Search Results (191)

Background: Drug-resistant tuberculosis remains a major challenge in resource-limited settings, particularly in rural regions of the Eastern Cape Province, where limited laboratory infrastructure, constrained access to advanced molecular diagnostics, shortages of specialized healthcare personnel, and prolonged diagnostic turnaround times can delay appropriate treatment initiation. This study examined whether routinely detectable genomic resistance markers could be integrated with parsimonious machine learning approaches to support early risk stratification for isoniazid (INH) and/or rifampicin (RIF) resistance and multidrug-resistant tuberculosis (MDR-TB). Methods: We conducted a retrospective analysis of clinical, demographic, and genomic data from 207 Mycobacterium tuberculosis isolates representing 207 unique patients. Resistance was classified as INH and/or RIF resistance or MDR-TB (concurrent resistance to both drugs). Predictors included age, sex, and canonical resistance-associated mutations (katG S315T, inhA −15C>T, and rpoB codon substitutions). Logistic regression was used to estimate adjusted odds ratios (aORs), while Random Forest models were applied to assess non-linear feature importance. Internal validation was performed using 10-fold cross-validation. A systems network analysis mapped the integration of model-derived risk bands into Clinical Governance structures and Community-Engaged Education pathways, including interventions delivered by Community Health Workers (CHWs). Results: INH and/or RIF resistance was identified in 58.9% of isolates, with 21.7% classified as MDR-TB. The most frequently detected mutations were katG S315T (29.0%) and rpoB S450L (26.6%). Logistic regression identified rpoB S450L (aOR 4.20; 95% CI: 2.10–8.45) and katG S315T (aOR 2.85; 95% CI: 1.40–5.80) as the strongest independent predictors, while age and sex were not statistically significant. Models demonstrated strong internal discrimination (AUCs of 0.96 for INH and/or RIF resistance and 0.99 for MDR-TB). Risk stratification categorized 18% of patients as high risk. Scenario-based modelling suggested that prioritizing high-risk patients for reflex Line Probe Assay testing could reduce the median time to appropriate treatment from 14 to 3 days and may reduce progression from isoniazid-resistant TB to MDR-TB under specified operational assumptions. Conclusions: Mutation-informed predictive modelling demonstrates strong internally validated discrimination and provides a structured framework for risk-stratified intervention. Integrating probability-based risk thresholds within Clinical Governance systems and community-level support structures, including CHW-led adherence and education strategies, may support earlier treatment optimization in high-burden rural settings. External validation and prospective implementation studies are required before broader programmatic adoption. Full article

Species composition is an important indicator for the condition, functioning, and ecosystem service provision of salt marshes, making the mapping of species composition valuable for their management. Previous studies have demonstrated that the combined use of unoccupied aerial vehicle (UAV)-mounted multispectral cameras and machine learning (ML) can provide effective mapping of vegetation communities in these habitats. However, to date, these studies have predominantly focused on relatively species-poor salt marshes in North America. There has been no published testing of these combined UAV-ML methods in the salt marshes of northwestern Europe, which contain different often more diverse assemblages. Consequently, this study investigated whether applying recent methodological advances can accurately map National Vegetation Classification communities in three locations in the United Kingdom, each comprising two salt marsh sites, one established and one restored. Sites consisted of a mix of established and restored salt marshes of different ages, enabling a complementary assessment of how these methods perform in communities at different stages of development. The applied random forest ML models were found to produce highly accurate maps of salt marsh vegetation communities, with a mean overall accuracy of 94.7%. No relationship was found between the age of restoration sites and the accuracy of the classifications, showing these methods may be applied at a range of stages of community development and offer wider applicability for saltmarsh management and monitoring. The findings of this study demonstrate that advances in the combined use of drones and machine learning provide a readily transferrable method for mapping standardised vegetation communities in both established and restored northwestern European salt marshes and therefore likely other salt marshes globally. Consequently, this study demonstrates that both researchers and practitioners may confidently use these methods to create improved assessments of both marsh condition and function. Full article

China’s age structure is undergoing profound demographic shifts, making accurate spatial information on age-stratified populations essential for policy-making, resource allocation, and risk assessment. However, census data are primarily aggregated by administrative units, offering coarse spatial resolution that constrains their integration and application with other gridded datasets. Using township-level population counts for four age groups (0–14, 15–59, 60–64, and ≥65 years) from the 2020 Seventh National Population Census across 38,572 townships, we developed an age-stratified downscaling framework. This framework integrates a random forest model with age-filtered Points of Interest (POI) data and other multi-source geospatial covariates to generate a 100 m resolution age-stratified population density weighting layer. Through township-level data dasymetric mapping, we produced the township-based 100 m Age-Stratified Population Grid Data (Township-ASPOP). Since township-level data represent the finest publicly available spatial unit of demographic statistics in China, we further validated the accuracy of Township-ASPOP by generating County-based 100 m Age-Stratified Population Grid Data (County-ASPOP) through dasymetric mapping using county-level age-stratified population data. The results demonstrate that County-ASPOP achieves superior predictive accuracy, with R² values of 0.95, 0.95, 0.85, and 0.86, and Root Mean Square Error (RMSE) values of 1743, 6829, 900, and 2033 persons per township for the four age groups, respectively—significantly outperforming the contemporaneous WorldPop dataset (R² = 0.69, 0.72, 0.64, and 0.60). The accuracy of Township-ASPOP is no less than that of County-ASPOP and effectively captures realistic spatial settlement patterns. This study establishes a reproducible framework for generating age-stratified population grid data and provides critical data support for policy formulation and resource allocation. Full article

Tree pollen allergies are influenced by regional atmospheric pollen concentrations and flora distribution. Climate change and urban landscaping have altered airborne pollen profiles in Ukraine, potentially affecting sensitization patterns. We examined 7518 patients (57.63% children) sensitized to at least one of 26 molecular components from 19 tree species using ALEX testing (2020–2022). Atmospheric pollen data from Ukrainian aerobiology stations were integrated with clinical data. Regional sensitization was mapped using the Geographic Information System, and Bayesian network modeling determined hierarchical relationships. Sensitization to Cry j 1 (46.01%), Bet v 1 (41.67%), and Fag s 1 (34.38%) dominated across age groups. High Fagales sensitization correlated with elevated atmospheric Betula, Alnus, and Corylus pollen concentrations, confirming environmental exposure-sensitization relationships. Bayesian modeling identified Bet v 1 as the root allergen (89.43% accuracy) driving cascading sensitization to other Fagales and non-Fagales allergens. Unexpectedly high Cry j 1 sensitization despite minimal atmospheric Cryptomeria presence suggests Thuja and Ambrosia cross-reactivity. Fagales sensitization dominated 10 of 17 regions, correlating with forest geography and urban landscaping. This study validates aerobiological monitoring’s clinical relevance. Diagnostic protocols should prioritize Bet v 1 while interpreting Cry j 1 positivity as potential cross-reactivity. Climate-driven shifts in atmospheric pollen patterns require ongoing coordinated aerobiological and clinical surveillance. Full article

Current mainstream remote sensing approaches to forest age estimation frequently neglect interspecific differences in functional traits, which may limit the accurate representation of species-specific tree growth strategies. This study develops and validates a technical framework that incorporates multi-source remote sensing and tree species functional trait heterogeneity to systematically improve the accuracy of plantation age mapping. We constructed a processing chain—“multi-source feature fusion–species identification–heterogeneity modeling”—for a typical karst plantation landscape in southeastern Yunnan. Using the Google Earth Engine (GEE) platform, we integrated Sentinel-1/2 and Landsat time-series data, implemented a Gradient Boosting Decision Tree (GBDT) algorithm for species classification, and built age estimation models that incorporate species identity as a proxy for the growth strategy heterogeneity delineated by the Plant Economic Spectrum (PES) theory. Key results indicate: (1) Species classification reached an overall accuracy of 89.34% under spatial block cross-validation, establishing a reliable basis for subsequent modeling. (2) The operational model incorporating species information achieved an R² (coefficient of determination) of 0.84 (RMSE (Root Mean Square Error) = 6.52 years) on the test set, demonstrating a substantial improvement over the baseline model that ignored species heterogeneity (R² = 0.62). This demonstrates that species identity serves as an effective proxy for capturing the growth strategy heterogeneity described by the Plant Economic Spectrum (PES) theory, which is both distinguishable and valuable for modeling within the remote sensing feature space. (3) Error propagation analysis demonstrated strong robustness to classification uncertainties (γ = 0.23). (4) Plantation structure in the region was predominantly young-aged, with forests aged 0–20 years covering over 70% of the area. Despite inherent uncertainties in ground-reference age data, the integrated framework exhibited clear relative superiority, improving R² from 0.62 to 0.84. Both error propagation analysis (γ = 0.23) and Monte Carlo simulations affirmed the robustness of the tandem workflow and the stability of the findings, providing a reliable methodology for improved-accuracy plantation carbon sink quantification. Full article

Mapping forest growth potential across varying environments is challenging, especially when field measurements are limited. In this study, we integrated Airborne Laser Scanning (ALS) terrain derivatives and Sentinel-2 spectral indices to model Site Index (SI), using forest plantations, at 10-m spatial resolution across three ecologically distinct regions in British Columbia (Aleza Lake, Deception, and Eagle Hills). Random Forest regression models were calibrated using field-measured SI and a multistep variable-selection procedure that included Variance Inflation Factor (VIF) screening followed by model-based variable importance assessment. Model performance was evaluated using repeated 10-fold cross-validation. The combined ALS–Sentinel-2 models substantially outperformed single-source models, yielding cross-validated R² values of 0.63, 0.44, and 0.56 for Aleza Lake, Deception, and Eagle Hills, respectively, compared with R² values of 0.40, 0.40, and 0.46 for ALS-only models. Key predictors consistently included terrain metrics, such as the Topographic Position Index (TPI) and the Topographic Wetness Index (TWI), along with satellite-derived chlorophyll-sensitive indices including S2REP (Sentinel-2 red-edge position), MTCI (MERIS terrestrial chlorophyll), and GNDVI (Greenness Normalized Difference Vegetation Index). A general model using predictors common to all regions performed comparably (R² = 0.63, 0.41, 0.52), demonstrating the transferability and operational potential of the approach. These findings demonstrate that integrating ALS-derived terrain metrics with Sentinel-2 spectral indices provides a robust, age-independent framework for capturing spatial variability in forest productivity across landscapes. This multi-sensor fusion approach enhances traditional SI methods and single-sensor models, providing a scalable and operational tool for forest management and long-term planning in changing environmental conditions. Full article

Modern medium-voltage (MV) distribution networks face increasing reliability challenges driven by aging assets, climate variability, and evolving operational demands. In Colombia and across Latin America, reliability metrics, such as the System Average Interruption Frequency Index (SAIFI), standardized under IEEE 1366, serve as key indicators for regulatory compliance and service quality. However, existing analytical approaches struggle to jointly deliver predictive accuracy, interpretability, and traceability required for regulated environments. Here, we introduce CRITAIR (Criticality Analysis through Interpretable Artificial Intelligence-based Recommendations), an integrated framework that combines predictive modeling, explainable analytics, and regulation-aware reasoning to enhance reliability management in MV networks. CRITAIR unifies three components: (i) a TabNet-based predictive module that estimates SAIFI using outage, asset, and meteorological data while producing global and local attributions; (ii) an agentic retrieval-and-reasoning stage that grounds recommendations in regulatory evidence from RETIE and NTC 2050; and (iii) interpretable reasoning graphs that map decision pathways. Evaluations conducted on real operational data demonstrate that CRITAIR achieves competitive predictive performance—comparable to Random Forest and XGBoost—while maintaining transparency through sparse attention and sequential feature explainability. Also, our regulation-aware reasoning module exhibits coherent and verifiable recommendations, achieving high semantic alignment scores (BERTScore) and expert-rated interpretability. Overall, CRITAIR bridges the gap between predictive analytics and regulatory governance, offering a transparent, auditable, and deployment-ready solution for digital transformation in electric distribution systems. Full article

Objectives: Previously, we identified four open-angle glaucoma (OAG)-associated autoantibodies (anti-ETNK1, anti-VMAC, anti-NEXN, and anti-SUN1) using proteome-wide autoantibody screening by wet protein arrays. The objective of this exploratory study was to evaluate the diagnostic performance of these four glaucoma-associated autoantibodies using automated machine learning. Methods: Plasma samples from 119 patients with OAG and 35 patients with cataracts as controls were enrolled for the study. All machine-learning analyses were performed in Python 3.9.16 (GCC 11.2.0) using scikit-learn 1.2.2 and PyCaret 3.0.1. Variables included plasma levels of the autoantibodies, age, sex, and intra-ocular pressure (IOP). Probability calibration (Platt/sigmoid and isotonic) was assessed with reliability curves and Brier scores. Model explainability was examined with permutation importance, SHAP values, and an ablation analysis removing one autoantibody at a time. Results: The tuned random forest achieved an out-of-fold (OOF) area under the receiver-operating characteristic curve (ROC–AUC) of 0.852 (±0.040), an average precision (AP) of 0.950, and an F1 score of 0.865. Isotonic mapping improved agreement between predicted and empirical probabilities. Among these four autoantibodies, VMAC was the most important factor for the model’s prediction. Conclusions: A machine learning model using four autoantibodies from blood samples showed potential for diagnosing OAG. Full article

Accurate detection of machinery-induced strip roads after forest operations is fundamental for assessing soil disturbance and supporting sustainable forest management. However, in Mediterranean pine forests where canopy openings after boom-corridor thinning are moderate, the effectiveness of different remote sensing techniques remains uncertain. Previous studies have shown that LiDAR-based methods can reliably detect logging trails in different forest stands, but their direct transfer to structurally simpler, even-aged Mediterranean stands has not been validated. This study addresses this gap by testing whether UAV-derived RGB imagery can achieve comparable accuracy to LiDAR-based methods under the canopy conditions of boom-corridor thinning. We compared four approaches for detecting strip roads in a black pine (Pinus nigra Arn.) plantation on Mount Amiata (Tuscany, Italy): one based on high-resolution UAV RGB imagery and three based on LiDAR data, namely Hillshading (Hill), Local Relief Model (LRM), and Relative Density Model (RDM). The RDM method was specifically adapted to Mediterranean conditions by redefining its return-density height interval (1–30 cm) to better capture areas of bare soil typical of recently trafficked strip roads. Accuracy was evaluated against a GNSS-derived control map using nine performance metrics and a balanced subsampling framework with bootstrapped confidence intervals and ANOVA-based statistical comparisons. Results confirmed that UAV-RGB imagery provides reliable detection of strip roads under moderate canopy openings (accuracy = 0.64, Kappa = 0.27), while the parameter-tuned RDM achieved the highest accuracy and recall (accuracy = 0.75, Kappa = 0.49). This study demonstrates that RGB-based mapping can serve as a cost-effective solution for operational monitoring, while a properly tuned RDM provides the most robust performance when computational resources are sufficient to work on large point clouds. By adapting the RDM to Mediterranean forest conditions and validating the effectiveness of low-cost UAV-RGB surveys, this study bridges a key methodological gap in post-harvest disturbance mapping, offering forest managers practical, scalable tools to monitor soil impacts and support sustainable mechanized harvesting. Full article

Background: The accelerating global population aging underscores the urgency of addressing public health challenges. Sarcopenia and depression are prevalent, interrelated conditions in older adults, yet prevailing research often treats depression as a static state, neglecting its longitudinal progression and limiting predictive capability for sarcopenia. Methods: Using data from four waves (2011–2018) of the China Health and Retirement Longitudinal Study (CHARLS), we identified distinct depressive symptom trajectories via Group-Based Trajectory Modeling. Seven machine learning algorithms were employed to develop predictive models for sarcopenia risk, incorporating these trajectory patterns and baseline characteristics. Results: Three depressive symptom trajectories were identified: ‘Persistently Low’, ‘Persistently Moderate’, and ‘Persistently High’. Tree-based ensemble methods, particularly Random Forest and XGBoost, demonstrated superior and robust performance (mean accuracy: 0.8265 and 0.8178; mean weighted F1-score: 0.8075 and 0.8084, respectively). Feature importance analysis confirmed depressive symptoms as a core, independent predictor, ranking third (5.7% importance) in the optimal Random Forest model, only after BMI and cognitive function, and surpassing traditional risk factors like age and waist circumference. Conclusions: This study validates that longitudinal depressive symptom trajectories provide superior predictive power for sarcopenia risk compared to single-time-point assessments, effectively mapping mental health trajectories to physical risk. The robust ML framework not only enables early identification of high-risk individuals but also reveals a multidimensional risk profile, highlighting the intricate mind–body connection in aging. These findings advocate for integrating dynamic mental health monitoring into routine geriatric assessments, demonstrating the potential of AI to facilitate a paradigm shift towards proactive, personalized, and scalable prevention strategies in public health and clinical practice. Full article

In developed countries such as Spain, where the population is increasingly aging, retirement planning and longevity risk represent major societal challenges. In Spain, in particular, a significant proportion of household wealth is concentrated in real estate, primarily in the form of owner-occupied housing. For this reason, one emerging financial product in the retirement savings space is the reverse mortgage (RM). This study examines the determinants of acceptance of this financial product using survey data collected from Spanish individuals. The intention to take out an RM is explained through performance expectancy (PE), effort expectancy (EE), social influence (SI), bequest motive (BM), financial literacy (FL), and risk (RK). The analysis applies machine learning techniques: decision tree regression is used to visualize variable interactions that lead to acceptance; random forest to improve predictive capability; and Shapley Additive Explanations (SHAP) to estimate the relative importance of predictors. Finally, Importance–Performance Map Analysis (IPMA) is employed to identify the variables that merit greater attention in the acceptance of RMs. SHAP values indicate that PE and SI are the most influential predictors of intention to use RMs, followed by BM and EE with moderate importance, whereas the positive influence of RK and FL is more reduced. The IPMA highlights PE and SI as the most strategic drivers, and RK and BM act as relevant barriers to the widespread adoption of RMs. Full article

A previous study demonstrated that spontaneous forest recovery can result in the development of functional mixed forests in post-mining areas. A critical step in this process is the establishment of climax woody species in the understory of pioneer trees. In this case study, we utilise repeated sampling to evaluate the establishment, initial survival, and growth of pedunculate oak (Quercus robur) and European beech (Fagus sylvatica) seedlings, and to newly assess Norway spruce (Picea abies) during unassisted forest recovery on a post-mining site after coal mining near Sokolov in North Bohemia. Detailed mapping of beech and oak seedlings was conducted in 2009 and 2012 (i.e., 14 and 11 years after the site was reclaimed). Now, we have resurveyed these seedlings, which has allowed us to evaluate their survival and growth. We have also mapped spruce seedlings and estimated their age from annual branch whorls. In the original study, most seedlings were found on the northern site near the edge of the post-mining area and the surrounding landscape, which serve as seed sources. Beech shows the best survival and growth on the northern site, where the greatest number of new seedlings also appear. In contrast, oaks demonstrate much higher mortality than beech overall, with the highest mortality observed on the northern site and the highest survival on the southern site, where most of the new seedlings also appeared. Interestingly, however, surviving oaks grew faster on the northern site. Across microtopography, seedlings of all three tree species were most frequent on the slopes of micro-undulations. Beech individuals were taller in depressions, whereas oaks did not consistently demonstrate a size advantage across microhabitats. Spruce colonised vigorously and was the most abundant of the three species across microhabitats. Age-frequency analyses suggest an annual mortality rate of 3%–9%. Browsing damage was observed on 19% of beech seedlings and 9% of oak seedlings. The study shows that pioneer tree stands are suitable nursing sites for studied climax tree species, which can colonise these sites several kilometres away from mature trees, and their establishment involves a complex interplay between distance to seed source and local microclimatic conditions. Our resurvey indicates that later successional stages may increasingly be shaped by shade-tolerant beech and spruce under the developing canopy. Full article

Accurate precipitation data are essential for effective water resource management. This study aimed to correct precipitation values from the ERA5_Ag reanalysis dataset using observational data from 20 meteorological stations located in the Tensift basin, Morocco. Five machine learning models were evaluated: MLP, XGBoost, CatBoost, LightGBM, and Random Forest. Model performance was assessed using RMSE, MAE, R², and bias metrics, enabling the selection of the best−performing model to apply the correction. The results showed significant improvements in the accuracy of precipitation estimates, with R² ranging between 0.80 and 0.90 in most stations. The best model was subsequently used to correct and generate raster maps of corrected precipitation over 42 years, providing a spatially detailed tool of great value for water resource management. This study is particularly important in semi−arid regions such as the Tensift basin, where water scarcity demands more accurate and informed decision−making. Full article

Background/Objectives: Multiple sclerosis (MS) is an autoimmune disorder affecting the central nervous system, characterised by the degradation of myelin, which results in various neurological symptoms. This study aims to utilise radiomics features to evaluate the predictive value of IVIM diffusion parameters, namely, the true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f), in relation to disability severity, assessed using the Expanded Disability Status Scale (EDSS), and mobility in patients with relapsing–remitting MS. Methods: This retrospective cross-sectional study analysed MRI data from 197 patients diagnosed with multiple sclerosis (MS). Quantitative intravoxel incoherent motion (IVIM) parameters were obtained using a 1.5 Tesla MRI scanner. Clinical information collected included age, disease duration, number of relapses, status of disease-modifying therapy (DMT), and the need for mobility assistance. Machine learning (ML) techniques, such as XGB, Random Forest, and ANN, were employed to explore the relationships between radiomic IVIM parameters and these clinical variables. Results: IVIM radiomics achieved high accuracy in lesion phenotyping. Random Forest distinguished enhancements from non-enhancing lesions with 96% accuracy and AUC = 0.99 with IVIM-f and D* maps. CNN also reached ~92% accuracy (AUC 0.97) with IVIM-f. For disability prediction, IVIM-D and D* radiomics strongly correlated with EDSS: Random Forest achieved 89% accuracy (AUC = 0.90), while CNN achieved 90% accuracy (AUC = 0.95). Mobility impairment was predicted with the highest performance—RNN achieved 96% accuracy (AUC = 0.99) across IVIM-f features. In contrast, relapse history, disease duration, and treatment status were poorly predicted (<75% accuracy). Conclusions: ML analyses of IVIM metrics provided independent predictors of functional impairment and disability in MS. Our novel approach may be used to improve diagnostic accuracy and develop personalised treatment strategies for MS patients. Full article

Juglans neotropica Diels, classified as endangered on the IUCN Red List, plays a crucial role in the resilience of Andean montane forests in southern Ecuador—a megadiverse region encompassing coastal, Andean, and Amazonian ecosystems. This study examines how climatic, edaphic, and topographic gradients influence the species’ phenotypic traits across six source localities—Tibio, Merced, Tundo, Victoria, Zañe, and Argelia—all of which are localities situated in the provinces of Loja and Zamora Chinchipe. By integrating long-term climate records, slope mapping, and soil characterization, we assessed the effects of temperature, precipitation, humidity, soil moisture, and terrain steepness on leaf presence, fruit maturation, and tree architecture. Over the past 20 years, temperature increased by 1.5 °C (p < 0.01), while precipitation decreased by 22%, disrupting local edaphoclimatic balances. More than 2000 individuals were measured in forest stands, with estimated ages ranging from 11 to 355 years. ANOVA results revealed that Tundo and Victoria exhibited significantly greater DBH, height, and volume (p ≤ 0.05), with Victoria showing a 30% larger DBH than Argelia, the lowest-performing provenance. Soils ranged from loam to sandy loam, with slopes exceeding 45% and pH levels from slightly acidic to neutral. These findings confirm the species’ pronounced phenotypic plasticity and ecological adaptability, directly informing site-specific conservation strategies and long-term forest management under shifting climatic conditions. Full article