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Rapid and accurate extraction of water bodies from high-spatial-resolution remote sensing images is of great value for water resource management, water quality monitoring and natural disaster emergency response. For traditional water body extraction methods, it is difficult to select image texture and features, the shadows of buildings and other ground objects are in the same spectrum as water bodies, the existing deep convolutional neural network is difficult to train, the consumption of computing resources is large, and the methods cannot meet real-time requirements. In this paper, a water body extraction method based on lightweight MobileNetV2 is proposed and applied to multisensor high-resolution remote sensing images, such as GF-2, WorldView-2 and UAV orthoimages. This method was validated in two typical complex geographical scenes: water bodies for farmland irrigation, which have a broken shape and long and narrow area and are surrounded by many buildings in towns and villages; and water bodies in mountainous areas, which have undulating topography, vegetation coverage and mountain shadows all over. The results were compared with those of the support vector machine, random forest and U-Net models and also verified by generalization tests and the influence of spatial resolution changes. First, the results show that the F1-score and Kappa coefficients of the MobileNetV2 model extracting water bodies from three different high-resolution images were 0.75 and 0.72 for GF-2, 0.86 and 0.85 for Worldview-2 and 0.98 and 0.98 for UAV, respectively, which are higher than those of traditional machine learning models and U-Net. Second, the training time, number of parameters and calculation amount of the MobileNetV2 model were much lower than those of the U-Net model, which greatly improves the water body extraction efficiency. Third, in other more complex surface areas, the MobileNetV2 model still maintained relatively high accuracy of water body extraction. Finally, we tested the effects of multisensor models and found that training with lower and higher spatial resolution images combined can be beneficial, but that using just lower resolution imagery is ineffective. This study provides a reference for the efficient automation of water body classification and extraction under complex geographical environment conditions and can be extended to water resource investigation, management and planning.

The monitoring and analysis of the spatiotemporal distribution of anthropogenic carbon emissions is an important part of realizing China’s regional “dual carbon” goals; that is, the aim is for carbon emissions to peak in 2030 an to achieve carbon neutrality by 2060, as well as achieving sustainable development of the ecological environment. The column-averaged CO2 dry air mole fraction (XCO2) of greenhouse gas remote sensing satellites has been widely used to monitor anthropogenic carbon emissions. However, selecting a reasonable background region to eliminate the influence of uncertainty factors is still an important challenge to monitor anthropogenic carbon emissions by using XCO2. Aiming at the problems of the imprecise selection of background regions, this study proposes to enhance the anthropogenic carbon emission signal in the XCO2 by using the regional comparison method based on the idea of zoning. First, this study determines the background region based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) dataset and potential temperature data. Second, the average value of the XCO2 in the background area was extracted and taken as the XCO2 background. On this basis, the XCO2 anomaly (XCO2ano) was obtained by regional comparison method. Finally, the spatiotemporal variation characteristics and trends of XCO2ano were analyzed, and the correlations between the number of residential areas and fossil fuel emissions were calculated. The results of the satellite observation data experiments over China from 2010 to 2020 show that the XCO2ano and anthropogenic carbon emissions have similar spatial distribution patterns. The XCO2ano in China changed significantly and was in a positive growth trend as a whole. The XCO2ano values have a certain positive correlation with the number of residential areas and observations of fossil fuel emissions. The purpose of this research is to enhance the anthropogenic carbon emission signals in satellite observation XCO2 data by combining ODIAC data and potential temperature data, achieve the remote sensing monitoring and analysis of spatiotemporal changes in anthropogenic carbon emissions over China, and provide technical support for the policies and paths of regional carbon emission reductions and ecological environmental protection.

Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.

Precision therapy for glioma remains a major challenge due to tumor heterogeneity. The Origin Recognition Complex Subunit 6 (ORC6) is a crucial regulator of DNA replication initiation. This study aims to investigate the expression of ORC6 in gliomas and its relationship with survival rates and malignancy, while screening potential drugs targeting its functional network. By integrating multiple bioinformatics approaches with structure-based virtual screening, retrospective RNA sequencing data analysis was performed using patients from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. A protein-protein interaction (PPI) network was constructed from ORC6-coexpressed genes to identify core hubs. Molecular docking was employed to screen a library of natural compounds and known drugs against these hub targets. Research has revealed that ORC6 is significantly upregulated in high-grade gliomas, with its elevated expression associated with poor survival outcomes and immune inflammatory responses. Network analysis identified five core hub genes (ORC1, ORC2, MCM2, MCM6, CDC45) central to DNA replication. Molecular docking revealed that several compounds, including the natural flavonoid Baicalein and the FDA-approved drug Palbociclib, exhibited high binding affinity to these hub targets. ORC6 represents a highly promising novel target for precision therapy in glioma. Potential approaches to target this pathway include disrupting the ORC6-replication axis using existing drugs (such as palbociclib) or natural products (such as baicalin).

A novel surface plasmon resonance (SPR) refractive index (RI) sensor based on the D-type dual-mode photonic crystal fiber (PCF) is proposed. The sensor employs a side-polished few-mode PCF that facilitates the transmission of the fundamental and second-order modes, with an integrated microfluidic channel positioned directly above the fiber core. This design minimizes the distance to the analyte and maximizes the interaction between the optical field and the analyte, thereby enhancing the SPR effect and resonance loss for improved sensing performance. Au-TiO2 dual-layer material was coated on the surface of a microfluidic channel to enhance the penetration depth of the core evanescent field and tune the resonance wavelength to the near-infrared band, meeting the special needs of chemical and biomedical detection fields. The finite element method was utilized to systematically investigate the coupling characteristics between various modes and surface plasmon polariton (SPP) modes, as well as the impact of structural parameters on the sensor performance. The results indicate that the LP11b_y mode exhibits greater wavelength sensitivity than the HE11_y mode, with a maximum sensitivity of 33,000 nm/RIU and an average sensitivity of 8272.7 nm/RIU in the RI sensing range of 1.25–1.36, which is higher than the maximum sensitivity of 16,000 nm/RIU and average sensitivity of 5666.7 nm/RIU for the HE11b_y mode. It is believed that the proposed PCF-SPR sensor features both high sensitivity and high resolution, which will become a critical device for wide RI detection in mid-infrared fields.

Emerging evidence suggests that long noncoding RNA (lncRNA) plays pivotal roles in regulating various biological process in human cancers. Titin-antisense RNA1 (TTN-AS1) has been regarded as a tumor promoting lncRNA in numerous cancers. However, the clinical significance and biological function of TTN-AS1 in lung adenocarcinoma (LUAD) remain unclear. In the present study, we revealed that the expression of TTN-AS1 was upregulated in LUAD tissues and cell lines. High TTN-AS1 expression was associated with TNM stage and lymph node metastasis of LUAD patients. In addition, high expression of TTN-AS1 was correlated with poor postoperative prognosis of LUAD patients. Knockdown of TTN-AS1 significantly inhibited the growth, proliferation, migration, and invasion ability of LUAD cells in vitro. Then, by using bioinformation analysis and luciferase reporter experiment, we identified that TTN-AS1 could function as a competing endogenous RNA (ceRNA) by sponging miR-142-5p to regulate the expression of cyclin-dependent kinase 5 (CDK5) in LUAD. Since CDK5 is a key regulator in the process of epithelial mesenchymal transition (EMT), we detected the expression of EMT-related proteins, consequently, EMT was suppressed by knockdown of TTN-AS1 while this phenomenon was rescued by miR-142-5p inhibitor. Taken above, our study revealed that TTN-AS1 played an important role in LUAD progression. TTN-AS1/miR-142-5p/CDK5 regulatory axis may serve as a novel therapeutic target in the treatment of LUAD.

Background To investigate related factors for postoperative pathological upgrading of cervical biopsy to cervical cancer (CC) in patients with cervical intraepithelial neoplasia (CIN)3 after conical resection. Methods This retrospective study collected data from patients diagnosed with CIN3 by cervical biopsies at the author’s Hospital between January 2012 and December 2022. The primary outcome was the pathological results of patients after conical resection. The pathological findings were categorized into the pathological upgrading group if postoperative pathology indicated CC, while those with normal, inflammatory, or cervical precancerous lesions were classified into the pathological non-upgrading group. The factors associated with upgrading were identified using multivariable logistic regression analysis. Results Among 511 patients, there were 125 patients in the pathological upgrading group (24.46%). The patients in the upgrading group were younger (47.68 ± 9.46 vs. 52.11 ± 7.02, P  < 0.001), showed a lower proportion of menopausal women (38.40% vs. 53.02%, P  = 0.0111), a lower proportion of HSIL (40.00% vs. 57.77%, P  = 0.001), a higher rate of HPV-16/18 positive (25.60% vs. 17.36%, P  = 0.011), a higher rate of contact bleeding (54.40% vs. 21.50%, P  < 0.001), lower HDL levels (1.31 ± 0.29 vs. 1.37 ± 0.34 mmol/L, P  = 0.002), higher neutrophil counts (median, 3.50 vs. 3.10 × 109/L, P  = 0.001), higher red blood cell counts (4.01 ± 0.43 vs. 3.97 ± 0.47 × 1012/L, P  = 0.002), higher platelet counts (204.84 ± 61.24 vs. 187.06 ± 73.66 × 109/L, P  = 0.012), and a smaller platelet volume (median, 11.50 vs. 11.90 fL, P  = 0.002).The multivariable logistic regression analysis showed that age (OR = 0.90, 95% CI: 0.86–0.94, P  < 0.001), menopausal (OR = 2.68, 95% CI: 1.38–5.22, P  = 0.004), contact bleeding (OR = 4.80, 95% CI: 2.91–7.91, P  < 0.001), and mean platelet volume (OR = 0.83, 95% CI: 0.69–0.99, P  = 0.038) were independently associated with pathological upgrading from CIN3 to CC after conical resection. Conclusion Age, menopausal, contact bleeding, and mean platelet volume are risk factors of pathological upgrading from CIN3 to CC after conical resection, which could help identify high risk and susceptible patients of pathological upgrading to CC.

Accurately and precisely grasping the spatial distribution and changing trends of China’s regional population is of great significance in new urbanization, economic development, public health, disaster assessment, and ecological environmental protection. To monitor and evaluate the long-term spatiotemporal characteristics of the population distribution in China, a population monitoring estimation model was proposed. Based on remote sensing data such as nighttime light (NTL) images, land use data, and data from the fifth, sixth, and seventh censuses of China, the population spatiotemporal distribution in China from 2000 to 2020 was analyzed with a random forest algorithm. This study obtained spatial distribution maps of population density at a 1 km x 1 km resolution in 2000, 2010, and 2020. The results revealed the trend of the spatiotemporal pattern of population change from 2000 to 2020. It shows that: the accuracy assessment using the 2020 census population of townships/streets as a reference shows an R2 of 0.67 and a mean relative error (MRE) of 0.44. The spatial pattern of the population in 2000 and 2010 is generally unchanged. In 2020, population agglomeration is evident in the east, with a slight increase in the proportion of the population in the west. The patterns of population agglomeration and urbanization also change over time. The population spatiotemporal distribution obtained in this study can provide a scientific reference for urban sustainable development and promote the rational allocation of urban resources.

We perform a systematic study of the Maxwell quasinormal spectrum in a mirror-like cavity following the generic Robin type vanishing energy flux principle, by starting with the Schwarzschild black holes in this paper. It is shown that, for black holes in a cavity, the vanishing energy flux principle leads to two different sets of boundary conditions. By solving the Maxwell equations with these two boundary conditions both analytically and numerically, we observe two distinct sets of modes. This indicates that the vanishing energy flux principle may be applied not only to asymptotically anti-de Sitter (AdS) black holes but also to black holes in a cavity. In the analytic calculations, the imaginary part of the Maxwell quasinormal modes are derived analytically for both boundary conditions, which match well with the numeric results. While in the numeric calculations, we complete a thorough study on the two sets of the Maxwell spectrum by varying the mirror radius rm, the angular momentum quantum number ℓ, and the overtone number N. In particular, we proclaim that the Maxwell spectrum may bifurcate for both modes when the mirror is placed around the black hole event horizon, which is analogous to the spectrum bifurcation effects found for the Maxwell fields on asymptotically AdS black holes. This observation provides another example to exhibit the similarity between black holes in a cavity and the AdS black holes.

We generalize our previous studies on the Maxwell quasinormal modes around Schwarzschild-anti-de-Sitter black holes with Robin type vanishing energy flux boundary conditions, by adding a global monopole on the background. We first formulate the Maxwell equations both in the Regge–Wheeler–Zerilli and in the Teukolsky formalisms and derive, based on the vanishing energy flux principle, two boundary conditions in each formalism. The Maxwell equations are then solved analytically in pure anti-de Sitter spacetimes with a global monopole, and two different normal modes are obtained due to the existence of the monopole parameter. In the small black hole and low frequency approximations, the Maxwell quasinormal modes are solved perturbatively on top of normal modes by using an asymptotic matching method, while beyond the aforementioned approximation, the Maxwell quasinormal modes are obtained numerically. We analyze the Maxwell quasinormal spectrum by varying the angular momentum quantum number ℓ, the overtone number N, and in particular, the monopole parameter 8πη2. We show explicitly, through calculating quasinormal frequencies with both boundary conditions, that the global monopole produces the repulsive force.