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This paper aims to solve the technical problems such as poor drillability， large formation inclination， difficult well deflection control， and easy wall instability in the middle and deep Carboniferous and Permian hard formation in the Hashan area of Shengli western exploration area and realize the efficient exploration and benefit development of oil and gas reserves. Based on the current conventional drilling technology， the applicability and process optimization of segmented combined drilling technology were studied， and supporting technologies for speed acceleration and safety construction related to gas drilling were proposed for the Carboniferous and Permian hard formation of dolomitic rock and igneous rock， forming the regional construction standards. After the transformation from gas drilling to drilling fluid construction， the combined drilling technology of “gas drilling + ‘impregnated bit + turbine/high-speed screw’” was preferred. According to the formation characteristics， efficient impregnated bit series of different sizes were developed to achieve well deflection control and improve the rate of penetration. The combined drilling technology was successfully applied in Hashan3 and Hashan2 wells and achieved good results. During gas drilling， the rate of penetration in Well Hashan3 reached 5.2 m/h， more than three times faster than the conventional drilling method. During drilling fluid construction， the technology of “impregnated bit + turbine/high-speed screw” was adopted， and the formation inclination was 30°. The well deflection was well controlled， and the rate of penetration was close to 1 m/h， which showed a good regional application effect compared with conventional drilling technology. The field application shows that the combined drilling technology of “gas drilling + ‘impregnated bit + turbine/high-speed screw’” is an effective means to increase the speed and efficiency of middle and deep drilling in the Hashan area of Shengli west exploration area. However， the technology also has bottlenecks. Combined with the applicability research of particle impact drilling （PID） technology， it is suggested that the PID technology should be introduced and improved in the next step， so as to achieve speed and efficiency breakthrough in regional drilling.

AIM: To investigate the effect of ankyrin repeat domain 49 (ANKRD49) on epithelial-mesenchymal transition (EMT) in NCI-H1299 cells, and to explore its mechanism. METHODS: The ANKRD49 was over-expressed in NCI-H1299 cells. The morphological changes of ANKRD49-overpressing NCI-H1299 cells were observed under microscope. The mRNA and protein expression levels of EMT-related markers [E-cadherin, transforming growth factor-β1 (TGF-β1), vimentin and α-smooth muscle actin (α-SMA)] and EMT-related transcription factors (Snaill, Slug, Twist and ZEBl) were detected by RT-qPCR Western blot. Immunofluorescence staining was performed to observe the localization and expression of E-cadherin and vimentin in ANKRD49-overexpressing cells or control cells. Immunohistochemical method was performed to examine the levels of E-cadherin, a-SMA, Snail, Slug and ZEB1 in lung tissues of nude mice inoculated with ANKRD49-overexpressing H1299 cells or control cells. RESULTS: Compared with the control group, the ANKRD49 overexpressing cells showed mesenchymal cell morphology (fusiform and less tight connections). RT-qPCR and Western blot results showed that the mRNA and protein levels of mesenchymal markers vimentin and α-SMA in ANKRD49 overexpressing cells were significantly higher than those in cells of control group, while the mRNA and protein levels of epithelial marker E-cadherin were lower than those in cells of control group. Compared with control group, the im-munofluorescence intensity of E-cadherin of H1299 cells decreased in after ANKRD49 overexpression, while that of vimentin increased significantly. Snail, Slug and ZEB1 expression were significantly elevated in ANKRD49 overexpressing cells compared with control group. The levels of E-cadherin in lung tissues of nude mice inoculated with ANKRD49-overexpressing H1299 cells declined, while the levels of α-SMA, Snail, Slug and ZEB1 increased compared with those in control mice. CONCLUSION: ANKRD49 promoted EMT of NCI-H1299 cells by increasing the expression of Snail1, Slug and ZEB1 and consequent downreguation of E-cadherin and upregulation of vimentin and α-SMA. [ABSTRACT FROM AUTHOR]

The literature on corporate political activity (CPA) generally views nonmarket actions aimed at influencing political actors (e.g., lobbying or campaign contributions) as related but separate activities from market actions. This study demonstrates how firms' core market actions (e.g., market entry or geographic expansion) can function as CPA. We theorize two mechanisms through which firms leverage market actions as CPA: "pork" (i.e., ones that primarily benefit a politician's constituents) and "perk" (i.e., ones that directly benefit the politician). We document these mechanisms through an empirical analysis of data from the U.S. airline industry in 1990–2019. Specifically, we find that airlines increase the supply of flights from the airports in the home district of the chair of the Transportation Committee in the U.S. House of Representatives (pork). We also find that the airlines increase the supply of nonstop flights to Washington, DC. from the Chair's district (perk). We use counterfactual estimation methods and exogenous turnovers in committee leadership to provide causal evidence. Moreover, the observed increase in flight supplies is negatively associated with formal policy changes in Congress, and with text mining techniques, we find that this effect is stronger for bills related to aviation safety and security. We contribute to the literature on CPA by demonstrating a blurred boundary between market and nonmarket actions, which helps explain firms' competitive actions that cannot be explained by market considerations alone. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.17026. [ABSTRACT FROM AUTHOR]

Groundwater contamination induced by anthropogenic activities has long been a global issue. Characterizing and modeling contaminant transport processes is crucial to groundwater protection and management. However, challenges still exist in process complexity, data constraint, and computational cost. In the era of big data, the growth of machine learning has led to new opportunities in studying contaminant transport in groundwater systems. In this work, we introduce a new attention‐based graph neural network (aGNN) for modeling contaminant transport with limited monitoring data and quantifying causal connections between contaminant sources (drivers) and their spreading (outcomes). In five synthetic case studies that involve varying monitoring networks in heterogeneous aquifers, aGNN is shown to outperform LSTM‐based (long‐short term memory) and CNN‐ based (convolutional neural network) methods in multistep predictions (i.e., transductive learning). It also demonstrates a high level of applicability in inferring observations for unmonitored sites (i.e., inductive learning). Furthermore, an explanatory analysis based on aGNN quantifies the influence of each contaminant source, which has been validated by a physics‐based model with consistent outcomes with an R2 value exceeding 92%. The major advantage of aGNN is that it not only has a high level of predictive power in multiple scenario evaluations but also substantially reduces computational cost. Overall, this study shows that aGNN is efficient and robust for highly nonlinear spatiotemporal learning in subsurface contaminant transport, and provides a promising tool for groundwater management involving contaminant source attribution. Plain Language Summary: Groundwater contamination caused by human activities is a longstanding global challenge. Accurately characterizing and modeling the movement of contaminants is crucial for the protection and management of groundwater resources. However, the complexity of the processes, limitations in data availability, and high computational demands pose significant challenges. In the age of big data, machine learning offers new avenues for exploring contaminant transport in groundwater. In this study, we introduce a novel machine learning model called an attention‐based graph neural network (aGNN) designed to model contaminant transport with sparse monitoring data and to analyze the causal relationships between contaminant sources and observed concentrations at specific locations. We conducted five synthetic case studies across diverse aquifer systems with varying monitoring setups, where aGNN demonstrated superior performance over models based on other approaches. It also proved highly capable of making inferences about pollution levels at unmonitored sites. Moreover, an explanatory analysis using aGNN effectively quantified the impact of each contaminant source, with results validated by a physics‐based model. Overall, this study establishes aGNN as an efficient and robust method for complex spatiotemporal learning in subsurface contaminant transport, making it a valuable tool for groundwater management and contaminant source identification. Key Points: A novel graph‐based deep learning method is proposed for modeling contaminant transport constrained by monitoring dataThe proposed model quantifies the contribution of each potential contaminant source to the observed concentration at an arbitrary locationThe deep learning method substantially reduces the computational cost compared with a physics‐based contaminant transport model [ABSTRACT FROM AUTHOR]

To study the characteristics of nitrogen (N) loss on slopes, different vegetation (bare soil, alfalfa), slopes (5°, 10°, 15°), and rainfall intensities (40, 60, 80 mm/h) were set as variable factors in simulated rainfall experiments. Surface runoff accounts for 60.38–96.16% of total runoff and most N loss (57.69–88.67% of NO3−-N). Alfalfa can reduce average concentrations of N loss in runoff and reduce N loss in surface runoff by more than 48.29%, as well as subsurface runoff by 3.8%. Average N loss in subsurface runoff exceeds that of surface runoff. Rainfall intensity most affects N loss from surface runoff in bare soil conditions, and slope most affects N loss in subsurface runoff. Rainfall intensity in alfalfa treatments most influences runoff volume and N loss. The comprehensive effects of rainfall intensity, slope, and vegetation cover on the total loss of various forms of nitrogen in surface runoff can be described using a linear correlation equation, with a correlation coefficient between 0.84 and 0.91. [ABSTRACT FROM AUTHOR]

Rainfall intensity and slope gradient are the main drivers of slope surface runoff and nitrogen loss. To explore the distribution of rainfall runoff and nitrogen loss on the Miyun Reservoir slopes, we used artificial indoor simulated rainfall experiments to determine the distribution characteristics and nitrogen migration paths of surface and subsurface runoff under different rainfall intensities and slope gradients. The initial runoff generation time of subsurface runoff lagged that of surface runoff, and the lag time under different rainfall intensity and slope conditions ranges from 3.97 to 12.62 min. Surface runoff rate increased with increasing rainfall intensity and slope gradient; compared with a rainfall intensity of 40 mm/h, at a slope of 15°, average surface runoff rate at 60 and 80 mm/h increased by 2.38 and 3.60 times, respectively. Meanwhile, the subsurface runoff rate trended upwards with increasing rainfall intensity, in the order 5 > 15 > 10°. It initially increased and then decreased with increasing slope gradient, in the order 5 > 10 > 15°. Total nitrogen (TN) loss concentration of surface runoff shows a decrease followed by a stabilization trend; the concentration of TN loss decreases with decreasing rainfall intensity, and the stabilization time becomes earlier and is most obvious in 5° slope conditions. TN loss concentration in subsurface runoff decreased with increasing rainfall intensity, i.e., 40 > 60 > 80 mm/h. The surface runoff rainfall coefficient was mainly affected by rainfall intensity, a correlation between αs and slope gradients S was not obvious, and the fitting effect was poor. The subsurface runoff rainfall coefficient was mainly affected by slope gradient, the R2 of all rainfall intensities was <0.60, and the fitting effect was poor. The main runoff loss pathway from the Miyun Reservoir slopes was surface runoff, which was more than 62.57%. At the same time, nitrogen loss was subsurface runoff, more than 51.14%. The proportion of surface runoff to total runoff increases with the increase of rainfall intensity and slope, with a minimum of 62.57%, and the proportion of nitrogen loss from subsurface runoff also decreases with increasing rainfall intensity but does not change with slope gradient. The order of different runoff modulus types was mixed runoff (surface and subsurface runoff occur simultaneously) > surface runoff > subsurface runoff. The surface and mixed runoff modulus increased significantly with increasing rain intensity under different rain intensities and slope gradients. Overall, rainfall intensity significantly affected slope surface runoff, and slope gradient significantly affected nitrogen loss. [ABSTRACT FROM AUTHOR]

T cells play an important role in adaptive immunity. Mature T cells specifically recognize antigens on major histocompatibility complex molecules through T-cell receptors (TCRs). As the TCR repertoire is highly diverse, its analysis is vital in the assessment of T cells. Advances in sequencing technology have provided convenient methods for further investigation of the TCR repertoire. In the present review, the TCR structure and the mechanisms by which TCRs function in tumor recognition are described. In addition, the potential value of the TCR repertoire in tumor diagnosis is reviewed. Furthermore, the role of the TCR repertoire in tumor immunotherapy is introduced, and the relationships between the TCR repertoire and the effects of different tumor immunotherapies are discussed. Based on the reviewed literature, it may be concluded that the TCR repertoire has the potential to serve as a biomarker for tumor prognosis. However, a wider range of cancer types and more diverse subjects require evaluation in future research to establish the TCR repertoire as a biomarker of tumor immunity. [ABSTRACT FROM AUTHOR]

Background: Heart failure (HF), especially HF with reduced ejection fraction (HFrEF), presents complex challenges, particularly in the aging population where it often coexists with type 2 diabetes mellitus (T2DM)., Aim: To analyze the effect of dapagliflozin treatment on cardiac, renal function, and safety in patients with HFrEF combined with T2DM., Methods: Patients with T2DM complicated with HFrEF who underwent treatment in our hospital from February 2018 to March 2023 were retrospectively analyzed as the subjects of this study. The propensity score matching method was used, and a total of 102 eligible samples were scaled. The clinical efficacy of the two groups was evaluated at the end of the treatment, comparing the results of blood glucose, insulin, cardiac function, markers of myocardial injury, renal function indexes, and 6-min walk test (6MWT) before and after the treatment. We compared the occurrence of adverse effects on the treatment process of the two groups of patients. The incidence of adverse outcomes in patients within six months of treatment was counted., Results: The overall clinical efficacy rate of patients in the study group was significantly higher than that of patients in the control group ( P = 0.013). After treatment, the pancreatic beta-cell function index, left ventricular ejection fraction, and glomerular filtration rate of patients in the study group were significantly higher than control group ( P < 0.001), while their fasting plasma glucose, 2-h postprandial glucose, glycosylated hemoglobin, insulin resistance index, left ventricular end-systolic diameter, left ventricular end-diastolic diameter, cardiac troponin I, creatine kinase-MB, N-terminal pro b-type natriuretic peptide, serum creatinine, and blood urea nitrogen were significantly lower than those of the control group. After treatment, patients in the study group had a significantly higher 6MWT than those in the control group ( P < 0.001). Hypoglycemic reaction ( P = 0.647), urinary tract infection ( P = 0.558), gastrointestinal adverse effect ( P = 0.307), respiratory disturbance ( P = 0.558), and angioedema ( P = 0.647) were not statistically different. There was no significant difference between the incidence of adverse outcomes between the two groups ( P = 0.250)., Conclusion: Dapagliflozin significantly enhances clinical efficacy, cardiac and renal function, and ambulatory capacity in patients with HFrEF and T2DM without an increased risk of adverse effects or outcomes., Competing Interests: Conflict-of-interest statement: This study does not contain any conflict of interest., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)