Search Results (42,642)

This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies. Full article

As global climate change intensifies and extreme weather events become more frequent, understanding the historical spatial distribution of vegetation is of critical importance. However, most vegetation studies are temporally limited to the post-1980 period due to satellite data constraints. To bridge this gap, we integrated tree-ring width chronologies from the International Tree-Ring Databank with Landsat-derived Enhanced Vegetation Index (EVI) data and evaluated three machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)—to reconstruct annual, spatially explicit EVI for the period 1850–1985 in Diqing, Yunnan, China. RF regression was the best among the three with highest adjusted R² (0.90) and lowest Root Mean Square Error (0.032). The RF-based reconstruction indicated a consistent increase in regional EVI from 1991 to 2005. Breakpoint analysis identified three distinct sub-periods, each with unique spatiotemporal variation patterns. In current times, the EVI value shows a significant positive correlation with average temperatures in June, July, August, and December. In the contemporary period, it also correlates significantly and positively with winter average temperatures, March average precipitation, and spring average precipitation. The spatial pattern for the past 100 years reflects the succession of the local vegetation ecosystem and provides an insight into the influences of natural disturbances (low-temperature damages and droughts) on vegetation growth. This study demonstrates the feasibility of reconstructing high-resolution, long-term vegetation spatial dynamics using tree-ring proxies and machine learning. Full article

Accurate diagnosis of nitrogen status is essential for precision fertilization in winter wheat. Single-modal or single-temporal remote sensing often fails to capture the multidimensional crop responses to nitrogen stress. In this study, we propose a hybrid framework based on CNN-LSTM-XGBoost for interpretable classification of wheat nitrogen stress gradients using multimodal unmanned aerial vehicle (UAV) multispectral and thermal infrared (TIR) imagery. Field experiments were conducted at the Xinxiang base in Henan Province during the 2023–2024, following a randomized block design involving 10 cultivars, four nitrogen levels, and four water treatments. Multisource UAV images acquired at jointing, heading, and filling stages were used to construct a multimodal feature set consisting of manual features (spectral bands, vegetation indices (VIs), TIR, and their interaction terms) and seven temporal statistical features. A deep learning model (CNN-LSTM) was utilized to further extract deep spatiotemporal features, and its performance was systematically compared with traditional machine learning models. The results show that multimodal feature fusion significantly enhanced classification performance. The CNN-LSTM model achieved an accuracy of 89.38% with fused multimodal features, outperforming all traditional machine learning models. Incorporating multi-temporal features improved the F1_macro of the XGBoost model to 0.9131, a 9.42 percentage-point increase over using the single heading stage alone. The hybrid model (CNN-LSTM-XGBoost) achieved the highest overall performance (Accuracy = 0.9208; F1_macro = 0.9212; AUC_macro = 0.9879; Kappa = 0.8944). SHAP analysis identified TIR × NDRE as the most influential indicator, reflecting the coupled physiological response of reduced chlorophyll content and increased canopy temperature under nitrogen deficiency. The proposed multimodal, multi-temporal, and interpretable framework provides a robust technical foundation for UAV-assisted precision nitrogen management. Full article

Nicotine vaping has surged in recent years, particularly among young adults, and is strongly linked with concurrent alcohol use. Separately, chronic excessive alcohol use drives hypertension and cardiomyopathy, while nicotine vaping is linked to a modest rise in cardiovascular disease incidence and mortality. However, little is known about how concurrent use interacts to affect protein expression in the cardiovascular system. The aim of this study was to determine differential cardiac protein expression in mice exposed to concurrent chronic-plus-binge alcohol and nicotine vaping use. Male C57BL6/J mice received a 20-day 5% ethanol diet with 5 g/kg ethanol binges on days 10 and 20, alongside isocaloric controls. During this period, they were also exposed nightly to either 5% nicotine salt vapor, vegetable glycerin/propylene glycol vehicle vapor, or room air. The left ventricular free wall was collected and analyzed using discovery-based proteomics and subsequent Ingenuity Pathway Analysis. A total of 3144 proteins were identified across all groups. Compared to air-exposed, control-fed mice, 201 proteins were significantly altered by ethanol, 101 proteins by nicotine vaping, and 159 proteins by combined exposure. Both ethanol and nicotine vaping influenced pathways involved in lipid homeostasis, extracellular matrix remodeling, and mitochondrial bioenergetics; however, these alterations did not uniformly manifest in the dual-use group. This pattern highlights the nonadditive and potentially interaction-dependent nature of alcohol and nicotine vaping effects on cardiovascular protein expression patterns that may contribute to a distinct functional phenotype. Full article

Soil organic carbon (SOC) plays a critical role in the terrestrial carbon cycle, yet its spatial patterns and drivers in arid regions remain poorly understood. This study aims to clarify SOC distribution mechanisms in the Akesai region, where limited water–heat conditions and land use create high environmental heterogeneity. Four machine learning models were applied to predict SOC content and produce high-resolution spatial maps, and SHAP analysis was used to quantify the contributions of key environmental variables. The Gradient Boosting model had the best performance (R² = 0.675; RMSE = 1.304 g kg⁻¹), followed by XGBoost, LightGBM, and Random Forest. The results indicated that the main factors controlling SOC variation were NDVI, DEM, sand, clay, mean temperature, and ERVI. Furthermore, NDVI and clay parameters were positively associated with promoted SOC accumulation, while sand showed a negative effect. Spatially, higher SOC values were found in mountainous zones and vegetated valleys, while low SOC values were observed in flat, arid plains. These findings demonstrate that incorporating vegetation-type indicators substantially improves large-scale SOC estimation and enhances our understanding of SOC spatial dynamics and the driving mechanisms in arid environments. This provides a scientific basis for carbon-stock assessment and sustainable land management. Full article

This study examines two wildfire events in the southern Amazon in August 2021, addressing the challenges in investigating the development of pyro-convective clouds in tropical regions. The analysis combines the Normalized Difference Vegetation Index, Fire Radiative Power derived from the Suomi-NPP and NOAA-20 satellites, and meteorological conditions from thermodynamic profiles and atmospheric modeling. The Meso-NH model was applied exploratorily with two simulations that allow convection, at a 2.5 km resolution. In the first case, a pyro-convective cloud (PyroCu) formed directly from active fires. In the second, a deep convective cloud developed over dispersed fire hotspots, exhibiting characteristics compatible with pyro-convective activity, although uncertainties remain regarding its classification as a true PyroCb. The results indicate that background thermodynamic instability primarily controls vertical plume development, modulating the influence of fire intensity. Incorporating high-resolution thermodynamic profiles into coupled atmospheric and chemical dispersion models can improve estimates of smoke injection height, complementing information on fire power. The results provide a basis for future developments related to understanding tropical pyro-convective clouds, showing how smoke dispersion may occur in the tropical region depending on the vertical structure of the atmosphere and fire intensity. Full article

Habitat loss and fragmentation threaten forest-dependent ungulates in subtropical mountain systems, yet integrative assessments linking habitat quality and landscape configuration remain limited. Here, we evaluated habitat suitability and identified core habitat patches for the endangered black muntjac (Muntiacus crinifrons) in Tongboshan National Nature Reserve using an Analytic Hierarchy Process–Habitat Suitability Index (AHP–HSI) framework integrated with camera-trap validation and landscape pattern analysis. Vegetation-related indicators (NDVI and vegetation type) were the dominant suitability drivers, and highly suitable habitats accounted for 62.9% of the reserve (8646.97 ha), forming three major forest blocks with low disturbance levels. Camera-trap detections (n = 58) showed strong concordance with model predictions (98.28% within moderately suitable or higher classes). Landscape metrics revealed contrasting spatial configurations between overall high-suitability habitats and optimal core patches, indicating that demographic source areas are embedded within fragmented peripheral mosaics. Medium patches and forested ridges may function as potential stepping stones and corridors facilitating movement across habitat clusters. These findings highlight the importance of maintaining functional connectivity and mitigating edge disturbances in buffer and experimental zones to ensure long-term population persistence and effective protected-area management for forest ungulates. Full article

Haloxylon ammodendron plantations constitute a dominant vegetation component of the desert–oasis ecotone in the arid and semi-arid regions of northwest China, playing a critical role in maintaining oasis stability and ecological security. However, the effects of converting natural desert ecosystems into plantations on the soil food webs of arthropods remain poorly understood, particularly with respect to how these effects vary across plantation age. To address this knowledge gap, we conducted a field investigation in the desert–oasis ecotone of the middle reaches of the Hexi Corridor, Gansu Province. Using pitfall trapping, we sampled two key arthropod taxa (arachnids and soil mesofauna) from control areas (natural deserts) and H. ammodendron plantations representing different ages (young and old). The results indicated that both young and old plantations were associated with significantly higher abundance and richness of arachnids, soil mesofauna, mites, and springtails compared with natural deserts, with springtail richness exhibiting a further significant increase in old plantations. Arachnid responses to plantation conversion were strongly structured by body size. Medium arachnid abundance increased in both young and old plantations, whereas large arachnid abundance increased only in young plantations and declined in older ones. In contrast, small arachnid abundance exhibited significant increases exclusively in old plantations. In addition, relationships between arachnid, mite and springtail abundance varied with plantation age: the ratio of large arachnids to mites and springtails declined significantly in old plantations relative to young ones, while the corresponding ratio for small arachnids showed an opposite pattern. Variations in soil mesofauna community composition were primarily explained by shrub cover, herbaceous cover, coarse sand proportion, silt-clay content, and soil soluble salt, which together accounted for 48.9% of observed variation. For arachnids, soil mesofauna as a food resource significantly enhanced abundance and richness. Moreover, shrub cover and silt-clay content were also drivers of arachnid community variation, jointly explaining 6.7% of variance. Overall, the establishment of H. ammodendron plantations promoted the diversity of both arachnids and soil mesofauna, but their relationships shifted dynamically with plantation age, leading to a reorganization of detrital food web structure and functioning. Full article

Background/Objectives: Skin carotenoid measurements have been proposed as an indicator to reflect of fruit and vegetable intake, but evidence from occupational settings remains limited. The primary aim of this study was to assess the association between fruit and vegetable intake and skin carotenoid levels in the workplace. The secondary aim was to examine the association of skin carotenoid levels with blood glucose levels and blood pressure (BP). Methods: This cross-sectional study included Japanese workers aged ≥20 years between 2022 and 2023. Skin carotenoid levels were measured, dietary intake was assessed using self-administered questionnaires, and data from workplace health check-up records were collected. Multiple regression analysis was conducted to examine the association between skin carotenoid levels and fruit and vegetable intake in 210 participants. Associations between skin carotenoid levels and log-transformed glycated haemoglobin (HbA1c), fasting blood glucose (FBG), systolic BP, and diastolic BP levels were examined in 162, 158, and 183 participants, respectively. Results: Skin carotenoid levels were positively associated with the number of vegetable dishes consumed and the frequency of fruit intake. A slight positive association was observed with HbA1c levels (partial regression coefficient = 0.00012), whereas no associations were found with FBG or BP. Conclusions: Skin carotenoid levels reflect self-reported fruit and vegetable intake, supporting their potential use as a non-invasive dietary assessment tool in workplace nutrition education. However, the associations observed with HbA1c were very small and of limited clinical significance, and the results should be interpreted with caution. Full article

Sustainable sources of natural antioxidants are increasingly important for circular bioeconomy strategies. Plant-derived waste streams represent an underexploited resource with significant potential for recovery of high-value antioxidant compounds such as carotenoids, polyphenols, and resveratrol. This review assesses potential alternative biomass sources, including nonhazardous wastes from agriculture, forestry, and fishing, as well as those from the manufacture of food products, beverages, and tobacco products. It evaluates their valorization potential using statistical evidence at the European level. EUROSTAT datasets were analyzed using XLSTAT 2025.2.0 through correlation analysis, Principal Component Analysis (PCA), Agglomerative Hierarchical Clustering (AHC), and k-means clustering. Variables included fresh vegetable production, plant waste generation, processed waste volumes, and national research and development expenditures and innovation. Correlation analysis revealed a strong association between total processed waste and research and development investments (r = 0.87), suggesting that technological capacity influences waste valorization. A moderate correlation (r = 0.55) between nonhazardous waste and processed quantities supports the operational feasibility of extracting antioxidants from residual biomass. PCA showed that Factor 1 (50.16% variance) is dominated by waste generation and processing capacity, whereas organic agriculture loads primarily on Factor 2 (21.6%). Cluster analyses grouped European countries by bioresource management efficiency, highlighting substantial heterogeneity in their readiness for valorization. The combined statistical evidence supports the use of plant-based waste streams as viable, sustainable feedstocks for antioxidant recovery. Strengthening processing infrastructure, harmonizing data reporting, and accelerating research and development investments are essential steps for integrating antioxidant extraction into circular bioeconomic processes. Full article

This study investigates the interplay between urban morphology, vegetation, and thermal environments by integrating mobile air temperature (AT) measurements with satellite-derived land surface temperature (LST). The case study is the city of Bologna (Italy). Correlation analysis revealed strong multicollinearity among morphological indicators, with building density and floor area ratio nearly collinear, while vegetation cover (PV) remained the most independent predictor. A composite urban density indicator (CUDI), derived through principal component analysis, was introduced to address redundancy among morphological metrics. Ordinary least squares regressions demonstrated significant associations, with PV exerting a pronounced cooling effect and CUDI amplifying both AT and LST. Model diagnostics confirmed statistical robustness, though residual spatial autocorrelation necessitated spatial regression approaches. Spatial lag models (SLMs) substantially improved explanatory power, highlighting spatial spillovers and neighborhood effects as central to understanding urban heat dynamics. Comparative analysis with spatial error models reinforced the dominance of SLM in capturing localized dependencies. Despite limitations in spatial coverage, temporal scope, and indicator transferability, findings emphasize the critical roles of vegetation and urban compactness in shaping thermal environments. This work underscores the necessity of integrating greening strategies with urban form management for effective heat mitigation and provides a methodological framework for analyzing urban heat islands through multi-source thermal and morphological data. Full article

Vegetation drought is a critical manifestation of ecosystem vulnerability in high-altitude, water-limited regions under climate change. The Yellow River Water Conservation Area (YRWC), as the core water source of the Yellow River Basin, is highly sensitive to variations in hydrothermal conditions. In this study, a Temperature–Vegetation–Precipitation Drought Index (TVPDI) was constructed to characterize the spatio-temporal dynamics of vegetation drought in the YRWC for 2003, 2012, and 2019. The XGBoost–SHAP framework was further employed to quantitatively analyze the nonlinear response characteristics and relative contributions of key factors within the TVPDI framework. Scenario-based spatial simulations of vegetation drought for 2035 are then conducted based on the GeoSOS-FLUS model. The results indicate that vegetation drought in the YRWC exhibits a relatively stable spatial pattern, with drought severity gradually intensifying from southeast to northwest and moderate drought as the dominant type. Precipitation is the key variable of TVPDI, followed by land surface temperature, while NDVI mainly plays a nonlinear regulatory role. Among external factors, atmospheric moisture conditions show relatively higher explanatory relevance, whereas topographic and human activity factors exert comparatively weaker influences. Scenario-based simulation results suggest that vegetation drought may be alleviated under low-emission pathways, whereas high-emission scenarios substantially exacerbate drought severity and associated risks. This study presents an interpretable, index-based analytical framework combined with scenario-based spatial simulation for characterizing vegetation drought in the YRWC, thereby providing scientific support for ecological management and climate adaptation strategies in the Yellow River Basin. Full article

Adulteration of high-value oils such as olive and camellia oil poses serious challenges to market integrity and consumer safety. This study develops a comprehensive, model-free marker library for high-throughput detection of single and multivariate adulteration across nine vegetable oils (olive, camellia, sesame, rapeseed, flaxseed, soybean, peanut, industrial hemp seed, and sunflower seed oils) using untargeted metabolomics via UHPLC-Q-TOF-MS. We identified 34 characteristic markers, including 9 confirmed by reference standards, such as hydroxytyrosol in olive oil, camelliasaponins in camellia oil, and sesamin in sesame oil, which are uniquely present in specific oils and absent in others. The method enables reliable qualitative screening of adulteration at levels as low as 5% without dependence on chemometric models. Validation using binary and multicomponent blends confirmed its robustness and specificity. In commercial sample analysis, adulteration was detected in 16.0% of olive oils (4/25) and 12.7% of camellia oils (7/55), with results consistent with regulatory findings. This work establishes the first integrated marker library for simultaneous screening of nine vegetable oils, offering a standardized, high-throughput tool for large-scale market surveillance that bridges the gap between discovery-based omics and routine regulatory practice. Full article

Induction of callus is an important step to produce high-quality seedlings, to promote the large-scale production of seedings, and to establish stable transgenic methods. To establish an efficient callus-based regeneration system for lily, in this study, we used the scales of Lilium regale as explants and employed plant tissue thin-layer culture to induce callus tissues. To examine the effects of different types and concentrations of plant growth regulators (PGRs) on the induction of lily callus tissues and plant regeneration, we designed orthogonal experiments using three PGRs: 6-BA, NAA, and PIC, with each regulator at three concentration levels. The results indicated that a suitable medium for inducing callus under the experimental conditions was 1.00 mg/L 6-BA + 0.05 mg/L NAA + 2.00 mg/L PIC, pH = 5.8 because in this medium, callus tissue showed a good balance of induction and contamination rate, as well as very low redifferentiation into bulbs. Under the experimental conditions, a suitable medium for callus expansion was 1 mg/L 6-BA + 0.5 mg/L NAA, pH = 5.8. We also showed that the induced callus tissues could develop into seedlings. These findings provide important references for optimizing in vitro culture systems of Lilium regale and offer supports for tissue culture studies of other lily species. Full article

Green Infrastructure (GI) is crucial for urban climate adaptation, providing ecosystem services like mitigating the urban heat island effect and enhancing stormwater management, alongside benefits for public health and biodiversity. Effective GI implementation remains challenging, particularly in dense, rapidly urbanized mid-Adriatic coastal cities, classified as climate hotspots like other Mediterranean contexts. This paper presents a replicable applied trans-scalar methodology for detailed GI design scenarios, developed through the EU-funded LIFE+ A_GreeNet project to bridge the theory–practice gap and enable pilot implementations in multiple Italian mid-Adriatic coastal municipalities. The research details a comprehensive, multi-disciplinary, five-phase process applied to the Sant’Antonio district of San Benedetto del Tronto—a dense, trafficked urban area projected to face “extremely strong heat stress” by 2050. Design interventions included spatial optimization, strategic species replacement, the creation of vegetated bioretention basins, and systematic pavement de-sealing. The application of the model demonstrated significant improvements: a substantial increase in permeable surface area (+194%), a measurable reduction in the UTCI index (average ENVI-MET simulated reduction of 1.17 °C by 2030), and a series of benefits resulting from increased green space and enhanced meteorological water management. This research offers local authorities a tangible model to accelerate climate-adaptive solutions, showing how precise GI design creates resilient, comfortable, and human-centered urban spaces. Full article