by MFEF1 and MFEF2 are fused with different weights to produce the fused score map, which is further handled by the Softmax function to generate the final extraction results for water and shadow areas. To evaluate the proposed framework, MRDED is trained and tested with large SAR images. To further assess the classification performance, a total of eight different classification frameworks are compared with our proposed framework. MRDED outperformed by reaching 80.12% in Pixel Accuracy (PA) and 73.88% in Intersection of Union (IoU) for water, 88% in PA and 77.11% in IoU for shadow, and 95.16% in PA and 90.49% in IoU for background classification, respectively.

An Improved Time-Series Model Considering Rheological Parameters for Surface Deformation Monitoring of Soft Clay Subgrade.

Building deformation models consistent with reality is a crucial step for time-series deformation monitoring. Most deformation models are empirical mathematical models, lacking consideration of the physical mechanisms of observed objects. In this study, we propose an improved time-series deformation model considering rheological parameters (viscosity and elasticity) based on the Kelvin model. The functional relationships between the rheological parameters and deformation along the Synthetic Aperture Radar ( SAR) line of sight are constructed, and a method for rheological parameter estimation is provided. To assess the feasibility and accuracy of the presented model, both simulated and real deformation data over a stretch of the Lungui highway (built on soft clay subgrade in Guangdong province, China) are investigated with TerraSAR-X satellite imagery. With the proposed deformation model, the unknown rheological parameters over all the high coherence points are obtained and the deformation time-series are generated. The high-pass (HP) deformation component and external leveling ground measurements are utilized to assess the modeling accuracy. The results show that the root mean square of the residual deformation is ±1.6 mm, whereas that of the ground leveling measurements is ±5.0 mm, indicating an improvement in the proposed model by 53%, and 34% compared to the pure linear velocity model. The results indicate the reliability of the presented model for the application of deformation monitoring of soft clay highways. The estimated rheological parameters can be provided as a reference index for the interpretation of long-term highway deformation and the stability control of subgrade construction engineering.

A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration.

Synthetic Aperture Radar (SAR) scene classification is challenging but widely applied, in which deep learning can play a pivotal role because of its hierarchical feature learning ability. In the paper, we propose a new scene classification framework, named Feature Recalibration Network with Multi-scale Spatial Features (FRN-MSF), to achieve high accuracy in SAR-based scene classification. First, a Multi-Scale Omnidirectional Gaussian Derivative Filter (MSOGDF) is constructed. Then, Multi-scale Spatial Features (MSF) of SAR scenes are generated by weighting MSOGDF, a Gray Level Gradient Co-occurrence Matrix (GLGCM) and Gabor transformation. These features were processed by the Feature Recalibration Network (FRN) to learn high-level features. In the network, the Depthwise Separable Convolution (DSC), Squeeze-and-Excitation (SE) Block and Convolution Neural Network (CNN) are integrated. Finally, these learned features will be classified by the Softmax function. Eleven types of SAR scenes obtained from four systems combining different bands and resolutions were trained and tested, and a mean accuracy of 98.18% was obtained. To validate the generality of FRN-MSF, five types of SAR scenes sampled from two additional large-scale Gaofen-3 and TerraSAR-X images were evaluated for classification. The mean accuracy of the five types reached 94.56%; while the mean accuracy for the same five types of the former tested 11 types of scene was 96%. The high accuracy indicates that the FRN-MSF is promising for SAR scene classification without losing generality.

A dorsally located giant posterior fossa neurenteric cyst in a Chinese woman.

Neurenteric cysts (NC) are rare, endodermal-derived intracranial lesions, commonly located anteriorly in the posterior cranial fossa. We describe a rare case of a giant posterior fossa NC (6.5 × 5.9 × 4.3cm) located dorsal to the brain stem in a Chinese woman with a 1 week history of cerebellar symptoms. To our knowledge, this is the largest documented cyst of this type and the third dorsally located NC in the posterior fossa.

Analyzing time-series microarray data reveals key genes in spinal cord injury.

Although many scholars have utilized high-throughput microarrays to delineate gene expression patterns after spinal cord injury (SCI), no study has evaluated gene changes in raphe magnus (RM) and somatomotor cortex (SMTC), two areas in brain primarily affected by SCI. In present study, we aimed to analyze the differentially expressed genes (DEGs) of RM and SMTC between SCI model and sham injured control at 4, 24 h, 7, 14, 28 days, and 3 months using microarray dataset GSE2270 downloaded from gene expression omnibus and unpaired significance analysis of microarray method. Protein-protein interaction (PPI) network was constructed for DEGs at crucial time points and significant biological functions were enriched using DAVID. The results indicated that more DEGs were identified at 14 days in RM and at 4 h/3 months in SMTC after SCI. In the PPI network for DEGs at 14 days in RM, interleukin 6, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), FBJ murine osteosarcoma viral oncogene homolog (FOS), tumor necrosis factor, and nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor) were the top 5 hub genes; In the PPI network for DEGs at 3 months in SMTC, the top 5 hub genes were ubiquitin B, Ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1), FOS, Janus kinase 2 and vascular endothelial growth factor A. Hedgehog and Wnt signaling pathways were the top 2 significant pathways in RM. These hub DEGs and pathways may be underlying therapeutic targets for SCI.

Proteomic comparison of 3D and 2D glioma models reveals increased HLA-E expression in 3D models is associated with resistance to NK cell-mediated cytotoxicity.

Three-dimensional cell culture techniques can better reflect the in vivo characteristics of tumor cells compared with traditional monolayer cultures. Compared with their 2D counterparts, 3D-cultured tumor cells showed enhanced resistance to the cytotoxic T cell-mediated immune response. However, it remains unclear whether 3D-cultured tumor cells have an enhanced resistance to NK cell cytotoxicity. In this study, a total of 363 differentially expressed proteins were identified between the 2D- and 3D-cultured U251 cells by comparative proteomics, and an immune-associated protein-protein interaction (PPI) network based on these differential proteins was constructed by bioinformatics. Within the network, HLA-E, as a molecule for inhibiting NK cell activation, was significantly up-regulated in the 3D-cultured tumor cells. Then, we found that the 3D-cultured U251 cells exhibited potent resistance to NK cell cytotoxicity in vitro and were prone to tumor formation in vivo. The resistance of the 3D-cultured tumor cells to NK cell lysis was mediated by the HLA-E/NKG2A interaction because the administration of antibodies that block either HLA-E or NKG2A completely eliminated this resistance and significantly decreased tumor formation. Taken together, our findings indicate that HLA-E up-regulation in 3D-cultured cells may result in enhanced tumor resistance to NK cell-mediated immune response.

Overexpression of MACC1 protein and its clinical implications in patients with glioma.

Metastasis associated in colon cancer 1 (MACC1) has been regarded as a novel potential therapeutic target for multiple cancers. However, the impact of MACC1 in glioma remains unclear. The aim of this study was to analyze the correlation of MACC1 expression with the clinicopathological features of glioma. MACC1 mRNA and protein expression levels in human glioma tissues were detected by quantitative real-time polymerase chain reaction and immunohistochemistry assays, respectively. MACC1 mRNA and protein expression were both significantly higher in glioma tissues than in corresponding noncancerous brain tissues (both P < 0.001). In addition, statistical analysis suggested that high MACC1 expression was significantly correlated with advanced pathological grade (P = 0.004) and that patients with high expression of MACC1 protein exhibited a poorer prognosis than those with low MACC1 expression. Furthermore, Cox multivariate analysis showed that MACC1 overexpression was an independent prognostic factor for predicting the overall survival of glioma patients. In conclusion, expression of MACC1 in glioma could be adopted as a candidate biomarker for the diagnosis of clinical stage and for assessing prognosis, indicating for the first time that MACC1 may play an important role in the tumor development and progression in glioma. MACC1 might be considered as a novel therapeutic target against this cancer.

Casticin induces human glioma cell death through apoptosis and mitotic arrest.

BACKGROUND: Malignant gliomas are the leading cause of morbidity and mortality in brain and central nervous system tumors. Recently, casticin has drawn wide attention to its critical role in tumor progression. However, the effect of casticin on glioma remains undefined. METHODS: Following treatment with casticin, cell viability, apoptosis, and cell cycle arrest were examined in U251 glioma cells. Additionally, the involved molecular mechanism was assessed by western blotting and flow cytometry. RESULTS: Casticin triggered an obvious dose-dependent decrease in U251, U87 and U373 glioma cell viability, and the growth inhibitory effect of casticin was correlated with cell cycle arrest and cell apoptosis. Further mechanistic analysis indicated that casticin induced G2/M phase arrest by attenuating the polymerization of tubulin. Furthermore, striking apoptosis was also confirmed, accompanied by the up-regulation of caspase-3, p53 and proapoptotic protein Bax. These effects were absent when the caspase inhibitor z-VAD-fmk or p53 inhibitor PFTα were applied, suggesting that casticin could trigger cell apoptosis in a caspase-3 and p53-dependent manner. CONCLUSION: These findings provide a prominent insight into how casticin abrogates the pathogenesis of glioma, and support its potential clinical prospect for further development of anti-brain cancer therapy.