Search Results (1,829)

Background/Objectives: Multidrug resistant (MDR) Salmonella represents a major global public health challenge within the One Health interface. This study aimed to characterize the genomic epidemiology of Salmonella isolates from Colombia and resolve the genetic architecture of novel MDR plasmids identified in foodborne strains. Methods: A total of 90 Salmonella isolates collected between 2002 and 2009 from various food sources and food-producing animals in Colombia were analyzed using whole-genome sequencing (WGS). Bioinformatics tools were employed for serotype prediction, multi-locus sequence typing (MLST), and resistome/virulome profiling. Long-read sequencing was utilized to close the complete sequences of representative MDR plasmids. Results: 45.6% of isolates exhibited antimicrobial resistance, with seven being classified as MDR. The major serotypes identified were Uganda (n = 20), Newport (n = 10), and Braenderup (n = 10). We characterized a novel 229,037 bp IncHI1 plasmid (pCFS0255-1) harboring a copper homeostasis and silver resistance island (CHASRI) integrated with tetracycline and macrolide resistance clusters. Additionally, a 99,288 bp IncI1 plasmid (pCFS0255-2) carrying a unique aminoglycoside resistance module was resolved. Conclusions: Our findings highlight the persistence of specific Salmonella lineages in the Colombian food chain and the role of hybrid plasmids in the co-selection of metal and antibiotic resistance. The study underscores the necessity of implementing WGS-based surveillance to track emerging MDR threats. Full article

Plants are constantly exposed to a wide range of abiotic stresses that have significant negative impacts on growth and yield. Plant acclimation to these stresses is governed by integrated classical phytohormone and plant peptide hormone signalling networks that control the ability of a plant to survive and adapt to extreme environments. Classical phytohormones, including abscisic acid, auxins, gibberellins, cytokinins, jasmonates, salicylic acid, brassinosteroids, and the recently recognised phytomelatonin, act in concert with peptide-based plant hormones, among which C-terminally encoded peptides (CEPs) play prominent roles in coordinating stress perception, signal transduction, and adaptive responses throughout the plant. These integrated networks control stomatal behaviour, photosynthesis, osmolyte and antioxidant levels, root architecture, and energy metabolism, thereby helping plants maintain homeostasis and optimise survival while sustaining minimal growth under unfavourable conditions. Under stressful conditions, these networks do not operate in isolation but form highly dynamic, context-dependent regulatory circuits in which each physiological process is simultaneously regulated by multiple hormones acting through convergent and overlapping signalling pathways. Phytomelatonin has emerged as a particularly important integrative node within these networks, functioning both as a potent direct antioxidant through sequential ROS-scavenging catabolite cascades and as a bidirectional regulator of classical phytohormone signalling under diverse abiotic stresses. New technologies in the fields of transcriptomics, proteomics, phosphoproteomics, metabolomics, and systems biology have provided new information on the dynamic relationships between classical phytohormones and plant peptide hormones, revealing candidate regulatory nodes and transcription factor networks that mediate stress adaptation at molecular, biochemical, and physiological levels. However, it is important to distinguish between correlative associations identified through omics profiling and causal regulatory relationships validated through rigorous genetic and biochemical experimentation, as most omics-derived candidates remain to be functionally established. Empirical studies demonstrate how these networks can be used to improve crops by increasing stress tolerance through modulating classical phytohormone and plant peptide hormone signalling, including through exogenous phytomelatonin application, CRISPR-mediated hormone pathway editing, and CEP pathway manipulation, to produce resilient cultivars without reducing yields. Although these advances represent significant progress, challenges remain, including the inherent complexity and redundancy of the networks, context-dependence and severity-dependence of hormonal responses, the persistence of a significant translational gap between laboratory findings and field application, and incomplete mechanistic understanding of peptide hormone roles under combined stress conditions. Addressing these challenges will require integrative multi-omics approaches, higher-order computational modelling, and rigorous field-based functional validation alongside emerging tools such as synthetic biology and precision breeding. Full article

This paper proposes a digital twin framework for robust parallel control of the mobile gin pole in ultra-high voltage (UHV) transmission line construction, aiming to improve safety and operational efficiency under uncertain conditions. The new framework integrates kinetic analysis, machine learning models, and multi-objective optimization algorithms to address the challenges of heavy-lifting operations in complex terrains. The method conducts finite-element kinetic analysis based on the actual structure of the mobile gin pole. A Tyrannosaurus Rex Optimization Algorithm (TROA) is employed to enhance the performance of the Extra Randomized Trees (ET) model for predicting key parameters such as maximum axial stress and shear stress. The framework leverages the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) to optimize safety and efficiency metrics by adjusting key control parameters. A digital twin system for the mobile gin pole was constructed to validate the proposed approach. Results indicate that: (1) The proposed prediction model achieved performance improvements with R², RMSE, and MSE of 0.9642, 19.6, and 7.42, respectively. Compared with baseline machine learning models, the proposed model achieved significant improvements of 21.5%, 19.2%, and 5.1% in R², RMSE, and MSE, respectively. (2) Experiments confirm that the proposed model maintains high prediction accuracy under noise interference and missing data scenarios, indicating strong robustness. (3) Under various operation conditions, the method reduces safety risks by up to 32.30% and improves operational efficiency by up to 42.73%. Case studies further verify the effectiveness of the proposed framework, demonstrating superior prediction accuracy, noise resistance, and computational efficiency compared to conventional control methods. The core methodological novelty of this study lies in integrating TROA, ET, NSGA-III, and digital twin technology into a unified framework for mobile gin poles. This framework adopts TROA-ET to convert finite-element-based kinetic analysis into a behavior–mechanics surrogate model. It further embeds the constructed surrogate model into an NSGA-III-driven digital twin parallel control architecture. In this way, the study contributes an integrated and computationally efficient solution for safety–efficiency co-optimization of mobile gin pole operations under uncertainty. Full article

Economic traits in livestock and poultry arise from the intricate interplay between genetic inheritance and environmental factors, mediated largely by epigenetic regulation. Histone modifications, particularly methylation and acetylation, serve as fundamental epigenetic mechanisms that dynamically remodel chromatin architecture and regulate gene expression in response to developmental and environmental cues. By bridging the gap between static DNA sequences and complex phenotypes, these dynamic marks offer a novel perspective for elucidating trait formation. This review examines the regulatory roles of histone modifications in shaping key economic traits, focusing on skeletal muscle development, fat deposition, and reproductive performance. Furthermore, we highlight two prospective strategies for integrating histone modification data into modern breeding programs: utilizing comprehensive epigenomic maps as novel biomarkers for precision selection, and implementing targeted nutritional regimens to program early phenotypic development. Despite substantial mechanistic advances, critical challenges persist, including high detection costs, inherent tissue specificity, and the necessity to validate transgenerational stability. Looking forward, the integration of multi-omics approaches is anticipated to propel animal breeding beyond traditional genomic selection toward an era of precise epigenomic design. Full article

Background: Maize shoot height is an important component of early vigor and plant architecture; however, its genetic basis during seedling development and its relationship with modern breeding remain insufficiently understood. This study aimed to investigate the genetic architecture of maize seedling shoot height across different breeding eras. Methods: Shoot height at 21 days after sowing was evaluated in 363 maize inbred lines representing three breeding eras in China. Genome-wide association analysis was performed to identify loci and candidate genes associated with shoot height variation, and selective sweep analysis was used to detect breeding-era differentiated genomic regions. Results: Modern breeding lines from the 2000–2010s exhibited significantly greater shoot height than lines from earlier breeding periods. Pearson’s correlation analysis revealed that 3-week shoot height showed highly significant positive correlations with plant height and ear height. Selective sweep analysis identified multiple differentiated genomic regions harboring previously reported height- and architecture-related genes, including ZmBR2, ZmLIL1, ZmNA1, ZmTE1, ZmSPL12, ZmBV1, ZmDIL1, ZmKN1 and ZmACS7. The GWAS identified 43 SNPs exceeding the GEC-derived suggestive threshold for shoot height, with the strongest and most continuous association signal located on chromosome 8. GWAS, together with LD analysis, haplotype analysis, and expression profiling, prioritized ZmGDCL (Zm00001d009163) as a promising candidate gene because of its strong association signal, local linkage disequilibrium support, broad expression profile, and significant haplotype effect on shoot height. Conclusions: Our results indicate that maize breeding has reshaped the genetic architecture of seedling shoot growth. ZmGDCL represents a promising candidate gene for future functional studies, while breeding-era differentiated regions provide useful genomic context for understanding maize architecture improvement. Full article

Background: Internode length (IL), a key component of plant height (PH), plays an important role in achieving the optimal architecture in wheat. However, the genetic mechanisms underlying internode elongation are not well understood. Methods: In this study, a recombinant inbred line (RIL) population derived from a cross between Bainong 4199 (BN4199) and Zhengyinmai 2 (ZYM2) was evaluated for PH and five ILs across two field locations over two years and genotyped using a 120 K liquid-phase chip. Results: A total of 141 quantitative trait loci (QTL) associated with PH and the five ILs were mapped onto 20 chromosomes, except for chromosome 5D. Among these, 37 stable QTL were identified on chromosomes 1B, 2B, 2D, 4B, 5A, 7A, 7B and 7D, accounting for 3.86–25.97% of the phenotypic variation. Meanwhile, 23 co-localized QTL associated with at least two traits were detected, with QTL cluster regions on chromosomes 2D, 4B, 5A, 7A, and 7B. Moreover, the total additive effects of the QTL combinations increased with the number of QTL, which indicates the effectiveness of pyramid breeding. Additionally, based on gene function annotation, the cloning and characterization of rice orthologs, and analysis via the QTG miner module of the wheat integrative gene regulatory network (wGRN) platform, 63 candidate genes (e.g., Rht1, Rht8, TB1 and ZnF-B) were prioritized within the stable QTL intervals, and their tissue expression patterns were analyzed. Conclusions: Collectively, these findings not only deepen our understanding of the genetic basis of PH and ILs in wheat but also lay a foundation for the further validation and functional characterization of candidate genes, enabling the optimization of plant architecture through marker-assisted selection (MAS) to ultimately improve agronomic performance and yield potential. Full article

Moyamoya angiopathy (MMA) is a rare, chronic progressive cerebrovascular condition characterized by bilateral stenosis or occlusion of the terminal internal carotid arteries and their major branches. This progressive occlusion triggers the development of telangiectatic and fragile vessels at the base of the brain, creating the characteristic angiographic appearance of a “puff of smoke.” Depending on the etiology, MMA is classified as Moyamoya Disease (MMD) when idiopathic and primary or Moyamoya Syndrome (MMS) when associated with underlying systemic conditions. While the RNF213 gene, particularly the p.R4810K variant, is recognized as the major susceptibility locus for MMD in East Asian populations, it does not fully account for the global genetic landscape or the phenotypic diversity of the disease. This review provides a comprehensive overview of the genetic architecture of the entire MMA spectrum, exploring loci beyond RNF213. We analyze the role of genes involved in vascular smooth muscle cell contractility (ACTA2, MYH11), TGF-β signaling, and DNA repair mechanisms that drive MMS, alongside the genetic basis of syndromic forms associated with neurofibromatosis type 1, trisomy 21, and RASopathies. Understanding these diverse genetic drivers is crucial for early diagnosis, risk stratification, and the development of targeted molecular therapies. Full article

Severe mental disorders, including schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD), are leading causes of global disability, yet current treatments remain largely symptomatic and fail to alter disease trajectories. Converging evidence from genetics, longitudinal studies, and systems neuroscience supports a dimensional and transdiagnostic architecture of psychopathology, involving shared polygenic risk and overlapping neurodevelopmental and circuit-level alterations. Traditional approaches—such as post-mortem brain analysis, neuroimaging, and animal models—have delineated core molecular perturbations (e.g., dopaminergic, glutamatergic, and GABAergic dysfunction), as well as informed translational frameworks for mechanistic investigation, but remain constrained by restricted access to dynamic processes and incomplete recapitulation of human-specific biology. The advent of human-derived cellular models, particularly human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs), has partially addressed these limitations, enabling the study of patient-specific neurodevelopment and synaptic function in vitro. Within this evolving landscape, the olfactory neuroepithelium (ONE) has emerged as an accessible source of neural progenitors, obtainable through minimally invasive procedures, providing a window into living human neurobiology. ONE-derived cells retain donor-specific genetic and epigenetic signatures while recapitulating disease-relevant phenotypes across major psychiatric disorders, including altered neurodevelopmental dynamics, synaptic gene expression, and inflammatory profiles. Here, we present a narrative review of the principal cellular and tissue models used in biological psychiatry, examining their respective strengths, limitations, and translational relevance across experimental contexts. By situating these approaches within a unified framework, we aim to clarify their complementarity, identify current gaps, and outline future directions, highlighting the emerging potential of ONE-based models to bridge genetic risk, cellular dysfunction, and clinical phenotype, thereby advancing precision psychiatry. Full article

Unmanned aerial vehicles (UAVs) serve as both communication relays and aerial couriers in modern urban logistics networks. Conventional trajectory optimization methods assume perfect localization and isotropic free-space tracking signal propagation, which limits their effectiveness in urban canyons. To address the positional uncertainty and signal blockage from buildings, we propose a digital twin-driven framework for continuous trajectory and resource optimization in UAV swarms. We model an urban environment containing random high-rise structures, applying a non-line-of-sight (NLoS) uncertainty to reflect realistic communication degradation. The digital twin (DT) architecture utilizes a dual-layer spatial representation that captures a dynamically decaying positional uncertainty radius of the recipient. We define a strict visual localization boundary that initiates deterministic target tracking with a state transition mechanism. To manage the complexity of swarm routing, we apply Density-Based Spatial Clustering of Applications with Noise (DBSCAN), assigning one UAV courier and one logistics transfer station to each cluster. The system executes a continuous re-optimization loop using an adaptive multi-objective Genetic Algorithm. This framework jointly minimizes cumulative outage probability and total flight time while enforcing a signal-to-noise ratio threshold and throughput constraints. This continuous adaptation mechanism mitigates NLoS blockage risks, supporting reliable communication and efficient delivery in Global Navigation Satellite System (GNSS)-degraded and obstacle-dense urban environments. Full article

The evolution of Osteichthyes began with a split into two major lineages: Sarcopterygii (lobe-finned fishes) and Actinopterygii (ray-finned fishes). In one lineage—sarcopterygians—some groups evolved robust internal bones and limb-like fins and ultimately gave rise to semi- and fully terrestrial tetrapods; the other lineage—actinopterygians—remained primarily aquatic and later radiated into the diverse teleosts. Repeated mass extinction events and ongoing genetic divergence allowed novel functions and new niches to be exploited, a pattern especially evident in recent analyses of teleost diversification. Lobe-finned fishes characteristically possess an endoskeleton fin architecture, whereas ray-finned fishes bear dermal fin rays built on a different structural plan. Primitive Osteichthyes also show an early origin of paired air-spaces (lungs), but many derived actinopterygians modified this ancestral condition into a dorsal swim bladder. Imagining a world without sarcopterygians or tetrapods highlights how teleosts might have convergently colonized many terrestrial-associated niches; although significant developmental and structural hurdles would have made such a transition challenging, this thought experiment underscores the cascading ecological consequences that the loss of a major clade can produce. Ecosystems thrive on diversity and adaptability, and episodes of environmental upheaval—such as the Silurian and Devonian extinctions—often catalyze rapid evolutionary change. Full article

Amaranth (Amaranthus spp.) is a multifaceted genus of C4 plants with significant nutritional and agronomic potential, yet it remains underutilized in mainstream agriculture. Despite growing interest in Amaranth, most germplasm studies have used either phenotypic or molecular approaches alone, lacking integration. Multivariate methods have not been systematically applied to identify promising genotypes, and species-specific selection indices for grain Amaranth remain unexplored. To address these gaps, this study comprehensively characterized 84 Amaranth genotypes representing multiple species (A. caudatus, A. cruentus, A. hypochondriacus, A. hybridus, A. spinosus, A. powellii, A. tricolor, and 38 accessions of unknown taxonomic status) using field experiments in a randomized complete block design with three replications and DNA barcoding with chloroplast (psbA-trnH) and nuclear (ITS) markers. Analysis of variance revealed highly significant differences (p < 0.01) among genotypes for all six agronomic traits evaluated, confirming substantial genetic variability with grain yield exhibiting the widest variation (CV = 28.55%), ranging from 0.25 to 125.56 g/plant. High broad-sense heritability estimates (0.79–0.99) coupled with high genetic advance, particularly for grain yield (117.54%), indicated that these traits would respond favorably to selection. Path analysis and stepwise regression identified early flowering, long inflorescences, and heavy seeds as the primary determinants of grain yield, collectively explaining 27% of yield variation. Mahalanobis D² analysis identified nine multivariate outliers with distinct phenotypic profiles, among which G39 emerged as the most promising breeding candidate, combining exceptional yield (90.50 g/plant) with desirable architecture, long inflorescence, and large seeds. Principal component analysis further resolved trait complexes, identifying 11 PC1-selected promising genotypes as donors for plant architecture and three PC2-selected promising genotypes as donors for seed size characteristics. Molecular analysis revealed distinct genetic relationships. A. caudatus (kiwicha) exhibited limited haplotype diversity indicating a narrow genetic base, while A. cruentus and A. hypochondriacus showed broader diversity, with the nuclear ITS network providing clearer resolution than chloroplast markers due to biparental inheritance. Outlier genotypes, including G82, G83, G13, G10, and G39, occupied unique haplotype positions, confirming that their phenotypic distinctiveness corresponds to genuine genetic differentiation. The novelty of this study lies in integrating multivariate biostatistical techniques (heritability, path analysis, Mahalanobis D², PCA, and stepwise regression) with two complementary DNA barcode systems (chloroplast and nuclear) within a single germplasm collection. This integrated approach provides breeders with well-characterized germplasm, validated selection criteria, and prioritized parental materials for Amaranth improvement. Further multi-location and multi-season evaluations are recommended to ensure the stability and adaptability of these promising germplasm accessions. Full article

Automated defect detection in industrial casting processes is important for improving product quality while reducing the cost of manual inspection. In this work, two deep convolutional neural network (CNN) architectures, InceptionV3 and InceptionResNetV2, are evaluated for the binary classification of defects in submersible pump impellers. A genetic algorithm (GA) is used to optimize key hyperparameters, including dropout rate, learning rate, and dense layer configuration, while model complexity is assessed through Pareto-based analysis. Single-run optimization results show that InceptionV3 achieves high classification accuracy (99.0%) with lower model complexity than InceptionResNetV2 (98.75%). Repeated experiments using different random seeds demonstrate relatively stable performance across runs, with InceptionV3 achieving an accuracy of 0.9913 ± 0.003 and InceptionResNetV2 achieving 0.9860 ± 0.0076. Additional experiments were conducted using random-search baselines and classification-head ablation studies (Flatten vs. Global Average Pooling). These experiments showed that optimization strategy and architectural design choices influence both predictive performance and computational complexity. The environmental impact of the training process is evaluated using CodeCarbon, with energy consumption ranging from 0.083 to 0.098 kWh and carbon emissions ranging from 2.008 to 2.401 g CO₂eq for InceptionV3 and InceptionResNetV2, respectively. Overall, the results suggest that the most effective configuration depends on the evaluated architecture and experimental setting, highlighting the importance of balancing accuracy, model complexity, and computational efficiency in industrial defect detection systems. Full article

Plant height is a key component of sunflower (Helianthus annuus L.) plant architecture. It strongly influences lodging resistance, mechanized harvestability, and yield stability. However, the genetic basis of plant height in sunflowers remains underexplored. This study aimed to develop an F₂ population consisting of 715 individuals from a cross between the dwarf inbred line 150A and the tall inbred line PT326. Bulked segregant analysis coupled with whole-genome resequencing was employed to identify loci associated with plant height. Using three complementary analytical methods, a major quantitative trait locus, qPH15, was identified on chromosome 15. This locus was subsequently fine-mapped, using Kompetitive Allele Specific PCR (KASP) markers and recombinant screening in F₂ and F₃ populations, narrowing it to a 64.66-kb region containing three annotated genes. Among these, HanXRQr2_Chr15g0707451, which encodes an NAC transcription factor designated HaNAC7, was identified as the most promising candidate gene. Haplotype analysis of HaNAC7 across 148 sunflower accessions revealed 4 polymorphic sites defining 6 haplotypes with substantial differences in plant height. The shortest haplotypes, Hap2 and Hap3, were associated with reduced plant height and were predominantly found in Asian germplasm. These findings suggest that HaNAC7 is a strong candidate gene underlying qPH15 and provide useful molecular markers and favorable allelic resources for improving sunflower plant architecture. Full article

While a conserved core microbiome is shared across healthy individuals, significant interindividual taxonomic variation exists; however, the specific influence of genetic ancestry on supragingival plaque structure in eubiosis remains unclear. This systematic review analyzed evidence regarding taxonomic variations in supragingival plaque associated with ethnicity in systemically healthy populations. A search was conducted in PubMed, Scopus, ScienceDirect, and Scielo following PRISMA 2020 guidelines, covering literature up to October 2025. Cross-sectional studies using genomic sequencing or metagenomics were included, with quality assessed via the GRADE system. Six studies met eligibility criteria. Results identified a universal core microbiome structurally dominated by Corynebacterium spp. and Streptococcus spp. However, distinct ethnic-specific taxonomic signatures emerged, such as the enrichment of Fusobacterium spp. in African Americans and Corynebacterium spp. in Caucasians, alongside the exclusive presence of Sneathia spp. in Burmese individuals. Although a basal microbial architecture necessary for homeostasis exists, ethnicity acts as a biological filter defining distinctive bacterial profiles and differential susceptibilities. These findings suggest that while the core microbiome is conserved, the composition of peripheral species in the dental plaque hedgehog structure varies according to ancestry. This supports a transition from standardized dental care to personalized medicine oriented towards the patient’s biological heritage. Full article

Product defect inspection is critical in industrial applications; however, it remains increasingly challenging in mass production environments, particularly for glossy or curved surface products. Conventional inspection of such surfaces typically relies on manual visual examination using gauges and operator judgment, which is time consuming and prone to inconsistency. This study proposes a robust defect detection framework for curved and reflective surfaces using a KEYENCE displacement laser sensor. The system integrates the Dijkstra algorithm, the Nearest Neighbor Algorithm, and a Genetic Algorithm to optimize the laser scanning path for structured image data generation. To validate the proposed framework, datasets were generated from both healthy and defective samples and used to train multiple deep learning models. A comparative analysis was conducted using YOLOv8, YOLOv9, YOLOv10, and YOLOv11 architectures. Experimental results demonstrate that YOLOv11 achieved the best overall performance, attaining an mAP50 score of 0.844 while also exhibiting lower computational complexity and faster inference. Full article