Browse Articles
Discover research articles across all indexed journals
A geometric morphometric approach for detecting different reproductive stages of a free-ranging killer whale Orcinus orca population
Deep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach
Biomechanical evaluation of the modified proximal femoral nail for the treatment of reverse obliquity intertrochanteric fractures
Automated mechanical ventilator design and analysis using neural network
Bio-inspired preparation of Ag NPs, rGO, and Ag/rGO nanocomposites for acoustical, antioxidant, and plant growth regulatory studies
Research on the bearing performance of HSCA high-strength preloaded expansion piles in calcareous sand foundation
Diversity and composition of vegetation and soil seed banks after sand dune restoration by oil mulching and plantations
Abstract Soil seed bank (SSB) is valuable reserves of seeds hidden in the soil and are especially important for the preservation and establishment of vegetation under adverse environmental conditions. However, there is a lack of knowledge on the effects of restoration measures on SSB, especially in arid ecosystems. Here, we assess the impacts of oil mulching (1 and 3 years after mulching) and plantations (15-year-old) on the diversity and composition of SSB and aboveground vegetation (AGV) in comparison with those in non-restored areas (i.e., control). Based on the results, species richness was poor in the studied area (36 species belonging to 16 families), with a lower contribution of SSB than of AGV (11 and 34 species, respectively). The largest number of exclusive species was observed in the planted treatment for both SSB and AGV (4 and 5 species, respectively), while the lowest was found in the 1-yr mulching treatment. The mean comparison of the diversity indices revealed that the highest values occurred in the plantation and 3-yr-mulching plots. The seed density in the plantation area was significantly greater (4145 ± 694 seeds/m2) than that in the other areas (3-yr-mulching > 1-yr-mulching > control treatments (145 ± 53, 65 ± 28, and 43 ± 22, respectively). The results of the DCA showed that the plantation treatment was completely separated from the other treatments in terms of the plant composition of the AGV, and the treatment closest to that area was 3-yr mulching, which indicates the positive effect of time since mulching on plant composition. The results of this study suggest that there is a trade-off between the short-term and medium-term effects of oil mulching, such that in the early years, oil mulching has a negative effect on the AGV and SSB, but its positive effects increase with time since mulching. It can be concluded that mulching, along with afforestation, creates a favorable microclimate and improves the diversity and composition of AGV and SSB.
Development of detection system for lead ions in mixture solutions using UV-Vis measurements with peptide immobilized microbeads
Comprehensive pan-cancer analysis reveals NTN1 as an immune infiltrate risk factor and its potential prognostic value in SKCM
Cardiovascular disease risk in Korean patients with systemic lupus erythematosus compared to diabetes mellitus and the general population
Fluid flow impacts endothelial-monocyte interactions in a model of vascular inflammatory fibrosis
Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach
Rubber intercropping with arboreal and herbaceous species alleviated the global warming potential through the reduction of soil greenhouse gas emissions
Health literacy and influencing factors in university students across diverse educational fields in Kazakhstan
Pseudogenization of the Slc23a4 gene is necessary for the survival of Xdh-deficient mice
Characteristics of transition to turbulence in a healthy thoracic aorta using large eddy simulation
Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007–2016
Abstract Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS. After excluding adolescents with missing diagnostic variables, the dataset included 2,459 adolescents via NHANES data from 2007–2016. We used LASSO regression and 20-fold cross-validation to screen for the variables with the greatest predictive value. The dataset was divided into training and validation sets in a 7:3 ratio, and SMOTE was used to expand the training set with a ratio of 1:1. Based on the training set, we built eight machine learning models and a multifactor logistic regression model, evaluating nine predictive models in total. After evaluating all models using the confusion matrix, calibration curves and decision curves, the LGB model had the best predictive performance, with an AUC of 0.969, a Youden index of 0.923, accuracy of 0.978, F1 score of 0.989, and Kappa value of 0.800. We further interpreted the LGB model using SHAP, the SHAP hive plot showed that the predictor variables were, in descending order of importance, BMI age sex-specific percentage, weight, upper arm circumference, thigh length, and race. Finally, we deployed it online for broader accessibility. The predictive models we developed and validated demonstrated high performance, making them suitable for large-scale, non-medical primary screening and early warning of adolescent Metabolic syndrome. The online deployment of the model allows for practical use in community and school settings, promoting early intervention and public health improvement.