Search Results (1,027)

Rotaviruses (RVs) are important pathogens which induce gastroenteritis in different kinds of animals, including mammals and birds. Rotaviruses are divided into nine species (RVA-RVD and RVF-RVJ), and RVA-RVC and RVH can infect both humans and pigs. It is vital to understand the genetic diversity and evolution of porcine rotavirus (PoRV) for effective prevention and control of this disease. In this study, 5320 intestinal tissue samples and fecal swabs were collected from different pig farms in Guangxi Province, southern China, from 2022 to 2025. These samples were tested for PoRV species A (PoRVA), PoRVB, PoRVC, and PoRVH using the multiplex RT-qPCR. The positive samples of PoRVA were further selected to amplify and analyze the VP4, VP6, and VP7 gene sequences. The phylogenetic trees were constructed based on the PoRVA VP4, VP6, and VP7 gene sequences. Bayesian time-dynamic analysis and recombination analysis were performed for the PoRVA VP4 gene. The results indicated that the PoRVA, PoRVB, PoRVC, and PoRVH positivity rates were 16.92% (900/5320), 0.51% (27/5320), 12.71% (676/5320), and 6.22% (331/5320), respectively. Fifty-two VP4, VP6, and VP7 gene sequences were obtained from the 52 selected PoRVA-positive clinical samples. The nucleotide and amino acid identity analysis of the obtained PoRVA VP4, VP6, and VP7 genes indicated that the genetic diversity of the VP4 gene was higher than that of the VP6 and VP7 genes. The phylogenetic trees based on the VP4, VP6, and VP7 genes revealed that the predominant strains of PoRVA in Guangxi Province were the G9P[13]I5 genotype. Bayesian analysis indicated that the population size of PoRVA kept steady with no significant expansion from its discovery in the 1970s to approximately 2016, then exhibited gradual growth. Sequence analysis of the PoRVA VP4 gene revealed substitutions and recombination in the PoRVA strains, and one strain was derived from recombination of a porcine-originating strain and a human-originating strain. This study provided useful information on the molecular characteristics and genetic diversity of PoRVA and supplied important clues for in-depth research on the cross-species transmission of PoRVA. Full article

Arbuscular mycorrhizal fungi (AMF) are crucial symbiotic microorganisms in terrestrial ecosystems, playing a vital role in maintaining orchard soil health and productivity. However, how organic–and Ca, Mg, Zn fertilizers co-application affect vertical stratification and ecological functions of arbuscular mycorrhizal fungi (AMF) in perennial fruit orchards remains unclear. Based on a five-year in situ peach trial, we established three fertilization regimes: low- (LWF), medium- (MWF), and high-input (HWF) regimes. We systematically analyzed the AMF community structure, diversity, and their correlations with soil physicochemical properties, as well as peach tree physiology, fruit yield, and quality across two soil depths: 0–20 cm (topsoil) and 20–40 cm (subsoil). HWF significantly inhibited AMF root colonization rates and spore density (p < 0.05), while reducing community α-diversity AMF α-diversity (p < 0.05), characterized by the enrichment of genera such as Glomus and a decrease in the relative abundance of Rhizoglomus. Redundancy analysis (RDA) identified available Zn (AZn) and Mg (WMg) as key drivers of this restructuring. Integrating RDA results into depth-specific partial least squares structural equation models (PLS-SEM), we found that subsoil AZn/WMg indirectly boosted yield by reshaping AMF composition (β = 0.34, p = 0.006), mediated via improved canopy status (NDVI, PRI). Total effect analysis confirmed the dominant role of subsoil pathways. These findings challenge the prevailing topsoil-centric view of soil microbial ecology and underscore the importance of considering the full soil profile when evaluating the impacts of agricultural practices on beneficial symbionts. We conclude that sustainable management strategies should account for depth-dependent AMF responses to maintain both productivity and belowground biodiversity across the entire rooting zone. Full article

Litter and understory are key components of forest ecosystems, playing vital roles in soil carbon inputs and microhabitat regulation. Their removal is a common forest management practice: litter removal (L) reduces fire and pest risks while promoting nutrient cycling, whereas understory removal (U) alleviates nutrient competition between understory plants and trees. However, the combined effects and underlying mechanisms of litter removal (L) and understory removal (U) on soil nematode communities remain unclear. This study investigated the individual and interactive effects of L and U on soil nematode communities in a coniferous–broadleaved mixed forest in a subtropical–warm temperate transition zone. The results showed that L significantly reduced soil nematode abundance by 18.1%, increased the relative abundance of bacterivores and omnivores-predators by 21.1% and 103.3%, respectively. U significantly elevated the relative abundance of fungivores by 30.8%, while both L and U reduced the relative abundance of plant-parasitic nematodes. Significant interactive effects between L and U were observed on soil nematode abundance, relative abundances of fungivores, plant-parasites, maturity index, and plant parasite index. Litter plus understory removal (LU) alleviated soil nematode resource limitation through synergistic regulation of resource pulses and microhabitat modification. Faunal analysis based on structure and enrichment indices indicated that LU enhanced food web structural complexity and resource-use efficiency. Redundancy analysis identified microbial biomass carbon, microbial biomass nitrogen, and soil total carbon as key drivers of nematode community differentiation. This study reveals the synergistic regulatory patterns of litter and understory vegetation on soil nematode communities, providing a theoretical reference for understanding the responses of soil nematodes to understory disturbance during the growing season in climate transitional forests, and offering basic data for long-term monitoring and forest soil management. Full article

Rapid urbanisation and climate change intensify heat, drought, wind exposure, and salinity risks in arid coastal cities, while simultaneously increasing dependence on trees for microclimate regulation and public-space quality. However, integrated evidence linking taxonomic concentration, spatial structure, tree dimensions, and current vitality remains scarce for Central Asian cities on the Caspian coast. This study characterised 13,951 trees across 10 green spaces in Aktau, Kazakhstan, belonging to 10 species, 10 genera, and 7 families, using a comprehensive inventory and an integrated taxonomic, spatial, and vitality framework. Stand density ranged from 27.1 to 510.3 trees ha⁻¹. Ailanthus altissima and Platycladus orientalis accounted for 42.74% and 23.13% of all trees, respectively, and together formed 65.87% of the inventory. Two species, two genera, and one family exceeded the indicative 10/20/30 diversification thresholds. The Shannon index ranged from 1.19 to 2.00, Pielou evenness from 0.52 to 0.87, and the share of the dominant species from 24.2% to 63.2%. Of all trees, 12,205 (87.48%) were classified as in good condition, but three sites accounted for 83.0% of all satisfactory or unsatisfactory trees. Maclura pomifera and Gleditsia triacanthos had the highest proportions of trees in good condition (96.3% and 94.2%), whereas Catalpa speciosa had the lowest (66.0%). The inventory, therefore, reveals a favourable current condition, but limited taxonomic redundancy and localised management hotspots. The findings provide a baseline for climate-resilient urban landscape planning based on gradual diversification, site-specific irrigation and soil management, control of invasive recruitment, and repeated monitoring of condition, growth, and mortality. Full article

Urban greenbelts are vital for biodiversity and ecosystem services but face threats from urban expansion. This study assessed the population structure and identified potential threats to woody plants in Arusha city’s greenbelts (nature areas and riparian forests). Woody plants were sampled across 53 grid cells (200 m × 200 m) using stratified random sampling and the Braun-Blanquet relief method. Remote sensing processed 2015 and 2022 satellite images. ArcGIS 10.8.2 software facilitated field data collection coordinates, the satellite imageries and spatial analyses. Standard plot sizes of 400 m² were systematically selected for data collection. Significant differences in tree species diversity and abundance were observed within nature areas (t = 18.6, p = 0.001; t = 5.48, p = 0.001) and riparian forests (t = 21.4, p = 0.001; t = 13.8, p = 0.001). No significant differences were found between eastern and western nature areas (t = 1.06, p = 0.338; t = −1.55, p = 0.181) while within riparian forests, only species diversity differed significantly (t = 2.66, p = 0.011). However, tree species abundance differed significantly between nature areas and riparian forests (t = −2.97, p = 0.01) with riparian forests having higher abundance of native trees compared to nature areas and with significant abundance of native trees compared to non-native trees (t = 14, p = 0.001). These findings emphasize the conservation of Arusha’s greenbelts, aligning with SDGs 3 (well-being), 6 (water quality), 11 (sustainable cities) and 15 (ecosystem conservation). Full article

Satellite -derived environmental features can predict county-level life expectancy (LE) across the contiguous United States with a mean absolute error of 1.08 years over two decades, without using any census or sociodemographic inputs. We assembled 61,680 county-year observations across 3084 counties from 2000–2019, integrating features from 11 satellite and gridded data streams. The data streams include the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, Sentinel-1 synthetic aperture radar, Sentinel-2 and Landsat optical imagery, the United States Department of Agriculture (USDA) Cropland Data Layer, the European Commission Joint Research Centre (JRC) Global Surface Water layer, the Copernicus Digital Elevation Model, the European Space Agency Climate Change Initiative (ESA CCI) soil moisture record, and the Food and Agriculture Organization (FAO) gridded livestock densities. After a supervised pruning step that removed low-importance variables, a Random Forest regressor was trained and evaluated using 5-fold cross-validation grouped by county. The grouping places all 20 years of each county exclusively in either the training set or the test set, which prevents spatial information leakage between folds. Coefficient of determination, mean absolute error, and root mean squared error are reported as $R^2=0.631\pm 0.013$, MAE $=1.08\pm 0.02$ years, and RMSE $=1.48\pm 0.04$ years. Moran’s I, a measure of residual spatial autocorrelation, is $0.0988$ ($p=0.001$), which supports geographic generalisation. Multimodal fusion reduces unexplained variance by approximately one-third relative to the strongest single-modality baseline (MODIS land surface temperature alone, $R^2=0.442$). TreeSHAP attribution analysis reveals a feature hierarchy in which nighttime land surface temperature features carry roughly $6.16\times$ the cumulative attribution weight of all daytime channels combined. The model response shows a protective inflection near a minimum overnight temperature of about 7.5 °C. Because all input streams are globally available, the framework is architecturally extensible to regions where civil registration and vital statistics systems are incomplete; however, the trained model and its thresholds require recalibration against local mortality data before application outside the contiguous United States. With that caveat, the approach supports satellite-based monitoring of United Nations Sustainable Development Goal (UN SDG) Target 3.9. Full article

Due to climate change and urbanization, green roofs are vital for urban climate resilience. However, there is currently no consensus on the photosynthetic carbon uptake capacity of green roof plants in cities. This study investigated and compared nine common green roof plant species in Xiamen, a typical coastal city in southeastern China. Their photosynthetic parameters were measured across all four seasons to evaluate photosynthetic carbon uptake capacity, and the differences among the nine plant species were evaluated. Results show: (1) Significant interspecific differences exist. Ligustrum japonicum (Lj) had the highest seasonal average daily carbon uptake per unit leaf area (8.86 g m⁻² d⁻¹) and per unit area (93.10 g m⁻² d⁻¹), approximately 9 times that of the lowest-performing species per unit leaf area (Pedilanthus tithymaloides) and 11 times that of the lowest-performing species per unit area (Tradescantia spathacea). (2) The variation in carbon uptake capacity among different green roof plant species followed a pattern consistent with that of their net photosynthetic rate and leaf area index. (3) Overall, the carbon uptake capacity of the nine plant species exhibited a trend of trees > shrubs > herbs. In summary, Lj demonstrated the highest photosynthetic carbon uptake capacity among the nine species examined, suggesting its potential as a promising species for enhancing photosynthetic carbon uptake on green roofs in southeastern coastal cities. Full article

Background/Objectives: Acute type A aortic dissection (ATAAD) has high preoperative mortality, and an interpretable multivariable model based on clinically accessible data is crucial for clinical risk stratification. Methods: The data for this study were obtained from West China Hospital, Sichuan University. Centroid regression was used to construct the predictive model, with logistic regression, classification and regression tree, explainable boosting machine and extreme gradient boosting as the reference. Variables were screened by iterative selection, the literature review and clinical experience. Model performance was evaluated by accuracy, sensitivity, precision, Youden’s index, AUROC and AUPRC. Results: The vital signs and tests of 361 ATAAD patients during the first 24 h of their first admission were included in the final analysis. Centroid regression outperformed logistic regression, achieving accuracy (90.7% vs. 81.5%), sensitivity (0.813 vs. 0.741), specificity (0.983 vs. 0.900), Youden’s index (0.796 vs. 0.641), AUROC (area under the receiver operating characteristic curve, 0.953 vs. 0.843) and AUPRC (area under the precision–recall curve, 0.978 vs. 0.863) in the test set. It revealed that the use of α-blocker (the weights w = −1.20) and hydrochlorothiazide (w = −1.20), clinical features like dyspnea (w = −0.94), chest pain (w = 0.91) and lactate dehydrogenase (w = −0.95) were variables that had the greatest impact on model prediction. Conclusions: The centroid regression model not only has relatively high predictive performance and interpretability but also can be easily implemented in hospital systems to provide a practical and cost-effective tool for ATAAD preoperative risk stratification. Full article

Accurate canopy thickness measurement is vital for advancing smart orchard sprayers and reducing pesticide use. We present three FMCW radar-based algorithms to estimate canopy thickness and assess their accuracy through experiments. Laboratory tests on simulated canopies showed a strong correlation between estimated and actual thickness. However, stability and relative error were significantly affected by leaf area density (LAD) and detection distance. When distance was 40.0–110.0 cm, LAD 1.5–4.2 m²/m³, and actual thickness 30.0–120.0 cm, the coefficient of variation and relative error remained within ±14%. Preliminary outdoor trials on three isolated tree canopies served strictly as an early proof-of-concept, not a full orchard evaluation. The Db algorithm performed poorly, with relative error up to 74.9%, whereas the Da and Dc methods proved robust, with maximum relative errors of 11.93% and −15.19%, respectively. These findings highlight that, with further refinement, Da and Dc algorithms are highly promising for precision variable-rate spraying systems in orchards. Full article

Cork oak (Quercus suber L.) forests play a crucial role in the Mediterranean region, providing essential ecological, social, and economic services. Increasing pressure from wildfires, pests, diseases, and climate change has led to a progressive decline of these ecosystems, making the development of innovative post-stripping management strategies urgent. This study evaluates the effectiveness of kaolin application on cork oak trees immediately after cork removal in a mixed forest in Sant Celoni (Barcelona, Spain). Short- and long-term physiological responses were assessed through stomatal conductance and chlorophyll fluorescence (OJIP test), while sap flux density (J_s) was continuously monitored over a four-month period (July–October 2023) using IoT-based TreeTalker® Cyber (Nature 4.0 s.r.l., Viterbo, Italy). Proximal vegetation indices (Normalized Difference Vegetation Index, NDVI; and Normalized Difference Red Edge, NDRE) were also evaluated but showed no significant differences among treatments (p > 0.05). Kaolin-treated trees (K) maintained significantly higher photosynthetic performance and stem water transport capacity compared to untreated stripped trees (nK), with effects persisting up to 140 days after stripping. These findings support kaolin application as a viable and low-cost tool for mitigating post-stripping physiological stress in cork oak forest management. Further research across multiple sites and consecutive harvesting cycles is recommended to fully assess its long-term implications for tree vitality and cork productivity. Full article

Biomass valorization plays a vital role in achieving carbon neutrality and circular economy frameworks. Owing to its carbon-rich structure, biomass represents a promising feedstock to produce bio-based hydrocarbons via biological and thermochemical pathways. While biological conversion routes have been extensively studied, their deployment at commercial scale is constrained by high capital costs and low product yields. In contrast, thermochemical conversion technologies are increasingly being explored as viable large-scale biomass valorization routes. This review presents a comprehensive assessment of thermochemical pathways, with particular emphasis on hydrothermal liquefaction (HTL). The review identifies hydrothermal liquefaction (HTL) as a strategically advantageous route for wet and heterogeneous biomass valorization, due to simultaneous yields of liquid biocrude, and solid hydrochar. The review emphasizes the application of biocrude upgradation processes like hydrodeoxygenation under biphasic solvent systems using sulfided NiMo and CoMo catalysts. Further, the review also establishes hydrochar as a tunable functional material rather than a mere byproduct for applications in fields of energy production, soil amendment, and heterogeneous catalysis. The review article examines technology readiness levels of different biomass valorization techniques, and suggests that while combustion, anaerobic digestion, torrefaction, and transesterification are commercially mature, HTL and carbon capture utilization and storage (CCUS)-integrated fuel synthesis pathways remain at intermediate readiness. Additionally, the review carries out an in-depth study on artificial intelligence and machine learning (AI and ML) applications in biomass valorization, where it observes that Tree-based ensemble models, particularly Random Forest and XGBoost, show strong performance for several HTL prediction tasks, while Gaussian Process Regression and neural network–Bayesian optimization approaches provide additional advantages for uncertainty estimation and process-level optimization. Finally, the future research opportunities in biomass valorization and AI/ML application in HTL-process optimization have been identified for improving the bio-based fuel production techniques. Full article

Pollen viability is a crucial determinant of reproductive success in plants. Given the enormous threat posed to the common ash (Fraxinus excelsior L.) by ash dieback, it is important to investigate the potential disease’s effects on pollen viability and germination. Thus, we conducted an analysis of these pollen characteristics across three distinct forest stands in southern Bavaria, with up to 23 ash trees per study site. These ash trees exhibited varying degrees of ash dieback-related damage symptoms, enabling us to assess differences between mildly and severely affected trees (via Mann–Whitney-U/Wilcoxon tests, complemented by linear mixed-effects modelling). Pollen viability was assessed using the TTC test, while pollen germination capacity was evaluated on a sucrose–agar medium. Our findings revealed no statistically significant differences in pollen viability between mildly affected and severely diseased trees, as indicated by both the TTC test and pollen germination assay when applying non-parametric analyses (Mann–Whitney U and Kruskal–Wallis tests). Nevertheless, a consistent tendency towards higher pollen viability was observed in healthier ash trees. When accounting for the hierarchical structure of the data using linear mixed-effects modes, tree vitality showed a significant effect on pollen viability, whereas a substantial proportion of the observed variation was explained by interannual differences. These results indicate that ash trees generally retain the capacity to produce viable pollen across different levels of disease severity, but vitality-related effects are subtle and context-dependent. However, severely diseased trees produced few or no flowers, substantially reducing the likelihood that their pollen contributes to fertilization. We therefore conclude that ash dieback primarily limits reproductive success in common ash mainly by reducing flower and pollen production, whereas pollen viability itself is strongly driven by interannual differences. Consequently, no consistent pattern of declining pollen viability with increasing disease severity emerged. Full article

Background/Objectives: Acute care use (ACU)—emergency department visits, inpatient hospitalizations, and observation stays—drives morbidity and costs among adults with diabetes. We developed and evaluated machine-learning models to predict 1-year ACU risk using electronic health record (EHR) data and neighborhood-level data. Methods: We performed a retrospective cohort study using de-identified EHR data from a large urban academic health center, including adults (≥18 years) with diabetes (N = 23,052). The index date was defined as one year before each patient’s last encounter, and ACU was assessed during the subsequent year. We modeled 180 predictors spanning demographics, Area Deprivation Index (ADI), prior healthcare utilization, vitals/BMI, comorbidities, medications, and laboratory results. Decision tree and gradient-boosted models (XGBoost, LightGBM, CatBoost) were tuned with Optuna using 8-fold stratified cross-validation, optimizing area under the receiver operating characteristic curve (AUC). To improve class-balanced classification performance under outcome imbalance, we selected post hoc probability thresholds that maximized Macro F1 and quantified interpretability with permutation feature importance. Results: ACU occurred in 30.53% of patients (7039/23,052). Boosted models achieved AUC ≈ 0.78, with LightGBM performing best (AUC = 0.7839). Macro F1–optimized thresholds (<0.5; typically 0.375–0.40) improved class-balanced performance versus a 0.5 cutoff. Across boosted models, prior utilization features dominated, followed by discharge-related factors and neighborhood deprivation; comorbidities and laboratory results contributed. Conclusions: In this single urban academic health-system cohort of adults with diabetes, EHRbased boosted models demonstrated moderate discrimination for predicting 1-year ACU and identified interpretable predictive signals. Threshold optimization improved class-balanced statistical performance. Prior utilization, care transitions, and neighborhood deprivation emerged as dominant predictive features. External and temporal validation are needed before broader application. Full article

Exploring the interaction relationship among multiple ecosystem services is vital for maintaining ecosystem function. However, traditional approaches are limited in their ability to: (i) characterize complex interactions and (ii) visualize the spatial connectivity of various ecosystem services delivered by social–ecological systems. To address these challenges, a framework for constructing spatial networks of multiple ecosystem services was proposed. The framework is implemented by: (i) estimating the spatial distribution of multiple ecosystem services using the InVEST model, and (ii) generating network nodes and edges with geographical attributes based on the minimum cumulative resistance model and a multiresolution segmentation method. We conducted a case study in the Guangdong–Hong Kong–Macao Greater Bay Area and examined the topological features of the spatial networks using complex network indicators. For each network, winding and multiple edges connected adjacent nodes and formed continuous linkages across the entire study area, indicating that the proposed framework is feasible for capturing the spatial connectivity of multiple ecosystem services. The different ecosystem service networks exhibited conspicuous spatial heterogeneity and generally maintained relatively high connectivity, as evidenced by their tree-like structure with winding pathways and the distribution of multi-edge nodes, indicating that each ES was predominantly connected with multiple other ecosystem services. Meanwhile, nodes with high values of degree centrality and clustering coefficient were mainly concentrated in coastal and mountainous regions. This study advances the representation of complex interactions among multiple ecosystem services from a spatial perspective, thereby facilitating a deeper understanding of the interaction mechanisms underlying ecosystem functioning. Full article

Although the green belt around Greater Lomé (GBGL) is a vital ecological buffer, it is currently facing significant degradation. This decline appears to be associated with a combination of various socioeconomic uses by the local community and formal operations of established businesses. Grounded in the cultural materialism framework, this study aims to contribute to a better understanding of the dynamics of the socioeconomic uses of the green belt around Greater Lomé in a context of degradation and investigates the dynamics of these socioeconomic uses and their environmental impacts through a multidisciplinary methodology. This approach combines anthropological analysis based on field observation, 53 semi-structured interviews and 5 focus groups, a quantitative questionnaire survey (n = 384) and an analysis of land use and land cover (LULC) dynamics derived from Landsat imagery (2003–2023). The results reveal six main types of socioeconomic uses of the GBGL (notably land transactions, agriculture, breeding and grazing, exploitation of wood energy, timber and utility wood, sand mining, and waste disposal), which lead to complex social dynamics ranging from conflicts to alliances among stakeholders. The LULC dynamics analysis indicates a staggering 468.26% expansion in built-up areas over the last 20 years, at the expense of swamp vegetation/gallery forest (−76.79%), tree-and-shrub savanna (−53.47%) and plantations (−49.43). This study provides a scientific basis supporting the urgent necessity to establish the GBGL as a legally protected entity and argues in favour of an inclusive management model that is designed to reconcile the socioeconomic survival needs of local populations with sustainable preservation of essential ecosystem services. Full article