Search Results (2,993)

Background and Objectives: Type 2 diabetes mellitus (T2DM) causes retinal microvascular changes that precede clinically apparent diabetic retinopathy (DR). We aimed to identify which optical coherence tomography angiography (OCTA) biomarkers best distinguish eyes with T2DM without clinical DR from healthy controls and to evaluate machine learning classifiers trained on a comprehensive 68-parameter OCTA panel. Materials and Methods: In this prospective case–control study, 80 patients with T2DM without clinical DR and 33 controls underwent 3 × 3 mm macular OCTA using an Optovue RTVue Avanti System. After outlier screening, 221 eyes (155 T2DM, 66 controls) were analyzed. Sixty-eight OCTA parameters were extracted, covering FAZ morphometry (including foveal density FD-300), SCP and DCP vessel density and layer thickness, outer-retina and choriocapillaris flow, and a full retinal-thickness map. Between-group comparisons used the Mann–Whitney U test with Benjamini–Hochberg FDR correction. Logistic regression, random forest, and XGBoost classifiers were evaluated with patient-grouped 10-fold cross-validation; feature importance was quantified via SHAP. Results: Forty-two of 68 parameters reached FDR significance (q < 0.05). Deep capillary plexus vessel density was the most discriminative family (whole image rb = −0.66, q = 2.5 × 10⁻¹³; parafovea rb = −0.64). FD-300 was reduced in T2DM (median 47.55% vs. 51.86%; rb = −0.57; q = 1.0 × 10⁻¹⁰) and emerged as the top SHAP feature (mean |SHAP| = 0.81). FAZ circularity decreased without FAZ-area enlargement, and outer-retina flow was paradoxically elevated (rb = +0.39), consistent with a projection artifact. XGBoost using all 68 features achieved a patient-grouped cross-validated AUC of approximately 0.91, compared with 0.85 for conventional SCP + DCP whole-image density. No parameter correlated with current HbA1c in T2DM (all q > 0.98), and the well-controlled (<7%) and poorly controlled (≥7%) subgroups were indistinguishable across five of six principal biomarkers, consistent with metabolic memory. FD-300 remained independent after adjustment for hypertension, hyperlipidemia, and age (OR = 0.76; 95% CI 0.69–0.84; p < 0.001). Conclusions: A multi-compartment OCTA panel outperforms conventional two-layer vessel-density metrics in detecting preclinical diabetic microvasculopathy, although external validation is required before clinical use. FD-300 is the single most informative biomarker, while choriocapillaris and retinal thickness measures provide complementary, compartment-specific signals. Because the OCTA signature is decoupled from the current HbA1c, screening should not be deferred in well-controlled T2DM. Full article

Wearable inertial sensing has considerable potential for process-level analysis of upper-limb function, but further evidence is needed to understand how it can be applied within ecologically structured immersive virtual reality (VR) tasks. Most VR-based functional assessments rely primarily on outcome-level indicators, such as task completion time, success rate, or error count, which may not fully capture how a task is executed. This exploratory study investigated whether wearable IMU signals collected during an immersive VR sushi-making task could support binary detection of a core upper-limb manipulation phase and provide additional information about task execution beyond global performance outcomes. A total of 45 participants contributed usable motion recordings for this study, with five Xsens DOT sensors placed on the hands, forearms, and waist. Three signal modalities were analysed, including acceleration (ACC), gyroscope angular velocity (GYR), and Euler angles. The downstream recognition problem was formulated as a binary classification task (Placing vs. Non-Placing), and a self-supervised learning (SSL) pretrain–fine-tune strategy was evaluated against conventional machine learning and from-scratch deep learning baselines using five subject-wise validation splits. The strongest overall performance was achieved with hand-mounted accelerometer signals, with LeftHand–ACC achieving a Macro-F1 of $0.712\pm 0.128$ and RightHand–ACC achieving $0.679\pm 0.118$. Under both hand-ACC settings, SSL fine-tuning showed higher mean Macro-F1 than the Balanced Random Forest baseline and the same deep architecture trained from scratch. Recognition performance varied substantially across sensor locations, signal modalities, and task segments, with distal upper-limb sensors generally outperforming waist-based configurations. Cross-age analyses further showed that within-cohort and cross-cohort performance did not fully align, indicating sensitivity to age-related distribution shift. Beyond classification, Log Dimensionless Jerk (LDLJ) derived from the Placing action showed a significant positive association with Cognitron motor control time cost ($r=0.636$, $p<0.001$). These findings suggest that wearable IMU sensing can provide preliminary process-level information during immersive VR functional tasks, including task-phase detection, sensing-configuration comparison, cross-cohort generalisation assessment, and exploratory motion-quality analysis. The results should be interpreted as evidence of feasibility rather than as a mature biomechanical or clinical assessment model. Full article

Background: Pelvic inflammatory disease (PID) is a prevalent chronic inflammatory condition among women. The Composite Dietary Antioxidant Index (CDAI), a measure of dietary antioxidant capacity, has been associated with various inflammatory diseases, but evidence concerning its association with PID remains limited. Methods: The final analytic sample included 4539 women. CDAI was calculated from six dietary antioxidant components: vitamin A, vitamin C, vitamin E, carotenoids, zinc, and selenium. Survey-weighted multivariable logistic regression models were used to evaluate the association between CDAI and self-reported history of treated PID, incorporating the sampling weights, strata, and primary sampling units of NHANES. Restricted cubic spline (RCS) analysis was used to assess both linear and non-linear associations. Subgroup analyses and a machine learning model based on random forest, combined with SHapley Additive exPlanations (SHAP) value ranking, were conducted to evaluate the relative importance of individual components of CDAI. Results: In the fully adjusted spline model including smoking status, CDAI was inversely associated with the odds of self-reported history of treated PID, with no statistical evidence of nonlinearity. Compared with the lowest quartile (Q1), the odds ratios (ORs) for self-reported history of treated PID across higher quartiles of CDAI were as follows: Q2 (OR = 0.682, 95% CI: 0.485–0.959, p = 0.036), Q3 (OR = 0.524, 95% CI: 0.334–0.819, p = 0.009), and Q4 (OR = 0.666, 95% CI: 0.380–1.167, p = 0.167). Among the components of CDAI, vitamin E intake showed an independent inverse association with the odds of self-reported history of treated PID. The SHAP value interpretation indicated that vitamin A, vitamin C, and carotenoids were the three components in CDAI with the highest predictive contribution. Furthermore, subgroup analysis demonstrated a significant interaction effect of age on the association between CDAI and PID. Conclusions: This cross-sectional study suggests an inverse association between CDAI and self-reported history of treated PID, particularly in spline analyses; however, the quartile-based fully adjusted results were non-monotonic and attenuated after adjustment for smoking status. These findings provide hypothesis-generating evidence for future longitudinal and mechanistic studies on antioxidant-related dietary patterns and PID-related reproductive health. Full article

Algorithmic prediction models are increasingly used to support decision-making in high-stakes environments, including emergency departments (ED). However, aggregate performance metrics may obscure systematic differences in classification errors or calibration across subgroups. This study presents a stage-wise, multi-metric, and interpretable fairness auditing framework for ED prediction. The framework compares mitigation strategies across data-, model-, and decision-level interventions, evaluates subgroup fairness using complementary classification and calibration criteria including equalized odds difference (EOD) and expected calibration error (ECE) disparity, and incorporates interpretability analyses based on SHapley Additive exPlanations (SHAP) and the calibration adjustment difference (CAD) to characterize changes in feature-contribution patterns and subgroup-specific probability adjustments after mitigation. The framework was applied to 126,819 ED encounters from MIMIC-IV-ED using measurements recorded within the first 2 h after arrival, and penalized logistic regression and random forest models were compared under reweighting, reduction, and multicalibration. Baseline AUROC values were 0.748 ± 0.028 for random forest and 0.746 ± 0.028 for penalized logistic regression. Reduction and multicalibration largely preserved discrimination performance, whereas reweighting was associated with reduced AUROC and AUPRC. Reweighting most clearly reduced EOD-based classification disparity, particularly for age, yielding reductions of 80.6% in random forest and 86.4% in penalized logistic regression. By contrast, multicalibration most consistently reduced ECE-based calibration disparity for sex and age but did not consistently improve EOD-based classification disparity. In the interpretability analyses, SHAP indicated that data- and model-level mitigation altered feature-contribution patterns, whereas CAD showed that decision-level mitigation produced subgroup-specific probability adjustments that varied in direction and magnitude across groups. These findings reveal trade-offs among discrimination performance, classification fairness, and calibration fairness, indicating that fairness mitigation should be guided by a clearly defined target fairness objective. Pre-deployment fairness auditing should therefore combine complementary fairness metrics with interpretability analyses to evaluate both subgroup-level outcomes and unintended changes in model behavior. Full article

From the perspective of applied surface science, heritage textiles from the Kashgar region can be regarded as fragile fibrous surface systems in which stains, abrasion, yarn breakage, yarn shedding, holes, and color fading represent measurable surface-degradation phenomena. However, manual inspection of these complex, woven, embroidered, and aged surfaces is time-consuming and difficult to standardize. To support non-contact surface-condition documentation, this study proposes an improved YOLOv8-based framework, YOLOv8-MABFT, for surface defect detection and condition-level assessment of Kashgar heritage textiles. The model integrates the C2f-Faster-EMA module and an RT-DETR-informed decoder head to improve the detection of weak-boundary and fine-grained surface defects. A dataset of 8247 high-resolution annotated images was constructed, covering six surface-degradation categories: stains, broken yarn, yarn shedding, holes, abrasion, and color fading. Experimental results show that YOLOv8-MABFT achieves an F1-score of 94.6%, a precision of 91.4%, a recall of 98.0%, and an mAP@0.5 of 94.0%, outperforming Faster R-CNN, SSD, YOLOv5n, YOLOv7n, and YOLOv8n while maintaining lightweight computational characteristics. CAM-based visualizations indicate that the improved model focuses more consistently on defect-related surface regions rather than surrounding decorative textures. Based on detected defects, seven surface-condition variables were constructed and input into a Random Forest classifier for four-level condition prediction. SHAP analysis shows that Distribution and Severity are the main contributors to condition classification. Overall, the proposed framework provides an applied surface-science tool for non-contact surface defect detection, surface-condition documentation, and preliminary condition-level assessment of fragile textile substrates. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

Forests are among the most biodiverse terrestrial ecosystems, in which small mammals play an important ecological role. Their presence is influenced by various habitat parameters, including vegetation structure, microclimate, food resources, and human-driven forest management practices. The aim of our study was to assess rodent diversity across different forests, with a particular focus on forest stand age and forest plant communities. For this purpose, we analyzed 17 years of forest rodent monitoring data across five forest plant communities. This study represents the first long-term monitoring results of small mammals in managed forests of continental Croatia. Trapping was conducted as part of routine rodent monitoring in state-owned forests, incorporating data collected from various research projects. The results showed that lowland forests, particularly floodplain oak forests, exhibited higher biodiversity compared to other forest plant communities. In addition, younger stands exhibited higher species richness than older stands. Canonical correspondence analysis (CCA) indicated that Microtus species were associated with lowland forests and younger stands. Overall, the findings demonstrate that both forest plant communities and stand age play important roles in shaping rodent diversity in continental Croatian forests. The obtained data provide a basis for optimizing forest management practices in continental forests. Full article

The risk of wildfires is increasing due to high temperatures and dry weather conditions caused by climate change. Outbreaks and spread of wildfires are usually conditioned by weather, topography, and fuel characteristics. In the Republic of Korea (hereafter, the ROK), most wildfires are caused by anthropogenic factors rather than natural ones. However, the current forest fire forecasting system being operated in the ROK does not account for anthropogenic factors. To analyze the impact of human and physical factors on wildfire occurrence, a binary logistic regression model was constructed using data from the Gangwon and Gyeongbuk provinces from January 2022 to August 2025. The dependent variable was defined as the occurrence of a wildfire, while the independent variables comprised meteorological, seasonal, stand, and anthropogenic factors. To address multicollinearity, variables with high correlation coefficients were excluded from the independent variables, which were selected by three estimating approaches, including logistic regression and two machine learning techniques (namely, Random Forest and XGBoost). With machine learning, the variables with high feature importance were identified. The explanatory power of the logistic regression analysis with independent variables selected by the machine learning models was about 1.3 times higher than that of the model using variables adjusted solely for multicollinearity. The results of logistic regression analysis revealed that weather and coniferous forests are the most important factors fostering wildfires, while the mean stand age was the most significant factor in hindering wildfires. Among the anthropogenic factors, forest road density acted as a suppressor of wildfire spread rather than a promoter of occurrence. Conversely, trail density tends to increase the risk of wildfire occurrence. Among forest management activities, plantation forests may increase the risk of forest fires, although this remains uncertain. These findings suggest that preventing wildfires requires a paradigm shift in forest resource management policies, including extending forest rotation ages and converting coniferous forests to broadleaf forests. Meanwhile, it also indicates the need to restrict the expansion of hiking trails and improve regulations regarding hiker access and behavior to prevent wildfires. Full article

Background/Objectives: Obstructive sleep apnea (OSA) and related sleep disorders affect a substantial proportion of hospitalized patients, with an estimated 48% pooled prevalence of undiagnosed OSA in cardiac inpatients and up to 80% of moderate-to-severe community OSA cases carrying no formal diagnosis at the time of hospital admission. In parallel, frailty—a state of heightened physiological vulnerability arising from cumulative multi-system biological decline—is present in 40–80% of inpatients and shares deep, bidirectional neurobiological pathways with sleep-disordered breathing through circadian dysregulation, intermittent hypoxia, hypothalamic–pituitary–adrenal axis activation, and chronic low-grade inflammation. Despite this convergence, no prior study has integrated validated, administratively computable frailty phenotyping with a machine learning framework specifically designed to predict inpatient sleep disorder diagnosis—and OSA in particular—at the point of hospital admission. The present study addresses this gap by developing an admission-time, explainable machine learning framework for the prediction of inpatient sleep disorder diagnoses (ICD-10 G47.x, encompassing OSA G47.3, insomnia G47.0, hypersomnia, and circadian rhythm disorders) and of insomnia specifically (ICD-10 G47.00). Methods: We developed and evaluated a suite of five binary classification models—XGBoost, Random Forest, LightGBM, CatBoost, and Decision Tree—using 9682 balanced hospitalization episodes from the MIMIC-IV (version 2.2) database. The predictor set comprised 23 admission-time structured features across three domains: (i) frailty and comorbidity burden, including the Hospital Frailty Risk Score (HFRS) derived from ICD-10 codes, the Elixhauser comorbidity index, prior admission history, and six binary disease flags (obesity, hypertension, type 2 diabetes, heart failure, COPD, and depression/anxiety); (ii) physiological and laboratory biomarkers from the first 24 h of care, including minimum SpO₂, heart rate variability, hemoglobin, creatinine, albumin, and arterial blood gas parameters; and (iii) sociodemographic and administrative variables encompassing age, sex, ethnicity, insurance type, and admission acuity. Model performance was assessed through five-fold stratified cross-validation and bootstrap confidence intervals (n = 1000 iterations), with predictor importance quantified using SHapley Additive exPlanations (SHAP). Results: XGBoost achieved the strongest aggregate performance across all evaluation metrics, attaining an area under the receiver operating characteristic curve (AUC) of 0.871 (95% CI: 0.856–0.887), accuracy of 79.6%, F1-score of 0.820, and sensitivity of 94.9%, correctly identifying 903 of 952 true positive cases in the held-out test set; all gradient boosting frameworks substantially outperformed the Decision Tree baseline (AUC 0.836). SHAP analysis identified the HFRS and Elixhauser index as the two dominant predictors, followed by depression/anxiety, obesity, hypertension, and minimum SpO₂—a hierarchy that recapitulates the canonical clinical phenotype of obstructive sleep apnea in frail inpatients rather than that of primary insomnia, indicating that the model is preferentially capturing the OSA–frailty axis within the broader G47.x outcome. The predicted probability outputs were well-calibrated across all risk deciles. Conclusions: Frailty-derived features, in combination with admission-time clinical and physiological data, can predict inpatient sleep disorder diagnoses—predominantly OSA—with high sensitivity and well-calibrated risk estimates. The deployable, interpretable nature of the XGBoost model makes it directly suitable for integration into clinical decision support systems, offering a screening tool that requires no dedicated instrumentation beyond routine admission data. By flagging high-risk patients at the moment of admission, the framework provides a concrete mechanism for accelerating referral for definitive diagnostic confirmation (overnight oximetry, polysomnography) and earlier initiation of CPAP and related therapies, with direct implications for reducing the persistent diagnostic gap, perioperative risk, and preventable adverse outcomes in frail hospitalized populations. Full article

The accurate estimation of lithium-ion battery state of health (SOH) is crucial for battery monitoring, safety, and degradation assessment; however, it remains challenging because of the nonlinear nature of battery degradation, measurement noise, and variability in the battery aging trajectory. This study aims to solve these problems by proposing a hybrid attention-based BiLSTM–RF model, which combines wavelet-based signal denoising, incremental capacity analysis (ICA)-based feature extraction, stacked Bidirectional Long Short-Term Memory (BiLSTM) networks, multi-head self-attention, principal component analysis (PCA)-based feature compression, and ensemble regression using a Random Forest (RF) model with adaptive weighted fusion. The proposed framework was tested on the NASA battery datasets (B0005, B0006, B0007 and B0018) and was further validated on the Oxford Battery Degradation Dataset using leave-one-battery-out cross validation conditions. Experimental results indicated that, in general, the proposed framework outperformed the evaluated benchmark models (CNN-LSTM, BiLSTM, and RF models) in terms of the prediction error, with a minimum RMSE value of 0.0229 for NASA battery B0007 and 0.0024 for Oxford Cell3. Ablation analysis also showed that the combination of wavelet denoising, PCA compression, temporal sequence learning and ensemble regression played a role in the overall SOH estimation performance. These results show that the proposed hybrid approach is effective and stable for SOH estimation in different battery degradation trajectories under the tested experimental conditions. Full article

Background/Objectives: Canine athletes have a higher energy requirement and are more susceptible to nutrient depletion, electrolyte imbalance, and metabolic stress than sedentary pets. The objective of this study was to characterize the plasma metabolome of American Foxhound dogs following a bout of unstructured exercise. Methods: Thirty-nine adult American Foxhound dogs (32 intact males, 7 spayed females; age: 6.2 ± 3.1 yr; BW: 36.3 ± 5.3 kg) were allotted to a standard performance diet (CTRL) or NUTRO^® Natural Choice^® Adult High Endurance Formula (TEST). After 80 d in the study, blood samples were collected prior to (0 h), and 3 h and 25 h post-exercise (average: 17.7 km run over 2–3 h). Plasma samples of the 10 top performers of each treatment group were analyzed for untargeted metabolite profiling. Results: Of the 566 named metabolites identified, >200 and >185 metabolites were impacted (p < 0.05) by exercise and diet, respectively. Principal component analysis indicated distinct clustering by diet. Random forest analysis highlighted several metabolites having a high degree of predictive accuracy based on diet and exercise, with most related to amino acid, lipid, xenobiotic, and cofactor and vitamin metabolism. Relating to exercise, glycolytic end-products and citric acid cycle intermediates were increased at 3 h post-exercise. Similarly, tocopherols and omega-3 polyunsaturated fatty acids were higher in dogs fed TEST than those fed CTRL during recovery, indicating a lower oxidative stress and anti-inflammatory response. Conclusions: Overall, the data suggest a protective effect (lower susceptibility to oxidative stress and muscle fatigue) of feeding a nutrient-fortified diet for dogs undergoing unstructured exercise. Full article

To investigate the rheological properties and compositional changes in SBS-modified bitumen under different aging conditions in the unique environmental conditions of the Xinjiang region, this study selected a local 70# base bitumen from Xinjiang and prepared modified bitumen by adding 4.0%, 4.5%, and 5.0% SBS modifier, respectively. RTFOT and PAV were used to simulate the short-term thermal-oxidative aging and long-term oxidative pressure aging processes of the bitumen samples, respectively. The three key indicators and dynamic rheological properties of the bitumen were tested for the original sample, as well as before and after short-term thermal-oxidative aging and long-term oxidative pressure aging. Thin-layer chromatography/flame ionization detection (TLC/FID) was used to analyze the migration patterns of the samples’ chemical components, and a random forest model was employed to establish a quantitative mapping between the four components of the modified bitumen and the rutting factor over a wide temperature range. The results indicate that aging weakens the improvement effect of SBS on the high-temperature performance of bitumen. However, 4.5% SBS-modified bitumen subjected to long-term oxidative pressure aging still maintains the best high- and low-temperature performance, elastic recovery capacity, and fatigue resistance compared to other dosage levels. It also has the highest bitumen content, which verifies the high-temperature performance of this dosage at the component level. Therefore, the optimal SBS dosage is recommended to be 4.5%. Notably, as the SBS content increases, it significantly regulates the increase in heavy fraction content during the aging process, while the decrease in light fraction content is not significantly affected by the content. Based on the random forest algorithm, a mapping relationship between fractions and properties under fully aged conditions was established. This study provides a theoretical basis for research on the modification and aging mechanisms of Xinjiang bitumen. Full article

Background: Plasma biomarkers are promoted as scalable tools for the staging of Alzheimer’s disease (AD), yet head-to-head comparisons against the clinical scales used to define diagnostic labels remain scarce. Reported gains from machine learning fusion of clinical and biomarker features may reflect label circularity rather than biological signals, and quantifying this circularity is a central aim of the present work. Methods: From the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we assembled 655 participants (CN = 296, MCI = 168, and AD = 191) with concurrent plasma biomarkers (pT217, A$\beta$42/40, NfL, and GFAP), clinical scales (MMSE, CDR-SB, and FAQ), APOE genotype, and demographics. Three pre-specified feature sets (clinical-only, biomarker plus demographic–genetic, and full fusion) were compared across four classifiers (Logistic Regression, SVM, Random Forest, and XGBoost) using repeated, nested cross-validation (5-fold × 3 outer, 5-fold inner) with balanced class weighting. Because the external Center for Neurodegeneration and Translational Neuroscience (CNTN) cohort ($n=130$) measures pT181 rather than pT217 and lacks A$\beta$42/40, external evaluation used a separate reduced feature panel (NfL, GFAP, APOE, age, sex, and education), not the proposed pT217-inclusive panel. Results: Clinical scales alone reached a three-class AUC-OVR of $0.9539\pm 0.0041$, and fusion reached $0.9559\pm 0.0046$, an indistinguishable gain. Because MMSE, CDR-SB, and FAQ partly determine ADNI diagnostic labels, both estimates are circularity-inflated upper bounds and do not reflect independent classification power. Independent of this circularity, the internal plasma plus demographic–genetic model still achieved AUC-OVR $=0.7455\pm 0.0150$, with pT217 as the dominant contributor. Pairwise discrimination was excellent for CN vs. AD ($1.0000$) and MCI vs. AD ($0.9739$) but markedly weaker for CN vs. MCI ($0.9302$ for fused and $0.6972$ for plasma only). The separate reduced-feature model, which contains neither pT217 nor A$\beta$42/40, transferred to CNTN with AUC-OVR $=0.702$ (95% CI $0.635$–$0.764$). Conclusions: Apparent fusion gains in ADNI are largely a consequence of label circularity. After removing the circular clinical features, the internal pT217-inclusive plasma model supports three-class CN/MCI/AD screening at AUC $\approx 0.74$ and a reduced panel without pT217 transfers to an independent cohort at AUC $\approx 0.70$. These values provide a realistic performance estimate for blood-based AD staging under the current feature set, diagnostic label structure, and cohort design, and richer feature sets or pathology-anchored labels may shift this estimate. MCI detection remains the principal bottleneck. Full article

Background/Objectives: Middle-aged Korean women (aged 40–65 years) face compounded physiological and psychosocial health burdens, yet controlled evidence for non-pharmacological interventions in this population remains limited. This systematic review and meta-analysis aimed to quantify the effects of forest therapy on health-related outcomes in middle-aged Korean women and to identify program characteristics associated with differential therapeutic effects. Methods: Ten databases were searched for controlled studies published from January 2000 to February 2025 following PRISMA 2020 and PICOTS-SD criteria; only controlled studies conducted in Korea were included in the meta-analysis. Of 9563 records screened, 24 controlled Korean studies (RCT, n = 13; NRCT, n = 11; k = 128 effect sizes) met inclusion criteria. A three-level random-effects model with robust variance estimation (RVE) was used as the primary analysis. Results: The primary three-level RVE model, applied to 24 controlled Korean studies, yielded a pooled Hedges’ g = 0.596 (95% CI: 0.432–0.760); a supplementary standard random-effects model yielded g = 0.542 (95% CI: 0.420–0.664). Substantial heterogeneity and potential publication bias were observed; overall evidence certainty was rated Low (GRADE). Conclusions: These findings provide preliminary, low-certainty evidence (overall GRADE: Low) that forest therapy may benefit middle-aged Korean women. They do not justify broad clinical or policy adoption at present. High-quality, independently conducted international RCTs and standardized trials outside Korea are required to confirm and generalize these findings. Full article

Forests in the Qinling Mountains play a critical role in maintaining regional ecosystem services, yet long-term, high-resolution forest disturbance datasets at the regional scale remain limited, particularly in mountainous and cloud-prone environments. Existing forest disturbance products are largely based on Landsat imagery and optimized for global-scale applications, which may constrain their performance at regional scales. In this study, we developed a 30 m resolution forest disturbance dataset for the Qinling Mountains spanning 1999–2025 by integrating Landsat and Sentinel-2 time-series imagery with the LandTrendr algorithm. Annual Normalized Burn Ratio time series were generated through multi-sensor fusion of Landsat and Sentinel observations, improving temporal continuity and data availability. Based on these annual composites, LandTrendr was applied to produce consistent annual forest disturbance maps. A comprehensive validation framework was implemented using 2000 visually interpreted disturbance sample points and 60 independently documented disturbance events. The results show strong temporal agreement between detected and reference disturbance years, with a regression slope of 0.89 and an R² of 0.93. Spatial validation based on disturbance events yielded an overall accuracy of 90.95%. Comparative analyses indicate that the proposed dataset exhibits improved spatiotemporal consistency relative to existing forest disturbance products, including Global Forest Change (GFC) and the Forest Age Dataset of China (FAGE), particularly in complex mountainous terrain. This study provides a long-term, regionally optimized forest disturbance dataset for the Qinling Mountains and demonstrates the applicability of Landsat–Sentinel annual composites for reliable forest disturbance monitoring in mountainous regions. Full article