Search Results (19,317)

Background: Fibrotic remodelling of the lamina cribrosa (LC) is a defining pathological feature of glaucomatous optic neuropathy and contributes to progressive optic nerve head deformation and axonal vulnerability. LC cells from glaucomatous donors exhibit a myofibroblast-like phenotype characterised by excessive extracellular matrix (ECM) production, a process associated with chronic cellular stress. cAMP responsive element-binding protein 3-like 1 (CREB3L1) is an endoplasmic reticulum–resident transcription factor implicated in stress-responsive regulation of collagen synthesis and matrix homeostasis. The role of CREB3L1 in glaucomatous LC cells, however, remains poorly defined. Methods: Primary human LC cells derived from donors with confirmed glaucoma (GLC; n = 3) and age-matched non-glaucomatous controls (NLC; n = 3) were examined. CREB3L1 expression was assessed at the mRNA and protein levels using quantitative RT-PCR and Western immunoblotting. The functional effects of CREB3L1 suppression were evaluated using siRNA-mediated knockdown in GLC cells, followed by analysis of ECM gene transcription (α-smooth muscle actin, collagen type I alpha 1, fibronectin) and cellular metabolic activity using an MTS assay. Results: CREB3L1 mRNA and protein expression were significantly elevated in GLC cells compared with NLC cells. siRNA-mediated knockdown of CREB3L1 effectively reduced its expression in GLC cells and was associated with significant suppression of profibrotic ECM gene transcription. In addition, CREB3L1 knockdown resulted in a marked reduction in cellular metabolic activity in glaucomatous LC cells. Conclusions: These findings identify CREB3L1 as a regulator of ECM-associated gene expression and cellular behaviour in glaucomatous lamina cribrosa cells. While preliminary, the data suggest that CREB3L1 may contribute to pathological fibrotic remodelling at the optic nerve head. Further mechanistic and in vivo studies will be required to determine whether modulation of CREB3L1-mediated pathways represents a viable therapeutic strategy in glaucoma. Full article

The Brazilian Amazon has been endangered by agro-extractivism, a development model characterized by the expansion of the agricultural frontier to produce raw commodities embedded in power-asymmetrical commodity chains. Recently, the socio-biodiversity economy has emerged as an alternative development model, aimed at reconciling local development with nature conservation. While the environmental and social contrasts between the two models are well documented, the commercial dimension of the socio-biodiversity economy remains underexplored. These two models are typically approached as separate systems, yet their coexistence and interaction within the same actors and across interconnected value chains has not been empirically examined. In this paper, we provide a qualitative analysis of dynamics and upgrading mechanisms in two cocoa value chains in the Brazilian Amazon: raw (bulk) and fine-flavor (fino) cocoa. Through this comparison, we examine how each chain differs in terms of commercial relations and how socio-biodiversity economy and agro-extractivism interact within the commercial sphere. The research took place in three municipalities along the Transamazon highway between March and September 2024. Data were gathered through semi-structured interviews with cocoa producers, buyers, and supporting actors such as NGOs, companies, and public agencies, complemented by participant observation and participation in cocoa-related events. Findings suggest that the bulk and fino cocoa chains present distinct commercial configurations, the former displaying agro-extractivist patterns, the latter consistent with the socio-biodiversity economy. Cocoa production in the region is part of an emergent socio-biodiversity economy that remains commercially embedded in agro-extractivism. Notably, farmers engage in both chains as part of their livelihood strategies, while relying predominantly on the bulk trade. We argue that the fino cocoa chain may represent a pathway for transforming commercial relations in the region, provided that the structural conditions sustaining agro-extractivist patterns in the bulk chain are addressed. More broadly, we show that production-level transitions toward sustainable farming do not automatically translate into the transformation of commercial relations, and call for greater analytical attention to the commercial dimension of socio-biodiversity economies. Full article

Reproductive performance is a key determinant of overall livestock productivity. In both beef and dairy systems, reproductive failure represents a leading cause of cow culling. Reproductive traits are complex in nature and present a low heritability in general. Additionally, the collection of such phenotypes usually relies on indirect measures of fertility, such as conception success. Therefore, further investigation into genetic and non-genetic factors of reproductive traits in cattle is necessary. The hosts’ microbiome plays a crucial role in vertebrate biology, including reproduction. We, therefore, hypothesize that microbiome indicators may serve as a biomarker of fertility. This study explored the relationship between vaginal microbiome profiles and pregnancy among three beef cattle genetic groups using field data. Vaginal swabs were collected from 74 cows at Fort Keogh, MT, including 23 Angus, 23 Hereford Line 1, and 28 crossbreds, and DNA was extracted and analyzed via 16S rRNA gene amplification. Significant differences in alpha diversity (p < 0.05) were found among Line 1 cows compared to Angus and crossbreds in many indicators of alpha diversity. Pregnancy status did not influence alpha diversity of samples significantly, but trends toward significance were observed. PERMANOVA analysis indicated that genetic groups and pregnancy status affected microbial composition (p < 0.05), but their interaction was not significant. Each genetic group showed unique compositions of operational taxonomic units (OTUs), with higher proportions of Ureaplasma and Mycoplasma families in Line 1. Additionally, variations in microbial communities were observed between pregnant and non-pregnant cows, with certain uncultured bacteria more prevalent in non-pregnant cows. While field data are useful for such studies and represent a real production system, better-designed experiments are necessary to validate findings and test hypotheses. These results suggest variation in vaginal microbiomes across breeds and pregnancy status, emphasizing the need for further research to identify factors affecting these changes. Full article

Query–service matching in customer service systems faces a critical challenge of accurately aligning user queries expressed in colloquial language with formally defined services while balancing business objectives. Traditional keyword-based and embedding approaches fail to capture complex semantic nuances and cannot provide interpretable explanations. We address this problem by proposing a novel reasoning-enhanced framework that leverages large language models (LLMs) for structured multi-criteria evaluation. Our key innovation is a reasoning-first scoring architecture where the model generates detailed explanations before numerical scores, reducing score variance by 18% through conditional mutual information. We introduce a controlled stochastic perturbation mechanism with theoretically derived optimal parameters that balance diversity and relevance, alongside a knowledge distillation pipeline enabling 960× model compression (480B→0.5B parameters) while retaining 94% performance. Rigorous theoretical analysis establishes Pareto optimality guarantees for multi-criteria evaluation, information-theoretic entropy reduction bounds, and PAC learning guarantees for distillation. Experimental validation on real-world telecommunications data demonstrates 89% Precision@1 (15.3% improvement over baselines), 23% diversity enhancement, and 96× latency reduction, with deployment cost decreasing 1200× compared to direct LLM inference. This work bridges the gap between LLM capabilities and production deployment requirements through principled mathematical foundations and practical system design. Full article

This study investigates whether external terms of trade (TOT) and mining-sector GDP in Peru share a stable long-run relationship. Although mining has played a central role in the country’s growth trajectory, its performance remains highly exposed to international price cycles, raising questions about its structural sustainability under persistent external shocks. Using quarterly data for 2001–2024, the analysis applies Johansen cointegration techniques and estimates a bivariate Vector Error Correction Model (VECM) to evaluate long-run co-movement and short-run adjustment dynamics. The results identify a single cointegrating relationship in which mining GDP acts as the primary adjustment variable, gradually correcting deviations from long-run equilibrium, while short-run TOT shocks do not exert direct contemporaneous effects on mining growth. The estimated speed of adjustment is low, suggesting a prolonged convergence process consistent with the capital-intensive and rigid structure of the mining sector. Robustness exercises—including estimation with heteroskedasticity and autocorrelation consistent (HAC) standard errors and an extended specification incorporating gross fixed capital formation—confirm the stability of the long-run relationship. These findings indicate that the structural sustainability of mining output depends on the interaction between external price dynamics and the sector’s capacity to adjust to persistent international shocks. The study concludes that, in the Peruvian case, structural sustainability in the mining sector is not determined solely by global price trends, but is also conditioned by domestic productive and institutional factors that govern the speed of adjustment in the presence of sustained external volatility. Full article

Broccoli (Brassica oleracea L. var. italica) is prone to rapid yellowing when stored at ambient temperature after harvest due to membrane damage. Here, freshly harvested broccoli florets were stored in a static magnetic field (5 mT) at 20 °C. The current results demonstrated that a static magnetic field lowered postharvest yellowing (chlorophyll breakdown), water loss, and oxidative stress. An integrated transcriptome and metabolome analysis suggested that static magnetic field-mediated alleviation of postharvest yellowing and senescence of broccoli florets involved the following factors: (1) downregulating the expression of genes related to organ senescence; (2) delaying the breakdown of chlorophylls through preventing the upregulation of chlorophyll degradation-related genes and the increase in oxidative stress; (3) alleviating cellular energy imbalance by upregulated fatty acid oxidation (as indicated by decreased free fatty acids) to reduce water loss and oxidative stress and to maintain membrane integrity; (4) increasing the abundances of lysophospholipids and sphingolipids and preventing the decrease in phosphatidylcholine abundance to lower water loss and oxidative stress, inhibit ethylene production, delay chlorophyll degradation, and keep membrane integrity; (5) reducing water loss via increasing cutin, suberin, and wax biosynthesis and stomatal closure brought about by upregulated expression of phospholipase D genes; and (6) preventing the increase in malondialdehyde (MDA) content, electrolyte leakage, and weight loss rate. To conclude, this work provided some novel data elucidating the underlying mechanism by which a static magnetic field delayed postharvest yellowing and senescence of broccoli florets. A static magnetic field could retard postharvest deterioration of broccoli florets, thereby providing a clean and non-thermal method for their green preservation. Full article

Music popularity prediction is a fundamental problem in music information retrieval, with important implications for digital content dissemination and creative decision-making on streaming platforms. In this study, music popularity prediction is formulated as a supervised regression problem, and six widely-used tree ensemble models (Random Forest, XGBoost, CatBoost, LightGBM, Extra Trees, and Decision Tree) are systematically evaluated using large-scale Spotify data. Among these models, Random Forest achieves the best predictive performance on this dataset (RMSE = 6.79, MAE = 5.10, and $R^2$ = 0.6658), followed by Extra Trees ($R^2$ = 0.6378) and Decision Tree ($R^2$ = 0.6328). Bayesian hyperparameter optimization based on a Tree-structured Parzen Estimator with an Expected Improvement acquisition function is conducted over 50 trials with 5-fold cross-validation to ensure robust model selection. Shapley value decomposition via SHAP analysis reveals that temporal recency dominates feature importance, far surpassing traditional musical attributes, while acoustic intensity (loudness) exhibits a U-shaped contribution pattern with optimal values at moderate intensity levels. Further SHAP dependence analysis uncovers non-linear relationships, indicating substantial popularity advantages for recent releases and optimal loudness levels around $-5$ to 0 dB. These findings suggest that streaming popularity is primarily governed by temporal exposure dynamics and production-related characteristics rather than intrinsic musical structure, offering both theoretical insights for music information retrieval research and suggestive empirical patterns that may inform future investigations into digital music ecosystems. Full article

The transition to a circular economy is a strategic direction of the European Union, and the agri-food sector is essential in this transformation through resource consumption, climate impact and an economic role in the food chain. This study analyses the relationship between the circularity of the economy and the sustainable performance of agri-food systems in the EU-27, using Eurostat data for the period of 2014–2023. Circularity is operationalised through a composite index built from the circularity of materials and resource productivity, aggregated through principal component analysis and complemented by an alternative measure based on the average of the standardised components. Sustainable performance is assessed through economic indicators (value added and output in agriculture, value added in the food industry), environmental indicators (greenhouse gas emissions from agriculture) and, complementary, energy indicators (energy intensity in the food industry), the latter being analysed separately on the available observations. The results do not indicate clear aggregate differences in sustainable performance associated with circularity measured at the macro level over the analysed period, underlining the importance of connecting circularity objectives with interventions and indications specific to the agri-food chain for monitoring and policy design at the EU level. Full article

Background/Objectives: Natural medicine analysis remains challenging due to chemical diversity. To the best of our knowledge, the comprehensive identification of multiple chemical constituents in Xiaoban Kangfu (XBKF) capsules has not been reported. Therefore, a combined approach utilizing ultra-high-performance liquid chromatography quadrupole-Exactive Orbitrap mass spectrometry (UHPLC-Q-Exactive Orbitrap MS) and molecular network analysis needs to be developed to comprehensively characterize the chemical constituents of XBK capsules in heat-clearing and toxin-eliminating granules, thereby enhancing annotation accuracy and enabling visualization. Methods: Firstly, chromatographic and mass spectrometry conditions were optimized to achieve good separation and a rich signal response. Subsequently, the literature searches, database consultations, and reference standards were employed to enhance annotation reliability. Finally, the raw data acquired under optimized conditions were uploaded to Global Natural Products Social (GNPSs), enabling component visualization by linking precursor ions of similar structural features with identical colors. Results: A total of 170 compounds were identified from this medicinal resource for the first time, including 50 flavonoids, 34 phenolic acids, 16 terpenoids, 14 quinones, 14 organic acids, eight coumarins, ive carbohydrates, and 29 other compounds. Conclusions: This study establishes a robust UHPLC-Q-Exactive Orbitrap MS/MS strategy for the comprehensive chemical profiling of XBKF capsules. The use of the presented validated analytical method for the comprehensive quality control of XBKF capsules is highly promising, offering fast, highly sensitive, and reliable analysis. Full article

Environmental transparency in food manufacturing requires efficient carbon footprint data collection, yet multi-departmental coordination often creates time-consuming, fragmented processes that impede adoption. This study applies lean office methodologies to optimize carbon footprint assessment processes in food manufacturing. Using a case study approach at a Thai food processing facility, we implemented flow process charts, value stream mapping, eight waste analysis, and ECRS methodology to evaluate the data collection process for strawberry jam production. The baseline assessment documented 142 activities across 12 departments, requiring 17,540 min. The lean interventions included establishing a centralized cross-functional team, developing standardized data collection templates, implementing a unified digital repository system, and consolidating redundant verification procedures. The improved process reduced activities from 142 to 63, decreased the required time from 17,540 to 11,190 min (36.2% reduction), and eliminated 95.8% of non-value-added activities while maintaining regulatory compliance. These efficiency gains enable more frequent environmental assessments and facilitate the broader adoption of carbon footprint measurement within resource-constrained manufacturing contexts. The study demonstrates that lean principles effectively optimize environmental assessment processes themselves, providing a replicable framework adaptable across diverse food manufacturing facilities and product lines while addressing critical adoption barriers including resource constraints and administrative complexity. Full article

Soil salinity in wheat represents a severe threat to global productivity, requiring a deep understanding of physiological adaptation mechanisms to ensure food security in the context of continuous agricultural land degradation. The study aim was to assess the impact of a salinity gradient (0–75 mM NaCl) on the dynamics of stomatal opening and chlorophyll content of the varieties Glosa, Taisa, Boema and Granny. The methodology integrated four joint classes, of which two were from detailed physiological parameters, stomatal features and chlorophyll content, and two morphological characteristics, growth visual indices and biomass allocation. All data was corroborated into an original hierarchical synthesis model presented in a multi-layered sunburst plot. The most relevant results indicate that the concentration of 45 mM NaCl represents the osmotic adjustment threshold, where the active accumulation of ions decreases the internal osmotic potential, facilitating an influx of water that maximizes guard cell turgor and, implicitly, stomatal width. Maximal physiological parameters and biomass ranked the variety Granny first, followed by Taisa. Despite stomatal increases, Boema ranked third and Glosa showed overall decreased development and the lowest plant biomass. These findings validate the use of interconnected effects analysis as a screening tool for identifying the salinity responses of wheat varieties. Full article

In response to the growing need for accurate forecasting of electricity generation from PV installations, which is crucial both for enhancing self-consumption and for balancing the power grid, this study presents a comparative analysis of selected machine learning models. The research focuses on the XGBoost algorithm and LSTM neural networks, applied to predict PV energy production based on meteorological data and historical generation records from four medium-sized PV installations (30–50 kWp) located in Poland. Meteorological data were retrieved from open sources and combined with actual production measurements to build the training dataset. This paper discusses the challenges posed by these data at the given latitude, as well as issues related to processing data from newly launched installations. The performance of both approaches was evaluated in short- and medium-term forecasting, with particular attention to prediction accuracy, robustness to noisy data, and the ability to capture nonlinear relationships. Full article

This study investigated the relationship between digital governance and sustainable development across the European Union (EU-27) during the period 2015–2023. Although digital transformation has become a central policy priority, empirical evidence on how e-government adoption contributes to sustainability performance remains limited. Using panel data from Eurostat and the UN Sustainable Development Solutions Network, the analysis employed advanced econometric techniques, including Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Method of Moments Quantile Regression (MMQR), to explore both long-run relationships and heterogeneous effects across countries. The model incorporates key indicators such as the percentage of individuals using e-government services, Gross Domestic Product (GDP) per capita growth, and Research and Development (R&D) expenditure, capturing, respectively, digital governance adoption, innovation potential, and economic capacity, as essential drivers of sustainable development. Results indicate a strong and statistically significant positive association between digital governance adoption and sustainable development outcomes. The quantile regression analysis reveals that this effect is more pronounced in countries with higher innovation intensity and stronger economic capacity, suggesting that digital governance amplifies sustainability benefits in countries with more advanced institutional and technological infrastructures. Robustness checks confirm the stability of the findings across multiple estimation techniques. The results underscore the need for inclusive and innovation-driven digital strategies to ensure that the benefits of digital governance are equitably distributed, ultimately enhancing the EU’s progress towards the Sustainable Development Goals. Full article

As a core pastoral region of the Qinghai–Tibet Plateau, Qinghai Province faces substantial threats to livestock production from tick-borne diseases. This study aimed to investigate the prevalence of six bacterial pathogens in dominant tick species from Qinghai Province, to provide baseline epidemiological data for local tick-borne disease surveillance. A total of 1025 questing ticks were collected from key pastoral regions of Qinghai Province during April to May in 2024 and 2025. All ticks were morphologically identified as belonging to 1 family (Ixodidae), 2 genera, and 4 species. Dermacentor nuttalli was the dominant species with a relative dominance of 66.83% (685/1025, 95% CI: 63.92–69.61%), followed by Haemaphysalis qinghaiensis at 30.83% (316/1025, 95% CI: 28.11–33.69%), Dermacentor silvarum at 1.95% (20/1025, 95% CI: 1.27–2.98%), and Dermacentor niveus at 0.39% (4/1025, 95% CI: 0.15–1.01%). PCR detection was performed for six target pathogens, with an overall Brucella spp. DNA detection rate of 0.78% (8/1025, 95% CI: 0.40–1.53%) and an overall Rickettsia spp. detection rate of 16.29% (167/1025, 95% CI: 14.16–18.67%). Statistical analysis showed that the prevalence of Brucella spp. and Rickettsia spp. differed significantly between the two dominant tick species (Fisher’s exact test/χ² test, all p < 0.001). No Brucella or Rickettsia pathogens were detected in D. silvarum and D. niveus. Notably, detection of Brucella spp. DNA does not confirm the presence of viable bacteria or tick vector competence. This study fills the regional data gap of tick-borne pathogens in Qinghai, and provides reference for the prevention and control of local tick-borne zoonotic diseases. Full article

Precision agriculture employs advanced technologies to enhance farm productivity and sustainability; however, selecting the most appropriate tools can be challenging for small and medium-sized farms. This study conducts a comparative analysis of ten key precision agriculture technologies (PATs): remote sensing, GPS, GIS, VRT, soil & crop sensors, DSS, UAVs/Drones, AI & ML-based precision farming, autonomous agricultural machinery, and IoT-based smart farming. The analysis employs a neutrosophic set-based multi-criteria decision-making (MCDM) framework. Domain experts evaluated ten representative technologies using a structured questionnaire based on ten critical criteria, including spatial-temporal accuracy, data acquisition latency, scalability, robustness, interoperability, environmental resilience, economic feasibility, and agro-ecological impact. A hybrid MCDM methodology was employed, integrating neutrosophic entropy and DEMATEL to construct criterion weights. Furthermore, we utilized neutrosophic DEMATEL to identify inter-criterion causal relationships. Neutrosophic TOPSIS, enhanced by a newly proposed hybrid Cosine-Jaccard similarity measure, was introduced to rank the alternatives under conditions of uncertainty. The findings reveal that IoT-based smart farming solutions achieved the highest overall score, followed by remote sensing and decision-support system (DSS) platforms. At the same time, variable-rate technology and sensor networks received lower rankings. The findings underscore the appropriateness of particular PATs for small and medium-scale farming contexts and illustrate the effectiveness of neutrosophic MCDM in addressing ambiguity and indeterminacy. The comparative insights provide direction for researchers, policymakers, and practitioners in prioritizing precision agriculture technologies and strategies to enhance sustainable practices in small and medium-scale farming. Full article