Search Results (1,401)

Ecosystem restoration on Mediterranean islands is often hindered by the residual effects of past land use and invasive species. Decades of holm oak forest exploitation, the establishment of secondary pine plantations, and the introduction of invasive mammals have altered habitat configurations. Consequently, converting these conifer stands to promote the recovery of native Quercus ilex L. communities has become a conservation priority. This study investigates the regeneration constraints of Q. ilex in Mediterranean insular environments, focusing on the inhibitory role of conifer-derived litter and seed predation by invasive rodents and lagomorphs. We integrated an ex situ experiment (384 acorns) testing germination under varying local pine-forest soil and commercial substrate conditions, with an in situ field experiment (300 acorns) across five areas, comparing three treatments: closed cages (exclusion of all mammals), open cages (exclusion of lagomorphs), and unfenced controls. Results indicate that, while ex situ, local pine-forest soil significantly favoured germination over the commercial mixture, predation represents the main obstacle in situ, outweighing any soil-mediated effects. Seedling emergence was substantially reduced by early predation and, to a lesser extent, by litter presence. These findings highlight the necessity of integrated management strategies in insular ecosystems. Full article

Pectinases are a group of enzymes widely applied in agri-food processes. This study aimed to isolate and characterize pectinase-producing yeasts and yeast-like fungi from soil and humus samples collected in a Moroccan argan forest, a region characterized by arid to semi-arid climatic conditions, with emphasis on screening and evaluating their pectinolytic activity. Among nine isolated strains, four exhibited detectable pectinolytic activity on pectin agar medium. Two promising isolates were molecularly identified by ITS region sequencing as Coniochaeta pulveracea PX765016 and Coniochaeta ligniaria PX765017. Notably, C. pulveracea PX765016 showed the highest pectinolytic potential, with a pectinolytic degradation index of 4.2 on pectin agar. This strain also exhibited maximal pectinase production after 96 h of submerged fermentation in YEPD medium under optimized conditions of pH 4, 30–35 °C, and 0.5% (w/v) pectin. The crude enzyme obtained under these conditions exhibited a specific activity of 559.90 ± 11.62 U/mg. The enzyme was subsequently subjected to sequential purification comprising ammonium sulfate precipitation, dialysis, and gel filtration chromatography on a Sephadex G-100 column, yielding a 2.99-fold purification with a final recovery of 14%. The purified enzyme exhibited optimal activity at pH 6.0 and 40–55 °C, with a reaction time of 20 min. Kinetic analysis of pectin hydrolysis revealed a Michaelis–Menten constant (Km) of 7.33 mg pectin per mL and a maximum reaction velocity (Vmax) of 1666.7 U/mg. To the best of our knowledge, this is the first report of pectinase production by a member of the genus Coniochaeta, and the first characterization of pectinase activity from C. pulveracea. Full article

Disturbance events and subsequent management practices significantly shape the ecological legacies of affected sites. This study evaluated the impacts of a 2022 derecho and the subsequent forest management on forest structure and arthropod diversity by comparing affected forests at Fogwell Forest Nature Preserve and Fox Island County Park with control forests at Blue Cast Springs and Hammer Wald Nature Preserves. Arthropod communities were sampled using pitfall traps, while forest structure was assessed through detailed surveys of understory, midstory, and overstory vegetation. Results indicated a decrease in overall arthropod diversity across all sites since 2016, variably attributed to forest maturation, climatic variability, and the 2022 disturbance, with some taxa showing declines, such as Formicidae and Curculionidae. Fogwell exhibited a significant decline in arthropod diversity, likely linked to the derecho, while Fox Island’s diversity aligned more closely with undisturbed control sites. Notable midstory reductions were observed across sites over time, especially at Fox Island, due to harvest and storm impacts. Meanwhile, overstory diversity varied between properties. Regression modeling revealed that forest management practices at Fox Island may have mitigated the disturbance’s effects, aiding arthropod recovery. All in all, these findings highlight the importance of forest management strategies in influencing biodiversity and ecological recovery post-disturbance. Full article

Individual tree crown (ITC) segmentation in structurally complex mixed forests remains challenging under limited annotation, uneven effective height-structure support, and severe inter-crown adhesion. Existing end-to-end instance segmentation methods often require substantial instance-level annotation, and their cross-domain transferability can degrade when applied to plots with different forest structures. This study proposes a probabilistic prior-constrained instance reconstruction framework that treats semantic segmentation output as an interpretable canopy prior and reconstructs object-level crowns through a structured post-processing pipeline. A height-aware canopy support mask (HCSM) converts the probability field into a credible operational domain through hysteresis thresholding, morphological reconstruction, and a height constraint. Constrained recovery within the support domain (E2GROW) repairs coverage deficiency through spatially bounded boundary adjustment with guard rails on area ratio and buffer distance. Selective splitting then addresses residual merge errors through branch-specific seed-guided partitioning, including an aggressive Voronoi reference branch and a more conservative LOCAL/marker-controlled watershed branch with explicit trigger and child-object filtering criteria. An instance-level evaluation loop based on Gate-3 Recall, a precision proxy, and threshold-crossing audits is used during module development as an iterative safeguard. On a single 500 × 500 m mixed conifer–broadleaf plot with 306 reference crowns retained for evaluation, the high-Recall VORv1 branch improves Recall from 0.369 to 0.673 over the internal R2 baseline produced by the semantic-prior-to-instance initialization procedure, whereas the balanced E2GROW configuration achieves the highest F1_proxy with fewer predicted objects; the overall gain originates from two distinct mechanisms: threshold-crossing boundary recovery for coverage-deficient crowns and local structural decomposition for merged crown groups. Sensitivity analysis indicates that the support-domain construction is stable across the explored parameter ranges, and that the two splitting branches realize a structural Recall–precision trade-off with no evidence of simple additive gains. The framework is modular and auditable, and its demonstrated applicability is strongest for annotation-scarce closed-canopy plots where a usable semantic canopy prior and height information are available. The reported evidence represents a single-site, within-plot methodological demonstration. Full article

This study evaluated the effects of maternal overfeeding during mid-to-late gestation on maternal productivity, metabolic status, reproductive recovery, and calf performance in Hanwoo cattle using conventional statistics and machine learning (ML) approaches. A total of 243 pregnant cows were assigned to either a control group or an overfeeding group from gestation day 90 to parturition. The overfeeding treatment increased nutrient supply to approximately 140–145% of the control level. Maternal body weight (BW), body condition score (BCS), serum metabolites, and reproductive traits were evaluated throughout gestation and postpartum, while calf growth, morphometrics, and metabolic traits were assessed at birth and weaning. Calves were further classified into growth- or meat-quality-oriented genotypes using SNP-based profiling. Overfeeding increased maternal BW gain and BCS during gestation and reduced circulating non-esterified fatty acid concentrations, indicating improved maternal energy status. However, overfed cows showed a longer interval to postpartum estrus return. Calf birth weight was not significantly affected by maternal overfeeding, whereas calf growth and morphometric traits at weaning were more strongly influenced by parity, sex, and genotype. Machine learning models identified gestational BW, metabolic indicators, calf feed intake, and genotype as major predictors of maternal and calf outcomes, with random forest and XGBoost showing superior predictive performance compared with linear models. These findings suggest that parity- and genotype-informed nutritional management combined with ML-based prediction may support precision feeding strategies in beef cattle production systems. Full article

Background/Objectives: The trajectory of influenza hospitalization burden from pre-COVID-19 pandemic baseline through post-pandemic recovery remains poorly characterized at the national level. This study characterized phase-stratified burden and seasonal structure, quantified racial and ethnic disparities, and assessed whether post-pandemic seasons represent anomalous departures from pre-pandemic expectations. Methods: Sixteen complete seasons of FluSurv-NET surveillance data (2009–2010 through 2024–2025; 509 observation weeks) were analyzed across pre-pandemic, disruption, and recovery phases using OLS regression with effect-size estimation, bootstrapped age-adjusted rate ratios, seasonal-trend decomposition (STL), Prophet time-series forecasting, and Isolation Forest anomaly detection. Results: Mean peak weekly hospitalization rate nearly doubled from pre-pandemic to recovery (5.1 to 11.1 per 100,000), cumulative seasonal burden increased from 46.3 to 87.0 per 100,000, and median peak timing advanced from MMWR week 9 to week 50. STL decomposition revealed a marked shift from weak pre-pandemic seasonality (Fs = 0.14) to substantially stronger annual regularity (Fs = 0.98) across three recovery seasons, with threefold amplitude increase. Non-Hispanic Black persons had rate ratios of 1.72, 2.16, and 1.99 relative to White persons across phases; American Indian and Alaska Native persons showed the highest disruption-phase ratio (2.24, 95% CI 1.90–3.53), based on two contributing seasons. A flat-growth Prophet model detected first exceedance in February 2020, outperforming a linear-growth specification on held-out validation. Isolation Forest identified 2017–2018, 2023–2024, and 2024–2025 as robust anomalies across all contamination thresholds. Conclusions: Post-COVID-19 pandemic influenza recovery is characterized by intensified and restructured seasonality, persistent racial and ethnic disparities, and anomalous burden exceeding pre-pandemic projections, identified independently by time-series forecasting and unsupervised anomaly detection. Full article

Background and Objectives: Emergence agitation (EA) is a common neurobehavioral disturbance during recovery from sevoflurane anesthesia in pediatric patients, particularly after ophthalmic surgery. Clinically deployable and rigorously validated risk stratification approaches remain limited. We aimed to develop and internally validate an explainable machine learning model to estimate individualized EA risk after pediatric ophthalmic surgery. Materials and Methods: This retrospective cohort study included 1029 children aged 3–7 years who underwent ophthalmic surgery under sevoflurane anesthesia between 2016 and 2025. EA was defined as clinically significant agitation requiring active management in the post-anesthesia care unit. Four machine learning algorithms (regularized logistic regression, random forest, XGBoost, and CatBoost) were developed using stratified patient-level 5-fold cross-validation. Performance was evaluated using pooled out-of-fold predictions. Discrimination, calibration, and classification metrics at the optimal Youden threshold were assessed. SHAP analysis was applied for interpretability. Results: EA occurred in 543 patients (52.8%). XGBoost showed comparable discrimination with slightly higher AUPRC (0.827) and sensitivity (0.796) compared with other models, while maintaining acceptable specificity (0.728). Calibration demonstrated good agreement between predicted and observed risk. SHAP identified airway management and anesthetic-related variables as key contributors. Conclusions: ML-based analysis identified clinically relevant perioperative factors associated with emergence agitation and may provide preliminary insight into perioperative risk stratification pending external validation. External validation is required before clinical implementation. Full article

Disentangling the combined effects of heat and ash in natural forest fires is challenging, hindering understanding of soil microbial post-fire responses. A 90-day simulated fire experiment with 16S rRNA sequencing monitored bacterial communities and functional potential in topsoil (0–10 cm) and subsoil (10–20 cm) under seven treatments: blank control/BC, dry ash/DA, wet ash/WA, low-intensity heating/LH, high-intensity heating/HH, charcoal smoldering combustion/CSC, and Fire, with samples collected every ten days. Results: (1) α diversity declined mainly in the topsoil, with reductions of 12.04–19.82% for Shannon, 1.23–2.86% for Simpson, and 16.03–31.34% for the Chao index. Subsoil only declined under CSC. (2) Both heating and ash treatments increased the relative abundance of low-abundance and endemic taxa. Heating significantly enriched thermotolerant, xerotolerant, and oligotrophic taxa, such as Ramlibacter. (3) Topsoil heating treatments separated from BC (p ≤ 0.01), ash clustered with BC; pH and water content drove differentiation (p ≤ 0.05). (4) Topsoil predicted function potential showed early suppression (0–20 d), mid recovery (30–60 d), and late enhancement (70–90 d) for most treatments, except WA with sustained suppression. Heat determines disturbance depth and initial bacterial loss, while ash reshapes soil properties to influence community reassembly, acting as sequential but distinct environmental filters, providing a framework for post-fire bacterial community reorganization. Full article

Aridification and hotter droughts are triggering forest die-off events characterized by high mortality rates and declines in forest productivity. The western Mediterranean Basin is a climate change hotspot where many of these die-off events have affected several tree and shrub species in recent decades. Yet, the responses of canopy greenness and cover, radial growth, and gross primary productivity (GPP) to climate in these die-off sites remain poorly understood across species and biomes. Here, we examined 44 sites across Spain, covering humid, dry sub-humid, and semi-arid biomes, and including nine tree and one shrub species. We obtained and correlated monthly climate data, satellite-derived vegetation indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index), tree-ring metrics (basal area increment, ring-width indices), and GPP. We assessed climate trends and relationships between climate, vegetation indices, growth, GPP, and resilience after five extreme drought years in the period 1984–2023. Climate warming impacted all sites, increasing vapor pressure deficit and reducing soil moisture availability, with semi-arid sites warming the most. Vegetation indices and growth showed the largest declines during extreme droughts in dry sub-humid and semi-arid sites. Correlations with climate variables highlighted strong sensitivity to drought stress, particularly regarding growth metrics. During die-off events, GPP significantly declined in the growing season, but no legacy effects were observed afterwards. Vegetation indices and growth partially recovered one year after drought, with resilience peaking for GPP in semi-arid sites. Hotter droughts constrain GPP and growth, especially in dry sub-humid and semi-arid forests. Forests and shrublands experiencing die-off are diagnostic monitors of drought-induced thresholds in ecosystem productivity. Full article

Nocturnal hypoglycemia (NH) following exercise represents a critical challenge in the management of type 1 diabetes (T1D), particularly in pediatric populations, where its occurrence is associated with severe adverse outcomes and increased caregiver burden. This study aimed to identify an interpretable early signature based on CGM-derived digital biomarkers of post-exercise NH risk in children and adolescents with T1D. CGM data from 49 pediatric subjects (DirecNet cohort) were used to extract several CGM metrics across two temporal configurations: (i) Exercise + Cumulative, where features were computed over the exercise window and over an extended window spanning from exercise onset through recovery (16:00–17:00 and 16:00–22:00); and (ii) Exercise + Post-exercise, where features were computed separately over two non-overlapping intervals, capturing the exercise phase and the subsequent recovery phase (16:00–17:00 and 17:00–22:00). A Random Forest classifier was trained within a Leave-One-Out Cross Validation framework, incorporating variance inflation factor (VIF)-based multicollinearity filtering, minimum redundancy–maximum relevance (mRMR) feature selection, and SMOTE-based class balancing. The Exercise + Post-exercise configuration achieved superior performance: balanced accuracy (BA) = 76.9%, F1-score = 0.71, Area Under Receiver Operating Characteristic Curve (ROC-AUC) = 0.75, outperforming the Exercise + Cumulative configuration; this result was achieved using only five features: CONGA-15_EX (short-term glucose variability during exercise) emerged as the most robust predictor, alongside below_54 and above_250 (time spent in hypoglycemic and hyperglycemic ranges), MAG (mean absolute glucose change), and GRADE_hypo (hypoglycemia risk score). The generalizability of the temporal framework was further supported by independent validation on the OhioT1DM free-living cohort, where the Exercise + Post-exercise configuration (BA = 76.3%, ROC-AUC = 0.804) again outperformed the cumulative approach. These results suggest that a small set of interpretable CGM-derived features, extracted from the exercise and recovery windows, can effectively discriminate pediatric T1D subjects at risk of NH, supporting the development of lightweight CGM-only decision support tools for safer exercise management. Full article

The Mega-Tsunami of March 2011 in eastern Japan caused severe damage in the coastal black pine (Pinus thunbergii) forests along the Pacific coast. To evaluate post-disturbance forest recovery, tree-ring samples from 30 trees at Ishinomaki coastal forest were analyzed for the period 2006–2020 using tree-ring indices and stable carbon isotope discrimination (Δ¹³C). The results revealed a strong decline in radial growth immediately after the tsunami, indicating severe growth suppression during the years 2011–2014. Simultaneously, Δ¹³C values decreased, suggesting reduced stomatal conductance and acute physiological stress associated with the initial salinity effect at the root zone. Although isotopic signals indicated gradual physiological adjustment in subsequent years, radial growth recovery occurred more slowly. Most trees returned to pre-disturbance growth levels within approximately 3–5 years and later exceeded pre-disturbance growth levels, likely due to reduced competition following the mortality of nearly 40% of trees after the tsunami. However, recovery trajectories differed markedly among individual trees, with some trees showing prolonged growth suppression beyond 6 years. This variability may reflect highly localized or tree-level factors, including intrinsic differences in individual resilience, while spatial autocorrelation analysis did not indicate significant clustering of recovery time across the stand. We conclude that black pine coastal forests show a high degree of resilience, showing physiological recovery in a short period (3–4 years). Although growth recovery took longer, initial tree mortality promoted the growth of the surviving trees beyond pre-disturbance values. Full article

Understanding post-fire visible greenness change is important for assessing spatial heterogeneity in mountainous burned landscapes, but satellite observations often cannot capture local variation. This study developed a workflow using Unmanned Aerial Vehicle (UAV) Red–Green–Blue (RGB) imagery for RGB-interpreted burn severity classification and Green Leaf Index (GLI)-derived visible greenness change analysis three years after fire. The workflow integrated object-based Random Forest (RF) classification, bi-temporal GLI difference ($\Delta$GLI) detection, and terrain-stratified analysis under RGB-only conditions. Object-based multi-feature representation, including a 41-dimensional (41D) feature set of color, texture, and gradient metrics, supported local burn severity mapping, although performance gain over the 23-dimensional (23D) set was modest and not statistically significant. The burned area was dominated by high and moderate severity classes. GLI-derived analysis showed limited visible greenness increase (mean $\Delta$GLI = 0.0058), with slightly more than half of pixels being positive; high severity areas had higher $\Delta$GLI, while low severity areas showed limited or negative values. $\Delta$GLI also varied across terrain, being higher on steeper slopes, mid-to-upper elevations, and east-facing aspects. The workflow provides a practical local-scale approach for post-fire analysis using high-resolution UAV RGB imagery, with results interpreted as case-specific visible greenness patterns rather than comprehensive ecological recovery. Full article

Elastic springback is a critical challenge in sheet metal bending that directly affects dimensional accuracy and manufacturing efficiency. This study presents a comparative experimental and machine learning-based analysis of elastic springback behavior in three widely used sheet metals like stainless steel, aluminum, and copper, which are subjected to folding bending. The influence of key process parameters, namely sheet thickness (0.5 to 1.5 mm) and bending temperature (room temperature to 200 °C), was systematically examined under cold working. A cost-effective localized heating approach using a direct flame was introduced to enhance process control and reduce elastic recovery without the complexity associated with heated dies. Experimental results revealed substantial variability in elastic springback, ranging from 0.15% to 12.41%, emphasizing the fact that they are nonlinear in nature. Statistical evaluation confirmed that sheet thickness is the dominant factor governing elastic springback, while material type and temperature exhibit secondary yet meaningful effects. To improve predictive capability, five regression models (Linear, Polynomial, Support Vector, Random Forest, and Gradient Boosting) were developed and assessed. Among them, Random Forest demonstrated superior performance with the lowest prediction errors and strongest explanatory power, achieving an R² of approximately 0.85. Cross-validation further validated its robustness and generalization capability. Feature importance and SHapley Additive exPlanations (SHAP) analyses reinforced the primary role of thickness in determining elastic recovery behavior. The findings provide practical insights for selecting materials and process conditions to minimize elastic springback while highlighting the effectiveness of ensemble learning techniques for accurate prediction. This work contributes a consistent framework for enhancing bending precision and supports data-driven decision-making in modern manufacturing environments. Full article

In rocky mountainous regions characterized by shallow, barren soils and water scarcity, non-rainfall water, such as condensation, plays a crucial ecological role in mitigating seasonal drought in forest trees. To enhance the water-use capacity of vegetation, this study utilized a previously developed eco-friendly PVA–CS/SA–Ca²⁺ hydrogel. The primary objective was to elucidate the synergistic mechanisms by which the hydrogel optimizes condensed water utilization and drives the ecophysiological recovery of Pinus tabuliformis and Platycladus orientalis, two keystone afforestation species in northern China. Utilizing a controlled environmental chamber to simulate the condensation and humidification process, the experiment established three treatments: a control group (CK), a pot-sealed group (PS, to isolate soil water absorption), and a hydrogel-amended group (Hydrogel-Root Wrapping, HRW). To comprehensively evaluate the water utilization mechanisms, the amount of condensed water captured by the system was quantified, and hydrogen isotope tracing techniques were employed to precisely track water transport pathways and contribution rates. Concurrently, key physiological parameters were systematically determined, including leaf water potential, stomatal conductance, leaf water content, net photosynthetic rate, and transpiration rate. The results demonstrated the following: (1) the hydrogel significantly enhanced the condensation water capture capacity of the system. The net mass gains of the Pinus tabuliformis and Platycladus orientalis systems under the HRW treatment reached 26.3 g and 32.9 g, respectively, which represented 1.17 and 1.30 times those of the CK treatment, and 1.52 and 1.54 times those of the PS treatment. (2) Isotope tracing confirmed that both tree species possess significant Foliar Water Uptake (FWU) capacity. Following condensation, the δ²H values in the leaves of Platycladus orientalis and Pinus tabuliformis surged to 113.5‰ and 85.3‰, respectively, with stem δ²H values increasing by 31‰ and 22‰ compared to their initial baseline. (3) The introduction of the hydrogel in the HRW treatment provided 11.2% and 10.9% of the stem water supply for Platycladus orientalis and Pinus tabuliformis, respectively, thereby reducing their dependence on soil water by 8.3% and 13.1%. In contrast, there was no significant difference in the fractional contribution of condensation water to stem water between the PS and CK treatments. (4) Regarding physiological responses, the application of the hydrogel material effectively improved the physiological status of the plants. The leaf water potentials of Pinus tabuliformis and Platycladus orientalis increased to −0.15 MPa and −1.32 MPa, respectively. Concurrently, stomatal conductance (3.25 and 3.64 mm·s⁻¹) and leaf water content (58.4% and 67.4%) were significantly higher than those in the other treatments. In summary, the hydrogel can significantly enhance the capture, conversion, and utilization efficiency of condensation water by vegetation, effectively optimizing the water supply dynamics of the system. This provides key theoretical and technical support for ecological afforestation in difficult sites within rocky mountainous areas. Full article

Background/Objectives: Opioid-free anesthesia (OFA) has emerged as a potential alternative to opioid-based anesthesia (OBA) to reduce opioid-related adverse effects. This meta-analysis compares OFA and OBA with respect to postoperative pain and opioid consumption. Methods: PubMed, Cochrane, and Embase libraries were searched for OFA studies published through 12 June 2025. Randomized controlled trials (RCTs) conducted on adult humans were selected; observational studies, studies including neuraxial anesthesia, and RCTs currently awaiting approval were excluded. A forest plot was used to summarize findings of a random-effects meta-analysis to compare OFA (treatment) and OBA (control). Results: Of 1446 citations found, twenty-nine articles met our inclusion criteria. Twenty-six studies reported pain scores with a 0–10 scale. OFA was associated with lower postoperative pain scores (Hedges’ g = –0.34; 95% CI –0.55 to –0.13; p < 0.001; I² = 84%) but with high heterogeneity, limiting clinical significance or strong interpretation of results. Eleven trials were analyzed for opioid use, showing a small reduction with OFA (Hedges’ g = –0.55; 95% CI –1.10 to –0.005; p = 0.048; I² = 96.22%). Subgroup outcomes favored OFA, with an overall reduction in pain found specifically in endoscopic abdominal surgeries. Some secondary outcomes also indicated potential improved recovery profiles through OFA for certain surgeries. Conclusions: OFA was associated with statistically significant lower postoperative pain scores, along with opioid consumption, but with a small effect size and high heterogeneity when compared to OBA. This is potentially comparable in pain control and opioid consumption with limited clinical significance. Overall, outcomes support the continued controlled study of OFA as an alternative to conventional analgesia. Full article