Search Results (268)

Background: The nasolabial angle is widely used in orthodontic diagnosis, but its composite geometric nature complicates interpretation. Whether it primarily reflects skeletal or soft-tissue influences remains unclear, as no previous study has quantitatively partitioned its angular components using multivariable regression in a large adult sample. Objective: The objective of this study is to quantify the relative angular contributions of skeletal classification and soft-tissue parameters to NLA morphology using a multivariate cephalometric model. Methods: A retrospective analysis of 504 adult lateral cephalograms was conducted. Subjects were categorized by sagittal (Class I, II, or III) and vertical skeletal patterns (hyperdivergent, normodivergent, and hypodivergent). Measured variables included the NLA, nasal inclination (N/FH), upper lip inclination (L/FH), upper lip thickness, and maxillary incisor inclination. Correlation and multivariable regression analyses were performed. Results: Although the NLA differed across sagittal classes (p = 0.001; η² = 0.047), upper lip inclination demonstrated the strongest association with the NLA (r = 0.766; R² = 0.588). In multivariable analysis, L/FH exhibited the largest standardized contribution (β = 0.752, p < 0.001), while vertical pattern and lip thickness were not independently associated. Class II showed a modest independent association (β = 0.083, p = 0.006). Conclusions: Nasolabial angle variation largely reflects upper lip orientation rather than skeletal classification alone. These findings support component-based interpretation, emphasizing separate evaluation of lip inclination during esthetic orthodontic assessment. Full article

The accurate delineation of mega-environments (MEs) and the rigorous evaluation of test locations are critical for optimizing regional variety trial schemes, particularly when addressing unbalanced datasets from multi-year, multi-location wheat (Triticum aestivum L.) trials. This study aimed at refining the regional wheat trial framework in the Huanghuai Winter Wheat Region (HWWR) of China using an integrated BLUP-GGE biplot approach, which combines best linear unbiased prediction (BLUP) values with genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis to account for temporal variability and experimental error. We systematically evaluated the BLUP-GGE biplot approach, focusing on its goodness of fit and its ability to resolve inter-location relationships. We further assessed test location representativeness, discriminating ability, and overall desirability via the BLUP-GGE biplot, and contrasted ME delineation outcomes between the traditional “which-won-where” polygon method and the test location clustering-based approach. The BLUP-GGE biplot explained 72.9% of total phenotypic variation, with all location vectors displaying positive correlations (maximum angle = 88.8°), confirming the ecological homogeneity of the target region and yielding robust evaluation results. Based on the ideal tester view, Puyang was identified as the most desirable location, followed by Zhumadian, Shangqiu, and Huixian, while Lianyungang and Suqian exhibited relatively poor comprehensive performance. MEs delineated by the “which-won-where” method showed strong inter-ME correlations and insufficient differentiation, whereas the location clustering-based method markedly enhanced inter-ME discrimination (maximum vector angle > 60°), stably partitioning the HWWR into three distinct MEs with clear cultivar–ME interaction patterns: ME1 (Lianyungang, Suqian, Fuyang, Suzhou, Guoyang, Huixian, Huai’an, Xinmaqiao, Huayin, and Yangling), ME2 (Luoyang, Xinxiang, Zhumadian, Shangqiu, Puyang, and Luohe), and ME3 (Baoji, Xuzhou, Yuanyang, Sheyang, and Xingyang). This study confirms the superiority of the BLUP-GGE biplot for analyzing unbalanced multi-year multi-environment trial data and validates a robust clustering strategy for ME delineation. The findings provide a scientific basis for optimizing wheat regional trial systems and facilitating precise cultivar deployment in the HWWR, and offer a reference for analogous studies on other crops or ecological regions. Full article

Hydrological fluctuations drive community dynamics in floodplain lakes, yet their integration into water resource management remains limited. Here, we integrated environmental DNA (eDNA) metabarcoding with hydroacoustic surveys to investigate vertebrate community assembly in China’s largest freshwater lake (Poyang Lake) during the winter dry season. We detected 65 vertebrate species, with Cypriniformes dominating. Beta-diversity partitioning revealed that turnover dominated taxonomic and functional dissimilarity, while phylogenetic beta diversity was characterized by nestedness, which is consistent with environmental filtering. Functional richness declined with water depth, coinciding with hydroacoustic vertical size stratification, indicating niche partitioning along depth gradients. Null model analysis showed stochastic processes (ecological drift) dominated regional assembly (72.97%), whereas joint species distribution modeling attributed explained variation to environmental factors (28.9%, notably water depth) and species associations (29.7%) at local scales. This hierarchical framework, regional stochasticity shaping the species pool and local deterministic filtering structuring communities, reframes environmental flow conceptualization: effective management must preserve the full spectrum of hydrological variability and maintain bathymetrically diverse habitats that support functional niche differentiation. The integrated eDNA-hydroacoustic approach offers a non-invasive, high-resolution toolkit for biological assessment within regulatory water quality frameworks. Full article

Reconciling hydropower development with aquatic biodiversity conservation is a central challenge for sustainable river management worldwide. Cascade dam configurations, in which multiple impoundments are arranged in series along a single channel, impose longitudinal environmental gradients that restructure biological communities across trophic levels. Whether the resulting multi-trophic responses are independently driven by shared abiotic gradients (environmental filtering) or mechanistically coupled through direct food-web interactions (trophic cascading) remains unresolved. We surveyed phytoplankton, zooplankton, and benthic macroinvertebrates simultaneously at seven stations along a 430 km gradient downstream of Danjiangkou Dam in the Hanjiang River, the largest tributary of the Yangtze River and the source of China’s South-to-North Water Diversion Middle Route, over eight seasonal campaigns (2015–2017). Variance partitioning, piecewise structural equation modeling, Mantel tests, and co-occurrence network analysis were applied to partition environmental and trophic pathways. Environmental filtering dominated community restructuring at all three trophic levels, while the biotic proxy for direct trophic interactions explained less than 0.4% of community variation, consistent with weak detectable trophic coupling at seasonal resolution. Distance from Danjiangkou Dam shaped downstream transparency and turbidity gradients that mediated trophic-level-specific responses along distinct environmental axes (pH and water temperature for phytoplankton, conductivity for zooplankton, and transparency for benthic macroinvertebrates). Benthic macroinvertebrates were systematically decoupled from the pelagic analytical framework, absent from the cross-trophic co-occurrence network and structured more by spatial configuration than by water-column variables. Hub species in the network were associated with downstream mineralized conditions, confirming that network architecture reflects shared environmental preferences rather than biotic interactions. These findings support a management shift from single-dam mitigation toward cascade-scale coordination of environmental flow regimes, sediment connectivity, and substrate restoration as integrated strategies for sustaining multi-trophic biodiversity in regulated rivers. Full article

Accurate fresh weight (FW) estimation is essential for growth monitoring and yield prediction in greenhouse fruit vegetables, but remains challenging due to the dynamic allocation between vegetative and reproductive organs. This study aimed to systematically evaluate modeling strategies for FW estimation in sweet pepper and identify which approach is most suitable under conditions of dynamic biomass partitioning. Non-destructive morphological measurements were collected under greenhouse cultivation, and allometric models based on geometric equations were established as baselines. Their performance was compared with machine learning (ML) models and ensemble learning frameworks. To address limited data availability, numerical data augmentation with Gaussian noise and a variational autoencoder was applied. Among the allometric models, the stick model combined with a sigmoid function showed the highest performance, with an $R^2$ of 0.80 for shoot FW and 0.54 for fruit FW. All ML models outperformed the allometric models, and the ensemble model achieved the highest predictive accuracy, with an $R^2$ of 0.96 for shoot FW and 0.89 for fruit FW. Data augmentation further improved predictive performance across all ML models, particularly for fruit FW prediction. Feature contribution analysis revealed that temporal progression was the dominant predictor of fruit FW, while structural traits played the primary role in shoot FW estimation. Ensemble-based ML, combined with data augmentation, provides a methodological framework for non-destructive FW estimation of sweet pepper in controlled environments such as greenhouses and smart farming systems. Full article

Hopea celebica Burck is an endangered dipterocarp endemic to Sulawesi, Indonesia, occurring in two ecologically contrasting habitats: karst and ultrabasic forests. These environments differ markedly in soil composition and topography, potentially driving ecological specialization and genetic divergence. To investigate the genetic variation and genetic structure of this species, we applied newly developed microsatellite (SSR) markers, together with the chloroplast DNA sequences of the trnL–trnF region. Genotypes at 15 SSR loci were determined for 255 individuals collected from six populations covering the range of the species’ distribution across karst and ultrabasic forests. Genetic diversity was consistently higher in karst than in ultrabasic populations. DIYABC and VarEff analyses revealed a historical bottleneck and earlier recovery in the karst populations. Analysis of molecular variance (AMOVA) revealed that 35% of the genetic variation was partitioned between habitat types (F_RT = 0.345, p = 0.001). Bayesian clustering (STRUCTURE), principal coordinate analysis (PCoA), and UPGMA dendrograms consistently showed two distinctive clusters corresponding to habitat type. Chloroplast haplotypes differed between populations in the karst and ultrabasic forests. These results suggest that populations in the karst and ultrabasic forests have undergone a long history of differentiation without migration. The strong habitat-related genetic structure likely reflects ecological isolation and early-stage speciation. We recommend treating the karst and ultrabasic populations as distinct conservation units to preserve the evolutionary potential and adaptive capacity of H. celebica under ongoing environmental change. Full article

Noise contamination is a common challenge in the analysis of time series data, where stochastic perturbations can obscure deterministic dynamics and complicate the interpretation of signals from chaotic and physiological systems. Reliable identification of noise regimes and their intensity is therefore essential for robust analysis of dynamical and biomedical signals, where incorrect attribution of stochastic perturbations can lead to misleading interpretations of system behavior. For this reason, the present study examines the role of complexity-based descriptors for identifying stochastic perturbations in time series and analyzes how these metrics respond to different noise regimes across heterogeneous dynamical systems. A supervised learning approach based on complexity descriptors was developed to analyze controlled perturbations in multiple signal types. Gaussian, pink, and low-frequency noise disturbances were injected at predefined intensity levels into the Rössler and Lorenz chaotic systems, the Hénon map, and synthetic electrocardiogram signals, while AR(1) processes were used for validation on inherently stochastic signals. From these systems, eighteen entropy-based, fractal, statistical, and singular value decomposition-based complexity metrics were extracted from either raw signals or reconstructed phase spaces. These features were used to perform three classification tasks that capture different aspects of noise characterization, including detecting the presence of noise, identifying the perturbation type, and discriminating between different noise intensities. In addition to predictive modeling, the study evaluates the complexity profiles and feature relevance of the metrics under varying perturbation regimes. The results show that no single complexity metric consistently discriminates noise regimes across all systems. Instead, system-specific relevance patterns emerge. Under given experimental constraints (data partitioning, machine learning algorithm, etc.), Approximate Entropy provides the strongest discrimination for the Lorenz system and the Hénon map, the Coefficient of Variation, Sample and Permutation Entropy dominate classification for ECG signals, and the Condition Number and Variance of first derivative together with Fisher Information are most informative for the Rössler system. Across all datasets, the proposed framework achieves an average accuracy of 99% for noise presence detection, 98.4% for noise type classification, and 98.5% for noise intensity classification. These findings demonstrate that complexity metrics capture structural and statistical signatures of stochastic perturbations across a diverse set of dynamic systems. Full article

Atomic charges play a central role in the analysis of molecular electronic structure and are widely used in the development of computational models. We introduce a simple and computationally efficient extension of Hirshfeld’s 1977 stockholder partitioning method, called scaled Hirshfeld, in which neutral proatom densities are scaled to construct a promolecular density better adapted to the molecular electron density. We present a fixed-point iterative algorithm to compute the proatom scaling coefficients and show that this formulation is equivalent to the information-theoretic additive variational Hirshfeld method with a minimal basis. This equivalence establishes a rigorous mathematical foundation for the scaled Hirshfeld method and ensures size consistency as well as the existence of a unique solution. Numerical results demonstrate that the proposed approach yields charges larger than those obtained with the original Hirshfeld method, while retaining computational efficiency and providing an improved description of molecular dipole moments and electrostatic potentials. Full article

The generative mechanisms underlying multifractal scaling in complex systems remain a fundamental unsolved problem, limiting our ability to distinguish healthy from pathological dynamics, predict system failures, or understand how scale-invariant organization emerges across vastly different physical domains. We resolve this challenge by introducing threshold sensitivity analysis—an extension of Chhabra–Jensen’s direct method—as a framework that classifies cascade types by examining how scaling reliability varies across moment orders q. Different q values systematically probe weak fluctuations (negative q) versus strong fluctuations (positive q), and the coefficient of determination ($r^2$) of partition function regressions quantifies scaling reliability at each q. Analyzing $r^2\left(q\right)$ patterns in 280 cardiac recordings (healthy controls through fatal heart failure), 200 financial time series (global equity markets and currencies, 2000–2025), and 80 climate stations (tropical to continental zones, 2000–2025), we discover a universal diagnostic signature: symmetric expansion of valid scaling behavior under relaxed $r^2$ thresholds, spanning both weak and strong fluctuations. This threshold sensitivity fingerprint—predicted by synthetic cascade simulations but never before validated empirically—uniquely identifies additomultiplicative cascades, hybrid processes that randomly alternate between additive stabilization and multiplicative amplification. Critically, this symmetric signature persists universally across domains: cardiac dynamics maintain consistent patterns across health and disease states, financial markets show varying robustness across asset classes (currencies more variable than US equities) while preserving a hybrid structure, and climate systems exhibit geographical variations (subtropical/continental stronger than tropical) without altering fundamental cascade type. These findings suggest that additomultiplicative organization is a unifying feature of complex adaptive systems, offering a resolution to decades of debate between additive and multiplicative models. The $r^2\left(q\right)$ profiling provides a mechanistic diagnostic capable of detecting early dysfunction, assessing system resilience, and revealing how environmental constraints shape—but do not determine—the fundamental principles governing multifractal complexity. Full article

Fine particulate matter (PM2.5) and ozone (O₃) are the key factors restricting the continuous improvement of air quality in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZT). Due to the potential correlation between variations in urban PM2.5–O₃ concentration, the analysis of its composite pollution characteristics is helpful in formulating accurate and thorough air control policies. Based on the long-term concentration change in PM2.5 and O₃, this study analyzed the features and synergistic factors of PM2.5–O₃ pollution in the CZT by using spatial autocorrelation and a linear driving model of PM2.5–O₃. The results showed that from 2017 to 2023, under the current Chinese atmospheric environment standard, the CZT saw four combined pollution days. However, if the daily limit values were viewed in line with Grade II of the WHO transition period (O₃: 120 μg/m³, PM2.5: 50 μg/m³), the combined pollution days would reach 111. The concentration of O₃ in Zhuzhou and Xiangtan was about 10 μg/m³ lower than that in Changsha. Lower SO₂ levels in Changsha might influence the partitioning of OH radicals and reactive nitrogen species, potentially affecting local O₃ formation efficiency. NO₂ and meteorological conditions jointly influence the co-variation in PM2.5 and O₃, with NO₂ playing a more significant role in PM2.5 formation. The long-term time series and daily concentrations of PM2.5 and O₃ in the CZT showed opposing values, but there were short-term synergistic events on the scale of daily concentrations, and the time period was typically 3–10 days. Low humidity and strong sunlight may cause antagonistic events in which the concentration of O₃ rises rapidly. Under static and stable weather conditions with low wind speed, no rainfall and moderate humidity, the concentration of PM2.5 and O₃ rose alternately on sunny and cloudy days, demonstrating synergistic growth. Full article

Elevation is a strong proxy for the thermal environment because it causes a predictable drop in temperature and food availability. This restricts cave-dwelling bats to species with specific metabolic traits, such as torpor or migration to avoid cold stress. In this context, we aimed to reveal how thermal niche differentiation structures 25 cave-dwelling bat assemblages along elevation gradients in two of the largest Bulgarian mountains—Stara Planina and Rhodopi. Multivariate PERMANOVA showed significant differences in bat assemblages among elevation groups (F = 1.616, p = 0.046), with altitude and temperature explaining 32.4% of the variance (p = 0.001). A high degree of species turnover (91.12% dissimilarity), driven by temperature niches, was observed: mesophilic Rhinolophus species dominated warm, low-elevation caves, while cold-adapted Myotis species were more common at high elevations. SIMPER analysis identified R. euryale as an indicator in low-elevation caves (p = 0.012) and the M. myotis/blythii complex at high elevations (p = 0.003). Alpha diversity showed no variation across elevation groups (p = 0.293), indicating that species turnover occurs without overall changes to local diversity. Mid-elevation assemblages lacked specific indicator species and resembled high-elevation communities, forming an ecotone. Thermal niche partitioning, as a physiological filter, shapes cave-dwelling bat assemblages and affects climate change range-shift predictions. Full article

Soybean (Glycine max (L.) Merr.) is a globally important legume valued for the high protein, oil, and carbohydrate content of its seeds. However, it is difficult to simultaneously optimize the content of these three macronutrients due to their negatively correlated metabolic pathways and complex quantitative inheritance patterns. In this study, a genome-wide association study (GWAS) was conducted to elucidate the genetic architecture underlying the seed protein, oil, and carbohydrate content in 328 soybean accessions evaluated over two years using near-infrared spectroscopy. Significant negative correlations (r = −0.323 to −0.656, p < 0.001) were observed between the three traits, confirming the trade-off in carbon partitioning during seed development. The GWAS identified nine significant loci distributed across chromosomes 4, 6, 8, 9, 10, 16, and 18 as stable quantitative trait loci (QTLs) regulating the three traits. Of these, three pleiotropic loci (qProOil.4, qProOil.16, and qOilCarb.6) were found to be associated with multiple seed compositional traits. Haplotype analysis revealed seven haplotype blocks with distinct phenotypic variation, indicating that they have the potential for use as trait-specific markers in marker-assisted selection. Functional annotation of the stable QTL regions identified 22 putative genes, among which five candidate genes, Glyma.06g201700, Glyma.08g281900, Glyma.09g164900, Glyma.13g155600, and Glyma.16g209800 were likely to be involved in carbon allocation, protein biosynthesis, lipid metabolism, and carbohydrate modification pathways based on their relative expression levels. Overall, this study enhances the understanding of the genetic and molecular mechanism controlling the composition of soybean seed and identifies promising genomic targets for precision breeding programs aimed at improving nutritional quality. Full article

The natural regeneration of planted forests in karst landscapes is severely constrained by extreme substrate heterogeneity and fragile edaphic conditions. However, the relative importance and interaction pathways of environmental versus stand structural drivers remain poorly quantified. In this study, 54 plots (10 m × 10 m) were surveyed across Cupressus funebris plantations in the karst landscape of the Lijiang River Basin, southern China. To identify the key factors and causal pathways governing regeneration, redundancy analysis (RDA), variation partitioning, partial least squares structural equation modeling (PLS-SEM), and threshold analyses were applied. Regeneration exhibited pronounced spatial heterogeneity, with 42.6% of plots showing complete recruitment failure and a characteristic inverted J-shaped size class distribution. The analysis identified soil rock fragment content (as a negative constraint) and canopy gap area (as a positive driver) as the two dominant predictors. PLS-SEM revealed that environmental factors influence regeneration primarily through an indirect pathway mediated by stand structure ($R^2=0.683$) rather than through direct effects. Threshold analyses identified quantitative benchmarks for key drivers, including a gap area breakpoint of approximately 10 ${\mathrm{m}}^2$ and a presence–absence effect of soil rock fragments. These findings contribute to a more sophisticated mechanistic understanding of forest regeneration in karst ecosystems and provide an empirical foundation for silvicultural management that aims to encourage natural regeneration and ecological restoration of degraded karst plantations. Full article

Fructokinase (FRK) initiates fructose phosphorylation, channeling carbon into central metabolic pathways, yet its functional diversity and regulatory networks in C₄ cereals remain poorly understood. Here, we performed a comprehensive pan-genome analysis of the FRK gene family in foxtail millet (Setaria italica), identifying 697 SiFRKs across 110 accessions and revealing extensive presence–absence variation shaped by evolution and domestication. Among nine characterized members in the reference genome, SiFRK4 exhibited broad and high expression, a diurnal rhythm, and substantial natural variation. Biochemical assays confirmed its fructokinase activity in vitro. We discovered a novel physical interaction between SiFRK4 and the key photoreceptor Phytochrome C (SiPhyC), which co-localized in the cytoplasm. Functional analysis of SiPhyC mutants demonstrated that loss of SiPhyC disrupted carbohydrate homeostasis, elevating fructose while depleting sucrose and starch. Our findings reveal a physical and genetic link between the light-signaling component SiPhyC and the metabolic enzyme SiFRK4, suggesting their interaction influences carbon partitioning. This study provides foundational insights into the sugar metabolism network of a resilient C₄ model crop and identifies potential targets for metabolic engineering and breeding. Full article

Scientific flood season segmentation serves as the foundation for determining the flood-limited operating water levels across different periods, providing crucial support for reservoir flood control safety operations and optimal water resource utilization. Under the background of climate change, the traditional static flood-limited water level management model based on fixed dates struggles to adapt to variations in flood season patterns. This study aims to establish a scientifically sound flood season segmentation scheme, providing a basis for dynamic control of flood-limited water levels across different periods, thereby improving water resource utilization efficiency while ensuring flood control safety. This study focuses on the Wuluwati Reservoir and employs the circular distribution method and the Fisher optimal partition method to conduct its flood season segmentation calculations. First, the circular distribution method is used to analyse the concentration and periodic characteristics of flood occurrences in the basin. Subsequently, the Fisher optimal partition method is applied to perform statistical segmentation of the historical hydrological series. Based on this analysis, the flood season of the Wuluwati Reservoir is comprehensively determined as: the pre-flood season from 1 June to 2 July, the main flood season from 3 July to 27 August, and the post-flood season from 28 August to 30 September. To objectively evaluate the rationality of the segmentation results, the improved Cunderlik method was employed to examine the rationality of 15 segmentation schemes based on relative superiority degree. The results show that the scheme with the main flood season from 3 July to 23 August achieves the highest relative superiority degree (0.930). The comprehensively determined segmentation of this study (3 July–27 August) encompasses this optimal interval, demonstrating that the flood season segmentation for the Wuluwati Reservoir is reasonable and effective. Full article