Search Results (102,382)

Background: Polyamines, including putrescine (PU), spermidine (SPD), and spermine (SPM), are ubiquitous bioactive compounds essential for cell proliferation, genomic stability, autophagy, and the regulation of oxidative and inflammatory responses. Growing evidence, particularly for SPD, suggests that polyamine-rich diets may protect against age-related conditions such as cardiovascular disease, metabolic syndrome, and neurodegenerative disorders. As endogenous polyamine synthesis declines with age, dietary intake becomes increasingly important, especially in older adults. Methods: This study estimated each polyamine (PU, SPD and SPM) and total polyamine intake in the Spanish population using food consumption data from the Spanish Ministry of Agriculture, Fisheries and Food. Intakes were evaluated across four age groups, and major dietary sources were identified. Results: Total polyamine intake increased with age, reaching 393 µmol/day in adults over 65 years. PU accounted for 49% of total intake, followed by SPD (29%) and SPM (22%). Plant-based foods were the primary contributors to SPD intake, particularly vegetables (36%), fruits (26%), and cereals (18%). PU intake was also predominantly plant-derived, mainly from fruits (58%) and vegetables (23%), whereas SPM intake was largely associated with meat products (59%). A theoretical Mediterranean diet model yielded a slightly higher total polyamine intake of 406.6 µmol/day and a substantially greater SPD intake than that observed in older adults (193.99 µmol/day versus 121.62 µmol/day). Conclusions: Overall, estimated polyamine intake in the Spanish population fell below the optimal level of 540 µmol/day proposed in the literature. These findings highlight the need for public health strategies promoting consumption of polyamine-rich foods, particularly vegetables, legumes, whole grains, and fruits, to support healthy aging and reduce the risk of age-related diseases. Full article

The discharge of metal-complex dyes from textile industries poses significant environmental challenges due to their chemical stability and resistance to conventional biological treatment. This study examined the degradation of Acid Black 194 (AB–194), a 1:2 chromium-complex azo dye, using Co²⁺-activated peroxymonosulfate (PMS). A central composite design based on response surface methodology was used to evaluate the effects of Co²⁺ (5.93–20.07 µM), PMS (1.67–7.33 mM), and dye (13.79–56.21 mg L⁻¹) concentrations on decolorization and mineralization. The polynomial models demonstrated strong predictive accuracy (R² > 0.9896), identifying Co²⁺ and dye concentrations as the most influential factors. Under optimal conditions (18.0 µM Co²⁺, 6.5 mM PMS, 20.0 mg L⁻¹ dye), 99.19% decolorization was achieved at 30 min and 41.43% TOC removal at 240 min. Degradation kinetics were described by a mechanistic model incorporating 15 elementary reactions that comprise the Co²⁺/Co³⁺ redox cycle, radical generation, and dye oxidation, yielding a global R² of 0.9617. Estimated rate constants for dye oxidation (k₁₄ = 3.52 × 10⁹ M^–1 s^–1 for and k₁₅ = 2.00 × 10¹⁰ M^–1 s^–1 ) were consistent with values reported for aromatic compounds in sulfate radical systems. Radical contribution analysis confirmed sulfate radicals as the principal oxidizing species, accounting for 96.75% of the overall process. Full article

Introduction: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is widely used for diagnosing pulmonary diseases causing mediastinal lymphadenopathy. However, non-diagnostic results may occur. This study investigated factors associated with non-diagnostic cytological results in EBUS-TBNA. Methods: This retrospective study included patients who underwent EBUS-TBNA at a tertiary hospital between March 2019 and December 2023. Data on demographics, biopsy techniques, cyto-/histopathological results, sonographic lymph node measurements, and pre-procedural PET-CT SUVmax values were recorded. Cytological results were classified as diagnostic or non-diagnostic. We analyzed the characteristics and associated factors of patients who were non-diagnostically identified. Results: Among 776 patients undergoing EBUS-TBNA, 502 (64.7%) were male, with a mean age of 61.5 ± 12.6 years. A total of 1110 lymph nodes were sampled. Of the patients, 14.1% had a non-diagnostic cytology. Among the diagnosed patients, cytological findings showed 58.9% non-malignant, 41.1% malignant. The most sampled station was station 7 (72.9%), with an average of 5.9 ± 1.4 aspirations. Diagnostic cases had significantly more aspirations (p = 0.022) and sampled larger lymph node sizes (p < 0.001). Each 1 mm increase in lymph node size raised the likelihood of diagnostic results by 1.04 times (adjOR = 1.04, 95% CI = 1.02–1.08, p = 0.002). The largest lymph node size significantly predicted diagnostic results (AUROC = 0.611, p < 0.001). A cut-off of 19.55 mm had 67.0% sensitivity and 52.2% specificity. Conclusion: Sampled larger lymph nodes increase diagnostic yield in EBUS-TBNA, reducing the need for repeat procedures and enabling earlier treatment, thereby decreasing morbidity and mortality. Full article

The construction of green mines is a core strategy for promoting ecological civilization in China’s mining sector, yet its long-term ecological effects require quantitative assessment. Using a cement-grade limestone mine operated by Linyi Zhonglian Cement Co., Ltd. in Shandong Province as an illustrative case, we employed Landsat 8 OLI/TIRS imagery acquired in 2015, 2020, and 2025 to develop a five-indicator framework for assessing ecological environment quality. The selected indicators comprised greenness (NDVI), wetness, dryness (NDBSI), land surface temperature (LST), and dust concentration (MECDI). These five indicators were subsequently integrated via principal component analysis to generate the Mine Ecological Quality Index (Mine-EQI). Using this index, we applied the Theil–Sen median slope estimator alongside zonal statistics to examine ecological change trajectories across the full study area and three functional zones—the industrial square, haul roads, and active mining area—over the 2015–2025 period. The ecological outcomes attributable to the green mine policy were then quantified. The results show that (1) the mean Mine-EQI of the study area decreased from 0.3713 in 2015 to 0.3460 in 2025, exhibiting a slight overall decline. However, the rate of decline decreased from −6.1% during 2015–2020 to −0.7% during 2020–2025, yielding a Temporal Change Intensity index (TCI) of +88.5%, indicating that the ecological degradation trend has been effectively curbed. (2) Significant spatial heterogeneity was observed. The industrial square showed substantial improvement (Theil–Sen slope = +0.0726), while the haul roads (slope = −0.0705) and mining area (slope = −0.0408) continued to exhibit degradation trends. The improved areas (9.7% of the study area) were spatially coincident with green mine engineering projects. (3) The dust indicator (MECDI) decreased by 24.7% during 2020–2025, and the vegetation index (NDVI) increased by 19.5% over the decade, representing the dominant contributors to ecological improvement. This study reveals that China’s green mine policy has yielded remarkable ecological improvements in relatively stable functional zones such as industrial squares. In contrast, ecological restoration within persistently disturbed areas, including haul roads and mining pits, demands long-term sustained investment and governance. By integrating remote sensing techniques with policy analysis, this research establishes a replicable framework for evaluating progress toward sustainable mining practices. The findings directly support the monitoring of SDG 12 (Responsible Consumption and Production) and SDG 15 (Life on Land), providing a quantitative pathway to balance mineral resource extraction with ecological protection—a core sustainability challenge for resource-dependent regions. Full article

Blue honeysuckle (Lonicera caerulea L.) is subject to environmental stressors, leading to variability in both severe fruit drop during development and fruit quality. Zinc, an essential micronutrient, is critical to sustainable fruit tree production by enhancing yield and nutritional quality. Different forms of zinc fertilizers, particularly nano-zinc versus conventional ionic zinc, exhibit marked differences in absorption efficiency and agronomic performance, thereby determining their practical efficacy. In this two-year study, we evaluated the effects of foliar-applied zinc forms, ZnO nanoparticles (30, 50, and 90 nm) and ionic zinc (ZnCl₂ and ZnSO₄), applied at the young fruit, veraison, and maturity stages on yield and fruit quality. Results showed that ZnO nanoparticles were more effective than ionic zinc at 80 mg/L. In particular, among the ZnO NP treatments, 90 nm ZnO NPs exhibited the best overall effect, significantly improving fruit quality. The 30 nm ZnO NPs treatment performed best in terms of single fruit weight, yield per plant, and fruit firmness. This study highlights the potential of nano-zinc to enhance productivity and quality in blue honeysuckle, providing a theoretical basis for selecting optimal zinc fertilizer types and particle sizes in specialty berry production, with implications for sustainable, high-quality fruit cultivation. Full article

This study proposes a standards-based Reference AI Business Model Canvas (Reference AI-BMC) that translates the use-case descriptors of ISO/IEC TR 24030 into the nine blocks of the Business Model Canvas, addressing the lack of a structured translation layer between AI standards and business-model design. Using ten selected fields of the ISO/IEC TR 24030 use-case template, a two-round Delphi process derives consensus-based mapping rules from expert judgments; Latent Dirichlet Allocation is used as a field-level semantic analysis to provide interpretive context for the Delphi-derived mappings. Primary mappings are reported as default translation references that met the 80% strict-consensus threshold, secondary mappings as context-dependent relations, and the adjudicated dual-mapping exception A5 (Threats/Challenges → Cost Structure) as a separately documented case. After converting the finalized primary mapping rules into a coding manual, three independent coders applied them to 81 AI use cases; the Layer 1 coding yielded Krippendorff’s α = 1.000, descriptively indicating no observed coder disagreement under the specified coding conditions. The Reference AI-BMC contributes a standards-based, consensus-derived translation layer for systematically organizing AI use cases in business-model terms, offering a structured starting point for early use-case workshops, preliminary portfolio screening, and standards-aware AI service design discussions. Together, these results position the Reference AI-BMC as a standards-based, consensus-derived reference layer for organizing AI use cases in BMC terms, with its applicability bounded by the ISO/IEC TR 24030 descriptor structure and the specified mapping procedure. Full article

Considerable debate remains regarding the mechanisms responsible for the reduction in organic matter (OM) pores in post-mature shales. To address this issue, complementary techniques including scanning electron microscopy (SEM), total organic carbon (TOC) analysis, and helium porosity measurement were employed to characterize the microstructure and porosity of post-mature shales from the Qiongzhusi Formation in the southwestern Upper Yangtze region, China. The results show that OM pores in these shales are poorly developed and exhibit highly irregular morphologies. Notably, the degree of OM pore development is negatively correlated with TOC. Interestingly, in samples with TOC < 2.5 wt.%, well-preserved spongy migrated OM is still observable under SEM. The average porosity of Qiongzhusi mudstones is 1.8%; siltstone samples with TOC < 2 wt.% yield an average porosity of 3.5%, whereas samples with TOC > 4 wt.% have an average porosity of only 1.9%. These findings do not support the hypothesis that graphitization causes the significant destruction of OM pores in post-mature shales. Instead, we propose that compaction has been the dominant factor controlling OM pore destruction. Accordingly, we introduce a “depth window” for the development of high-quality shale gas reservoirs: Beyond a certain maximum paleoburial depth, compaction leads to extensive OM pore destruction and a marked decline in reservoir quality. This study advances our understanding of pore evolution in post-mature shales and provides practical guidance for shale gas exploration. Full article

This study develops a stochastic degradation-based reliability framework for mechanical systems subject to interacting operational stresses and imperfect maintenance. The degradation dynamics are formulated in cumulative damage space and modeled using a geometric Itô diffusion process, in which the drift term incorporates a multiplicative degradation kernel representing the combined influence of load, speed, misalignment, and environmental exposure. Imperfect maintenance is represented through a continuous attenuation functional embedded within the drift structure, allowing maintenance actions to reduce degradation growth without restoring the system to an as-good-as-new condition. Using a logarithmic transformation, the multiplicative stochastic differential equation is converted into an additive diffusion process, enabling analytical treatment via Itô’s lemma. A closed-form reliability expression is then obtained through first-passage analysis, yielding a lognormal survival function governed directly by the degradation dynamics. Numerical evaluation demonstrates physically consistent wear-out behavior and confirms the stability of the derived reliability formulation. The model further enables reliability-based maintenance optimization through preventive replacement analysis. Sensitivity results indicate that system reliability is strongly influenced by the degradation growth parameter governing the stochastic drift. The proposed framework provides a mathematically tractable connection between stochastic degradation modeling, reliability theory, and maintenance optimization. Beyond its application to conveyor belt systems, the formulation offers a general analytical structure for reliability assessment of degrading engineering systems governed by multiplicative stochastic dynamics. Full article

Crop yield responses to reduced solar radiation are central to the design of agrivoltaic systems, yet crop-specific patterns and critical shading thresholds remain insufficiently characterized across diverse environments. This study evaluates yield responses across a global dataset of 546 observations from 66 studies, including agrivoltaic, shading, and agroforestry systems. Relative yield was analyzed in relation to reduction in solar radiation (RSR), crop type, and environmental variables using exploratory analysis, multiple linear regression, and tree-based ensemble models. Crop responses varied systematically across crop types. Fruits, berries, and fruity vegetables maintained or increased yield under lower shading levels, while forages, leafy vegetables, cereals, and tubers showed gradual declines, and maize and grain legumes exhibited the strongest sensitivity. Across models, yield responses were non-linear, with relatively stable yields at lower shading levels followed by accelerated declines beyond approximately 50–60% RSR. Climatic conditions further influenced these patterns, with crops in higher-radiation and warmer environments maintaining yields more effectively under partial shade. These findings demonstrate that crop yield responses depend on crop type, shading intensity, and environmental context, providing an agronomic basis for crop selection and agrivoltaic system design. Full article

In plant factories with artificial lighting (PFALs), spectral regulation serves as the predominant factor governing plant growth and development. The implementation of red-enriched spectral regimens during cultivation promotes biomass accumulation, whereas blue-dominant spectra enhance the biosynthesis of phytochemicals and nutritional compounds in plants. Nevertheless, systematic investigations into the effects of staged spectral regimens on both plant development and secondary metabolite biosynthesis remain limited. This study evaluated four distinct stage-specific dynamic lighting regimens (T1–T4) under a constant total photosynthetic photon flux density (PPFD) of 200 μmol·m⁻²·s⁻¹. The treatments utilized three distinct red-to-white photon flux ratios (R:W = 3:1, 1:1, and 1:3) administered sequentially during critical developmental phases of Pak choi: the seedling stage, the early growth stage (15 days after transplanting, DAT), and the late growth stage (16–30 DAT). The effects of these treatments on biomass production, morphological development, photosynthetic pigments, nutritional metabolites, antioxidant levels and radical quenching capacity were evaluated. The results demonstrated that the T4 treatment significantly enhanced biomass production, increasing shoot fresh weight by 51.3% compared to the T1 treatment at the late growth stage. The application of a higher red-light proportion (HR, R:W = 3:1) during the seedling stage significantly increased leaf area by 70% compared to the low red-light treatment (LR, R:W = 1:3). Regarding nutritional quality, while carotenoid content showed no significant differences among treatments, higher blue-light proportions selectively stimulated the biosynthesis of chlorophyll, vitamin C, and soluble proteins. Specifically, the T3 treatment enhanced certain traits during the early growth stage, whereas the T2 treatment best maintained specific antioxidant capacities (FRAP and flavonoids) at the late growth stage prior to harvest. Notably, nitrate levels were not significantly affected by the spectral shifts. This study establishes that the temporal modulation of red-to-white spectral ratios enables the targeted optimization of either crop yield (T4) or specific harvest-stage nutritional attributes (T2) in Pak choi. Full article

Accurate crop row detection is identified as a critical prerequisite for autonomous agricultural navigation, yet it remains challenging in complex field environments. To achieve a balance between segmentation accuracy, robustness, and real-time performance, an improved crop row segmentation and navigation method based on the DeepLabV3+ framework was developed. MobileNetV2 was adopted as the backbone to minimize computational costs, while feature representation was enhanced through integrated attention mechanisms and multi-scale fusion. Specifically, split-attention convolution was integrated into the backbone, a DenseASPP + SP module was employed for multi-scale contextual capture, and a Convolutional Block Attention Module (CBAM) was added to refine feature responses. Experimental results demonstrated that the proposed method outperformed mainstream models, achieving a mean Intersection over Union (mIoU) of 93.42% and an f1-score of 96.8%. The model maintained a lightweight architecture with 8.35 M parameters and a real-time speed of 32 FPS. Furthermore, crop row anchor points were extracted and processed via DBSCAN clustering and RANSAC fitting to generate high-precision navigation lines. Validation showed that the middle crop row yielded the highest fitting accuracy with minimal angular and lateral errors. This study provides an efficient visual perception solution for intelligent field operations. Full article

Background/Objectives: Machine learning approaches are widely used in modern medical diagnostics, including cancer detection. The results can be significantly improved by aggregating individual measurements, and appropriate aggregation methods should be established. Methods: We applied various measurement aggregation strategies both before and after machine learning modeling to two datasets of X-ray diffraction images: human breast biopsy samples and canine claw samples. Two classifiers, Random Forest and Logistic Regression, were used to determine classification metrics: the area under the receiver operating characteristic curve (ROC-AUC) and balanced accuracy. Results: We found that all aggregation types improve classification metrics, with aggregation after modeling yielding better performance. Depending on the dataset and approach, either classifier can produce better results. For human breast samples, Random Forest with the logit aggregation strategy provides an ROC-AUC exceeding 0.9. For the canine dataset, both Random Forest with the logit aggregation strategy and Logistic Regression with the median of cancer probabilities achieve an ROC-AUC of about 0.85. Conclusions: We examined several simple, straightforward aggregation methods for patient diagnosis based on multiple measurements per patient and achieved significant improvements in classification metrics. Full article

Acorn flours from the six Mediterranean Quercus species (Quercus cerris, Quercus petraea, Quercus robur, Quercus ilex, Quercus pubescens, and Quercus rotundifolia) were systematically fractionated for polyphenols using ultrasonic-assisted extraction with seven solvent systems varying in polarity and composition (water at 20 and 40 min; acetone, ethanol, and methanol at 20% and/or 70% v/v). The total polyphenol content (TPC), non-tannic phenolic content (NTPC), tannin content (TC), antioxidant potential (DPPH, ABTS, and FRAP), and individual phenolic profiles through high-performance liquid chromatography (HPLC) were determined. The results showed that the botanical species primarily determined the TPC and TC, while the solvent composition significantly influenced the NTPC yield. Q. cerris yielded the highest average TPC (105.1 ± 3.8 mg GAE/g) and TC, supported by a gallotannin-dominated profile. Conversely, Q. rotundifolia exhibited the lowest values but the highest NTPC/TPC ratio (32.0%). Q. ilex featured species-exclusive ellagitannins, while Q. pubescens showed the highest specific antioxidant activity. For the targeted recovery, 20% acetone is recommended for tannins and 70% ethanol for the non-tannic fractions. These findings establish a species-resolved framework for valorizing acorn flours as functional ingredients, identifying high-tannin species requiring detannification and “sweet” varieties suitable for direct food application. Full article

Genetic diversity within maize inbred populations is essential for sustaining genetic gain in breeding programmes. This study evaluated 280 maize inbred lines with two checks using an augmented block design (22 × 14). At harvest, 271 lines and two checks were analysed, with nine entries excluded due to poor survival. Using both descriptive (24) and quantitative (19) traits, significant variations were observed across many traits. Descriptive traits varied among the genotypes, as revealed by graphical analysis and correlation heatmaps. The likelihood ratio test (LRT) for lines showed significant differences for several quantitative traits with moderate–high heritability, while anthesis–silking interval, tassel length, ear position, ear aspect, bad husk cover, number of plants, and number of ears per plant exhibited low heritability. High genetic advance as a percentage of the mean was observed for grain yield, plant height, grain texture, number of plants, number of kernels, and grain weight per plant. Positive associations were observed among genotypic coefficient of variation, genetic advance, and heritability. Grain yield showed significant positive correlations with yield-related traits and morphological traits, but negative correlations with flowering traits. The first 10 principal components explained 86.17% of total variation, with flowering traits contributing most to variability in PC 1. Cluster analysis grouped genotypes into 10 clusters, with substantial genetic divergence within and between cluster groups. In conclusion, the study revealed considerable genetic diversity, supporting the selection of superior parents in breeding programmes and developing improved maize varieties to enhance productivity. Full article

Soybean is one of the most important crops worldwide, but its productivity is frequently challenged by abiotic stresses such as drought and heat, which impair physiological and metabolic processes. Biostimulants have emerged as sustainable tools to improve plant performance under adverse conditions. This study evaluated the effects of foliar application of a new biostimulant, “SB”, on soybean photosynthetic efficiency, antioxidant metabolism, biometric traits, and grain yield. SB was applied at different doses (0.5, 1.0, 1.5, and 2.0 L ha⁻¹) at the V₄ and R₁ growth stages during two seasons (2023/2024 and 2024/2025). Foliar SB application enhanced soybean leaf chlorophyll levels, RuBisCO activity, and gas exchange parameters, resulting in higher photosynthetic rates, carboxylation efficiency, and water use efficiency. In addition, foliar SB application reduced hydrogen peroxide and malondialdehyde accumulation, indicating lower oxidative damage and improved redox balance. These physiological and metabolic improvements contributed to greater root development and plant height and significant increases in yield components. Grain yield was consistently improved by all SB application rates, but the 1.5 L ha⁻¹ dose produced the most stable and positive effects across both seasons, with an average increase of more than 500 kg ha⁻¹ compared to the control. Overall, foliar SB application proved to be an efficient and promising management strategy to enhance soybean resilience and productivity under variable climatic conditions. Full article