Search Results (58)

White clover (Trifolium repens L.) is a major legume in Mediterranean agroecosystems. This study systematically evaluates 15 autochthonous white clover populations from the Trikala region of Greece, focusing on chemical composition and derived nutritional indices relevant for germplasm characterization and breeding. Fifteen local populations were evaluated under controlled pot cultivation over two consecutive years. Clonal plants were harvested at the early flowering stage. Key traits—crude protein (CP), Ash, Fat, crude fibre (FIBRE), acid detergent fibre (ADF), neutral detergent fibre (NDF), digestible dry matter (DDM), dry matter intake (DMI), and relative feed value (RFV)—were measured. Combined ANOVA revealed significant differences among populations for all traits (p ≤ 0.001), while genotype × year interactions were present but generally minor compared to genotypic effects. Broad-sense heritability was high across most traits (H² = 90.8–99.4%), demonstrating strong genetic control. CP showed positive correlations with DDM, DMI, and RFV, whereas ADF and NDF were negatively correlated with intake and digestibility. Canonical and discriminant analyses showed that a reduced set of traits (CP, Ash, FIBRE, RFV) contributed strongly to differentiation among populations. Hierarchical clustering (heatmap) confirmed these groupings based on fibre and digestibility-related traits. Populations such as Dendrochori and Gorgogyri consistently showed favorable chemical and nutritional profiles, while Fiki and Dendrochori showed the highest stability across years. The present study highlights substantial genetic variability among local white clover populations and identifies trait structures of relevance for germplasm characterization. These findings enhance the characterization of genetic diversity in Trifolium repens and support its potential use in future breeding research under Mediterranean environments. Full article

Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine–metabolic disorder associated with insulin resistance (IR), visceral adiposity, and increased cardiometabolic risk. The visceral adiposity index (VAI) is a validated surrogate marker of adipose tissue dysfunction, but its relationship with circulating neurotrophins and adipokine balance in PCOS remains incompletely understood. In this study, 100 women with PCOS were stratified into lower- (n = 50) and higher-risk (n = 50) groups according to VAI. Anthropometric measures, fasting glucose and insulin concentrations, lipid profile, and serum levels of brain-derived neurotrophic factor (BDNF), nerve growth factor-β (NGFβ), leptin, adiponectin, and resistin were assessed. HOMA-IR, adipokine ratios and atherogenic indices were calculated. Multivariate regression showed that BDNF was independently associated with VAI and non-HDL cholesterol, whereas NGFβ was independently linked to HDL cholesterol and estradiol, highlighting neurotrophin relationships with metabolic and endocrine parameters beyond general adiposity. Correlation heatmap and network analyses demonstrated interconnected clusters linking visceral adiposity, IR, dyslipidemia, adipokine imbalance, and neurotrophins, with the leptin/adiponectin ratio emerging as a central integrative marker. These findings suggest that within a PCOS population, VAI-defined cardiometabolic risk is associated with distinct neurotrophin–adipokine signatures, highlighting neurotrophin–adipokine networks underlying visceral adiposity-driven cardiometabolic and endocrine risk. Full article

This study aimed to develop a robust morphometric-based framework for classifying Apis cerana populations using deep learning and machine learning approaches. Previous studies on Apis cerana population differentiation have primarily relied on manual morphometrics or genetic markers, which are labor-intensive and often lack scalability for large image-based datasets. Forewing landmarks were automatically detected through a deep learning model employing a heatmap regression and Hourglass Network architecture. The extracted coordinates were processed by Principal Component Analysis (PCA) for dimensionality reduction, and shape alignment was further refined through Procrustes ANOVA to minimize non-biological variation. Nine machine learning algorithms were trained and compared under identical preprocessing and validation settings. Among them, the Extra Trees classifier achieved the highest accuracy (99.7%) in distinguishing the three populations—A. cerana cerana from China and A. cerana indica from Thailand, the northern and southern populations. After applying error-based data filtering and retraining, classification accuracy improved further, with almost perfect population separation. The Procrustes ANOVA confirmed that individual variation significantly exceeded residual error (Pillai’s trace = 1.13, p < 0.0001), validating the biological basis of shape differences. Mahalanobis distance and permutation tests (10,000 rounds) revealed significant morphological divergence among populations (p < 0.0001). The integration of geometric alignment and ensemble learning demonstrated a highly reliable strategy for population identification, supporting morphometric and evolutionary studies in Apis cerana. Full article

Background: Herpes zoster (HZ) represents a substantial public health concern among aging populations, yet regional variability in clinical patterns and risk determinants remains insufficiently documented. In southeastern Romania, epidemiological data are limited, and the combined influence of demographic, behavioral, and metabolic factors on disease severity has not been systematically evaluated. Methods: We performed a retrospective observational study including 100 consecutive patients diagnosed with HZ between 2019 and 2023 in a dermatology department in southeastern Romania. Demographic characteristics, lifestyle behaviors, anthropometric status, clinical manifestations, and outcomes were extracted from medical records. Associations between categorical variables were assessed using Chi-square tests and Cramer’s V, while interaction patterns were explored through log-linear modeling. Heatmaps were generated in Python (version 3.10) using the Matplotlib library (version 3.7.1) to visualize distribution patterns and subgroup relationships. Results: The cohort showed a marked age dependence, with 77% of cases occurring in individuals ≥ 60 years, consistent with immunosenescence-driven reactivation. Women represented 59% of cases, and 84.7% of female patients were postmenopausal. Urban residents predominated (91%). Vesicular eruption (84%) and acute pain (79%) were the most frequent symptoms. Localized HZ was observed in 81% of cases, while ophthalmic involvement (11%) and disseminated forms (8%) were less common. Lifestyle factors significantly influenced clinical severity: smokers, alcohol consumers, and sedentary individuals exhibited higher proportions of postherpetic neuralgia (PHN) and ocular complications (p < 0.001). Overweight and obese patients demonstrated a higher burden of PHN, suggesting a role for metabolic inflammation, although BMI was not associated with incidence. No significant association between age category and complication type was detected, likely due to small subgroup sizes despite a clear descriptive trend toward increased severity with advanced age. Conclusions: These findings support a multifactorial model of HZ severity in southeastern Romania, shaped by age, lifestyle behaviors, hormonal status, and metabolic risk. While incidence patterns align with international data, the strong impact of modifiable factors on complication rates highlights the need for targeted prevention and individualized risk assessment. Results offer a regional perspective that may inform future multicenter investigations. Full article

Sarcopenia is highly prevalent among patients with chronic kidney disease (CKD) and chronic heart failure (CHF), yet the underlying immunometabolic mechanisms remain insufficiently understood. Short-chain fatty acids (SCFAs), inflammatory cytokines, and body-composition alterations may jointly contribute to the development of muscle dysfunction in this population. In this cross-sectional study, 80 patients with CKD and CHF underwent comprehensive clinical, biochemical, bioimpedance, inflammatory, and SCFA profiling. Sarcopenia was diagnosed according to EWGSOP2 criteria. Multivariable logistic regression, LASSO feature selection, correlation analysis, PCA, and Random Forest modeling were used to identify key determinants of sarcopenia. Sarcopenia was present in 39 (49%) participants. Patients with sarcopenia exhibited significantly lower body fat percentage, reduced ASM, and slower gait speed. Hexanoic acid (C6) showed an independent positive association with sarcopenia (OR = 2.24, 95% CI: 1.08–5.37), while IL-8 showed an inverse association with sarcopenia (OR = 0.38, 95% CI: 0.13–0.94), indicating that lower IL-8 levels were more frequently observed in individuals with sarcopenia. Correlation heatmaps revealed distinct SCFA–cytokine coupling patterns depending on sarcopenia status, with stronger pro-inflammatory clustering in C6-associated networks. The final multivariable model integrating SCFAs, cytokines, and body-composition metrics achieved excellent discrimination (AUC = 0.911) and good calibration. Sarcopenia in CKD–CHF patients represents a systemic immunometabolic disorder characterized by altered body composition, chronic inflammation, and dysregulated SCFA signaling. Hexanoic acid (C6) and IL-8 may serve as informative biomarkers of muscle decline. These findings support the use of multidimensional assessment and highlight potential targets for personalized nutritional, microbiota-modulating, and rehabilitative interventions. Full article

Urban park accessibility is often planned with fixed service radii, that is, circular walking catchments around each park defined by a maximum walking distance of about 1500 m, roughly a 15–20 min walk in this study, yet real visitation is uneven and dynamic, leaving persistent gaps between normative coverage and where people actually originate. We propose an interpretable discovery-to-parameter workflow that converts behavior evidence into localized accessibility and actionable planning guidance. Monthly Origin–Destination (OD) and heatmap samples are fused to construct visitation intensity on a 200 m grid and derive empirical park service boundaries. Multiscale Geographically Weighted Regression (MGWR) then quantifies spatial heterogeneity, and its local coefficients are embedded into the enhanced two-step floating catchment area (E2SFCA) model as location-specific supply weights and distance-decay bandwidths. Compared with network isochrones and uncalibrated E2SFCA, the MGWR–E2SFCA achieves higher Jaccard overlap and lower population-weighted error, while maintaining balanced coverage–precision across districts and day types. A $\Delta$-surface lens decomposes gains into corridor correction and envelope contraction, revealing where conventional radii over- or under-serve residents. We further demonstrate an event-sensitivity switch, in which temporary adjustments of demand and decay parameters can accommodate short-term inflows during events such as festivals without contaminating the planning baseline. Together, the framework offers a transparent toolset for diagnosing mismatches between normative standards and observed use, prioritizing upgrades in under-served neighborhoods, and stress-testing park systems under recurring demand shocks. For land planning, it pinpoints where barriers to access should be reduced and where targeted connectivity improvements, public realm upgrades, and park capacity interventions can most effectively improve urban park accessibility. Full article

This study addresses the critical conflict between livelihood preservation and commercial tourism in the residential historic districts of small-town China—a context often overlooked in urban studies. Taking Meizhou’s “One Town, Two Lanes” as a case, we propose a novel multi-source data fusion framework integrating POIs, population heatmaps, and questionnaire surveys. By applying Analytic Hierarchy Process (AHP), Poisson regression, and spatial correlation analysis, we quantitatively diagnose spatial disorders. The results reveal a dual-suppression mechanism: residential vitality, reliant on public services, is suppressed by commercial tourism, while tourist vitality is diminished by experience–quality gaps. This conflict manifests as pronounced vitality fractures. Our methodology and findings provide a replicable framework for diagnosing and resolving spatial conflicts in similar historic districts, emphasizing the imperative of prioritizing residential continuity. Full article

The development of tropical maize populations with high heterosis potential is essential for sustaining genetic progress in hybrid breeding programs, yet accurate selection remains challenging due to genotype–phenotype interactions and inbreeding depression. This study evaluated the efficiency of five selection strategies in recurrent selection programs using F₂ populations derived from commercial maize hybrids: Smith–Hazel Index (SHI), Base Index (BIA), Mulamba–Mock Index (MMI), REML/BLUP for grain yield (BLUP_GY), and REML/BLUP for inbreeding depression (BLUP_ID). Consistency among methods was assessed with a heatmap, and predicted genetic gains were compared with realized field performance. Predicted gains were highest with MMI and BIA for grain yield and ear weight, although realized results revealed discrepancies, particularly for BLUP-based approaches. Notably, BLUP_GY, which had the lowest predicted yield (4025 kg ha⁻¹), achieved a realized yield of 5620 kg ha⁻¹, surpassing BIA and SHI. This indicates that additive potential was underestimated in predictions, likely due to dominance and environmental effects in early F₂ cycles. Overall, BLUP-based methods proved effective in identifying progenies with higher additive value, and their integration with phenotypic indices is recommended to combine short-term realized gains with sustained genetic improvement. Full article

The study examined the association between body composition and beverage consumption and the risk of asthma and chronic obstructive pulmonary disease (COPD) and explored the single nucleotide polymorphisms (SNPs) involved in these associations by leveraging summary statistics from genome-wide association studies (GWAS) in nonoverlapping populations. The IEU OpenGWAS project was sourced for exposure datasets: body mass index, body fat percentage, fat-free mass, total body water mass, alcohol intake frequency, and coffee intake, and selected health outcome datasets: asthma and chronic obstructive pulmonary disease. Datasets were assessed and filtered using R, followed by a two-sample Mendelian randomization analysis. The MR Egger, weighted median, inverse variance weighted, simple mode, and weighted mode methods were used to examine the association between exposures and outcomes. Heterogeneity and pleiotropy analyses were used to evaluate the reliability of results. Additionally, SNPnexus was used to ascertain SNPs linked to established phenotypes, while SNP annotation was obtained from the Ensembl BioMart database via the biomaRt package. Genes belonging to overlapping groups were visualized using ComplexHeatmap. Higher body fat percentage (OR = 1.72, 95% CI: 1.23–2.41, p = 0.002), increased BMI (OR = 1.56, CI: 1.23–1.20, p = 2.53 × 10⁻⁴), and more frequent alcohol intake (OR = 1.34, CI: 1.08–1.68, p = 0.009) were associated with elevated COPD risk. Asthma risk was similarly increased with higher body fat percentage (OR = 1.60, CI: 1.23–2.21, p = 0.001), BMI (OR = 1.54, CI: 1.29–1.84, p = 2.23 × 10⁻⁶), fat-free mass (OR = 1.21, CI: 1.02–1.44, p = 0.032), and alcohol intake frequency (OR = 1.19, CI: 1.01–1.40, p = 0.039). Total body water mass and coffee intake were not associated with asthma and COPD. SNP annotation revealed that some genetic variants that influenced the association of the exposure variables with asthma and COPD were missense variants in several genes, including the evolutionarily highly conserved gene, SLC39A8 (rs13107325; C/A/T allele), and POC5 (rs2307111; T/A/C allele), as well as intronic variants in FTO (rs56094641; A/G/T allele) and NRXN3 (rs10146997; A/G allele). The discovery of the missense variants rs13107325 and rs2307111 in SLC39A8 and POC5, respectively, in addition to other intronic and synonymous SNPs suggests that these SNPs may have some roles in the development or progression of asthma and COPD. This may contribute to the identification of molecular signatures or biomarkers that forecast the risk, development, or therapeutic response of chronic lung diseases in persons with metabolic dysregulation, including obesity. Full article

In this study, we performed whole-genome resequencing (WGS) to investigate genomic variation and functional divergence among four wild Cymbidium species—C. ensifolium, C. sinense, C. kanran, and C. floribundum—collected from Fujian Province, China. A total of 350.58 Gbp of high-quality sequencing data was obtained from 13 samples, enabling comprehensive identification of SNPs and InDels. Genomic variants were unevenly distributed, with lower variation in gene-rich regions and higher levels in non-coding areas. Circos plots and variant density heatmaps revealed significant regional differences across chromosomes, with longer chromosomes exhibiting greater variant enrichment in 1 Mb windows. C. floribundum harbored the highest number of nonsynonymous SNPs and InDel-associated genes, whereas C. sinense and C. kanran had fewer mutations. KEGG pathway enrichment analysis revealed species-specific functional divergence, particularly in metabolism, stress response, and secondary metabolite biosynthesis. Population structure analysis and principal component analysis (PCA) indicated genetic differentiation among these species Notably, C. kanran exhibited high within-population genetic diversity. These findings provide essential genomic resources for the conservation and functional studies of wild Cymbidium species in subtropical China. Full article

Mental health disparities among those who self-identify as gay men in Peru remain a pressing public health concern, yet predictive models for early identification remain limited. This research aims to (1) develop machine learning and deep learning models to predict mental health issues in those who self-identify as gay men, and (2) evaluate the influence of demographic, economic, health-related, behavioral and social factors using interpretability techniques to enhance understanding of the factors shaping mental health outcomes. A dataset of 2186 gay men from the First Virtual Survey for LGBTIQ+ People in Peru (2017) was analyzed, considering demographic, economic, health-related, behavioral, and social factors. Several classification models were developed and compared, including Logistic Regression, Artificial Neural Networks, Random Forest, Gradient Boosting Machines, eXtreme Gradient Boosting, and a One-dimensional Convolutional Neural Network (1D-CNN). Additionally, the Shapley values and Layer-wise Relevance Propagation (LRP) heatmaps methods were used to evaluate the influence of the studied variables on the prediction of mental health issues. The results revealed that the 1D-CNN model demonstrated the strongest performance, achieving the highest classification accuracy and discrimination capability. Explainability analyses underlined prior infectious diseases diagnosis, access to medical assistance, experiences of discrimination, age, and sexual identity expression as key predictors of mental health outcomes. These findings suggest that advanced predictive techniques can provide valuable insights for identifying at-risk individuals, informing targeted interventions, and improving access to mental health care. Future research should refine these models to enhance predictive accuracy, broaden applicability, and support the integration of artificial intelligence into public health strategies aimed at addressing the mental health needs of this population. Full article

The ongoing global population growth and the corresponding rise in energy demand have contributed to increased greenhouse gas (GHG) emissions. The integration of alternative, locally sourced energy solutions such as biogas presents a promising strategy to partially offset conventional energy consumption. In this context, countries like Colombia—characterized by a high availability of organic waste such as palm oil mill effluent (POME), rice straw, and pig manure—have the potential to harness these residues for biogas production. This study integrates experimental assays of anaerobic co-digestion tests with the spatial analysis of substrate distribution through GIS tools, enabling the identification of optimal regions for biogas production. Methane yields reached 412 mL CH₄/g VS, comparable or superior to those reported in similar studies. In addition to laboratory assays, Geographic Information System (GIS) tools were used to generate a weighted heatmap index based on feedstock availability (POME, rice straw, pig manure) across 40 municipalities in Colombia. This integrated approach supports decentralized renewable energy planning and helps identify optimal locations for biogas plant development. Full article

Background: Greece is among the fastest-aging countries globally, with one of the highest proportions of elderly individuals. As a result, the prevalence of mild cognitive impairment (MCI) and dementia is among the highest in Europe. The distribution of affected individuals varies considerably across different regions of the country. Method: We estimated the number of people living with MCI or dementia in Greece and visualized these estimates using heatmaps by regions for four census years: 1991, 2001, 2011, and 2023 (the 2023 census was delayed due to the COVID-19 pandemic). Age- and sex-specific prevalence rates of MCI and dementia were obtained from the Hellenic Longitudinal Investigation of Aging and Diet. These prevalence rates were then applied to population data from each census to estimate the number of affected individuals per region. Results: There was a consistent increase in the number of people living with MCI, rising from 177,898 in 1991 to 311,189 in 2023. Dementia cases increased from 103,535 in 1991 to 206,939 in 2023. Projections based on future census data for 2035 and 2050 suggest that the number of people with MCI will reach 375,000 and 440,000, respectively, while dementia cases will increase to 250,000 in 2035 and 310,000 in 2050. Conclusion: Given that each person with dementia typically requires care from at least two caregivers over time, these projections highlight the profound impact the dementia epidemic will have on Greece. The heatmaps developed in this study can serve as valuable tools for policymakers in designing and implementing clinical care programs tailored to the needs of each region based on the projected burden of disease. Full article

Global warming has intensified the frequency and intensity of heatwaves, particularly in urban areas, significantly affecting residents’ daily activities. Extant studies have mainly concentrated on the relationship between socio-economic attributes and the impacts of heatwaves on urban populations. However, the relationship between the built environment and the impacts of heatwaves on urban population distribution has not received much attention. Furthermore, most studies have overlooked the temporal heterogeneity in heatwave impacts on population activities and distribution. Therefore, taking the central urban area of Nanchang as the case, this study investigated the impacts of heatwaves on population distribution and their temporal heterogeneity. Moreover, it identified the nonlinear relationships between built environment factors and population changes during heatwaves by using the XGBoost model and SHAP method. The results revealed that heatwaves exerted the largest impacts on population distribution during weekend nights, followed by weekend daytime and weekday nighttime, with the least impacts observed during weekday daytime. Furthermore, location and transportation factors significantly affected population changes during heatwaves across most time periods, with their influences being associated with policy factors such as the high-temperature leave policy for workers in industrial zones located in urban fringe areas and the cooling zone establishment policy for citizens in subway stations. Moreover, land use and building form factors exhibited significant temporal heterogeneity in their impacts on population changes during heatwaves. This temporal heterogeneity was fundamentally driven by individuals’ heat adaptation behaviors, the spatiotemporal patterns of their daily activities, and the diurnal variations in the built environment’s influence on local thermal environment. These findings provide valuable insights to proactively alleviate the adverse impacts of heatwaves. Full article

Accurate identification of urban agglomeration spatial range is essential for scientific regional planning, optimal resource allocation, and sustainable development, forming the basis for regional development policy. To improve the accuracy of identifying urban agglomeration boundaries, this study fuses nighttime light data, which reflects urban economic levels, with LandScan data representing population distribution and heatmap data indicating population mobility. This fusion allows for identification from a differentiated perspective of population distribution and mobility. We propose a new method for identifying the dynamic boundaries of urban agglomerations through multi-source data fusion. This method not only provides technical support for scientific regional planning but also effectively guides the functional positioning of edge cities and the optimization of resource allocation. The results show that the spatial range identified by NTL_LS has an accuracy of 80.37% and a kappa coefficient of 0.5225, while NTL_HM achieves an accuracy of 89.17% with a kappa coefficient of 0.7342, indicating that the fusion of economic level with population mobility data more accurately reflects the spatial range of urban agglomerations in line with real development patterns. By adopting a differentiated perspective on population distribution and mobility, we propose a new approach to identifying urban agglomeration spatial range. The research results based on this method provide more comprehensive and dynamic decision-making support for optimizing transportation layouts, allocating public resources rationally, and defining the functional positioning of edge cities. Full article