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Understanding the correlation between coal resistivity and methane content is critical for optimizing coalbed methane (CBM) recovery and ensuring mining safety. Existing studies mainly rely on empirical trend fitting, leaving a gap in model-driven analyses of resistivity dynamics during methane adsorption and desorption. This study develops a dual-coefficient electrical rock physics model integrating inorganic mineral composition, organic resistivity, methane adsorption–desorption behavior, and pore inclusion structures. Correction coefficients (0.2 for methane and 0.4 for organic resistivity) were introduced to address adsorption heterogeneity and structural complexity. Experimental validation on coal samples (density: 1.45 g/cm 3 , porosity: 5.5%) showed strong agreement between simulated and measured resistivity during adsorption (0.8882–3.6973 m 3 /t) and desorption (3.3974–2.1773 m 3 /t), with high correlation (R 2  = 0.9815 adsorption, 0.9956 desorption; P -values = 0.9861, 0.9763). Sensitivity analysis revealed that mineral composition (e.g., quartz, clay) and inclusion aspect ratios (0–1) notably affect resistivity. Flattened inclusions (low aspect ratios) reduce resistivity more than spherical ones, especially at methane volumes lower than 0.15 m 3 /t. Organic content inversely correlates with resistivity; when the volume fraction exceeds 0.92, pore structure effects diminish. This work links microscopic adsorption mechanisms to macroscopic electrical properties, providing a predictive framework for CBM resource evaluation, CO 2 storage monitoring, and coal mine hazard mitigation. The model adapts to diverse coal types and structural conditions, demonstrating broad applicability in research and industry.

As the primary domain of ideological and political education in higher education institutions, ideological and political courses must align with principles rooted in human psychology and education. Integrating educational psychology into ideological and political teaching in universities enhances the scientific, targeted, and forward-thinking nature of such education. The burgeoning exploration of knowledge graph applications has extended to machine translation, semantic search, and intelligent question answering. Diverging from traditional text matching, the knowledge spectrum graph transforms information acquisition in search engines. This paper pioneers a predictive system for delineating the relationship between educational psychology and ideological and political education in universities. Initially, it extracts diverse psychological mapping relationships of students, constructing a knowledge graph. By employing the KNN algorithm, the system analyzes psychological characteristics to effectively forecast the relationship between educational psychology and ideological and political education in universities. The system's functionality is meticulously detailed in this paper, and its performance is rigorously tested. The results demonstrate high accuracy, recall rates, and F1 values. The F1 score can reach 0.95enabling precise sample classification. The apex of the average curve for system response time peaks at approximately 2.5 s, maintaining an average response time of less than 3 s. This aligns seamlessly with the demands of practical online teaching requirements. The system adeptly forecasts the relationship between educational psychology and ideological and political education in universities, meeting response time requirements and thereby fostering the scientific and predictive nature of ideological and political teaching in higher education institutions.

A critical event in the pathogenesis of kidney fibrosis is the transition of macrophages into myofibroblasts (MMT). Exosomes play an important role in crosstalk among cells in the kidney and the development of renal fibrosis. However, the role of myofibroblast-derived exosomes in the process of MMT and renal fibrosis progression remains unknown. Here, we examined the role of myofibroblast-derived exosomes in MMT and kidney fibrogenesis. , transforming growth factor-β1 stimulated the differentiation of kidney fibroblasts into myofibroblasts and promoted exosome release from myofibroblasts. RAW264.7 cells were treated with exosomes derived from myofibroblasts. We found purified exosomes from myofibroblasts trigger the MMT. By contrast, inhibition of exosome production with GW4869 or exosome depletion from the conditioned media abolished the ability of myofibroblasts to induce MMT. Mice treatment with myofibroblast-derived exosomes (Myo-Exo) exhibited severe fibrotic lesion and more abundant MMT cells in kidneys with folic acid (FA) injury, which was negated by TANK-banding kinase-1 inhibitor. Furthermore, suppression of exosome production reduced collagen deposition, extracellular matrix protein accumulation, and MMT in FA nephropathy. Collectively, Myo-Exo enhances the MMT and kidney fibrosis. Blockade of exosomes mediated myofibroblasts-macrophages communication may provide a novel therapeutic target for kidney fibrosis.

Environmental noise is inevitable during seismic data acquisition, with major sources including heavy machinery, rivers, wind, and other environmental factors. During field data acquisition, it is important to assess the impact of environmental noise and evaluate data quality. In subsequent seismic data processing, these noise components also need to be eliminated. Accurate identification of noise traces facilitates rapid quality control (QC) during fieldwork and provides a reliable basis for targeted noise attenuation. Conventional environmental noise identification primarily relies on amplitude differences. However, in seismic data, high-amplitude signals are not necessarily caused by environmental noise. For example, surface waves or traces near the shot point may also exhibit high amplitudes. Therefore, relying solely on amplitude-based criteria has certain limitations. To improve noise identification accuracy, we use the Mel-spectrogram to extract features from seismic data and construct the dataset. Compared to raw time-series signals, the Mel-spectrogram more clearly reveals energy variations and frequency differences, helping to identify noise traces more accurately. We then employ a Vision Transformer (ViT) network to train a model for identifying noise in seismic data. Tests on synthetic and field data show that the proposed method performs well in identifying noise. Moreover, a denoising case based on synthetic data further confirms its general applicability, making it a promising tool in seismic data QC and processing workflows.

Background: Long non-coding RNA bladder cancer associated transcript 1 (BLACAT1) is oncogenic in several types of cancers. However, little is known concerning its expression and function in prostate cancer. Methods: Paired prostate cancer samples were collected, and the expression levels of BLACAT1, miR-29a-3p and disheveled segment polarity protein 3 (DVL3) were examined by quantitative real-time polymerase chain reaction (qRT-PCR); BLACAT1 shRNAs were transfected into PC-3 and LNCaP cell lines, and proliferative ability was detected by cell counting kit-8 (CCK-8) assay; qRT-PCR and Western blot were used to analyze the changes of miR-29a-3p and DVL3; dual-luciferase reporter gene assay was used to determine the regulatory relationships between miR-29a-3p and BLACAT1, and miR-29a-3p and DVL3. Results: BLACAT1 expression was significantly up-regulated in cancerous tissues of prostate cancer samples and positively correlated with the expression of DVL3, while negatively associated with miR-29a-3p. After the transfection of BLACAT1 shRNAs into prostate cancer cells, the proliferative ability and metastatic ability of cancer cells were significantly inhibited; BLACAT1 shRNAs could reduce the expression of DVL3 on both mRNA and protein expressions levels, the luciferase activity of BLACAT1 reporter was inhibited by miR-29a-3p, and DVL3 was validated as a target gene of miR-29a-3p. Conclusion: BLACAT1 expression is abnormally up-regulated in prostate cancer tissues. BLACAT1 can modulate the proliferative and metastatic ability of prostate cancer cells and have the potential to be the “ceRNA” to regulate the expression of DVL3 by sponging miR-29a-3p.

The classical pre-stack seismic inversion technique uses the Zoeppritz equation and its simplified versions to calculate the PP and PS reflection coefficients at different incidence angles, aiding in inverting the subsurface velocity and density parameters. Despite its widespread application, the amplitude variation with angle (AVA) inversion based on the Zoeppritz equation has limitations regarding the accuracy. The AVA neglects transmission losses and the effects of multiple reflections during seismic wave propagation, resulting in reduced resolution. In contrast, the propagation matrix theory offers a comprehensive range of reflection coefficients for P- and S-waves in multilayered media at arbitrary incidence angles, thereby theoretically enhancing the inversion accuracy. However, the seismic responses obtained using this method exist in the slowness–frequency domain and require constant slowness for consistency along a profile. This assumption is violated when variations in the P-wave velocity occur within the subsurface, affecting the incidence angle of propagating seismic waves. This study modifies the propagation matrix theory to compute AVA seismic responses and applies it to pre-stack multiparameter inversion. The effectiveness of the modified method was validated by deriving theoretical AVA seismic responses and comparing them to solutions from a typical layered media model. The modified theory was also employed for seismic pre-stack inversion. Numerical simulations and field data tests demonstrated that the new propagation matrix method offers a high accuracy and stability.

Methamphetamine (MA) is the most common and highly addictive substance abuse drug. Runt-related transcription factor 3 (RUNX3) and Zinc finger E-box-binding homeobox 1 (ZEB1) are associated with lung inflammation and fibrosis. However, the protein-protein interactions (PPIs) between RUNX3 and ZEB1 and its involvement in MA-induced chronic lung injury is still unclear. In this study, we evaluated lung injury using echocardiography, hematoxylin and eosin staining, and western blot analysis. The viability of alveolar epithelial cells (AECs) was assessed using cell counting kit-8. Molecular Operating Environment software, Search Tool for the Retrieval of Interacting Genes/Proteins database, co-immunoprecipitation, assay and confocal immunofluorescence assay were used to predict and identify the PPIs between RUNX3 and ZEB1. The expression of RUNX3 and ZEB1 were knockdown in AECs using siRNA. The results revealed that MA exposure increased the peak blood flow velocity of the pulmonary artery and the acceleration time of pulmonary artery blood flow. Further, exposure to MA also causes adhesion and fusion of the alveolar walls and altered AEC activity. A decrease in the expression of RUNX3 and an increase in the expression of ZEB1 and its downstream signaling molecules were observed on MA exposure. The PPIs between RUNX3 and ZEB1 were identified. Further, an increase in the protein binding rate of RUNX3-ZEB1 was observed in MA-induced lung injury. These results show interactions between RUNX3 and ZEB1. RUNX3 protects against lung injury; however, ZEB1 expression and the PPIs between ZEB1 and RUNX3 has deleterious effects on chronic lung injury induced by MA exposure. Our results provide a new therapeutic approach for the treatment of chronic lung injury due to MA exposure.

Based on the data of cores, thin sections, well logs, and test experiments, the characteristics and main controlling factors of lamellation fractures in continental shales of the third and fourth members of the Paleogene Qianjiang Formation in the Qianjiang Depression, Jianghan Basin, are studied. Lamellation fractures mainly develop along laminas in shales. They have various morphological characteristics such as straightness, bending, discontinuity, bifurcation, pinching out, and merging. Lamellation fractures with high density show poor horizontal continuity and connectivity characteristics. The average linear density of the lamellation fractures is mainly between 20 m-1 and 110 m-1, and the aperture is usually less than 160 μm. The density of lamellation fractures is related to their apertures. The smaller the apertures of lamellation fractures are, the higher the density is. The development degree of lamellation fractures is mainly controlled by mineral composition, type, thickness, density of lamination, contents of organic matter and pyrite, lithofacies, structural position, etc. Lamellation fractures develop well, especially under the conditions of medium dolomite content, large lamination density, small lamination thickness, and high total organic carbon (TOC) and pyrite contents. The influences of lithofacies on the lamellation fractures are complex. The lamellation fractures are most developed in carbonaceous layered limestone dolomite and carbonaceous layered dolomite mudstone, followed by stromatolite dolomite filled with carbonaceous pyroxene. The fractures in the massive argillaceous dolomites and carbonaceous massive mudstones are poorly developed. No fractures can be found in the carbonaceous dolomitic, argillaceous glauberites or salt rocks with high glauberite content. Structure is also an important factor controlling lamination fractures. Tectonic uplifts are beneficial to the expansion and extension of lamellation fractures, which increases fracture density. Therefore, when other influence factors are similar, lamellation fractures develop better in the high part of the structure than in the low part.

Seismic inversion in geophysics is an effective way to obtain underground rock properties from seismic survey data on the Earth’s surface. In particular, we can obtain much more information to characterize subsurface geological structure and lithology via pre-stack seismic inversion, with offset information added to the inversion, than by post-stack seismic inversion. However, pre-stack seismic inversion is usually a nonlinear and complicated process. In this article, we adopt a L1-2-norm as a constraint on pre-stack seismic inversion, promoting the generation of a sparse solution. We also propose a novel pre-stack seismic inversion method that reduces the complexity of the solving method by utilizing an objective function decomposition scheme. Comparison of calculation time, accuracy and sparsity of the inversion solutions indicates that the proposed algorithm has better accuracy and robustness. Moreover, considering the difficulty of regularization parameter selection, we develop an adaptive parameter selection strategy based on generalized Stein unbiased risk estimation (G-SURE) and incorporate it into the solving algorithm. The adaptive approach finds an appropriate regularization parameter in each iteration and obtains the optimal solution directly, which is beneficial for improving computational efficiency. A synthetic data test verifies that the adaptive method can converge to the optimal solution iteratively in the case of arbitrary initial regularization parameters. Finally, in application to real field data, we explain why the adaptive method is the better choice even though adaptive and non-adaptive methods can obtain solutions with similar accuracy.Article HighlightsA novel pre-stack seismic inversion method is proposed based on a proximal difference- of-convex algorithm (pDCA)A new adaptive regularization parameter selection strategy is proposed based on Generalized Stein unbiased risk estimation (G-SURE)Verification that one of the regularization parameters has a limited effect in L1-2-norm

Designing of a sliding mode observer was finished based on the motion model of autonomous underwater vehicle (AUV). A Flutter decrease strategy for the sliding mode observer was discussed and the sliding mode observer was applied to the fault diagnosis of the AUV. Simulation experiment without faults was designed. Simulation experiment of sensor fault diagnosis and field trial of thruster fault diagnosis were undertaken. The validity and feasibility of this method were validated based on the analysis of the simulation and field trial results.