Search Results (90)

Ballast compaction is a critical factor influencing ballast bed condition and the operational safety of heavy-haul railways. However, existing quantitative evaluation methods often suffer from overly idealized simulation models and limitations in signal processing and assessment frameworks. To address these issues, this study proposes a quantitative analysis approach for ballast compaction by integrating non-uniform medium simulation modeling, wavelet–Singular Value Decomposition (SVD) joint denoising, frequency–wavenumber (F-K) migration imaging, and wavelet packet decomposition (WPD)-based feature extraction. Forward simulations were conducted based on the constructed model, and the proposed methodology was validated using 1.5 GHz (gigahertz, 1 GHz = ${10}^9$ Hz) ground penetrating radar (GPR) data acquired from compaction experiments. The results demonstrate that wavelet–SVD joint denoising effectively suppresses deep coherent noise caused by strong reflections from sleepers, significantly enhancing the identification of deep effective signals and ensuring accurate localization and feature extraction of compaction zones. The Geometric Mean of WPD High/Low-Frequency Energy Ratio (GMHLFER) exhibits a strong positive correlation with the degree of compaction. In simulations, as the proportion of compacted material increased from $9\%$ to $21\%$, the GMHLFER rose from 21.555 to 26.581. In field tests, the value increased from 22.012 to 26.012 as compaction severity progressed from slight to severe, demonstrating stable responses across full-gradient compaction conditions and indicating high robustness and sensitivity. The proposed method provides an effective approach for quantitative characterization of ballast compaction in heavy-haul railways, and offers a promising technical pathway for intelligent inspection and condition assessment of railway ballast beds. Full article

Using perfluorooctanoic acid (PFOA) as a representative highly mobile solute to isolate hydrological controls, we investigated how slope influences the partitioning of vertical and lateral transport pathways. While vertical percolation has been widely examined using conventional column leaching tests, lateral transport driven by topographic gradients remain insufficiently quantified under controlled conditions. Here, laboratory-scale inclined leaching experiments were conducted to resolve the distribution of solute transport among vertical leachate, lateral runoff, and solid-phase retention under systematically varied slope angles (0°, 4°, 9°, and 20°), flow regimes, and leaching volumes. Results show that solute migration shifted from vertical-dominated transport under flat conditions (91% at 0°) to lateral-dominated export at moderate slopes, with lateral pathways accounting for up to 75% of the recovered mass at 9°. This pathway shift was well described by an exponential partitioning model, f₁(α) = f_max (1 − e^−kα), where f_max = 0.80 and k = 0.34°⁻¹ (R² = 0.97), indicating a critical crossover threshold at approximately 4° slope. Flow regime interacted with slope angle to modulate lateral transport efficiency: slower flow enhanced lateral export at moderate slopes, whereas faster flow promoted peak lateral transport under steeper conditions. In contrast, solid-phase retention remained consistently low (5–9%) across all treatments, indicating that the observed redistribution patterns were primarily governed by hydrological pathway partitioning rather than sorption processes. These results demonstrate that even modest topographic gradients can fundamentally alter solute transport pathways in sloped soils. The slope-dependent pathway partitioning framework developed here provides a process-based basis for incorporating lateral transport into hillslope hydrological models and for improving assessments of contaminant redistribution in both managed and natural landscapes. Full article

Background/Objectives: Melanoma is one of the deadliest types of skin cancer due to its ability to metastasize if not treated early. While targeted- and immune- therapies have significantly improved melanoma treatment outcomes, acquired drug resistance even with combined therapeutics remain prevalent. SIRT6 is a nuclear histone deacetylase that regulates DNA repair, metabolism, and chromatin remodeling. It is overexpressed in melanoma and its inhibition in melanoma is known to have anti-proliferative response, and alterations in pathways related to cell cycle, senescence, and metastasis. Methods: To deepen our understanding of the role of SIRT6 in melanoma, in this study we utilized RNA sequencing, proteomics, and Ingenuity Pathway Analysis on genetically modified human melanoma cells to determine the downstream mechanism of SIRT6 in melanoma. Results: SIRT6 knock down (KD) in A375 and G361 melanoma cells, with CRISPR/Cas9 or shRNA techniques, resulted in a significant decrease in proliferation and clonogenic survival of the cells. SIRT6 KD caused an altered expression of multiple genes associated with cell proliferation, mitotic regulation, invasion, cell death/senescence, and immunomodulation, including AURKB, ANLN, MYC, FOXM1, RABL6, E2F2, TP53, RBL1, OSM, TNF, IL1B, IL6, and IFNG. Comparative analysis at both transcription and translation levels revealed coordinated downregulation of proliferation, invasion, and migration and upregulation of targets related to cell death, apoptosis, and necrosis. Multi-omics analysis also predicted downregulation of signaling networks associated with MAP3K20, MYC, MKNK, and HMGCR. Conclusions: Given its involvement in tumorigenesis, this study underlines the importance of SIRT6 in melanoma and provides support to its potential as a novel therapeutic target for melanoma. Full article

Cell morphology and its mechanical properties are crucial factors in cancer development, affecting migration, invasiveness, and the potential risk of metastasis. However, most studies address these aspects separately, limiting the understanding of how morphological complexity relates to cellular mechanics. This work presents an integrated approach that simultaneously quantifies morphology and viscoelasticity in the human osteosarcoma cell line MG-63. Stress–relaxation experiments and optical imaging of the same cells were performed using a custom-built system that couples Atomic Force Microscopy (AFM) with an inverted optical microscope. Morphometric parameters were extracted from cell contours, while viscoelastic properties were obtained by fitting AFM data to the Fractional Kelvin (FK) and Fractional Zener (FZ) models. Among the morphological descriptors, the Shape Complexity (SC) was proposed. It is derived from the Lobe Contribution Elliptical Fourier Analysis (LOCO-EFA), which captures fine-scale contour features overlooked by conventional metrics. Experimental results show that, in MG-63 cells, higher SC values are associated with greater stiffness, indicating a correlation between cell shape complexity and cell stiffness. Furthermore, loading-rate analysis shows that the FZ model captures strain-rate-dependent stiffening more effectively than the FK model. This methodology provides a first approach to jointly analyzing quantitative morphological parameters and mechanical properties, underlining the importance of combined studies to achieve a comprehensive understanding of cell behavior. Full article

Malignant melanoma (MM) is a highly invasive and metastatic form of skin cancer. Betulinic acid (BA) and betulin (BE) possess pharmacological activities such as heat-clearing, detoxification, and anti-tumor effects, with BA showing potent selective cytotoxicity against melanoma cells. However, their underlying mechanisms in MM treatment remain unclear. Herein, this study systematically evaluated the anti-melanoma effects of BA and BE via integrated network pharmacology, in vitro and in vivo assays. Network pharmacology analysis revealed that BA and BE exerted anti-MM effects mainly by regulating apoptosis, angiogenesis and autophagy through the PI3K/AKT and MAPK signaling pathways. In vitro, both BA and BE inhibited colony formation and migration of B16-F10 cells, induced apoptosis by enhancing DNA damage and upregulating apoptotic protein expression, increased autophagic activity, and reduced ATP production and mitochondrial membrane potential (ΔΨm). These effects were closely associated with the inhibition of the PI3K/AKT/mTOR and MAPK pathways. Notably, BA showed stronger inhibitory effects than BE on the migration, invasion and tube formation of HUVECs. In vivo assays further confirmed that BA significantly suppressed melanoma growth in C57BL/6J mice by blocking the PI3K/AKT/mTOR and MAPK pathways. Collectively, BA and BE inhibit B16-F10 cell proliferation through the regulation of apoptosis and autophagy, with BA showing particularly promising potential as a candidate agent for MM therapy. Full article

By enhancing transient fidelity before geometric inversion, this work revisits the classical LCT-based non line-of-sight (NLOS)imaging paradigm and establishes a unified Bayesian sparse-enhancement framework for reconstructing hidden objects under photon-starved and hardware-limited conditions. We introduce sparse Bayesian learning (SBL) as a dedicated front-end transient restoration module, leveraging adaptive sparsity modeling to suppress background fluctuations while preserving physically consistent multipath returns. This lightweight and geometry-agnostic design enables seamless integration into existing LCT processing pipelines, granting the framework strong compatibility with diverse acquisition configurations. Comprehensive simulations and experiments on complex reflective targets demonstrate significant improvements in spatial resolution, boundary sharpness, and robustness to IRF-induced temporal blurring compared with traditional LCT and f-k migration methods. The results validate that transient quality remains a critical bottleneck in practical NLOS deployment, and addressing it via probabilistic sparsity inference offers a scalable and computationally affordable pathway toward stable, high-fidelity NLOS reconstruction. This study provides an effective signal-domain enhancement solution that strengthens the practicality of NLOS imaging in real-world environments, paving the way for future extensions toward dynamic scenes, multi-view fusion, and high-throughput computational sensing. Full article

Coal texture is an important factor in optimizing the characterization of coalbed methane (CBM) reservoirs, directly affecting key reservoir properties such as permeability, gas content, and production potential. This study develops an advanced methodology for coal texture classification in the Zhengzhuang Field of the Qinshui Basin, utilizing well-log data from 86 wells. Initially, 13 geophysical logging parameters were used to characterize the coal seams, resulting in a dataset comprising 2992 data points categorized into Undeformed Coal (UC), Cataclastic Coal (CC), and Granulated Coal (GC) types. After optimizing and refining the data, the dataset was reduced to 8 parameters, then further narrowed to 5 key features for model evaluation. Two primary scenarios were investigated: Scenario 1 included all 8 parameters, while Scenario 2 focused on the 5 most influential features. Five machine learning classifiers Extra Trees, Gradient Boosting, Support Vector Classifier (SVC), Random Forest, and k-Nearest Neighbors (kNN) were applied to classify coal textures. The Extra Trees classifier outperformed all other models, achieving the highest performance across both scenarios. Its peak performance was observed when 20% of the data was used for the test set and 80% for training, where it achieved a Macro F1 Score of 0.998. These findings demonstrate the potential of machine learning for improving coal texture prediction, offering valuable insights into reservoir characterization and enhancing the understanding of gas migration and accumulation processes. This methodology has significant implications for optimizing CBM resource evaluation and extraction strategies, especially in regions with limited sampling availability. Full article

One of the best-known flavonoid chrysin was coupled at position 7 with several trisubstituted phosphine derivatives with a flexible spacer, and their in vitro anticancer activities were investigated on 60 human tumor cell lines (NCI60) and on several gynecological cancer cells. The trisubstituted phosphines contained different substituents on the aromatic ring(s), e.g., methyl and methoxy groups or fluoro atoms. The phosphorus atom was substituted not only with aromatic rings but with cyclohexyl substituents. The ionic phosphonium building block is important because it allows the therapeutic agents to transfer across the cell membrane. Therefore, the pharmacophores linked to it can exert their effects in the mitochondria. Instead of the ionic phosphonium element, a neutral moiety, namely the triphenylmethyl group, was also added to the side chain, being sterically similar but without a charge and phosphorus atom. Most of the hybrids exhibited low micromolar growth inhibition (GI₅₀) values against the majority of the tested cell lines. Notably, conjugate 3f stood out, demonstrating nanomolar antitumor activity against the K-562 leukemia cell line (GI₅₀ = 34 nM). One selected compound (3i) with promising cancer selectivity elicited cell cycle disturbances and inhibited the migration of breast cancer. The tumor-selectivity of 3a and 3f was assessed based on their effects on non-tumor Chinese hamster ovary (CHO) cells using the CellTiter-Glo Luminescent Cell Viability Assay. Given their estimated half-maximal inhibitory concentration (IC₅₀) values on non-tumor CHO cells (2.65 µM and 1.15 µM, respectively), these conjugates demonstrate promising selectivity toward several cancer cell lines. The excellent results obtained may serve as good starting points for further optimization and the design of even more effective flavonoid- and/or phosphonium-based drugs. Full article

Oxygen diffusion in (Pu_xTh_1−x)O₂ mixed oxide crystals was investigated using molecular dynamics simulation. The model systems were isolated nanocrystals of 5460 and 15,960 particles, featuring a free surface. The oxygen diffusion coefficient D increased with decreasing thorium content, in accordance with the decrease in the melting temperature of (Pu_xTh_1−x)O₂ as x varied from 0 to 1. The temperature dependences D(T) exhibited non-linearity in the Arrhenius coordinates lnD = f(1/kT). The three linear segments of the plots corresponded to the superionic state, a transitional region, and the low-temperature crystalline phase. The transitional region was characterized by maximum values of the effective diffusion activation energy E_D(PuO₂) = 3.47 eV, E_D(ThO₂) = 5.24 eV and a complex collective mechanism of oxygen migration, which involved the displacement of anions into interstitial sites. At lower temperatures, an interstitialcy mechanism of oxygen diffusion was observed. The temperature dependence of D(PuO₂) showed quantitative agreement with low-temperature experimental data. Full article

Idiopathic pulmonary fibrosis (IPF) is characterized by excessive extracellular matrix (ECM) deposition driven by aberrant fibroblast-to-myofibroblast transition (FMT). However, the upstream regulators and downstream effectors of this process remain incompletely understood. Here, we identify acyl-CoA synthetase long-chain family member 4 (ACSL4), a lipid metabolic enzyme, as a critical mediator linking complement component 5a (C5a)/C5a receptor 1 (C5aR1) signaling to FMT via calcium signaling. In bleomycin (BLM)-induced pulmonary fibrosis of C57BL/6JGpt mice, and in C5a-stimulated primary lung fibroblasts, the expression of ACSL4 was markedly upregulated. Pharmacological inhibition of ACSL4 (PRGL493) or C5aR1 (PMX53) attenuated the deposition of ECM and suppressed the expression of fibrotic markers in vivo and in vitro. Mechanistically, the activation of C5a/C5aR1 signaling increased intracellular calcium levels and promoted the expression of ACSL4, while inhibition of calcium signaling (FK506) reversed the upregulation of ACSL4 and FMT-related changes, including the expression of α-smooth muscle actin (αSMA) and the migration of fibroblasts. Notably, inhibition of ACSL4 did not affect the proliferation of fibroblasts, suggesting its specific role in phenotypic transition. These findings demonstrate that ACSL4 functions downstream of C5a/C5aR1-induced calcium signaling to promote FMT and the progression of pulmonary fibrosis. Targeting ACSL4 may therefore offer a novel therapeutic strategy for IPF. Full article

This study proposes a Swin-ReshoUnet architecture with a three-level enhancement mechanism to address inefficiencies in multi-scale feature extraction and gradient degradation in deep networks for high-precision seismic exploration. The encoder uses a hierarchical convolution module to build a multi-scale feature pyramid, enhancing cross-scale geological signal representation. The decoder replaces traditional self-attention with ORCA attention to enable global context modeling with lower computational cost. Skip connections integrate a residual channel attention module, mitigating gradient degradation via dual-pooling feature fusion and activation optimization, forming a full-link optimization from low-level feature enhancement to high-level semantic integration. Simulated and real dataset experiments show that at decimation ratios of 0.1–0.5, the method significantly outperforms SwinUnet, TransUnet, etc., in reconstruction performance. Residual signals and F-K spectra verify high-fidelity reconstruction. Despite increased difficulty with higher sparsity, it maintains optimal performance with notable margins, demonstrating strong robustness. The proposed hierarchical feature enhancement and cross-scale attention strategies offer an efficient seismic profile signal reconstruction solution and show generality for migration to complex visual tasks, advancing geophysics-computer vision interdisciplinary innovation. Full article

To address the challenges in identifying NAPL contamination within low-permeability clay sites, this study innovatively integrates high-density electrical resistivity tomography (ERT) with a UNet deep learning model to establish an intelligent contamination detection system. Taking an industrial site in Shanghai as the research object, we collected apparent resistivity data using the WGMD-9 system, obtained resistivity profiles through inversion imaging, and constructed training sets by generating contamination labels via K-means clustering. A semantic segmentation model with skip connections and multi-scale feature fusion was developed based on the UNet architecture to achieve automatic identification of contaminated areas. Experimental results demonstrate that the model achieves a mean Intersection over Union (mIoU) of 86.58%, an accuracy (Acc) of 99.42%, a precision (Pre) of 75.72%, a recall (Rec) of 76.80%, and an F1 score (f1) of 76.23%, effectively overcoming the noise interference in electrical anomaly interpretation through conventional geophysical methods in low-permeability clay, while outperforming DeepLabV3, DeepLabV3+, PSPNet, and LinkNet models. Time-lapse resistivity imaging verifies the feasibility of dynamic monitoring for contaminant migration, while the integration of the VGG-16 encoder and hyperparameter optimization (learning rate of 0.0001 and batch size of 8) significantly enhances model performance. Case visualization reveals high consistency between segmentation results and actual contamination distribution, enabling precise localization of spatial morphology for contamination plumes. This technological breakthrough overcomes the high-cost and low-efficiency limitations of traditional borehole sampling, providing a high-precision, non-destructive intelligent detection solution for contaminated site remediation. Full article

This study investigates the impact of biogas slurry ratio, hole diameter and depth under hole irrigation on the soil wetting front migration distance and cumulative infiltration. In this study, a model describing the water transport characteristics of biogas slurry hole irrigation was developed based on the HYDRUS model. Results demonstrated that the HYDRUS model can be used for biogas slurry hole irrigation (NSE > 0.952, PBIAS ≤ ±0.34). Furthermore, the study revealed that the soil cumulative infiltration and soil wetting front migration distance decreased gradually with an increase in the biogas slurry ratio, while they increased gradually with an increase in the hole diameter and depth. The lateral and vertical wetting front migration distances exhibited a well-defined power function relationship with the soil’s stable infiltration rate and infiltration time (R² ≥ 0.977). The soil wetting front migration distance curve can be represented by an elliptic curve equation (R² ≥ 0.957). Additionally, there was a linear relationship between the cumulative infiltration and soil wetted body area (R² ≥ 0.972). Soil wetting front migration distance model ($X=4.442f_0^{0.375}{t}^{0.24}, Z=11.988f_0^{0.287}{t}^{0.124}, f_0=96.947K_s^{1.151}{D}^{0.236}{H}^{1.042}$, NSE > 0.976, PBIAS ≤ ±0.13) and cumulative infiltration model ($I=0.3365S$, NSE > 0.982, PBIAS ≤ ±0.10) established under biogas slurry hole irrigation exhibited good reliability. This study aims to determine optimal hole diameter, depth, and irrigation volume for biogas slurry hole irrigation by establishing a model for soil wetting front migration distance and cumulative infiltration based on crop root growth patterns, thereby providing a scientific basis for its practical application. Full article

The crystal–chemical behavior of two layered titanosilicate minerals with porous crystal structures, kupletskite, K₂NaMn₇²⁺Ti₂(Si₄O₁₂)₂O₂(OH)₄F, and kupletskite-(Cs), Cs₂NaMn₇²⁺Ti₂(Si₄O₁₂)₂O₂(OH)₄F, was investigated under high-temperature conditions using single-crystal and powder X-ray diffraction; infrared and optical absorption spectroscopy and electron-microprobe analysis. Both minerals undergo topotactic transformation to dehydroxylated and oxidized high-temperature (HT) modifications at temperature above 500 °C while maintaining the basic bond topology of the astrophyllite structure-type. The high-temperature structures show contraction of the unit-cell parameters similar to that of Fe²⁺-dominant astrophyllite, indicating that Mn²⁺ oxidizes along with Fe²⁺ in M(2)–M(4) sites. The oxidation of Mn²⁺ is confirmed by the increase of the Mn³⁺-related absorption (in optical spectra) that is inversely correlated with the intensity of O–H bands in the infrared spectra. The Fe,Mn-oxidation is also evident by the contraction of the M(2), M(3), and M(4)O₆ octahedra. The M(1)–O bond length increases slightly, indicating a preference for mono- and divalent cations to occupy the M(1) site in the heated structure; this may be due to site-selective oxidation and/or migration of unoxidized cations (as previously shown for lobanovite) to this site. The role of extra framework A-site cations (K, Cs) in thermal expansion of these minerals is discussed. Full article

This study investigates pegmatites with exceptionally rare mineralogical and chemical signatures, hosted by the 1.8 Ga peralkaline albite-enriched granite, which corresponds to the renowned Madeira Sn-Nb-Ta-F (REE, Th, U) deposit in Pitinga, Brazil. Four distinct pegmatite types are identified: border pegmatites, pegmatitic albite-enriched granite, miarolitic pegmatite, and pegmatite veins. The host rock itself has served as the source for the fluids that gave rise to all these pegmatites. Their mineral assemblages mirror the rare-metal-rich paragenesis of the host rock, including pyrochlore, cassiterite, riebeckite, polylithionite, zircon, thorite, xenotime, gagarinite-(Y), genthelvite, and cryolite. These pegmatites formed at the same crustal level as the host granite and record a progressive magmatic–hydrothermal evolution driven by various physicochemical processes, including tectonic decompressing, extreme fractionation, melt–melt immiscibility, and internal fluid exsolution. Border pegmatites crystallized early from a F-poor, K-Ca-Sr-Zr-Y-HREE-rich fluid exsolved during solidification of the pluton’s border and were emplaced in contraction fractures between the pluton and country rocks. Continued crystallization toward the pluton’s core produced a highly fractionated melt enriched in Sn, Nb, Ta, Rb, HREE, U, Th, and other HFSE, forming pegmatitic albite-enriched granite within centimetric fractures. A subsequent pressure quench—likely induced by reverse faulting—triggered the separation of a supercritical melt, further enriched in rare metals, which migrated into fractures and cavities to form amphibole-rich pegmatite veins and miarolitic pegmatites. A key process in this evolution was melt–melt immiscibility, which led to the partitioning of alkalis between two phases: a K-F-rich aluminosilicate melt (low in H₂O), enriched in Y, Li, Be, and Zn; and a Na-F-rich aqueous melt (low in SiO₂). These immiscible melts crystallized polylithionite-rich and cryolite-rich pegmatite veins, respectively. The magmatic–hydrothermal transition occurred independently in each pegmatite body upon H₂O saturation, with the hydrothermal fluid composition controlled by the local degree of melt fractionation. These highly F-rich exsolved fluids caused intense autometasomatic alteration and secondary mineralization. The exceptional F content (up to 35 wt.% F in pegmatite veins), played a central role in concentrating strategic and critical metals such as Nb, Ta, REEs (notably HREE), Li, and Be. These findings establish the Madeira system as a reference for rare-metal magmatic–hydrothermal evolution in peralkaline granites. Full article