Search Results (1,137)

Background: Rheumatoid arthritis (RA) is a chronic inflammatory disorder characterized by synovial hyperplasia and joint destruction. Previous studies have demonstrated that the alkaloids of Rushanhu (ARSHs), the dried root and stem of Zanthoxylum nitidum var. tomentosum, exhibit favorable therapeutic effects on RA, and this study aims to investigate the underlying molecular mechanisms involved. Methods: A complete Freund’s adjuvant (CFA)-induced arthritis model in male SD rats (n = 64) was used to evaluate ARSHs. Groups included control, model, methotrexate (MTX), and ARSH-treated. Therapeutic effects were assessed via arthritis index, paw swelling, and serum cytokines (IL-1β, IL-6, IL-17A). Network pharmacology identified bioactive alkaloids and core targets, validated by molecular docking. In vitro mechanisms (proliferation, apoptosis, signaling pathways) were examined in MH7A synovial cells. Results: ARSHs significantly attenuated joint inflammation and damage in CFA rats (* p < 0.01 vs. model), reducing pro-inflammatory cytokines. Fifteen alkaloids (e.g., dihydrochelerythrine, magnoflorine) and 24 targets (e.g., SRC, STAT3, MAPK3) were prioritized. Molecular docking confirmed strong binding (binding energy < −7.0 kcal/mol). In vitro, ARSHs suppressed MH7A proliferation and induced apoptosis via Bcl-2/Bax dysregulation and the inhibition of SRC/STAT3/MAPK3 phosphorylation. Conclusions: ARSHs mitigate RA pathogenesis by targeting the SRC/STAT3/MAPK3 signaling axis in synovial cells. This study provides mechanistic validation of ARSHs as multi-target phytotherapeutic agents against inflammatory arthritis. Full article

This paper uses survival analysis as a tool to assess credit risk in loan portfolios within the framework of the Basel Internal Ratings-Based (IRB) approach. By modeling the time to default using survival functions, the methodology allows for the estimation of default probabilities and the dynamic evaluation of portfolio performance. The model explicitly accounts for right censoring and demonstrates strong predictive accuracy. Furthermore, by incorporating additional information about the portfolio’s loss process, we show how to empirically estimate key risk measures—such as Value at Risk (VaR) and Expected Shortfall (ES)—that are sensitive to the age of the loans. Through simulations, we illustrate how loss distributions and the corresponding risk measures evolve over the loans’ life cycles. Our approach emphasizes the significant dependence of risk metrics on loan age, illustrating that risk profiles are inherently dynamic rather than static. Using a real-world dataset of 10,479 loans issued by Angolan commercial banks, combined with assumptions regarding loss processes, we demonstrate the practical applicability of the proposed methodology. This approach is particularly relevant for emerging markets with limited access to advanced credit risk modeling infrastructure. Full article

This research seeks to evaluate the effects of the preceding cyclical indicators and macroeconomic variables on the performance of the Brazilian stock market from January 2011 to December 2022. The objective is to identify how these factors influence the behavior of the main index representing this market. In this way, it was analyzed how shocks in the composite leading indicator of the economy (IACE) as well as the basic interest rate of the economy (SELIC), the broad national consumer price index (IPCA), the nominal exchange rate (in reals per dollar—BRL/USD) and the central bank economic activity index (IBC-Br) impact the performance of Brazilian stock market index (IBOVESPA). Using the vector autoregression (VAR) model with vector error correction (VEC), positive shocks were simulated in the IACE and the aforementioned macroeconomic variables to identify and compare their impacts on the index. The results obtained, through generalized impulse response functions, indicated that the shocks to the IACE, the exchange rate, and the inflation variables influenced the IBOVESPA in different and statistically significant ways. However, shocks to the economic activity index and the interest rate did not exert a statistically significant influence on the index, partially confirming the hypothesis, which was initially raised, that these factors influence the stock index in different ways. Full article

Background/Objectives: Perilla frutescens var. acuta Kudo, a member of the Lamiaceae family, has been previously reported to reduce neuroinflammation and potentially decrease Aβ plaque accumulation in 5XFAD mice. In this study, we aimed to evaluate the anti-neuroinflammatory potential of a standardized 60% ethanol extract of Perilla leaves (PE), optimized for commercial application. Methods: The inflammatory response was assessed in LPS-stimulated BV2 microglial cells, and the cognitive improvement was evaluated in an AD animal model induced by intracerebroventricular injection of Aβ. Results: Using LPS-stimulated BV2 microglial cells and an Aβ-injected ICR mouse model of Alzheimer’s disease, we found that PE significantly suppressed the LPS-induced production of nitric oxide and pro-inflammatory mediators, including IL-6, TNF-α, NF-κB, iNOS, and COX-2, along with inhibition of JNK and p38 MAPK activation. Furthermore, PE upregulated CREB and BDNF expression. In vivo, PE administration alleviated Aβ-induced cognitive deficits, which were associated with reduced expression of JNK, NF-κB, iNOS, and COX and increased CREB/BDNF signaling in the hippocampus. Behavioral assessments—including passive avoidance, Morris water maze, novel object recognition, and Y-maze tests—confirmed the improvement in cognitive function. Conclusions: Collectively, these findings demonstrate that PE exerts significant anti-neuroinflammatory and neuroprotective effects, supporting its potential as a functional ingredient for cognitive enhancement. Full article

The European Commission approval of some insect species for human consumption, starting with Tenebrio molitor (TM) in 2021, has drawn attention to the production of insect-derived protein flours and the sustainability of insect-rearing systems, particularly on a large scale. This has also highlighted the importance of utilizing byproducts, such as frass, and obtaining high-value-added products, such as biofertilizers. This study explored the potential for TM frass (TMF) to serve as a natural fertilizer for the cultivation of Canasta lettuce (Lactuca sativa var. capitata). Specifically, a series of tests was carried out to assess the efficacy of thermal treatment and to verify the trend of certain chemical and growth parameters as a function of the TMF percentage to be added to the potting soil. For this purpose, different percentages of both thermal-treated and untreated TMF and their effects on various growth parameters of Canasta lettuce were evaluated through pot trials. Furthermore, TMF was characterized by using scanning electron microscopy (SEM) to gain insights into its structural features and potential influence on soil–plant interactions. Our results show that heat treatment of TMF is essential to ensure plant survival, and at least in pots, TMF percentages above 5% of soil dry weight are not recommended. In our tests, the most suitable percentage was 4%. Full article

The rise in global supply chain pressure (GSCP) not only disturbs global sustainable development but also affects commodity prices. In this study, taking crude oil as an example, we use data from 1998 to 2024 and employ a structural VAR model to explore this effect. The empirical findings reveal that after a positive GSCP shock, crude oil prices rose immediately before the outbreak of global trade tensions in 2018. After 2018, however, prices decreased initially and then increased again about two months later. This response heterogeneity is primarily related to differences in the key drivers of GSCP between two periods. Full article

Sandy beaches are vital geomorphic units with ecological, social, and economic significance, playing a key role in coastal protection and ecosystem regulation. However, they are increasingly threatened by climate change and human activities, highlighting the need for large-scale, high-precision monitoring to support sustainable management. Existing remote-sensing-based sandy beach extraction methods face challenges such as suboptimal feature selection and reliance on single data sources, limiting their generalization and accuracy. This study proposes a novel sandy beach extraction framework that integrates multi-source data, feature optimization, and collaborative modeling, with Fujian Province, China, as the study area. The framework combines Sentinel-1/2 imagery, nighttime light data, and terrain data to construct a comprehensive feature set containing 44 spectrum, index, polarization, texture, and terrain variables. The optimal feature subsets are selected using the Recursive Feature Elimination (RFE) algorithm. Six machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Categorical Boosting (CatBoost)—along with an ensemble learning model, are employed for comparative analysis and performance optimization. The results indicate the following. (1) All models achieved the best performance when integrating all five types of features, with the average overall F1-score and accuracy reaching 0.9714 and 0.9733, respectively. (2) The number of optimal features selected by RFE varied by model, ranging from 19 to 36. The ten most important features across models were Band 2 (B2), Elevation, Band 3 (B3), VVVH_SUM, Spatial Average (SAVG), VH, Enhanced Water Index (EWI), Slope, Variance (VAR), and Normalized Difference Vegetation Index (NDVI). (3) The ensemble learning model outperformed all others, achieving an average overall accuracy, precision, recall, and F1-score of 0.9750, 0.9733, 0.9725, and 0.9734, respectively, under the optimal feature subset. A total of 555 sandy beaches were extracted in Fujian Province, covering an area of 43.60 km² with a total perimeter of 1263.59 km. This framework demonstrates strong adaptability and robustness in complex coastal environments, providing a scalable solution for intelligent sandy beach monitoring and refined resource management. Full article

This study proposes a CatBoost-enhanced hyperspectral modeling approach for accurate prediction of wood dyeing formulations. Using Pinus sylvestris var. mongolica veneer as the substrate, 306 samples with gradient dye concentrations were prepared, and their reflectance spectra (400–700 nm) were acquired. After noise reduction and sensitive band selection (400–450 nm, 550–600 nm, and 600–650 nm), spectral descriptors were extracted as model inputs. The CatBoost algorithm, optimized via k-fold cross-validation and grid search, outperformed XGBoost, random forest, and SVR in prediction accuracy, achieving MSE = 0.00271 and MAE = 0.0349. Scanning electron microscopy (SEM) revealed the correlation between dye particle distribution and spectral response, validating the model’s physical basis. This approach enables intelligent dye formulation control in industrial wood processing, reducing color deviation (ΔE < 1.75) and dye waste by approximately 25%. Full article

Background: Amyotrophic lateral sclerosis is a systemic, complex, multifactorial, and fatal neurodegenerative disease with various factors involved in its etiology. This study aimed to understand the effects of SNPs in the MTHFR, MTR, SLC19A1, and VAPB genes on protein functionality and structure and their influence on ALS susceptibility. Methods: The dbSNP and ClinVar databases were used for SNP data annotation, while UniProt and PDB provided protein sequences. We performed functional and structural predictions of SNPs using PolyPhen-2 and SNAP2. We modeled mutant proteins using AlphaFold 2 and visualized them in PyMOL to compare native and mutant forms. Results: Our results identified SNP rs74315431 as pathogenic, inducing structural and functional changes and exhibiting visible alterations in the three-dimensional structure. Although predicted as non-pathogenic, SNPs rs1801131, rs1805087, and rs1051266 caused protein structural alterations, a finding confirmed by three-dimensional visualization. SNP rs1801133 diverged from the others, being predicted as pathogenic but without causing changes in protein structure or function. Conclusions: Our study found a strong correlation between SNAP2-predicted alterations and those predicted by AlphaFold 2, whereas PolyPhen-2 results did not directly correlate with three-dimensional structure changes. Full article

Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a CAPM-style factor-based model that simulates risk via systematic factor exposures. The two models are applied to a technology-sector portfolio and evaluated under historical and rolling backtesting frameworks. Under the Basel III backtesting framework, both initially fall into the red zone, with 13 VaR violations. With rolling-window estimation, the return-based model shows modest improvement but remains in the red zone (11 exceptions), while the factor-based model reduces exceptions to eight, placing it into the yellow zone. These results demonstrate the advantages of incorporating factor structures for more stable exception behavior and improved regulatory performance. The proposed framework, fully transparent and reproducible, offers practical relevance for internal validation, educational use, and model benchmarking. Full article

This study evaluates the risk-adjusted performance of Hospitality REITs using multi-factor asset pricing models and downside risk measures with the aim of assessing their diversification potential and crisis sensitivity. Unlike prior studies that examine REITs in aggregate, this study isolates Hospitality REITs to explore their unique cyclical and macroeconomic sensitivities. This study looks at the risk-adjusted performance of Hospitality Real Estate Investment Trusts (REITs) in relation to more general REIT indexes and the S&P 500 Index. The study reveals that monthly returns of Hospitality REITs increasingly move in tandem with the stock markets during financial crises, which reduces their historical function as portfolio diversifiers. Investing in Hospitality REITs exposes one to the hospitality sector; however, these investments carry notable risks and provide little protection, particularly during economic upheavals. Furthermore, the study reveals that Hospitality REITs underperform on a risk-adjusted basis relative to benchmark indexes. The monthly returns of REITs show significant volatility during the post-COVID-19 era, which causes return-to-risk ratios to be below those of benchmark indexes. Estimates from multi-factor models indicate negative alpha values across conditional models, indicating that macroeconomic variables cause unremunerated risks. This industry shows great sensitivity to market beta and size and value determinants. Hospitality REITs’ susceptibility comes from their showing the most possibility for exceptional losses across asset classes under Value at Risk (VaR) and Conditional Value at Risk (CvaR) downside risk assessments. The findings have implications for investors and portfolio managers, suggesting that Hospitality REITs may not offer consistent diversification benefits during downturns but can serve a tactical role in procyclical investment strategies. Full article

Power distribution systems are witnessing a growing deployment of distributed, inverter-based renewable resources such as solar generation. This poses certain challenges such as rapid voltage fluctuations due to the intermittent nature of renewables. Volt-Var control (VVC) methods have been proposed to utilize the ability of inverters to supply or consume reactive power to mitigate fast voltage fluctuations. These methods usually require a detailed power network model including topology and impedance data. However, network models may be difficult to obtain. Thus, it is desirable to develop a model-free method that obviates the need for the network model. This paper proposes a novel model-free cooperative control method to perform voltage regulation and reduce inverter aging in power distribution systems. This method assumes the existence of time-series voltage and load data, from which the relationship between voltage and nodal power injection is derived using a feedforward artificial neural network (ANN). The node voltage sensitivity versus reactive power injection can then be calculated, based on which a cooperative control approach is proposed for mitigating voltage fluctuation. The results obtained for a modified IEEE 13-bus system using the proposed method have shown its effectiveness in mitigating fast voltage variation due to PV intermittency. Moreover, a comparative analysis between model-free and model-based methods is provided to demonstrate the feasibility of the proposed method. Full article

In this paper, we aim to provide a new algorithm for managing financial risk in portfolios containing multiple high-volatility assets. We assess the variability of volatility with the Volterra model, and we construct an estimator of the Value-at-Risk (VaR) function using quantile regression. Because of its long-memory property, the Volterra model is particularly useful in this domain of financial time series data analysis. It constitutes a good alternative to the standard approach of Black–Scholes models. From the weighted asymmetric loss function, we construct a new estimator of the VaR function usable in Multi-Functional Volterra Time Series Model (MFVTSM). The constructed estimator highlights the multi-functional nature of the Volterra–Gaussian process. Mathematically, we derive the asymptotic consistency of the estimator through the precision of the leading term of its convergence rate. Through an empirical experiment, we examine the applicability of the proposed algorithm. We further demonstrate the effectiveness of the estimator through an application to real financial data. Full article

Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets during the Trump (2017–2020) and Biden (2021–2024) governments. The 48-week return forecast of the Bitcoin–Gold correlation was also conducted by using the Bayesian Structural Time Series (BSTS) model. Results indicate that Bitcoin was the most volatile asset, while the U.S. Dollar remained the least volatile under both regimes. Under Trump, U.S. Dollar significantly influenced Oil and Bitcoin while Bitcoin and Gold were negatively linked to Oil and positively associated with U.S. Dollar. An inverse relationship between Bitcoin and Gold also emerged. Under Biden, Bitcoin, Gold, and U.S. Dollar all significantly affected Oil with Bitcoin showing a positive impact. Bitcoin and Gold remained negatively correlated though not significantly, and the Dollar maintained positive ties with both. Forecasts show a positive link between Bitcoin and Gold in the coming year. However, Bitcoin does not exhibit consistent characteristics of a safe-haven asset during the U.S. presidential transitions examined, largely due to its high volatility and unstable correlations with a traditional safe-haven asset, Gold. This study contributes to the understanding of shifting relationships between digital and traditional assets across political regimes. Full article

This study explores asymmetric volatility structures within multivariate hysteretic autoregressive (MHAR) models that incorporate conditional correlations, aiming to flexibly capture the dynamic behavior of global financial assets. The proposed framework integrates regime switching and time-varying delays governed by a hysteresis variable, enabling the model to account for both asymmetric volatility and evolving correlation patterns over time. We adopt a fully Bayesian inference approach using adaptive Markov chain Monte Carlo (MCMC) techniques, allowing for the joint estimation of model parameters, Value-at-Risk (VaR), and Marginal Expected Shortfall (MES). The accuracy of VaR forecasts is assessed through two standard backtesting procedures. Our empirical analysis involves both simulated data and real-world financial datasets to evaluate the model’s effectiveness in capturing downside risk dynamics. We demonstrate the application of the proposed method on three pairs of daily log returns involving the S&P500, Bank of America (BAC), Intercontinental Exchange (ICE), and Goldman Sachs (GS), present the results obtained, and compare them against the original model framework. Full article