Search Results (373)

Coptis chinensis var. brevisepala W. T. Wang & P. G. Xiao is an endemic and endangered medicinal plant in China whose wild populations are rapidly declining under the combined pressures of overharvesting, climate change, and habitat fragmentation. Using genotyping-by-sequencing, we analyzed 87 individuals from 15 populations in Zhejiang Province, China, and identified 155,611 high-quality SNPs. The species exhibited low genetic diversity and strong genetic differentiation among populations with restricted gene flow (population-averaged H_o = 0.066, H_e = 0.067, π = 0.078, F_IS = 0.029, F_ST = 0.503, Nm = 0.329, gRelMig = 0.136). Analysis of molecular variance showed that variation among populations accounted for 73.58% of the total genetic variation (p < 0.001). A phylogenetic tree, principal component analysis (PCA), and admixture analysis consistently resolved the 15 populations into two major groups, which could be further subdivided into four subgroups. Mantel and partial Mantel tests indicated that geographic isolation is the primary driver of genetic differentiation, while environmental factors such as ultraviolet radiation and low temperature may contribute to fine-scale divergence at local spatial scales. Furthermore, MMRR analysis provided further confirmation of the independent and dominant role of geographic isolation. This study provides key data on the genetic diversity and population structure of C. chinensis var. brevisepala and offers a genetic basis for developing regionally differentiated conservation strategies and promoting its sustainable utilization. Full article

Adlay (Coix lacryma-jobi L.) stands out as a vital health-promoting cereal due to its dual nutritional and medicinal properties; however, it remains significantly underdeveloped compared to major crops. The lack of mechanistic understanding of its caryopsis development and trait formation severely constrains targeted genetic improvement. While transformative technologies, specifically micro-computed tomography (micro-CT) imaging combined with AI-assisted analysis (e.g., Segment Anything Model (SAM)) and multi-omics approaches, have been successfully applied to unravel the structural and physiological complexities of model cereals, their systematic adoption in adlay research remains fragmented. Going beyond a traditional synthesis of these methodologies, this article proposes a novel, multidimensional framework specifically designed for adlay. This forward-looking strategy integrates high-resolution 3D phenotyping with spatial multi-omics data to bridge the gap between macroscopic caryopsis architecture and microscopic metabolic accumulation. By offering a precise digital solution to elucidate adlay’s unique developmental mechanisms, the proposed framework aims to accelerate precision breeding and advance the scientific modernization of this promising underutilized crop. Full article

Pedestrian safety in school zones is shaped by traffic conditions and street design characteristics, whose combined effects involve uncertainty and gradual transitions rather than sharp thresholds. This study presents an integrated assessment framework based on the analytic hierarchy process (AHP) and fuzzy logic to evaluate pedestrian traffic stress level (PTSL) at the street-segment scale in school environments. AHP is used to derive input-variable weights from expert judgments, while a Mamdani-type fuzzy inference system models the relationships between traffic and geometric variables and pedestrian stress. The model incorporates vehicle density, pedestrian density, lane width, sidewalk width, buffer zone, and estimated traffic flow speed as input variables, represented using triangular membership functions. Genetic Algorithm (GA) optimization is applied to calibrate membership-function parameters, improving numerical consistency without altering the linguistic structure of the model. A comprehensive rule base is implemented in MATLAB (R2024b) to generate a continuous traffic stress score ranging from 0 to 10. The framework is applied to street segments surrounding major schools in the study area, enabling comparison of spatial variations in pedestrian stress. The results demonstrate how combinations of traffic intensity and street geometry influence stress levels, supporting data-driven pedestrian safety interventions for sustainable school environments and low-stress urban mobility. Full article

Background: Plant architecture, particularly branching patterns, plays a crucial role in plant growth, photosynthetic performance, and yield. Spur-type apple, characterized by compact growth, early fruiting, high productivity, and manageable canopy structure, represent valuable germplasm for establishing dwarf and high-density apple orchards. While hybrid breeding of spur-type varieties offers significant potential for genetic advancement, severe segregation of traits in hybrid progeny and the difficulty of combining multiple favorable traits still significantly limit breeding efficiency. Moreover, the genetic basis and molecular mechanisms of the spur-type trait remain poorly understood at the genomic level, hindering the development of precise molecular breeding approaches. Methods: To address this, we used the spur-type line ‘0301-13-14’ and the non-spur-type line ‘0301-50-32’ from hybrid progenies of the spur-type cultivars ‘Miyazaki Spur Fuji’ and ‘Starkrimson’ to elucidate the regulatory mechanisms underlying apple branch formation and spur-type trait development by characterizing their branching traits, performing whole-genome resequencing analysis, and identifying candidate genes using bioinformatics analyses. Results: Anatomical observations revealed that the spur-type line ‘0301-13-14’ possessed smaller cells with a more compact spatial arrangement compared to the non-spur-type line ‘0301-50-32’. Whole-genome resequencing generated 5,003,968 high-quality single-nucleotide polymorphisms (SNPs) and 577,886 high-quality insertions/deletions (InDels). We further identified 29,157 candidate genes harboring predicted deleterious mutations (classified as high or moderate impact). Gene Ontology (GO) enrichment analysis indicated that genes associated with the spur-type trait were mainly enriched in molecular function and biological process categories. Specifically, variant genes related to molecular function were enriched in transferase and catalytic activities, while those in biological process were mainly involved in phosphorylation and phosphorus metabolism. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that candidate genes were significantly enriched in environmental information processing and metabolic pathways. Conclusions: These results will provide a genomic foundation for identifying genes controlling spur-type branching traits and facilitate the genetic improvement of spur-type apple. Full article

Vif and Vpr are HIV-1 accessory proteins that create optimal conditions for viral replication. They are considered as potential targets for the development of therapeutic agents. Natural amino acid substitutions in these proteins have previously been associated with disease progression. The aim of this study was to analyze the genetic diversity of Vif and Vpr in HIV-1 group M clades. A total of 5286 sequences were downloaded and analyzed. For 37 clades in group M, the consensus sequences, amino acid natural variation, and clade-specific amino acid residue substitutions (CSSs) were evaluated. Structural analysis and modeling of consensus sequences were performed for subtypes A1, B, C, and D. The average conservation degree in the HIV-1 group M was 86.4% for Vif and 91.3% for Vpr. In both proteins, the lowest amino acid diversity was observed in sub-subtype A6, and the highest in subtype B. In consensus sequences, the substitutions, which might influence pathogenesis, have been determined: in Vif—22H (11_cpx, 91_cpx) and 136P (A6, 01_AE, 15_01B, 59_01B, 89_BF1, 103_01B, 111_01C, 133_A6B), in Vpr—41N (06_cpx) and 55A (B, 07_BC, 35_01D, 56_cpx, 66_cpx, 66_BF1, 71_BF1, 85_BC, 137_0107). In functional motifs, CSSs associated with changes in the chemical properties of amino acid residues were noted. These findings could be taken into account for the development of therapeutic drugs in the future. No correlation was observed between the subtypes and the spatial organization of the oligomeric structures of Vif and Vpr. Using the structural analysis and modeling, it has been shown for the first time that Vif can interact with APOBEC3G as an oligomer. Full article

Freshwater ecosystems are increasingly threatened by pollution, hydromorphological alteration, invasive species, and loss of ecological connectivity, complicating the monitoring and conservation of native fish communities. Environmental DNA (eDNA) has emerged as a sensitive, non-invasive, and cost-effective tool for detecting species, including rare or low-abundance taxa, overcoming several limitations of traditional methods. However, its rapid expansion has generated methodological dispersion and heterogeneity in protocols. This systematic review and bibliometric analysis synthesize 131 articles published between 2020 and 2025 on the use of eDNA in freshwater fish conservation. Due to the strong methodological heterogeneity among studies, the evidence was synthesized through a structured qualitative approach under PRISMA standards. Results show rapid growth in scientific output since 2023. eDNA has proven highly effective in identifying key ecological patterns such as migration and spawning, detecting critical habitats, and supporting temporal and spatial assessments. It has also facilitated early detection of invasive species including Oreochromis niloticus, Oncorhynchus gorbuscha, and Chitala ornata, and improved monitoring of threatened native species, reinforcing conservation decision-making. Despite advances, challenges persist, including variability in eDNA persistence and transport, gaps in genetic reference databases, and a lack of methodological standardization. Future perspectives include detecting parasites, advancing trophic analyses, and integrating eDNA with ecological modeling and remote sensing. Full article

Sonneratia alba Sm. and Sonneratia caseolaris (L.) Engl. are two ecologically important components of mangrove communities in Thailand. However, their population genetic patterns in Thailand remain poorly understood. Here, we assessed the genetic diversity and population structure of 107 S. alba and 131 S. caseolaris individuals sampled across their full coastal range in Thailand using single-nucleotide polymorphism (SNP) markers. Population structure analyses consistently revealed strong genetic subdivision associated with geography: S. alba formed three clusters (including one admixed group), whereas S. caseolaris formed two clusters. In both species, populations were clearly separated between the Andaman Sea and the Gulf of Thailand, reflecting the isolating influence of the Malay Peninsula. Genetic differentiation between clusters was high (F_ST = 0.364 in S. alba and 0.321 in S. caseolaris). Genetic differentiation increased with geographic distance in S. caseolaris, whereas no such relationship was detected in S. alba. Both species exhibited low levels of genetic diversity (H_o = 0.173; H_e = 0.223 in S. alba and H_o = 0.290; H_e = 0.406 in S. caseolaris). Together, these results reveal pronounced spatial genetic structure and limited evolutionary connectivity between coastal regions, providing genome-wide insights into mangrove population differentiation with important implications for conservation and restoration. Full article

Understanding the spatial structure of large mammals is critical for conservation planning, especially under increasing habitat fragmentation. This study applies an integrated spatial analysis combining the DBSCAN density-based clustering algorithm and the α-hull method to delineate non-convex geographic ranges of the maned wolf (Chrysocyon brachyurus) across South America. Using 454 occurrence records filtered for ecological reliability, we identified 11 geographically isolated α-populations distributed across five countries and multiple biomes, including the Cerrado, Chaco, and Atlantic Forest. The sensitivity analysis of the α parameter demonstrated that values below 2 failed to generate viable polygons, while α = 2 provided the best balance between geometric detail and ecological plausibility. Our results reveal a highly fragmented distribution, with α-populations varying in area from 43,077 km² to 566,154.7 km² and separated by distances up to 994.755 km. Smaller and peripheral α-populations are likely more vulnerable to stochastic processes, genetic drift, and inbreeding, while larger clusters remain functionally isolated due to anthropogenic barriers. We propose the concept of ‘α-population’ as an operational unit to describe geographically and functionally isolated groups identified through combined spatial clustering and non-convex hull analysis. This approach offers a reproducible and biologically meaningful framework for refining range estimates, identifying conservation units, and guiding targeted management actions. Overall, integrating α-hulls with density-based clustering improves our understanding of the species’ fragmented spatial structure and supports evidence-based conservation strategies aimed at maintaining habitat connectivity and long-term viability of C. brachyurus populations. Full article

The European seabass (Dicentrarchus labrax) is one of the most emblematic coastal fish species in the Northeast Atlantic, with high commercial value for fisheries and aquaculture, and importance for sport and recreational fishing. Despite its socio-economic importance, the Iberian divisions, Cantabrian Sea (8c) and the Atlantic Iberian waters (9a), defined by the International Council for the Exploration of the Sea (ICES), lack stock delimitation data. Moreover, this species is missing basic biological information, a seasonal reproductive fishing ban, and the annual landings in this region are more than double the levels recommended by ICES. To investigate the population structure of D. labrax in these areas, 140 adult individuals (36–51 cm of total length) were collected between January and March 2025 in three locations along the Atlantic coast of the Iberian Peninsula: Avilés (n = 47), Peniche (n = 48), and Lagos (n = 45). Fish from each location were analyzed for body geometric morphometrics (truss network) and otolith shape contour (Elliptical Fourier Descriptors). Data were evaluated using univariate and multivariate tests to assess spatial differences and reclassification success among locations. Results revealed regional differences using body morphometry and otolith shape analyses. The overall reclassification success was 68% for truss networking, 51% for otolith shape, and 65% when both methods were combined. Despite the observed differences, the absence of clear, isolated populations supports the ICES definition of a single, though not homogeneous, European seabass stock in the Atlantic Iberian coastal waters. Nevertheless, individuals from Avilés exhibited distinctive morphometric patterns and otolith shapes, suggesting possible adaptations to local selective pressures in slightly different environments. Further studies integrating genetic tools, otolith chemistry, parasitic fauna and telemetry analyses, as well as other fish samples from adjacent areas such as the Bay of Biscay, are recommended to achieve a more comprehensive understanding of the population structure and migration patterns of this key species in the Atlantic Iberian coastal waters. Full article

Rumex crispus L. is a globally distributed invasive species that has naturalized in South Korea, where its use as a medicinal, edible, and ecological restoration resource continues to expand. However, its genetic background remains insufficiently understood, underscoring the need for species-specific molecular markers to enable accurate assessments of intraspecific genetic diversity and population structure. Using 19 newly developed microsatellite markers, we analyzed 120 plants from 6 populations in the riparian zone. A total of 166 alleles were detected, with a mean polymorphism information content of 0.637. Across the six populations, genetic diversity analysis showed mean observed (Ho = 0.304) and expected (He = 0.588) heterozygosity values indicative of heterozygote deficiency (inbreeding coefficient F_IS = 0.456–0.559). Genetic differentiation was low in AMOVA (10%) and F_ST (0.048–0.120) but higher in Jost’s D (0.096–0.342). STRUCTURE analysis identified two major genetic clusters (ΔK = 2), and spatial Bayesian clustering revealed six distinct genetic units (K = 6), suggesting that partial barriers to gene flow may have influenced population structure. These findings provide essential genetic insights that can support the effective control of R. crispus spread and its potential use as a valuable plant resource. Full article

The scarcity of reliably determined STELs for numerous chemicals severely impedes occupational health risk assessment. To address this gap, this study establishes and validates a suite of robust quantitative structure–activity relationship (QSAR) models to efficiently predict STELs for hydrocarbons and their derivatives. A dataset of 60 compounds was partitioned using Affinity Propagation clustering, and the validity of this division was verified using Tanimoto similarity analysis and Uniform Manifold Approximation and Projection (UMAP). Four optimal molecular descriptors, indicative of molecular size and spatial configuration, were identified using a genetic algorithm. These descriptors served as inputs for one linear model—multiple linear regression (MLR)—and three nonlinear models: support vector machine (SVM), back-propagation artificial neural network (BP-ANN), and extreme gradient boosting (XGBoost). All models were rigorously validated according to OECD principles. The results demonstrated that the XGBoost model achieved superior performance, with key metrics ($R^2$, $Q_{\mathrm{loo}}^2$, $Q_{\mathrm{ext}}^2$) all exceeding 0.9. Interpretability analysis using SHAP (SHapley Additive exPlanations) revealed that molecular size and symmetry descriptors (E3u, G2m) positively correlate with STEL, while the degree of unsaturation (n = CHR) shows a significant negative influence, providing novel mechanistic insights into the structure–toxicity relationship. Notably, 96% of the predictions fell within the defined applicability domain, confirming the model’s reliability. This study therefore serves as a rapid, accurate, interpretable, and reliable computational tool, with the potential to significantly inform and enhance occupational health and safety decision-making, especially for novel or data-poor chemicals. Full article

Osteogenesis imperfecta (OI) and Ehlers–Danlos syndrome (EDS) are inherited connective tissue disorders caused by diverse genetic defects, many of which affect collagen biosynthesis. However, the identified genetic variants do not always fully explain the clinical heterogeneity observed in patients, highlighting the need for advanced models and imaging techniques to assess collagen structure and fibroblast behavior at the microscopic level. In this study, we employed 5-week three-dimensional (3D) dermal fibroblast cultures derived from patients with haploinsufficient (HI) and dominant-negative (DN) OI, EDS, and healthy controls. Using label-free higher harmonic generation microscopy (HHGM), we visualized and quantified secreted collagen fibers and fibroblast morphology in situ. We analyzed fibroblast 3D orientation, collagen fiber diameter, collagen amount per cell, and the spatial alignment between fibroblasts and collagen fibers. HI OI fibroblasts secreted significantly less collagen than both control and EDS-derived cells, while EDS samples exhibited thinner collagen fibers compared to controls. Across all groups, collagen fiber orientation was strongly correlated with fibroblast alignment, in line with the role of fibroblasts in matrix organization. In healthy controls and HI OI samples, we observed a depth-dependent, counterclockwise rotation in fibroblast orientation from the culture bottom to the surface—a pattern that was less prominent in DN OI and EDS samples, potentially reflecting altered matrix guidance in diseased tissues. Overall, the quantity and quality of collagen, as well as fibroblast morphology and organization, were markedly altered in the OI and EDS model systems. These alterations may mirror tissue-level manifestations of the diseases, demonstrating the physiological relevance of patient-derived 3D fibroblast models for OI and EDS, as well as the power of harmonic generation microscopy in probing the cellular and extracellular consequences of disease-related gene defects in collagen or its biosynthetic pathways. Extensions of this methodological approach provide a way towards deeper understanding of tissue-level manifestations of collagen dysregulation in connective tissue disorders. Full article

Environmental degradation from rapid urbanization significantly threatens ecological resilience (ER). Nevertheless, accurately evaluating ER remains a persistent challenge. Prior studies’ limited attention to resilience’s cross-scale complexity has hindered evidence-based management. This study, based on long-term time series remote sensing and multi-source data, developed a cross-scale spatiotemporal ER analysis framework integrating landscape ecology and panarchy perspectives. A local “resistance–adaptation–recovery” substrate resilience evaluation was combined with telecoupling-based global network resilience to quantify multi-scale ER from 2000 to 2020. Key drivers across time scales were identified using a hybrid XGBoost–SHAP and genetic algorithm (GA)–optimized dynamic Bayesian network (DBN), and spatial optimization scenarios were simulated with patch-generating land use simulation (PLUS) model. ER decreased slightly from 0.4856 in 2000 to 0.4503 in 2020, with dynamic fluctuations across periods. A clear spatial pattern emerged, with higher ER in the east and lower in the west. Forest land contributed strongly to ER, while construction and cropland reduced it. Spatial composition factors—especially the proportions of forest and construction land—were dominant drivers, outweighing structural factors such as landscape pattern. DBN backward inference revealed nonlinear threshold effects among socio–natural–spatial drivers. Scenario-based simulations confirmed that regulating spatial composition via our optimization pathway can enhance ER. This is particularly effective when expanding forestland in mountainous regions while restraining the growth of built-up areas. This study proposes an integrated framework of “resilience assessment—driver analysis—spatial optimization,” which not only advances the theoretical basis for nested ER assessment but also offers a transferable approach for optimizing spatial patterns and sustainable land management, thereby enhancing ecological resilience in rapidly urbanizing regions. Full article

Urban land-use optimization plays a vital role in mitigating the escalating carbon emissions of rapidly growing cities. This study employs advanced computational intelligence to address urban carbon reduction through optimized spatial configurations. A Deep Reinforcement Learning (DRL) framework is proposed that integrates Points of Interest (POI), Areas of Interest (AOI), and Transportation System Data (TSD) to generate fine-grained carbon emission maps guiding land-use adjustments. In the case study of Hangzhou, China, results show that a carefully designed reward function enables the DRL agent to selectively optimize land-use structures, prioritizing the centralization of residential, dining, and commercial areas to form high-density, mixed-use urban clusters. This spatial reorganization leads to notable reductions in carbon emissions and improvements in resource-use efficiency. The proposed DRL-based framework provides a scientific basis for policy development toward sustainable land-use and urban density optimization. By merging advanced AI techniques with urban planning, this research contributes to the creation of low-carbon, resilient, and environmentally sustainable cities capable of addressing global climate challenges. The optimized DRL agent achieved carbon emission reductions of up to 15% compared to baseline configurations in the Hangzhou case study. Spatial concentration analysis revealed a 23% increase in residential area clustering and 31% increase in commercial zone centralization over 400 training episodes. The PPO-based model demonstrated superior performance compared to genetic algorithm and linear regression baselines, with lower policy loss (converging to <0.01) and critic loss (converging to <0.005) after early stopping at 400 episodes. However, this study is limited by its deterministic environment model, geographic specificity to Hangzhou, and exclusive focus on carbon reduction without incorporating socioeconomic constraints. Full article

The exponential growth of the Internet of Things (IoT) has made it increasingly vulnerable to cyberattacks, where malicious manipulation of network and sensor data can lead to incorrect data classification. IoT data are inherently heterogeneous, comprising sensor readings, network flow records, and device metadata that differ significantly in scale and structure. This diversity motivates transforming tabular IoT data into image-based representations to facilitate the recognition of intrusion patterns and the analysis of spatial correlations. Many deep learning models offer robust detection performance, including CNNs, LSTMs, CNN–LSTM hybrids, and Transformer-based networks, but many of these architectures are computationally intensive and require significant training resources. To address this challenge, this study introduces an evolutionary-driven framework that mathematically formalizes the transformation of tabular IoT data into image-encoded matrices and optimizes feature selection through metaheuristic algorithms. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Variable Neighborhood Search (VNS) are employed to identify optimal feature subsets for Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers. The approach enhances discrimination by optimizing multi-objective criteria, including accuracy and sparsity, while maintaining low computational complexity suitable for edge deployment. Experimental results on benchmark IoT intrusion datasets demonstrate that VNS-XGBoost configurations performed better on the IDS2017 and IDS2018 benchmarks, achieving accuracies up to 0.99997 and a significant reduction in Type II errors (212 and 6 in tabular form, reduced to 4 and 1 using image-encoded representations). These results confirm that integrating evolutionary optimization with image-based feature modeling enables accurate, efficient, and robust intrusion detection across large-scale IoT systems. Full article