This paper analyzes the long-run associations between environmental, financial, macroeconomic, and healthcare factors on life expectancy in E7 countries over the period 1990-2023. Special emphasis is placed on PM2.5 air pollution exposure, which has become one of the key environmental factors influencing population health and longevity. The analysis examines the effects of domestic credit to the private sector (DCPS), foreign direct investment (FDI), inflation (INF), exposure to PM2.5 air pollution (PM2.5), immunization coverage (IMM), and hospital beds per 1,000 people (HBEDS) using life expectancy (LE) as the dependent variable. Second-generation panel unit root and cointegration tests are used in the empirical framework. FMOLS and DOLS estimators are then used. The CCEMG estimator and FE-DKSE are used to evaluate robustness. The findings show a stable long-run connection between the variables. Financial development and FDI are positively associated with LE, but inflation and PM2.5 exposure are negatively associated with LE. Immunization coverage is generally positively associated with life expectancy, while hospital beds are negatively associated with LE. The robustness estimations generally support the baseline findings. This study adds to existing research by examining how finances, economy, environment, and health care impact LE in the E7 countries. It takes into account the connections and differences between these nations. The findings highlight that financial growth, economic stability, clean environments, and preventive healthcare play crucial roles in long-term health outcomes for large emerging economies.

High and stable maize yields are central to ensuring global food security. However, canopy growth differentiation caused by traditional water and fertilizer management patterns remains a key bottleneck constraining maize productivity improvement. This study aimed to elucidate the regulatory effects of water-fertilizer integration on maize canopy uniformity and yield.

Background/Objectives: Root canal treatment (RCT) may be required after full-coverage crown placement in initially vital teeth due to biological and restorative factors affecting pulp vitality. However, the timing and distribution of clinical variables associated with teeth requiring RCT after crown placement remain insufficiently described. The aim of this study was to evaluate this timing and distribution using retrospective data collected between 2011 and 2024. Methods: This study included 588 vital teeth treated with metal-ceramic or zirconia crowns between 2011 and 2024. Recorded variables were age, gender, tooth type and location, crown material, bruxism, number of abutment teeth, and time to RCT. Statistical analyses were performed using SPSS v25.0, and as data were not normally distributed (Shapiro-Wilk test), Mann-Whitney U, Kruskal-Wallis, and chi-square or Fisher's exact tests were used (p < 0.05). Results: Of the 588 teeth, 36.1% were from male and 63.9% from female patients, and most crowns were metal-ceramic (80.8%), followed by zirconia (19.2%). Significant associations were observed between tooth group and jaw location (p < 0.001), number of abutment teeth (p < 0.001), and crown material type (p < 0.001). RCT was most frequently recorded during the fifth year after crown placement (52.6% of cases), with a mean time of 4.13 ± 1.79 years. Tooth extraction was observed in 5.3% of teeth. Conclusions: This study provides descriptive information regarding the distribution and timing of RCT in initially vital teeth following fixed prosthodontic restorations. The findings should be interpreted as a characterization of affected cases rather than as evidence of risk factors or predictors of RCT occurrence.

Ride-hailing global positioning system (GPS) mobile probe data provide flexible urban traffic observations, but their sparse and uneven coverage makes model evaluation difficult because observed targets, valid predictions, and historical input support do not always coincide. This study audits ultra-short-term road-segment speed forecasting under sparse mobile sensing using a mask-consistent support-chain framework. A three-day GPS dataset is aggregated into 5 min speed observations over 1970 road segments and used as a controlled sparse-sensing case study rather than a general-purpose long-term forecasting benchmark. The evaluation protocol distinguishes the full test grid, the set of directly observed target speeds, model-valid prediction support, strict complete-history support, and common-support subsets for coverage-limited baselines. The directly observed target set is used as the primary relaxed support because it retains all verifiable ground-truth targets, while strict and common-support subsets are reported as sensitivity checks. Under this support-conditioned evaluation, the adaptive graph convolutional recurrent network (AGCRN) is associated with lower mean absolute error (MAE) among full-coverage models, the historical mean (HIST_MEAN) baseline is associated with lower root mean squared error (RMSE), and congestion recall remains below 0.24 for all full-coverage deep models. These complementary results indicate conditional and metric-dependent strengths rather than universal model superiority. Because the dataset covers only three consecutive days, weekday/weekend variation, incident-specific fluctuations, seasonal effects, and spatial transferability cannot be fully examined and are treated as limitations. Overall, the findings show that evaluation support should be reported as a first-order experimental factor alongside model accuracy under sparse mobile probe sensing.

This retrospective study evaluated the anatomical variations and morphological interrelationships of mandibular anterior, premolar, and first molar (MFM) teeth in a Turkish subpopulation using cone-beam computed tomography (CBCT), while categorizing and comparing root canal anatomies based on both the traditional Vertucci and contemporary Ahmed's classification systems. The study analyzed CBCT images of 9,504 mandibular teeth from 792 patients. Root canal configurations were classified utilizing both the Vertucci and Ahmed's systems. The study additionally evaluated root counts, distolingual root (DLR) presence in MFMs, bilateral agreement, and cross-group anatomical correlations. All anterior teeth were single-rooted but displayed significant internal morphological diversity. MFMs predominantly featured a two-rooted structure, with a DLR (Radix Entomolaris) observed in 5.3%-6.8% of cases. A significant positive correlation emerged between complex canal configurations in lateral incisors and the presence of an ipsilateral MFM DLR (p = 0.003, OR = 2.23). Males exhibited significantly higher anatomical complexity in premolars than females. Mandibular teeth demonstrate high morphological diversity and bilateral agreement. The statistically significant but weak correlation between lateral incisor canal complexity and MFM DLR presence may represent a preliminary indicator that requires further validation. Furthermore, the Ahmed's system provides broader descriptive coverage for for defining complex root canal anatomies compared to the traditional Vertucci classification.

Accurate environmental and climate modelling depends on the availability of large observational datasets, yet the generation of such data is often costly and logistically challenging for individual research groups. This limitation is particularly acute in New Zealand, where geographic isolation, complex topography, and strong climatic gradients require dense observational networks for robust national and regional scale modelling. Here we present New Zealand's national precipitation isotope database, compiled through a community data-collection effort. The database integrates historical and contemporary datasets with varied analytical methods, sampling strategies, and temporal coverage. To enable combined use of these data, we describe a standardisation framework, including weighting using gridded precipitation data, and alignment of sampling periods to calendar months. We then show how the database updates and improves the Precipitation Isotopes New Zealand (PINZ) machine-learning isoscapes. Incorporation of newly contributed data, particularly from inland and high-elevation regions, reduces overall root mean square error relative to earlier model versions and substantially expands the model's area of applicability in New Zealand. Our results highlight that further advances will benefit from the availability of submonthly precipitation isotope measurements and improved coverage of alpine environments. We show that community participation offers a practical pathway to strengthening national precipitation isotope products and their application across hydrology, climate science, and related environmental science disciplines.

Reliable and accurate farm-level forecasting of greenhouse gas concentrations from dairy cattle is important to the formulation of climate change mitigation strategies and policy planning in livestock systems. This study proposed an uncertainty-aware deep learning model that integrated data from multiple sources, including remote sensing and ground-based sensors, to forecast hourly concentrations of nitrous oxide, methane, and carbon dioxide in a controlled zero-grazing dairy system. The forecasting was implemented by formulating a causal multivariate time series using a 24-h lookback window with direct one-step-ahead prediction. A two-stage model evaluation protocol, model hyperparameter tuning via rolling cross-validation, and holdout Test Evaluation were implemented during model development. The model was evaluated using the coefficient of determination, root-mean-square error, and mean squared error. To evaluate model reliability, the study employed a dual-output gated recurrent unit to estimate the conditional mean and heteroscedastic variance, and Monte Carlo dropout and Gaussian Negative Log-Likelihood to quantify epistemic and aleatoric uncertainty. Results indicate stable generalisation across temporal folds and strong probabilistic calibration, with empirical 95% coverage ranging from 93.6 to 94.8% on the holdout test set. Feature selection indicates that rainfall, normalised difference vegetation index, humidity, temperature, trend, and season influence the prediction of concentration. The study shows that incorporating exogenous variables improves model performance. The proposed framework demonstrates proof of concept for controlled zero-grazing systems, with potential for broader application following multi-site validation.

Increasing emergency medical service (EMS) capacity within integrated delivery systems is essential for universal health coverage, particularly in low- and middle-income countries. However, in underdeveloped ethnic regions of western China, developing EMS capacity within county-level medical alliances (CLMAs) remains a critical challenge. Using classic grounded theory, we conducted semistructured interviews with 47 health care professionals from 11 CLMAs across Guangxi, a representative underdeveloped, multiethnic, mountainous region. Systematic three-level coding of 575 statements revealed 57 initial concepts, 15 categories, and four core dimensions: institutional deficiencies (root cause), inefficient coordination (key bottleneck), resource shortcomings (direct manifestation), and service efficacy constraints (final outcome). These dimensions form a closed-loop constraint model reinforced by reverse feedback. Crucially, service efficacy constraints-specifically, poor emergency care quality and collective public cognitive bias-do not merely represent outcomes but actively reinforce the institutional deficiencies that generated them, trapping the system in a low-level equilibrium. This finding explains why piecemeal interventions fail. Effective strengthening requires simultaneously targeting all four dimensions to disrupt the negative cycle, a transferable strategy for integrated health systems facing analogous constraints worldwide.

South Sudan is endemic for all preventive chemotherapy neglected tropical diseases, and counties including Awerial, Kapoeta North and Kajo-Keji face persistent transmission and high absenteeism and refusal rates for mass drug administration (MDA). To identify barriers, we conducted a mixed-methods root cause analysis (combining a quantitative KoboToolbox survey with focus group discussions and key informant interviews), using WI-HER's identify, design, apply/assess, record, and expand (iDARE) methodology with gender equity and social inclusion (GESI) at its core. Fundamental to our approach, we trained community-selected influencers (local leaders and respected community figures) in GESI principles and behavior change techniques, supporting them to co-design and implement locally contextualized solutions. Following influencer-led engagement in Awerial County, cohort members demonstrated a 90.9% improvement in MDA uptake. This pilot project demonstrates that sustainable increases in MDA coverage require a community-grounded approach that integrates localized barrier analysis, GESI-informed influencer training and ongoing community engagement.

The management of immature permanent teeth with pulpal necrosis and prior endodontic failure remains clinically challenging due to incomplete root development, persistent infection, and reduced structural integrity. Regenerative endodontic procedures (REPs) offer a biologically driven alternative to apexification; however, their use in retreatment scenarios is not well documented. This report describes the successful treatment of a previously treated immature maxillary central incisor in a 7-year-old patient presenting with a chronic apical abscess and a longstanding sinus tract. Prior endodontic therapy was inadequate, with insufficient obturation and a poorly adapted full-coverage restoration contributing to coronal leakage and reinfection. Management involved removal of the existing root filling material, limited mechanical instrumentation, chemical disinfection with low-concentration sodium hypochlorite, placement of a triple-antibiotic paste, and subsequent induction of intracanal bleeding. Mineral trioxide aggregate was placed over the resulting scaffold, followed by definitive coronal restoration. Clinical and radiographic evaluations at 3, 12, and 34 months demonstrated complete resolution of apical pathology, absence of clinical symptoms, increased dentinal wall thickness, and continued root maturation with apical closure. This case supports the potential application of regenerative endodontic therapy in previously treated immature teeth with persistent infection. Establishing conditions favorable to biologic healing may allow preservation of regenerative capacity even in retreatment contexts. Further investigation is needed to clarify indications, predictability, and long-term outcomes of REPs in similar cases.

The albic horizon is a bleached diagnostic layer formed through combined processes of iron-manganese leaching and clay illuviation. Its thickness and burial depth collectively determine the critical role in impeding water movement and restricting root extension. However, understandings of the spatial variation of the albic horizon remain limited. Traditional understandings primarily rely on extensive field surveys combined with geostatistical methods. To expand such understandings on a broader geographical area, we developed a digital soil mapping (DSM) framework to predict the thickness and burial depth of the albic horizon. We integrated 111 soil profile points containing albic horizons with a rich set of environmental covariates to construct reliable prediction models. Following the feature selection process of recursive feature elimination (RFE), a quantile regression forest (QRF) model was employed for spatial prediction, cross-validation, and uncertainty estimation. The results from 50 repetitions of 10-fold cross-validation demonstrated robust model performance, with R[2] of 0.38 and 0.29 for thickness and burial depth, respectively, and RMSE% accounting for 34% and 38% of their mean values. The prediction interval coverage percentage (PICP) indicated that approximately 86.9% and 90.6% of the validation samples for thickness and burial depth, respectively, fell within the predefined 90% prediction interval (PI), affirming the reliability of uncertainty estimation. The relative variable importance indicated that climate factors were the dominant determinants in predicting both albic horizon thickness and burial depth, highlighting the necessity of incorporating climate data in the spatial modeling of albic soils. The prediction maps indicated a general decreasing trend in both the thickness and burial depth from northeast to south across the study area. Large prediction uncertainty mainly occurred in areas where soil survey points were lacking, highlighting the need for targeted supplementary surveys. Our findings offer valuable references for other similar large-scale mapping of soil layer thickness (or depth) in plain agricultural regions.