Search Results (1,691)

Soil organic carbon (SOC) pool plays an extremely important role in regulating the global carbon (C) cycle and climate change. Atmospheric nitrogen (N) and phosphorus (P) deposition caused by human activities has significant impacts on soil C sequestration potential of terrestrial ecosystem. To investigate the effects of N and P deposition on soil C sequestration and C-N coupling relationship in broad-leaved evergreen forests, a 6-year field nutrient regulation experiment was implemented in subtropical Castanopsis sclerophylla forests with four different N and P additions: N addition (100 kg N·hm⁻²·year⁻¹), N + P (100 kg N·hm⁻²·year⁻¹ + 50 kg P·hm⁻²·year⁻¹), P addition (50 kg P·hm⁻²·year⁻¹), and CK (0 kg N·hm⁻²·year⁻¹). The changes in the C and N contents and stable isotope distributions (δ¹³C and δ¹⁵N) of different soil organic fractions were examined. The results showed that the SOC and total nitrogen (STN) (p > 0.05) increased with N addition, while SOC significantly decreased with P addition (p < 0.05), and N + P treatment has low effect on SOC, STN (p > 0.05). By density grouping, it was found that N addition significantly increased light fraction C and N (LFOC, LFN), significantly decreased the light fraction C to N ratio (LFOC/N) (p < 0.05), and increased heavy fraction C and N (HFOC, HFN) accumulation and light fraction to total organic C ratio (LFOC/SOC, p > 0.05). Contrary to N addition, P addition was detrimental to the accumulation of LFOC, LFN and reduced LFOC/SOC. It was found that different reactive oxidized carbon (ROC) increased under N addition but ROC/SOC did not change, while N + P and P treatments increased ROC/SOC, resulting in a decrease in SOC chemical stability. Stable isotope analysis showed that N addition promoted the accumulation of new soil organic matter, whereas P addition enhanced the transformation and utilization of C and N from pre-existing organic matter. Additionally, N addition indirectly increased LFOC by significantly decreasing pH; significantly contributed to LFOC and ROC by increasing STN accumulation promoted by NO₃⁻-N and NH₄⁺-N; and decreased light fraction δ¹³C by significantly increasing dissolved organic C (p < 0.05). P addition had directly significant negative effect on LFOC and SOC (p < 0.05). In conclusion, six-year N deposition enhances soil C and N sequestration while the P enrichment reduces the content of soil C, N fractions and stability in Castanopsis sclerophylla forests. The results provide a scientific basis for predicting the soil C sink function of evergreen broad-leaved forest ecosystem under the background of future climate change. Full article

The landscapes of the Arctic seem endless. But they are also subject to anthropogenic impact, especially in urbanized and industrial ecosystems. The population of the Arctic zone of Russia is extremely urbanized, and up to 84% of the population lives in cities and industrial settlements. In this regard, we studied the background soils of forests and tundras and the soils of settlements. The main signs of the urbanogenic morphogenesis of soils associated with the transportation of material for urban construction are revealed. The peculiarities of soils of recreational, residential, and industrial zones of urbanized ecosystems are described. The questions of diversity and the classification of soils are discussed. The specificity of bulk soils used in the construction of industrial structures in the context of the initial stage of soil formation is considered. For the first time, soils and soil cover of settlements in the central and southern parts of the Yamal region are described in the context of traditional pedology. It is shown that the construction of new soils and grounds can lead to both decreases and increases in biodiversity, including the appearance of protected species. Surprisingly, the forms of urban soil formation in the Arctic are very diversified in terms of morphology, as well as in the ecological functions performed by soils. The urbanization of past decades has drastically changed the local soil cover. Full article

Background/Objectives: Urban green space has been increasingly recognized as a determinant of maternal and child health. This study investigated the association between prenatal exposure to different types of green space and the risk of congenital anomalies in South Korea. Methods: We analyzed data from the National Health Insurance Service (N = 142,422). Green space exposure was measured at the area level and categorized into grassland and forest; statistical analysis was performed using generalized estimating equations and generalized additive models to analyze the associations. Additionally, subgroup and sensitivity analyses were performed. Results: GEE analysis showed that a 10% increase in the proportion of grassland in a residential district was associated with a reduced risk of nervous system (adjusted odds ratio [aOR]: 0.77, 95% confidence interval [CI]: 0.63–0.94) and genitourinary system anomalies (aOR: 0.83, 95% CI: 0.71–0.97). The subgroup analysis results showed significance only for male infants, but the difference between the sexes was not significant. In the quartile-based analysis, we found a slightly significant p-value for trend for the effect of forests on digestive system anomalies, but the trend was toward increasing risk. In a sensitivity analysis with different exposure classifications, the overall and nervous system anomalies in built green space showed that the risk decreased as green space increased compared to that in the lowest quartile. Conclusions: Our results highlight the importance of spatial environmental factors during pregnancy and suggest that different types of green spaces differentially impact the offspring’s early health outcomes. This study suggests the need for built environment planning as part of preventive maternal and child health strategies. Full article

Background: Peptides are a class of molecules that can be presented as good antimicrobials and with mechanisms that avoid resistance, and the design of peptides with good activity can be complex and laborious. The study of their quantitative structure–activity relationships through machine learning algorithms can shed light on a rational and effective design. Methods: Information on the antimicrobial activity of peptides was collected, and their structures were characterized by molecular descriptors generation to design regression and classification models based on machine learning algorithms. The contribution of each descriptor in the generated models was evaluated by determining its relative importance and, finally, the antimicrobial activity of new peptides was estimated. Results: A structured database of antimicrobial peptides and their descriptors was obtained, with which 56 machine learning models were generated. Random Forest-based models showed better performance, and of these, regression models showed variable performance (R² = 0.339–0.574), while classification models showed good performance (MCC = 0.662–0.755 and ACC = 0.831–0.877). Those models based on bacterial groups showed better performance than those based on the entire dataset. The properties of the new peptides generated are related to important descriptors that encode physicochemical properties such as lower molecular weight, higher charge, propensity to form alpha-helical structures, lower hydrophobicity, and higher frequency of amino acids such as lysine and serine. Conclusions: Machine learning models allowed to establish the structure–activity relationships of antimicrobial peptides. Classification models performed better than regression models. These models allowed us to make predictions and new peptides with high antimicrobial potential were proposed. Full article

Background/Objectives: The SARS-CoV-2 and influenza A (H1N1)pdm09 pandemics have resulted in high numbers of ICU admissions, with high mortality. Identifying risk factors for ICU mortality at the time of admission can help optimize clinical decision making. However, the risk factors identified may differ, depending on the type of analysis used. Our aim is to compare the risk factors and performance of a linear model (multivariable logistic regression, GLM) with a non-linear model (random forest, RF) in a large national cohort. Methods: A retrospective analysis was performed on a multicenter database including 8902 critically ill patients with influenza A (H1N1)pdm09 or COVID-19 admitted to 184 Spanish ICUs. Demographic, clinical, laboratory, and microbiological data from the first 24 h were used. Prediction models were built using GLM and RF. The performance of the GLM was evaluated by area under the ROC curve (AUC), precision, sensitivity, and specificity, while the RF by out-of-bag (OOB) error and accuracy. In addition, in the RF, the im-portance of the variables in terms of accuracy reduction (AR) and Gini index reduction (GI) was determined. Results: Overall mortality in the ICU was 25.8%. Model performance was similar, with AUC = 76% for GLM, and AUC = 75.6% for RF. GLM identified 17 independent risk factors, while RF identified 19 for AR and 23 for GI. Thirteen variables were found to be important in both models. Laboratory variables such as procalcitonin, white blood cells, lactate, or D-dimer levels were not significant in GLM but were significant in RF. On the contrary, acute kidney injury and the presence of Acinetobacter spp. were important variables in the GLM but not in the RF. Conclusions: Although the performance of linear and non-linear models was similar, different risk factors were determined, depending on the model used. This alerts clinicians to the limitations and usefulness of studies limited to a single type of model. Full article

Background: Tongue squamous cell carcinoma (TSCC) is an aggressive oral malignancy characterized by early submucosal invasion and a high risk of cervical lymph node metastasis. Accurate and timely diagnosis is essential, but it remains challenging when relying solely on conventional imaging and histopathology. This systematic review aimed to evaluate studies applying artificial intelligence (AI) in the diagnostic imaging of TSCC. Methods: This review was conducted under PRISMA 2020 guidelines and included studies from January 2020 to December 2024 that utilized AI in TSCC imaging. A total of 13 studies were included, employing AI models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest (RF). Imaging modalities analyzed included MRI, CT, PET, ultrasound, histopathological whole-slide images (WSI), and endoscopic photographs. Results: Diagnostic performance was generally high, with area under the curve (AUC) values ranging from 0.717 to 0.991, sensitivity from 63.3% to 100%, and specificity from 70.0% to 96.7%. Several models demonstrated superior performance compared to expert clinicians, particularly in delineating tumor margins and estimating the depth of invasion (DOI). However, only one study conducted external validation, and most exhibited moderate risk of bias in patient selection or index test interpretation. Conclusions: AI-based diagnostic tools hold strong potential for enhancing TSCC detection, but future research must address external validation, standardization, and clinical integration to ensure their reliable and widespread adoption. Full article

Background: Overcrowded emergency departments (EDs) create significant challenges for patient management and hospital efficiency. In response, Amiens Picardy University Hospital (APUH) developed the “Prediction of the Patient Pathway in the Emergency Department” (3P-U) model to enhance patient flow management. Objectives: To develop and clinically validate an explainable artificial intelligence (XAI) model for hospital admission predictions, using structured triage data, and demonstrate its real-world applicability in the ED setting. Methods: Our retrospective, single-center study involved 351,019 patients consulting in APUH’s EDs between 2015 and 2018. Various models (including a cross-validation artificial neural network (ANN), a k-nearest neighbors (KNN) model, a logistic regression (LR) model, and a random forest (RF) model) were trained and assessed for performance with regard to the area under the receiver operating characteristic curve (AUROC). The best model was validated internally with a test set, and the F1 score was used to determine the best threshold for recall, precision, and accuracy. XAI techniques, such as Shapley additive explanations (SHAP) and partial dependence plots (PDP) were employed, and the clinical explanations were evaluated by emergency physicians. Results: The ANN gave the best performance during the training stage, with an AUROC of 83.1% (SD: 0.2%) for the test set; it surpassed the RF (AUROC: 71.6%, SD: 0.1%), KNN (AUROC: 67.2%, SD: 0.2%), and LR (AUROC: 71.5%, SD: 0.2%) models. In an internal validation, the ANN’s AUROC was 83.2%. The best F1 score (0.67) determined that 0.35 was the optimal threshold; the corresponding recall, precision, and accuracy were 75.7%, 59.7%, and 75.3%, respectively. The SHAP and PDP XAI techniques (as assessed by emergency physicians) highlighted patient age, heart rate, and presentation with multiple injuries as the features that most specifically influenced the admission from the ED to a hospital ward. These insights are being used in bed allocation and patient prioritization, directly improving ED operations. Conclusions: The 3P-U model demonstrates practical utility by reducing ED crowding and enhancing decision-making processes at APUH. Its transparency and physician validation foster trust, facilitating its adoption in clinical practice and offering a replicable framework for other hospitals to optimize patient flow. Full article

Background/Objectives: The aim was to identify patterns of autonomic and neuroendocrine reactivity to an immersive virtual reality (VR) social-emotional stressor and explore their associations with perceived stress and eating behavior. Methods: This one-group pretest–posttest study included 30 children and adolescents with obesity (15 boys and 15 girls), aged 8 to 17 years. The VR protocol consisted of two consecutive phases: a 5 min relaxation phase using the Forest application and a 5 min stimulation phase using a cognitively engaging VR game designed to elicit social-emotional stress. Physiological responses were measured using heart rate variability (HRV) indices and salivary stress biomarkers, including cortisol and alpha amylase. Subjective stress and eating responses were assessed via visual analogue scales (VAS) administered immediately post-exposure. The Three-Factor Eating Questionnaire (TFEQ-R21C) was used to evaluate cognitive restraint (CR), uncontrolled eating (UE), and emotional eating (EE). Results: The cortisol reactivity was blunted and may reflect both the attenuated HPA axis responsiveness characteristic of pediatric obesity and the moderate psychological challenge of the VR stressor used in this study. Two distinct autonomic response patterns were identified via exploratory factor analysis: (1) parasympathetic reactivity, associated with increased RMSSD and SDNN and decreased LF/HF, and (2) sympathetic activation, associated with increased heart rate and alpha-amylase levels and reduced RR intervals. Parasympathetic reactivity was correlated with lower perceived stress and anxiety, but also paradoxically with higher uncontrolled eating (UE). In contrast, sympathetic activation was associated with greater cognitive restraint (CR) and higher anxiety ratings. Conclusions: This study demonstrates that immersive VR game exposure elicits measurable autonomic and subjective stress responses in children and adolescents with obesity, and that individual differences in physiological reactivity are relevantly associated with eating behavior traits. The findings suggest that parasympathetic and sympathetic profiles may represent distinct behavioral patterns with implications for targeted intervention. Full article

Background: Extremely premature neonates are at increased risk for respiratory complications, often resulting in recurrent hospitalizations during early childhood. Early identification of preterm infants at highest risk of respiratory hospitalizations could enable targeted preventive interventions. While clinical and demographic factors offer some prognostic value, integrating transcriptomic data may improve predictive accuracy. Objective: To determine whether early-life gene expression profiles can predict respiratory-related hospitalizations within the first four years of life in extremely preterm neonates. Methods: We conducted a retrospective cohort study of 58 neonates born at <32 weeks’ gestational age, using publicly available transcriptomic data from peripheral blood samples collected on days 5, 14, and 28 of life. Random forest models were trained to predict unplanned respiratory readmissions. Model performance was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC). Results: All three models, built using transcriptomic data from days 5, 14, and 28, demonstrated strong predictive performance (AUC = 0.90), though confidence intervals were wide due to small sample size. We identified 31 genes and eight biological pathways that were differentially expressed between preterm neonates with and without subsequent respiratory readmissions. Conclusions: Transcriptomic data from the neonatal period, combined with machine learning, accurately predicted respiratory-related rehospitalizations in extremely preterm neonates. The identified gene signatures offer insight into early biological disruptions that may predispose preterm neonates to chronic respiratory morbidity. Validation in larger, diverse cohorts is needed to support clinical translation. Full article

Background: Probiotics can modulate the microbiota and decrease uric acid levels. Objectives: This meta-analysis aimed to assess the effects of probiotics on uric acid levels. Methods: The keywords “probiotics”, “uric acid”, “gout”, “hyperuricemia” were searched in PubMed Medline, EMBASE, Web of Science, and Google Scholar. The search was limited to the English, French, Italian, and Spanish languages, and to the period between 1 January 2000 to 30 August 2024. We included RCTs and observational studies comparing probiotics to placebo. We excluded studies reporting (1) prebiotics, symbiotics, or postbiotics, (2) animal studies, and (3) case reports, commentaries, or reviews. Two independent reviewers performed quality assessment and data extraction. This meta-analysis was performed according to the PRISMA 2020 and AMSTAR 2 guidelines. The main outcome measure was uric acid levels “after–before” probiotic versus placebo interventions. Forest plots summarized the data using a random model. Results: Nine studies included 394 patients, of whom 201 were treated with probiotics and 193 with placebo. There was a statistically significant difference in favor of the probiotic group compared with the control group regarding the main outcome measure. However, substantial heterogeneity was noted, explained (after applying subgroup analysis and meta-regression) by the following moderators: continent, diseased/healthy, male sex, and monostrain probiotics. Conclusions: This meta-analysis demonstrates that probiotics reduced uric acid levels in Asian males who had disease and were treated with monostrain probiotics. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

The background for this study is the limitations of the conventional approach of using deforestation area multiplied by biomass densities or emission factors. We demonstrated how TanDEM-X and GEDI data can be combined to estimate forest Above Ground Biomass (AGB) change at the national scale for Tanzania. The results can be further recalculated to estimate CO₂ emissions and removals from the forest. We used repeated short wavelength, InSAR DEMs from TanDEM-X to derive changes in forest canopy height and combined this with GEDI data to convert such height changes to AGB changes. We estimated AGB change during 2012–2019 to be −2.96 ± 2.44 MT per year. This result cannot be validated, because the true value is unknown. However, we corroborated the results by comparing with other approaches, other datasets, and the results of other studies. In conclusion, TanDEM-X and GEDI can be combined to derive reliable temporal change in AGB at large scales such as a country. An important advantage of the method is that it is not required to have a representative field inventory plot network nor a full coverage DTM. A limitation for applying this method now is the lack of frequent and systematic InSAR elevation data. Full article

Background: Decoding mental attention states from electroencephalogram (EEG) signals is crucial for numerous applications such as cognitive monitoring, adaptive human–computer interaction, and brain–computer interfaces (BCIs). However, conventional EEG-based approaches often focus on channel-wise processing and are limited to intra-session or subject-specific scenarios, lacking robustness in cross-session or inter-subject conditions. Methods: In this study, we propose a hybrid feature learning framework for robust classification of mental attention states, including focused, unfocused, and drowsy conditions, across both sessions and individuals. Our method integrates preprocessing, feature extraction, feature selection, and classification in a unified pipeline. We extract channel-wise spectral features using short-time Fourier transform (STFT) and further incorporate both functional and structural connectivity features to capture inter-regional interactions in the brain. A two-stage feature selection strategy, combining correlation-based filtering and random forest ranking, is adopted to enhance feature relevance and reduce dimensionality. Support vector machine (SVM) is employed for final classification due to its efficiency and generalization capability. Results: Experimental results on two cross-session and inter-subject EEG datasets demonstrate that our approach achieves classification accuracy of 86.27% and 94.01%, respectively, significantly outperforming traditional methods. Conclusions: These findings suggest that integrating connectivity-aware features with spectral analysis can enhance the generalizability of attention decoding models. The proposed framework provides a promising foundation for the development of practical EEG-based systems for continuous mental state monitoring and adaptive BCIs in real-world environments. Full article

Background/Objectives: Heart rate variability (HRV) has been widely investigated as a predictor of disease and mortality across diverse patient populations; however, there remains no consensus on the optimal set or combination of time and frequency domain nor on nonlinear features for reliable prediction across clinical contexts. Given the relevance of the COVID-19 pandemic and the unique clinical profiles of these patients, this retrospective observational study explored the potential of HRV analysis for early prediction of in-hospital mortality using ECG signals recorded during the initial moments of ICU admission in COVID-19 patients. Methods: HRV indices were extracted from four ECG leads (I, II, III, and aVF) using sliding windows of 2, 5, and 7 min across observation intervals of 15, 30, and 60 min. The raw data posed significant challenges in terms of structure, synchronization, and signal quality; thus, from an original set of 381 records from 321 patients, after data pre-processing steps, a final dataset of 82 patients was selected for analysis. To manage data complexity and evaluate predictive performance, two feature selection methods, four feature reduction techniques, and five classification models were applied to identify the optimal approach. Results: Among the feature aggregation methods, compiling feature means across patient windows (Method D) yielded the best results, particularly for longer observation intervals (e.g., using LDA, the best AUC of $0.82\pm 0.13$ was obtained with Method D versus $0.63\pm 0.09$ with Method C using 5 min windows). Linear Discriminant Analysis (LDA) was the most consistent classification algorithm, demonstrating robust performance across various time windows and further improvement with dimensionality reduction. Although Gradient Boosting and Random Forest also achieved high AUCs and F1-scores, their performance outcomes varied across time intervals. Conclusions: These findings support the feasibility and clinical relevance of using short-term HRV as a noninvasive, data-driven tool for early risk stratification in critical care, potentially guiding timely therapeutic decisions in high-risk ICU patients and thereby reducing in-hospital mortality. Full article

Background: Brazilian Jiu-Jitsu (BJJ) is a physically demanding sport associated with a notable risk of musculoskeletal injuries. Understanding injury patterns, rehabilitation approaches, and psychological readiness to return to sport (RTS) is essential for prevention and management strategies. This study aimed to investigate injury characteristics among Italian BJJ athletes, assess their rehabilitation processes and psychological recovery, and identify key risk factors such as belt level, body mass index (BMI), and training load. Methods: A cross-sectional survey was conducted among members of the Italian BJJ community, including amateur and competitive athletes. A total of 360 participants completed a 36-item online questionnaire. Data collected included injury history, rehabilitation strategies, RTS timelines, and responses to the Injury-Psychological Readiness to Return to Sport (I-PRRS) scale. A Random Forest machine learning algorithm was used to identify and rank potential injury risk factors. Results: Of the 360 respondents, 331 (92%) reported at least one injury, predominantly occurring during training sessions. The knee was the most frequently injured joint, and the action “attempting to pass guard” was the most reported mechanism. Most athletes (65%) returned to training within one month. BMI and age emerged as the most significant predictors of injury risk. Psychological readiness scores indicated moderate confidence, with the lowest levels associated with playing without pain. Conclusions: Injuries in BJJ are common, particularly affecting the knee. Psychological readiness, especially confidence in training without pain, plays a critical role in RTS outcomes. Machine learning models may aid in identifying individual risk factors and guiding injury prevention strategies. Full article