Search Results (274)

Tunneling nanotubes (TNTs) are thin, actin-based intercellular bridges that enable long-range communication during cellular stress; yet the molecular pathway controlling their formation remains unclear. Here, using gain- and loss-of-function approaches in Cath. a-differentiated (CAD) neuronal cells, we identified a unidirectional regulatory pathway in which myosin-X (Myo10) functions upstream of the actin-bundling protein L-(LCP1) to drive TNT formation. Using Western blotting and fluorescence microscopy, we determined that overexpression of L-plastin significantly increased the proportion of TNT-connected cells, whereas L-plastin downregulation reduced TNT formation, demonstrating that L-plastin is both sufficient and necessary for maintaining normal TNT abundance. Having previously shown that Myo10 is required for TNT formation in CAD cells, we asked whether the relationship is reciprocal. Overexpression/downregulation of L-plastin had no effect on Myo10 protein levels. Conversely, Myo10 downregulation decreased endogenous L-plastin by ~30%, and Myo10 overexpression elevated L-plastin expression and TNT number, demonstrating that Myo10 acts as an upstream regulator of L-plastin. Dual-color 3D imaging revealed co-localization of Myo10 and L-plastin along TNT shafts and filopodia-like precursors (Proto-TNTs). Together, these findings demonstrate that Myo10-dependent TNT formation requires the bundling protein L-plastin, providing a framework for how stress-induced signaling cascades couple TNT initiation to actin-core stabilization during stress and disease. Full article

Ensuring the long-term performance of infrastructure in cold regions necessitates evaluating the frost durability of subgrade materials. This study comprehensively investigates the mechanical behavior of cement-stabilized silty clay, a common material for subgrade improvement, under freeze–thaw (F–T) cycles. A series of unconfined compressive strength (UCS) and resilient modulus (MR) tests were conducted to quantify the effects of cement content (3%, 6%, 9%), initial moisture content (OMC − 2% to OMC + 6%), and the number of F–T cycles (0 to 9). The results demonstrate that increasing the cement content significantly enhances the MR, with the most effective improvement observed up to 6%. Specifically, increasing cement from 3% to 6% boosted MR by 11.62% to 26.69%, while a further increase to 9% yielded a smaller gain of 4.59% to 12.60%, indicating an optimal content. Both UCS and MR peak at the optimum moisture content (OMC) and degrade markedly with F–T cycles, with the first cycle causing over 50% of the total MR loss in most cases. Properties tend to stabilize after approximately six cycles. The stabilized soil exhibits superior performance, with its MR being 2.29–2.43 times that of the original soil at OMC after nine F–T cycles. Furthermore, a logarithmic model (R² = 0.87–0.94) effectively captures the attenuation of MR with F–T cycles, while a strong linear relationship (R² = 0.90–0.96) exists between the initial moisture content and the degradation coefficient. An empirical predictive model for UCS, integrating cement content, moisture content, and F–T cycles, is proposed and shows excellent correlation with experimental data (R² > 0.92). Microstructural analysis reveals that the enhancement mechanism is attributed to hydration, cation exchange, and flocculation, which collectively form a stable cementitious network. The findings and proposed models provide critical quantitative insights for optimizing the design of frost-resistant cement-stabilized subgrades, thereby contributing to the enhanced durability and performance of overlying structures in seasonal freeze–thaw environments. Full article

Retrieval-Augmented Generation (RAG) is widely used for long-text summarization due to its efficiency and scalability. However, standard RAG methods flatten documents into independent chunks, disrupting sequential flow and thematic structure, resulting in significant loss of contextual information. This paper presents MOEGAT, a novel graph-enhanced retrieval framework that addresses this limitation by explicitly modeling document structure. MOEGAT constructs an Orthogonal Context Graph to capture sequential discourse and global semantic relationships—long-range dependencies between non-adjacent text spans that reflect topical similarity and logical associations beyond local context. It then employs a query-aware Mixture-of-Experts Graph Attention Network to dynamically activate specialized reasoning pathways. Experiments conducted on three public long-text summarization datasets demonstrate that MOEGAT achieves state-of-the-art performance. Notably, on the WCEP dataset, it outperforms the previous state-of-the-art Graph of Records (GOR) baseline by 14.9%, 18.1%, and 18.4% on ROUGE-L, ROUGE-1, and ROUGE-2, respectively. These substantial gains, especially the 14.9% improvement in ROUGE-L, reflect significantly better capture of long-range coherence and thematic integrity in summaries. Ablation studies confirm the effectiveness of the orthogonal graph and Mixture-of-Experts components. Overall, this work introduces a novel structure-aware approach to RAG that explicitly models and leverages document structure through an orthogonal graph representation and query-aware Mixture-of-Experts reasoning. Full article

This study examines how environmental, social, and governance (ESG) occurrences relate to firm performance and how these relationships depend on firms’ investments in brand capital. Using firm-level data spanning more than two decades, we analyze the effects of positive and negative ESG events on market-based (sales) and accounting-based (return on assets; ROA) performance for firms with and without brand capital investment (BCI). Using panel data on U.S. firms from 1995 to 2019, we compare firms that invest in brand capital through advertising with firms that do not. The results reveal an interesting asymmetric pattern. Specifically, BCI firms experience greater sales gains following positive ESG occurrences but incur significantly larger losses following negative ESG events. Interestingly, non-BCI firms benefit less from positive ESG activities but face smaller penalties from negative ESG occurrences. This study contributes to the marketing literature by examining brand capital investment and how ESG activities translate into performance gains versus when they impose performance costs for firms. Full article

Background and Objectives: Non-coding RNAs (ncRNAs) are genetic transcripts that do not produce proteins but are increasingly recognised for their roles in cellular processes and disease. Specifically, ncRNAs are implicated in the landscape activation of molecular triggers for different diseases, including cancer and viral infections. The function of Sense non-coding mitochondrial RNA-2 (SncmtRNA-2) is currently unknown. This study aims to investigate the roles of SncmtRNA-2 and hsa-miR-620 in Ras-induced cellular transformation. Materials and Methods: The study utilized isoforms V, K, and H of the Ras oncogene and analysed the expression of SncmtRNA-2 and hsa-miR-620 in response to Ras activity. Additionally, both in silico and in vitro analyses were performed to assess whether PML mRNA is a putative target of hsa-miR-620 although direct binding to the PML 3′UTR was not experimentally tested. Results: The research demonstrated that transformation induced by Ras isoforms V, K, and H resulted in decreased expression of both SncmtRNA-2 and hsa-miR-620. Further investigation revealed that hsa-miR-620 is produced by the processing of SncmtRNA-2. It was also shown that Ras increases the expression of Promyelocytic Leukemia Protein (PML). In silico prediction combined with miR-620 gain and loss of function experiments supports PML as a putative hsa-miR-620 target. Conclusions: Ras promotes cellular transformation by decreasing the expression of SncmtRNA-2 and hsa-miR-620, which may contribute to increased PML expression, suggesting but not demonstrating a possible regulatory relationship among these molecules in HPV-immortalised cells. These results highlight a potential SncmtRNA-2/miR-620/PML axis that requires further validation through direct interaction assays and functional necessity/sufficiency experiments. Full article

Forest cover dynamics strongly influence ecological integrity and resource sustainability, particularly in ecotonal landscapes, where vegetation is highly sensitive to climate variability, long-term climate change, and anthropogenic disturbances. This study examined Forest Land (FL), representing all areas of dense, canopy-forming woody vegetation with forest-like structure, aggregated from SANLC classes, in relation to eight other land cover classes across three periods: 1990–2014, 2014–2022, and 1990–2022. The study used South African National Land Cover datasets and the TerrSet–LiberaGIS Land Change Modeller to quantify changes in magnitude, direction, and source–sink relationships. Analyses included post-classification comparison to determine spatial changes, transition matrices to identify land-cover conversions, and asymmetric gain–loss metrics to reveal sources and sinks of forest change. The result shows that between 1990 and 2014, forests remained marginal and fragmented in the eastern central part of the study area, while shrubland increased from 40.4% to 60.2% at the expense of grasslands, cultivated land, bare land, wetlands, and forest land. From 2014 to 2022, FL regeneration was pronouncedly increased from 2% to 6%, especially along riparian corridors and reservoir margins, coinciding with shrubland decline (99.3%) and grassland recovery (261.2%). Over the entire 1990–2022 period, FL increased from 2.4% to 6% expanding into bare land, cultivated land, grassland, shrubland, and wetlands. Asymmetric analysis indicated that forests acted as a sink during the first period but as a source of ecological resilience in the second and final. These findings demonstrate strong vegetation feedback to hydrological and anthropogenic drivers. Overall, the findings underscore the potential for forest recovery to enhance biodiversity, ecosystem services, carbon storage, and hydrological regulation, while identifying priority areas for riparian conservation and integrated catchment management. Full article

The efficiency of centrifugal pumps is strongly influenced by impeller blade design; however, studies on double-curvature impellers remain limited. This research evaluates the impact of double-curvature impellers on pump performance through experimental measurements. Five impeller configurations were tested experimentally, and their hydraulic behavior was analyzed at three rotational speeds: 1400, 1700, and 1900 rpm. For each impeller–speed combination, 12 measurement points were recorded, capturing suction and discharge pressures, flow rate, rotational velocity, electrical parameters, and power consumption. Additionally, four impellers with double-curvature designs of 15%, 25%, and 35% were developed to improve flow guidance between blades and enhance the hydraulic performance of the pump. Quantitatively, the double-curvature impellers demonstrated performance improvements over the baseline configuration, achieving increases in hydraulic head of approximately 5–10% and peak efficiency gains of 4–8 percentage points (equivalent to 10–18% relative improvement), particularly in mid-range flow conditions. These enhancements confirm the beneficial role of blade double curvature in reducing internal losses and improving flow guidance. The results were used to derive head–flow and efficiency–flow relationships, demonstrating that specific double-curvature configurations can enhance pump performance compared to the original design. Full article

To enhance the monitoring and conservation efforts for China’s Class II endangered species, specifically the wild giant salamander and its ecosystems, this study addresses the urgent need to counteract the rapid decline of its wild population caused by habitat loss and insufficient surveillance. We present an innovative localization system based on passive Ultra-High-Frequency Radio Frequency Identification (UHF RFID) technology, employing a Double-Transform (D-Tr) methodology that integrates an enhanced 3D LANDMARC algorithm with GAIN generative adversarial networks. This system effectively reconstructs missing Received Signal Strength Indicator (RSSI) data due to environmental barriers by applying a log-distance path loss model. The D-Tr framework simultaneously generates RSSI sequences alongside their first-order differential characteristics, allowing for a comprehensive analysis of spatiotemporal signal relationships. Field tests conducted in the Hubei Xianfeng Zhongjian River Giant Salamander National Nature Reserve reveal that the positioning error consistently remains within 10 cm, with average accuracy improvements of 20.075%, 15.331%, and 12.925% along the X, Y, and Z axes, respectively, compared to traditional time-series models such as long short-term memory (LSTM) and gated recurrent unit (GRU). This system, designed to investigate the behavioral patterns and movement paths of farmed giant salamanders, achieves centimeter-level tracking of their cave-dwelling activities. It provides essential technical support for quantitatively assessing their daily activity patterns, habitat choices, and population trends, thereby promoting a shift from passive oversight to proactive monitoring in the conservation of endangered species. Full article

Glaucoma is one of the most common causes of irreversible visual impairment world wide. Providing low vision rehabilitation (LVR) services is a primary mode of support for patients with permanent vision loss. This retrospective, cross-sectional study evaluated the rate at which patients with severe open-angle glaucoma (OAG) were referred for LVR services at an academic medical center. Patient demographics, glaucoma severity, appointment history, performance on visual field (VF) testing, presenting visual acuity (VA), and change in best-corrected visual acuity (BCVA) after low vision refraction were abstracted from the electronic record and summarized by using descriptive statistics. Logistic regression analysis was used to assess the relationship between study variables and the likelihood of referral for LVR evaluation. Out of 522 patients with severe OAG, 88% of whom qualified as having low vision, 14 were referred for an LVR evaluation (2.7%). Referrals were most strongly associated with VA (adjusted odds ratio [aOR], 7.20; 95% confidence interval [CI], 2.11–24.64, p = 0.001) but not glaucoma-associated VF loss (aOR, 0.90; 95% CI, 0.24–3.37, p = 0.876). Thirteen of 14 patients referred for LVR completed visits (93%). More than one-third of those patients improved in their better-seeing eye after a low vision refraction, gaining an average of −0.18 ± 0.24 logMAR (half gaining ≥2-lines of BCVA). Patients with severe OAG are at risk of progressive visual disability from their eye disease. We found, however, that the majority of these patients were not referred to LVR services, despite meeting eligibility criteria and growing evidence demonstrating their potential benefit. Full article

The multimodal fusion of hyperspectral images (HSI) and LiDAR data for land cover classification encounters difficulties in modeling heterogeneous data characteristics and cross-modal dependencies, leading to the loss of complementary information due to concatenation, the inadequacy of fixed fusion weights to adapt to spatially varying reliability, and the assumptions of linear separability for nonlinearly coupled patterns. We propose QIE-Mamba, integrating selective state-space models with quantum-inspired processing to enhance multimodal representation learning. The framework employs ConvNeXt encoders for hierarchical feature extraction, quantum superposition layers for complex-valued multimodal encoding with learned amplitude–phase relationships, unitary entanglement networks via skew-symmetric matrix parameterization (validated through Cayley transform and matrix exponential methods), quantum-enhanced Mamba blocks with adaptive decoherence, and confidence-weighted measurement for classification. Systematic three-phase sequential validation on Houston2013, Muufl, and Augsburg datasets achieves overall accuracies of 99.62%, 96.31%, and 96.30%. Theoretical validation confirms 35.87% mutual information improvement over classical fusion (6.9966 vs. 5.1493 bits), with ablation studies demonstrating quantum superposition contributes 82% of total performance gains. Phase information accounts for 99.6% of quantum state entropy, while gradient convergence analysis confirms training stability (zero mean/std gradient norms). The optimization framework reduces hyperparameter search complexity by 99.6% while maintaining state-of-the-art performance. These results establish quantum-inspired state-space models as effective architectures for multimodal remote sensing fusion, providing reproducible methodology for hyperspectral–LiDAR classification with linear computational complexity. Full article

This study examines how sovereign risk shapes the financial performance of sustainable investments, using the MSCI Emerging Markets ESG Index as a reference. The analysis covers 24 emerging and frontier economies from Latin America, Asia, the Middle East, and Eastern Europe during 2016–2025, a period marked by major global disruptions such as the COVID-19 crisis and post-2022 financial tightening. Sovereign risk dimensions are extracted through Principal Component Analysis (PCA) applied to sovereign CDS spreads, identifying a systemic component linked to global shocks and a structural component associated with domestic fundamentals and governance quality. These factors are integrated into a quantile regression framework alongside control variables—oil prices, interest rates, and global equity indices—capturing key macro-financial transmission channels. Results show a nonlinear, quantile-dependent relationship: systemic risk intensifies ESG losses under adverse conditions, while structural improvements support gains in upper quantiles. Control variables behave as expected, confirming the macro-financial sensitivity of ESG performance. The findings reveal that ESG returns are state-dependent and strongly influenced by sovereign credit dynamics, especially in emerging markets where external shocks and institutional fragility intersect. Strengthening sovereign governance and integrating risk diagnostics into ESG assessments are essential steps to enhance resilience and credibility in sustainable finance. Full article

Three-dimensional gravity inversion technology involves inferring the underground density structure based on observed gravity anomaly data. In addition to gravity inversion based on physics-driven methods, deep learning, as a purely data-driven technique, is increasingly gaining attention in geophysical inversion problems. However, purely data-driven methods rely on the implicit relationships within the data during the inversion process, which results in a lack of clear physical significance. This study proposes a three-dimensional gravity inversion method that integrates physical equations with deep learning. Based on the U-Net architecture, the gravity forward equation is incorporated as a physical constraint term, and a composite loss function—comprising three-dimensional mean squared error, a depth-weighting function, and three-dimensional intersection-over-union loss—is constructed to enhance inversion accuracy. Numerical experiments indicate that this method outperforms traditional algorithms in terms of density recovery accuracy and boundary clarity. When applied to gravity anomaly data from the Tangshan earthquake region in China, this method successfully inverted the three-dimensional subsurface density structure, revealing a high-density anomaly beneath the seismic source area, which provides important evidence for understanding the regional earthquake generation mechanism. Full article

Background: Caring for dependent older relatives is thought to influence caregivers’ personal views of aging (VoA)—that is, perceptions regarding their own aging self. This study aimed to examine personal VoA, particularly felt age (FA) and awareness of age-related change (AARC), in caregivers of people with dementia (PwD) compared to non-caregivers, and to ascertain their relationship with caregiving-related burden and distress among dementia caregivers. Methods: Seventy dementia caregivers and 94 non-caregivers (age range: 45–85 years) reported their FA and completed a questionnaire assessing awareness of age-related gains (AARC-Gains) and losses (AARC-Losses) and a mood measure. Dementia caregivers’ burden and distress were also relieved. Results: No differences emerged between dementia caregivers and non-caregivers’ personal VoA. Different sociodemographic and health-related factors were related to AARC-Gains or AARC-Losses, but not felt age, in each group. AARC-Gains were associated with social status among non-caregivers, whereas AARC-Losses were related to chronological age and subclinical depression in non-caregivers, and to social status, self-rated health, and burden in dementia caregivers. A path model revealed a direct effect of burden, social status, and self-rated health, as well as an indirect one of subclinical depression through burden, on caregivers’ AARC-Losses. Conclusions: These findings confirm the interplay between VoA, sociodemographic, and health-related factors in adulthood and older age. They, then, suggest that the strains derived from caring for a PwD influence dementia caregivers’ personal VoA, particularly when their awareness of age-related losses is concerned. Full article

As an emerging production factor, data assets are gaining strategic prominence, yet their application in collateralized financing faces persistent challenges, including illiquidity and risk evaluation complexities. This study introduces an innovative $P_{max}$ model to enhance the Collateral Value of data assets through insurance mechanisms, systematically demonstrating the feasibility conditions under which risk transfer optimizes asset valuation and delineating implementation pathways to integrate data insurance with asset-backed financing. Building on the theoretical framework of Value-at-Risk (VaR), this study develops a dynamic valuation model to assess the value of the collateral before and after insurance. Our analysis shows that insurance coverage for potential losses significantly enhances financing viability when premiums satisfy $P_{max}$. Empirical analysis employing Monte Carlo simulations reveals a nonlinear positive correlation between pledgees’ risk tolerance thresholds and the maximum acceptable premium $P_{max}$. This study bridges theoretical gaps in understanding insurance-value relationships for data assets while providing conceptual foundations and operational blueprints to standardize data markets and foster financial innovation. Full article

Driven by carbon neutrality goals, electric–hydrogen coupled virtual power plants (EHCVPPs) integrate renewable hydrogen production with power system flexibility resources, emerging as a critical technology for large-scale renewable integration. As distributed energy resources (DERs) within EHCVPPs diversify, heterogeneous resources generate diversified market values. However, inadequate benefit allocation mechanisms risk reducing participation incentives, destabilizing cooperation, and impairing operational efficiency. To address this, benefit allocation must balance fairness and efficiency by incorporating DERs’ regulatory capabilities, risk tolerance, and revenue contributions. This study proposes a multi-stage benefit allocation framework incorporating risk–reward tradeoffs and an enhanced optimization model to ensure sustainable EHCVPP operations and scalability. The framework elucidates bidirectional risk–reward relationships between DERs and EHCVPPs. An individualized risk-adjusted allocation method and correction mechanism are introduced to address economic-centric inequities, while a hierarchical scheme reduces computational complexity from diverse DERs. The results demonstrate that the optimized scheme moderately reduces high-risk participants’ shares, increasing operator revenue by 0.69%, demand-side gains by 3.56%, and reducing generation-side losses by 1.32%. Environmental factors show measurable yet statistically insignificant impacts. The framework meets stakeholders’ satisfaction and minimizes deviation from reference allocations. Full article