Search Results (156)

Vertical stratification is a property of forest habitats related to the differential distribution of organisms according to the variation in the conditions, from the understory to the canopy. Here, we aimed to test whether butterfly assemblages from highly disturbed forests maintain the pattern of vertical stratification. We hypothesized that degraded forests would not exhibit vertical stratification due to the low variation in the microhabitat conditions along the vertical gradient, resulting from the canopy openness. To test this, we sampled fruit-feeding butterflies with bait traps, alternately disposed between the understory and canopy of three secondary forest fragments in a very fragmented Atlantic Forest landscape, for one year. We found that the vertical strata differed in terms of species composition, with a high contribution by the nestedness component on the beta diversity spatial variation. The understory assemblages had a higher abundance and were more diverse than the upper stratum. We demonstrated that vertical stratification is maintained even in disturbed forests; however, this does not necessarily provide support for a good quality and functioning ecosystem in these habitats. The butterfly assemblages recorded here are a subset of the species pool that inhabits conserved remnants. Thus, even being represented by species commonly found in disturbed habitats, the dynamic of vertical stratification of assemblages remains. Full article

Soil aggregates, which are highly influenced by land conversion, play key roles in driving soil nutrient distribution and microbial colonization. However, the role of soil aggregates in shaping the responses of microbial community composition and multiple ecosystem functions, especially ecosystem multifunctionality (EMF), to land conversion remains poorly understood. In this study, we investigated the impact of the conversion of a longan orchard (LO) to a conventional tea plantation (CTP) and organic tea plantation (OTP) on soil EMF at the aggregate level and explored the underlying mechanism. Our results showed that EMF was significantly reduced in the conventional tea plantation, with 3.44, 1.79, and 1.24 times for large macro-, macro-, and micro-aggregates. In contrast, it was relatively preserved in the organic tea plantation. Notably, micro-aggregates with higher microbial biomass supported more EMF than larger aggregates under the land conversion conditions. The EMF associated with soil aggregates was found to be regulated by the differences in nutrient content and microbial community composition. Random forest analysis, redundancy analysis, and Pearson analysis indicated that both soil nutrient and microbial community composition within soil aggregates jointly determined EMF. This study highlights that soil aggregation influences the stratification of nutrients and microbial communities, which leads to the differing response of aggregate-related EMF to land conversion. Full article

Cultivated land is crucial for food production and security. In complex environments like mountainous regions, the fragmented nature of the cultivated land complicates rapid and accurate information acquisition. Deep learning has become essential for extracting cultivated land but faces challenges such as edge detail loss and limited adaptability. This study introduces a novel approach that combines geographical zonal stratification with the temporal characteristics of medium-resolution remote sensing images for identifying cultivated land. The methodology involves geographically zoning and stratifying the study area, and then integrating semantic segmentation and edge detection to analyze remote sensing images and generate initial extraction results. These results are refined through post-processing with medium-resolution imagery classification to produce a detailed map of the cultivated land distribution. The method achieved an overall extraction accuracy of 95.07% in Tongnan District, with specific accuracies of 92.49% for flat cultivated land, 96.18% for terraced cultivated land, 93.80% for sloping cultivated land, and 78.83% for forest intercrop land. The results indicate that, compared to traditional methods, this approach is faster and more accurate, reducing both false positives and omissions. This paper presents a new methodological framework for large-scale cropland mapping in complex scenarios, offering valuable insights for subsequent cropland extraction in challenging environments. Full article

The mechanisms underlying severe allergic asthma are complex and unknown, meaning it is a challenge to provide the most appropriate treatment. This study aimed to identify novel biomarkers for stratifying allergic asthmatic patients according to severity, and to uncover the biological mechanisms that lead to the development of the severe uncontrolled phenotype. By using miRNA PCR panels, we analyzed the expression of 752 miRNAs in serum samples from control subjects (n = 15) and mild (n = 11) and severe uncontrolled (n = 10) allergic asthmatic patients. We identified 40 differentially expressed miRNAs between severe uncontrolled and mild allergic asthmatic patients. Functional enrichment analysis revealed signatures related to inflammation, angiogenesis, lipid metabolism and mRNA regulation. A random forest classifier trained with DE miRNAs achieved a high accuracy of 97% for severe uncontrolled patient stratification. Validation of the identified biomarkers was performed on a subset of allergic asthmatic patients from the CAMP cohort at Brigham and Women’s Hospital, Harvard Medical School. Four of these miRNAs (hsa-miR-99b-5p, hsa-miR-451a, hsa-miR-326 and hsa-miR-505-3p) were validated, pointing towards their potential as biomarkers for stratifying allergic asthmatic patients by severity and providing insights into severe uncontrolled asthma molecular pathways. Full article

Forest fragmentation and urban shrinkage have become the focus of attention in global ecological conservation, with the goal of achieving sustainable development. However, few studies have been concerned with urban forest patterns in shrinking cities. It is necessary to explore whether the loss of the population will mitigate urban forest degradation. Thus, in this study, 195 shrinking cities were identified based on demographic datasets to characterize the spatiotemporal patterns of urban forests in China against a depopulation background. To illustrate the explicit spatial evolution of urban forests in shrinking cities in China, in this study, we reclassified land-use products and determined the annual spatial variations from 2000 to 2022 using area-weighted centroids and landscape pattern indexes. The effects of different climatic and topographical conditions on the spatiotemporal variations in the urban forest patterns against population shrinkage were discussed. The results demonstrated that the forest coverage rate in the shrinking cities of China increased from 40.05 to 40.47% with a generally southwestern orientation, and the most frequent decrease appeared from 2010 to 2015. Except for the temperate humid and sub-humid Northeast China, with plains and hills, all geographical sub-regions of the shrinking cities exhibited growing urban forests. Relatively stable movement direction dynamics and dramatic area changes in climatic sub-regions with large forest coverage were observed. The urban forest centroids of shrinking cities at a lower elevation exhibited more fluctuating changes in direction. The urban forests in the shrinking cities of China were slightly fragmented, and this weakened condition was identified via the decelerating fragmentation. The urban forests of the shrinking cities in the warm-temperate, humid, and sub-humid North China and basin regions exhibited the most pattern variations. Therefore, it is emphasized that the monitoring of policy implementation is essential due to the time lag of national policies in shrinking cities, especially within humid and low-altitude regions. This research concludes that the mitigation of urban deforestation in the shrinking cities of China is greatly varied according to moisture and altitude and sheds light on the effects of the population density from a new perspective, providing support for urban forest management and improvements in the quality of residents’ lives. Full article

Soluble interleukin 1 receptor-like 1 (ST2) is a circulating protein demonstrated to be associated with cardiovascular diseases; however, it has not been studied as a biomarker for peripheral artery disease (PAD). Using a prospectively recruited cohort of 476 patients (312 with PAD and 164 without PAD), we conducted a prognostic study of PAD using clinical/biomarker data. Plasma concentrations of three circulating proteins [ST2, cytokine-responsive gene-2 (CRG-2), vascular endothelial growth factor (VEGF)] were measured at baseline and the cohort was followed for 2 years. The outcome of interest was a 2-year major adverse limb event (MALE; composite of major amputation, vascular intervention, or acute limb ischemia). Using 10-fold cross-validation, a random forest model was trained using clinical characteristics and plasma ST2 levels. The primary model evaluation metric was the F1 score. Out of the three circulating proteins analyzed, ST2 was the only one that was statistically significantly higher in individuals with PAD compared to patients without PAD (mean concentration in plasma of 9.57 [SD 5.86] vs. 11.39 [SD 6.43] pg/mL, p < 0.001). Over a 2-year period, 28 (9%) patients with PAD experienced MALE. Our predictive model, incorporating clinical features and plasma ST2 levels, achieved an F1 score of 0.713 for forecasting 2-year MALE outcomes. Patients identified as high-risk by this model showed a significantly increased likelihood of developing MALE (HR 1.06, 95% CI 1.02–1.13, p = 0.003). By combining clinical characteristics and plasma ST2 levels, our proposed predictive model offers accurate risk assessment for 2-year MALE in PAD patients. This algorithm supports risk stratification in PAD, guiding clinical decisions regarding further vascular evaluation, specialist referrals, and appropriate medical or surgical interventions, thereby potentially enhancing patient outcomes. Full article

Background: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0_HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms. Methodology: 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0_HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0_HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data. Results: The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0_HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards. Conclusions: The hypothesis for AtheroEdge™ 3.0_HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically. Full article

Cytokine-induced neutrophil chemoattractant 1 (CINC-1), a cluster of differentiation 95 (CD95), fractalkine, and T-cell immunoglobulin and mucin domain 1 (TIM-1) are circulating proteins known to be involved in inflammation. While their roles have been studied in neurological conditions and cardiovascular diseases, their potential as peripheral artery disease (PAD) biomarkers remain unexplored. We conducted a cross-sectional diagnostic study using data from 476 recruited patients (164 without PAD and 312 with PAD). Plasma levels of CINC-1, CD95, fractalkine, and TIM-1 were measured at baseline. A PAD diagnosis was established at recruitment based on clinical exams and investigations, defined as an ankle-brachial index < 0.9 or toe-brachial index < 0.67 with absent/diminished pedal pulses. Using 10-fold cross-validation, we trained a random forest algorithm, incorporating clinical characteristics and biomarkers that showed differential expression in PAD versus non-PAD patients to predict a PAD diagnosis. Among the proteins tested, CINC-1, CD95, and fractalkine were elevated in PAD vs. non-PAD patients, forming a 3-biomarker panel. Our predictive model achieved an AUROC of 0.85 for a PAD diagnosis using clinical features and this 3-biomarker panel. By combining the clinical characteristics with these biomarkers, we developed an accurate predictive model for a PAD diagnosis. This algorithm can assist in PAD screening, risk stratification, and guiding clinical decisions regarding further vascular assessment, referrals, and medical/surgical management to potentially improve patient outcomes. Full article

(1) Background: Among lung diseases, idiopathic pulmonary fibrosis (IPF) appears to be the most common type and causes scarring (fibrosis) of the lungs. IPF disease patients are recommended to undergo lung transplants, or they may witness progressive and irreversible lung damage that will subsequently lead to death. In cases of irreversible damage, it becomes important to predict the patient’s mortality status. Traditional healthcare does not provide sophisticated tools for such predictions. Still, because artificial intelligence has effectively shown its capability to manage crucial healthcare situations, it is possible to predict patients’ mortality using machine learning techniques. (2) Methods: This research proposed a soft voting ensemble model applied to the top 30 best-fit clinical features to predict mortality risk for patients with idiopathic pulmonary fibrosis. Five machine learning algorithms were used for it, namely random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and multi-layer perceptron (MLP). (3) Results: A soft voting ensemble method applied with the combined results of the classifiers showed an accuracy of 79.58%, sensitivity of 86%, F1-score of 84%, prediction error of 0.19, and responsiveness of 0.47. (4) Conclusions: Our proposed model will be helpful for physicians to make the right decision and keep track of the disease, thus reducing the mortality risk, improving the overall health condition of patients, and managing patient stratification. Full article

While machine learning (ML) models hold promise for enhancing the management of acute kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is crucial for accurate patient stratification and timely interventions. This study aimed to systematically summarize existing evidence to determine the effectiveness of ML algorithms for predicting mortality in patients with sepsis-associated AKI. An exhaustive literature search was conducted across several electronic databases, including PubMed, Scopus, and Web of Science, employing specific search terms. This review included studies published from 1 January 2000 to 1 February 2024. Studies were included if they reported on the use of ML for predicting mortality in patients with sepsis-associated AKI. Studies not written in English or with insufficient data were excluded. Data extraction and quality assessment were performed independently by two reviewers. Five studies were included in the final analysis, reporting a male predominance (>50%) among patients with sepsis-associated AKI. Limited data on race and ethnicity were available across the studies, with White patients comprising the majority of the study cohorts. The predictive models demonstrated varying levels of performance, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.60 to 0.87. Algorithms such as extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) showed the best performance in terms of accuracy. The findings of this study show that ML models hold immense ability to identify high-risk patients, predict the progression of AKI early, and improve survival rates. However, the lack of fairness in ML models for predicting mortality in critically ill patients with sepsis-associated AKI could perpetuate existing healthcare disparities. Therefore, it is crucial to develop trustworthy ML models to ensure their widespread adoption and reliance by both healthcare professionals and patients. Full article

Background/Objectives: Myokines have been demonstrated to be associated with cardiovascular diseases; however, they have not been studied as biomarkers for peripheral artery disease (PAD). We identified interleukin-7 (IL-7) as a prognostic biomarker for PAD from a panel of myokines and developed predictive models for 2-year major adverse limb events (MALEs) using clinical features and plasma IL-7 levels. Methods: A prognostic study was conducted with a cohort of 476 patients (312 with PAD and 164 without PAD) that were recruited prospectively. Their plasma concentrations of five circulating myokines were measured at recruitment, and the patients were followed for two years. The outcome of interest was two-year MALEs (composite of major amputation, vascular intervention, or acute limb ischemia). Cox proportional hazards analysis was performed to identify IL-7 as the only myokine that was associated with 2-year MALEs. The data were randomly divided into training (70%) and test sets (30%). A random forest model was trained using clinical characteristics (demographics, comorbidities, and medications) and plasma IL-7 levels with 10-fold cross-validation. The primary model evaluation metric was the F1 score. The prognostic model was used to classify patients into low vs. high risk of developing adverse limb events based on the Youden Index. Freedom from MALEs over 2 years was compared between the risk-stratified groups using Cox proportional hazards analysis. Results: Two-year MALEs occurred in 28 (9%) of patients with PAD. IL-7 was the only myokine that was statistically significantly correlated with two-year MALE (HR 1.56 [95% CI 1.12–1.88], p = 0.007). For the prognosis of 2-year MALEs, our model achieved an F1 score of 0.829 using plasma IL-7 levels in combination with clinical features. Patients classified as high-risk by the predictive model were significantly more likely to develop MALEs over a 2-year period (HR 1.66 [95% CI 1.22–1.98], p = 0.006). Conclusions: From a panel of myokines, IL-7 was identified as a prognostic biomarker for PAD. Using a combination of clinical characteristics and plasma IL-7 levels, we propose an accurate predictive model for 2-year MALEs in patients with PAD. Our model may support PAD risk stratification, guiding clinical decisions on additional vascular evaluation, specialist referrals, and medical/surgical management, thereby improving outcomes. Full article

Background: Carotid stenosis (CS) is an atherosclerotic disease of the carotid artery that can lead to devastating cardiovascular outcomes such as stroke, disability, and death. The currently available treatment for CS is medical management through risk reduction, including control of hypertension, diabetes, and/or hypercholesterolemia. Surgical interventions are currently suggested for patients with symptomatic disease with stenosis >50%, where patients have suffered from a carotid-related event such as a cerebrovascular accident, or asymptomatic disease with stenosis >60% if the long-term risk of death is <3%. There is a lack of current plasma protein biomarkers available to predict patients at risk of such adverse events. Methods: In this study, we investigated several growth factors and biomarkers of inflammation as potential biomarkers for adverse CS events such as stroke, need for surgical intervention, myocardial infarction, and cardiovascular-related death. In this pilot study, we use a support vector machine (SVM), random forest models, and the following four significantly elevated biomarkers: C-X-C Motif Chemokine Ligand 6 (CXCL6); Interleukin-2 (IL-2); Galectin-9; and angiopoietin-like protein (ANGPTL4). Results: Our SVM model best predicted carotid cerebrovascular events with an area under the curve (AUC) of >0.8 and an accuracy of 0.88, demonstrating strong prognostic capability. Conclusions: Our SVM model may be used for risk stratification of patients with CS to determine those who may benefit from surgical intervention. Full article

Accurate quantification of forest biomass (FB) is the key to assessing the carbon budget of terrestrial ecosystems. Using remote sensing to apply inversion techniques to the estimation of FBs has recently become a research trend. However, the limitations of vertical scale analysis methods and the nonlinear distribution of forest biomass stratification have led to significant uncertainties in FB estimation. In this study, the biomass characteristics of forest vertical stratification were considered, and based on the integration of random forest and least squares (RF-LS) models, the FB prediction potential improved. The results indicated that compared with traditional biomass estimation methods, the overall R² of FB retrieval increased by 12.01%, and the root mean square error (RMSE) decreased by 7.50 Mg·hm⁻². The RF-LS model we established exhibited better performance in FB inversion and simulation assessments. The indicators of forest canopy height, soil organic matter content, and red-edge chlorophyll vegetation index had greater impacts on FB estimation. These indexes could be the focus of consideration in FB estimation using the integrated RF-LS model. Overall, this study provided an optimization method to map and evaluate FB by fine stratification of above-ground forest and reveals important indicators for FB inversion and the applicability of the RF-LS model. The results could be used as a reference for the accurate inversion of subtropical forest biomass parameters and estimation of carbon storage. Full article

The persistent conflict between strict conservation and community welfare highlights the growing need to address sustainable livelihoods in forest protection programs. The Natural Forest Protection Program (NFPP) is a comprehensive forest protection program spearheaded by the Chinese government. It is designed to facilitate the conservation and restoration of forest ecosystems through a range of interventions, including logging ban, management, tending, and afforestation efforts. Drawing upon longitudinal micro-level household survey data spanning five consecutive years from 2017 to 2021, this research quantifies the sustainable livelihood levels of frontline participants in the NFPP by examining two dimensions: livelihood capital stock and livelihood transformation capacity. Additionally, it investigates the internal differentiation phenomenon within this cohort. The findings suggest that forest worker households engaged in tasks related to forest management, tending, and afforestation are the frontline participants in the NFPP, in contrast to management, technical, and service personnel. Moreover, these forest worker households exhibit a pattern characterized by a higher livelihood capital stock but a lower livelihood transformation capacity compared to non-forest worker households. Furthermore, within forest worker households, there is a significant group differentiation phenomenon, resulting in inter-group differentials in the sustainable livelihood levels based on geographical and seniority stratification criteria. The developers of the global forest protection program should prioritize addressing the sustainable livelihood issues of frontline participants in the program, especially the real problem of mismatches between livelihood capital stock and livelihood transformation capacity. This can be achieved through designing income incentives, stimulating consumption, and other means to enhance the relatively disadvantaged position of frontline participants while balancing the coordination and fairness of the protection program based on the aspects of both protection and development. Full article

Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB, with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity, followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies. Full article