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Gas well production involves various stages, including stable, variable, and declining production. However, existing production-capacity equations typically apply only to the stable production stage, limiting their effectiveness in evaluating gas well productivity across all stages. To address this, the material balance equation and Darcy's equation were employed to account for changes in average formation pressure due to pressure drop funnels. The concept of a pressure-conversion skin factor was introduced, and its approximation was developed, leading to the establishment and solution of a full-stage productivity equation. Numerical simulations were then conducted to verify the accuracy and applicability of this equation. The findings are as follows: ① The full-stage productivity equation remains effective even when production rates and pressure are not constant, with the only potential source of inaccuracy being the approximative solution for the pressure conversion-skin factor. ② Numerical simulations demonstrated that the approximate solution closely matched the numerical simulation results for average formation pressure across various production stages and fundamental parameters, showing a consistent trend and high precision. The approximate and independent approximation solutions for absolute open-flow capacity were nearly identical, indicating the full-stage productivity equation's applicability throughout the production of gas wells. ③ Application results revealed that the full-stage productivity equation offers superior accuracy compared to the modified isochronous well test. ④ The approximate solution generally provides slightly higher accuracy, and the independent approximate solution effectively eliminates the influence of gas leakage radius. Therefore, the use of the approximate solution is recommended to calculate the average formation pressure and the independent approximate solution to calculate the absolute open-flow capacity. The full-stage productivity equation developed in this study is not constrained by the production system, making it suitable for productivity evaluation across all stages of gas well production. This has significant implications for the effective development of gas fields. [ABSTRACT FROM AUTHOR]

In the field of geoinformation science, multiview, image-based 3D city modeling has developed rapidly, and image depth estimation is an important step in it. To address the problems of the poor adaptability of training models of existing neural network methods and the long reconstruction time of traditional geometric methods, we propose a general depth estimation method for fast 3D city modeling that combines prior knowledge and information propagation. First, the original image is downsampled and input into the neural network to predict the initial depth value. Then, depth plane fitting and joint optimization are combined with the superpixel information and the superpixel optimized depth value is upsampled to the original resolution. Finally, the depth information propagation is checked pixel-by-pixel to obtain the final depth estimate. Experiments were conducted using multiple image datasets taken from actual indoor and outdoor scenes. Our method was compared and analyzed with a variety of existing widely used methods. The experimental results show that our method maintains high reconstruction accuracy and a fast reconstruction speed, and it achieves better performance. This paper offers a framework to integrate neural networks and traditional geometric methods, which provide a new approach for obtaining geographic information and fast 3D city modeling. [ABSTRACT FROM AUTHOR]

Simple Summary: The v-raf murine sarcoma viral oncogene homolog B1 (BRAF) gene containing a 15-base pair (bp) deletion at L485-P490 is the cause of several cancers. We generated siRNA to specifically knock down BRAF mRNA containing the 15-bp deletion. This siRNA suppressed the expression of BRAF, harboring the deletion without affecting wild-type BRAF expression in BxPC-3 pancreatic ductal adenocarcinoma cells in vitro and in vivo. Cell growth and phosphorylation of downstream extracellular-signal-regulated kinase proteins were also repressed. An off-target effect is the most common side effect of siRNA therapy. In this study, we reveal that siRNA with a 2′-O-methyl chemical modification in the seed region of the siRNA guide strand reduced seed-dependent off-target effects. Pancreatic ductal adenocarcinoma (PDAC) is predicted to become the second-most common cause of death within the next 10 years. Due to the limited efficacy of available therapies, the survival rate of PDAC patients is very low. Oncogenic BRAF mutations are one of the major causes of PDAC, specifically the missense V600E and L485–P490 15-bp deletion mutations. Drugs targeting the V600E mutation have already been approved by the United States Food and Drug Administration. However, a drug targeting the deletion mutation at L485–P490 of the BRAF gene has not been developed to date. The BxPC-3 cell line is a PDAC-derived cell line harboring wild-type KRAS and L485–P490 deleted BRAF genes. These cells are heterozygous for BRAF, harboring both wild-type BRAF and BRAF with the 15-bp deletion. In this study, siRNA was designed for the targeted knockdown of 15-bp deletion-type BRAF mRNA. This siRNA repressed the phosphorylation of extracellular-signal-regulated kinase proteins downstream of BRAF and suppressed cell growth in vitro and in vivo. Furthermore, siRNAs with 2′-O-methyl modifications at positions 2–5 reduce the seed-dependent off-target effects, as confirmed by reporter and microarray analyses. Thus, such siRNA is a promising candidate therapy for 15-bp deletion-type BRAF-induced tumorigenesis. [ABSTRACT FROM AUTHOR]

Monitoring salinity information of salinized soil efficiently and precisely using the unmanned aerial vehicle (UAV) is critical for the rational use and sustainable development of arable land resources. The sensitive parameter and a precise retrieval method of soil salinity, however, remain unknown. This study strived to explore the sensitive parameter and construct an optimal method for retrieving soil salinity. The UAV-borne multispectral image in China's Yellow River Delta was acquired to extract band reflectance, compute vegetation indexes and soil salinity indexes. Soil samples collected from 120 different study sites were used for laboratory salt content measurements. Grey correlation analysis and Pearson correlation coefficient methods were employed to screen sensitive band reflectance and indexes. A new soil salinity retrieval index (SSRI) was then proposed based on the screened sensitive reflectance. The Partial Least Squares Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were employed to construct retrieval models based on the sensitive indexes. The results found that green, red, and near-infrared (NIR) bands were sensitive to soil salinity, which can be used to build SSRI. The SSRI-based RF method was the optimal method for accurately retrieving the soil salinity. Its modeling determination coefficient (R2) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R2, RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211. [ABSTRACT FROM AUTHOR]