Search Results (2,132)

The ADP-glucose pyrophosphorylase (AGPase) gene family plays an essential role in starch metabolism and stress adaptation. However, its function in antiviral defense remains largely uncharacterized. Cucumber (Cucumis sativus L.), a globally important vegetable crop, frequently experiences severe yield losses due to viral infections. In this study, we systematically identified five AGPase genes in cucumber, categorizing them into large and small subunits. Analysis of conserved motifs revealed ten conserved sequences, with the NTP (Nucleoside Triphosphate) transferase domain representing a signature feature of the AGPase family. Promoter regions contained multiple cis-regulatory elements associated with stress responses and hormone signaling. Transcriptomic profiling revealed tissue-specific expression patterns of CsAGP genes, with pronounced enrichment in leaves. Notably, CsAGP2, CsAGP4, and CsAGP5 were strongly induced under biotic and abiotic stresses. Of these, CsAGP4 exhibited rapid, transient induction specifically in the virus-resistant line ‘228’, but not in the susceptible line ‘65G’. Hormonal treatments showed that abscisic acid (ABA) rapidly activated most CsAGP genes and acted synergistically with viral infection to amplify CsAGP4 expression. Functional analysis via CRISPR/Cas9-mediated knockout of CsAGP4 revealed that the mutation disrupted starch granule formation and significantly altered resistance to watermelon mosaic virus (WMV) in ‘Poinsett 97’. Our work provides a systematic characterization of the AGPase gene family in cucumber and establishes its role in defense responses. Importantly, we identify CsAGP4 as a positive regulator of antiviral immunity, highlighting its potential as a target for breeding virus-resistant cucumber varieties. Full article

Efficient exploitation of evolutionary knowledge while preserving population diversity remains a central challenge in optimization. Existing knowledge-learning evolutionary algorithms primarily rely on successful experiences, overlooking structural information embedded in failed search attempts. This asymmetric learning limits adaptability and may cause premature convergence in high-dimensional landscapes. To address this issue, a failure-aware bidirectional evolutionary knowledge assimilation framework is developed within the honey badger optimization algorithm. Unsuccessful offspring are treated as negative knowledge carriers and transformed through symmetric adversarial reflection, enabling simultaneous extraction of positive and negative structural information. A time-dependent regulation mechanism dynamically adjusts knowledge assimilation intensity across evolutionary phases to balance exploration and exploitation. In addition, a continuous mutation spectrum transition strategy adaptively integrates Cauchy and Gaussian perturbations, facilitating smooth migration from global exploration to local refinement. Comprehensive experiments conducted on the CEC 2017 benchmark suite across 10, 30, and 50 dimensions validate the proposed framework, establishing a novel failure-aware bidirectional evolutionary learning paradigm for knowledge-driven optimization. The results demonstrate that our method achieves statistically significant and consistent performance improvements over classical baseline algorithms. Furthermore, its robustness and cross-domain adaptability are corroborated through successful application to a real-world constrained engineering problem: welded beam design. Full article

To achieve an optimal trade-off between production efficiency and energy benefits in complex manufacturing environments, this paper addresses the Green Flexible Job Shop Scheduling Problem (GFJSP) by establishing a multi-objective mathematical model that minimizes both makespan and total energy consumption. An Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) is proposed to solve this model. In the population initialization phase, chaotic mapping is integrated with multiple heuristic rules to generate a high-quality and uniformly distributed initial population. Furthermore, an enhanced elite selection mechanism is employed to effectively prevent premature convergence. Subsequently, adaptive crossover and mutation operators are designed to enable differentiated evolution across sub-populations, effectively coordinating global exploration and local exploitation. Finally, experimental results on the Brandimarte and Hurink benchmark datasets demonstrate the superiority of the proposed algorithm in terms of convergence and diversity, providing a robust solution for optimizing green industrial production scheduling. Full article

Background/Objectives: Serial passage in embryonated eggs is widely used to attenuate the infectious bronchitis virus (IBV) for vaccine production; however, the evolutionary processes underlying attenuation and immunogenicity remain incompletely understood. Here, we analyzed genome-wide viral evolution during serial passages to investigate how mutations emerge, persist, are lost, or become fixed over time and how these dynamics relate to changes in pathogenicity and immunogenicity. Methods: Deep sequencing was performed on 11 representative serial passages (P2–P79) of the UY/11/CA/18 strain, including two derivative lineages: P7 VIR (virulent) and P53 VAC (attenuated and immunogenic). Results: This study identified an early adaptive phase characterized by a limited set of mutations potentially associated with genome replication, viral RNA processing, and virion assembly, including a key change in non-structural protein 14 and variants in M and 3c (E). This phase was followed by a broader expansion of the variant spectrum across replicase genes and delayed accumulation of Spike protein variants. Most Spike changes emerged during later passages and exhibited transient dynamics, and only a subset reached a high frequency after the establishment of early replicase- and structural-associated changes. Consistent with these dynamics, P7 VIR diverged before the late accumulation of Spike variants and retained a pathogenic phenotype, whereas P53 VAC diverged after the emergence of early high-frequency variants but before the extensive late-stage Spike variation observed in P79, which was associated with reduced immunogenicity. Conclusions: These findings support a multi-step model of IBV attenuation in which progressive filtering of genome-wide variation shapes distinct evolutionary outcomes during serial passages. This evolutionary framework provides insight into the relationship between attenuation and immunogenicity and may help guide the rational design of live attenuated vaccines. Full article

To address the defects of the traditional egret swarm optimization algorithm (ESOA) in high-dimensional complex optimization problems, such as low optimization accuracy, weak ability to escape from local extrema, rapid decay of population diversity, and insufficient efficiency in the late convergence stage, an improved egret swarm optimization algorithm (IESOA) combining variable-factor weighted learning and adjacent generation dimension crossover strategy is proposed. Firstly, a dynamic change rule of core model parameters (exploration factor ω and exploitation factor μ) is constructed to adaptively adjust with the iteration process, so as to balance global exploration and local exploitation capabilities. Secondly, a multi-individual variable-factor weighted learning mechanism is designed to enable offspring individuals to inherit the position information of following individuals, sub-population optimal individuals, and global optimal individuals simultaneously, avoiding excessively fast assimilation of the population. Furthermore, an adjacent generation dimension crossover strategy is established to update the optimal individual based on the priority principle of absolute dimension difference, fully retaining the historical optimal dimension information. Finally, a preferred mutation reverse learning strategy is integrated to further enhance the local extremum escape ability and convergence accuracy of the algorithm. The IESOA is compared with eight algorithms, including PSO, DE, SBOA, BKA, HHO, DOA, and the original ESOA on CEC2014 and CEC2019 benchmark test suites. The results show that IESOA presents significant advantages in optimization accuracy, convergence speed, and stability. The algorithm is applied to three typical engineering optimization problems: reinforced concrete beam design, welded beam design, and pressure vessel design, which effectively reduces the structural design cost and verifies its application value in practical engineering. Full article

Anomaly detection performance in sensor data is highly sensitive to model hyperparameters, which is central to reliable monitoring in mobile Internet security and industrial IoT (IIoT) scenarios. We propose an IWOA-LightGBM-based anomaly detection method for sensor data. For machine learning-based anomaly detection methods, hyperparameter selection often determines model performance, so we propose an Improved Whale Optimization Algorithm (IWOA) and further use it to optimize the hyperparameters of the LightGBM algorithm. To avoid falling into local optima and accelerate algorithm convergence, the WOA is improved by integrating nonlinear convergence factor, adaptive inertia weight factor and stochastic differential mutation strategy. Experimental results show that during hyperparameter optimization for LightGBM model training, the IWOA achieves faster convergence and higher computational efficiency compared to the Whale Optimization Algorithm (WOA), with anomaly detection accuracy exceeding 90%. Full article

Triple-negative breast cancer is defined by the absence of druggable receptor targets and by a biologically dynamic phenotype that renders static, single-timepoint biomarker strategies fundamentally inadequate. Current predictive markers, including PD-L1 expression, tumor mutational burden, and genomic profiling, fail to capture the therapy-induced transcriptional reprogramming, spatial heterogeneity, and drug-tolerant persister states that drive resistance and relapse. In this review, we argue that RNA, particularly non-coding RNA (ncRNA), represents a complementary and state-aware platform for biomarker development in TNBC, capable of capturing transcriptional adaptation, regulatory threshold dynamics, and cell state transitions that static genomic markers cannot fully detect. Unlike messenger RNAs, which reflect active transcriptional programs, long non-coding RNAs and circular RNAs modulate the stability of state transitions and are specifically induced under conditions of therapeutic stress, immune exclusion, and drug tolerance, which are properties that make them suitable as potential early and sensitive indicators of adaptive reprogramming. We review the biological rationale for RNA as a state-aware readout across five dimensions: tumor plasticity, immune context, stress response, therapy adaptation, and microenvironment composition. An examination is conducted regarding how spatial transcriptomics can map RNA-defined resistant niches within TNBC, how serial liquid biopsy RNA measurements, including extracellular vesicle RNA and circulating tumor RNA, enable temporal monitoring of transcriptional state shifts before radiologic progression, and what analytical and clinical standards deployable RNA assays must meet. Finally, a state-guided adaptive management framework is proposed in which RNA signatures function as iteratively updated measurement layers informing therapy selection, on-treatment monitoring, and early resistance detection. This review outlines trial design models and defines the validation standards required before RNA-guided adaptation can enter clinical practice. Full article

Precision medicine is increasingly applied in the cancer clinic, adapting treatment to genomic alterations of the tumor. However, whether alterations disrupt the function of a protein can depend on if both alleles of a gene are altered. While massively parallel sequencing technologies can identify sequence aberrations, they are limited in resolving the corresponding haplotype information. In this proof-of-concept case study, we applied the linked-read droplet barcode sequencing (DBS) technology to resolve the haplotype complexity of colorectal cancer genomes on paired tumor and normal samples. Several cancer-related genes carried multiple mutations in either one or both haplotypes. Additionally, a number of haplotype-resolved large structural variants and copy number alterations were detected and phased with short somatic variants. Nearly all characterized oncogenic pathways harbored some of the identified short somatic variants. The study demonstrates that linked-read DBS technology can characterize complex genetic variations in a haplotype context and may provide essential information for personalized approaches. Full article

Antibiotic resistance is a growing global health threat. However, its evolution cannot be fully understood without considering antibiotic tolerance and persistence. Persister cells are phenotypic variants that survive lethal antibiotic exposure without heritable resistance, primarily through growth arrest, metabolic slowdown, and stress-adaptive states. Although persistence has been viewed as a transient survival phenomenon, increasing evidence suggests that it may also have a genetic basis by preserving populations during antibiotic-induced bottlenecks and enabling regrowth, mutation, and selection under certain conditions. This review examines the molecular mechanisms underlying persister formation, including toxin–antitoxin systems, stringent-response signaling, ATP depletion, translational arrest, and stress-response networks. We discuss how persistence contributes to antibiotic tolerance in biofilms, host environments, and recurrent infections, and how repeated antibiotic exposure may promote stepwise evolution from phenotypic survival to stable resistance in specific contexts. Evidence from experimental evolution, clinical observations, and system-level analyses supports a potential but context-dependent link between persistence and resistance. We also highlight therapeutic strategies targeting persister cells, including antipersister compounds, metabolic activation, combination therapies, bacteriophages, and alternative approaches. Finally, we outline future research directions, emphasizing single-cell technologies, systems biology, longitudinal clinical studies, and evolution-informed treatment design. A comprehensive understanding of persistence and its evolutionary implications is essential for improving treatment efficacy and limiting the emergence of long-term antibiotic resistance. Full article

Aspergillosis encompasses a heterogeneous spectrum of diseases caused by filamentous fungi of the genus Aspergillus, ranging from allergic airway disorders and chronic pulmonary infection to rapidly progressive invasive disease. Aspergillus fumigatus is the predominant pathogen worldwide, although other species, including Aspergillus flavus, Aspergillus terreus and cryptic species, contribute to morbidity and may exhibit intrinsic or acquired antifungal resistance. Early and accurate laboratory diagnosis is essential for timely treatment, appropriate antifungal selection, and stewardship. Traditional culture remains foundational, enabling confirmation of viable organisms, species-level identification, and antifungal susceptibility testing, but sensitivity is limited and turnaround times are prolonged. Non-culture approaches—including galactomannan, β-D-glucan, lateral flow assays, PCR, and next-generation sequencing—enhance diagnostic sensitivity, facilitate early detection, and allow identification of resistance-associated mutations. Optimal diagnostic performance is achieved through integrated, multimodal strategies combining laboratory tests with clinical and radiological findings. In invasive disease, concurrent use of biomarkers and molecular assays improves specificity and positive predictive value, while in allergic bronchopulmonary aspergillosis, immunological markers remain central. Future directions include standardised molecular protocols, novel antigenic and host-based biomarkers, and cost-effective, risk-adapted diagnostic algorithms to refine detection, guide therapy, and improve patient outcomes. Full article

Molecular testing has become an increasingly important adjunct in the evaluation of cytologically indeterminate thyroid nodules. These tests analyze genetic alterations associated with thyroid tumorigenesis, including point mutations, gene fusions, and gene expression profiles, with the aim of refining preoperative risk assessment and reducing unnecessary diagnostic surgery. Despite these advances, the clinical utility of molecular testing remains highly dependent on the context in which results are interpreted. Molecular alterations do not consistently correlate with tumor aggressiveness, and several mutations are observed in both benign and malignant thyroid lesions. In addition, the predictive performance of molecular tests is strongly influenced by the baseline prevalence of malignancy, which varies across clinical settings and is shaped by diagnostic thresholds and patient selection. This commentary summarizes the molecular landscape of thyroid tumors, the diagnostic performance of current molecular testing platforms, and their role in clinical decision-making. Emphasis is placed on the interpretation of molecular findings within a broader diagnostic framework that incorporates cytologic morphology, ultrasound-based risk stratification, and clinical context. A selective, risk-adapted approach to molecular testing may provide the most effective strategy for optimizing patient management while minimizing unnecessary intervention. Full article

The emergence of vancomycin-intermediate Staphylococcus aureus (VISA) threatens the efficacy of this last-line antibiotic. The GraSR two-component system is frequently mutated in VISA strains. Here, we demonstrate that the GraS(T136I) point mutation, identified in the clinical VISA isolate XN108, is a key determinant of reduced vancomycin susceptibility. Introducing this mutation into the susceptible strain Newman increased the vancomycin MIC from 1.5 to 4 mg/L, while its reversion in XN108 decreased the MIC from 12 to 8 mg/L. The mutation conferred common phenotypes, including thickened cell wall, decreased autolysis, and reduced cell surface negative charge via upregulation of the dltABCD operon and mprF. Notably, the GraS(T136I) mutation also upregulated virulence genes (efb, hlb, sbi, hld) and enhanced hemolytic activity. Interestingly, despite this hypervirulent profile, the mutant showed impaired long-term survival within macrophages. Our study reveals that a single GraSR mutation can co-regulate vancomycin resistance and virulence, offering new insights into the adaptation of S. aureus to antibiotic pressure. Full article

Oral squamous cell carcinoma (OSCC) is a biologically heterogeneous malignancy with poor clinical outcomes. Hypoxia and lipid metabolic reprogramming are important drivers of OSCC progression and treatment adaptation, and these processes are biologically interconnected. However, prognostic studies integrating hypoxia- and lipid metabolism-related features in OSCC remain limited. Here, transcriptomic data from TCGA-HNSC-OSCC were integrated with curated hypoxia- and lipid metabolism-related genes to identify candidate genes, construct a prognostic signature, and characterize its biological relevance through enrichment analysis, immune profiling, single-cell RNA-sequencing analysis, and RT-qPCR validation. A four-gene signature consisting of STC2, CAV1, ACADL, and PLA2G2D showed stable prognostic performance in the TCGA-HNSC-OSCC cohort and the external validation cohort GSE41613. The risk signature remained significantly associated with overall survival after adjustment for clinicopathological factors and retained prognostic discrimination across stage- and nodal status-defined subgroups. The high- and low-risk groups displayed distinct pathway, immune, mutational, and predicted drug sensitivity features. Notably, PLA2G2D showed the strongest association with differential immune infiltration, whereas single-cell analysis identified endothelial cells as a major CAV1-enriched population with active intercellular communication and dynamic state transitions. These findings define a hypoxia- and lipid metabolism-related prognostic signature and support its relevance to immune remodeling and endothelial cell context in OSCC. Full article

Duolang sheep, an indigenous breed of southern Xinjiang, are significant for their agricultural systems due to their adaptation to arid and semi-arid environments. This review integrates recent advancements in Duolang’s reproductive biology, genomic studies, and management strategies to address the breed’s reproductive efficiency under challenging ecological conditions. Reproductive traits such as puberty onset, estrous cycle characteristics, and seasonal breeding are influenced by complex genetic and several environmental factors. Numerous remarkable genomic findings highlight key loci related to fecundity, including the Booroola FecB mutation, as well as genes involved in steroidogenesis, folliculogenesis, and HPG axis regulation. Despite the genetic potential for increased prolificacy, Duolang sheep often exhibit low litter sizes, largely constrained by detrimental environmental factors and management practices. This review emphasizes the significance of integrating genetics, nutrition, and reproductive management to optimize productivity. Strategies such as nutritional flushing, hormone-based estrous synchronization, and selective breeding for increased litter size are discussed, with a focus on minimizing the risks associated with early puberty and lamb survival. Furthermore, the review explores the potential of genomic selection, marker-assisted breeding, and advanced reproductive technologies to enhance the breed’s performance. Finally, the review outlines future research directions, necessitating the development of genomic resources, precise breeding programs, and field trials on reproductive interventions to accelerate genetic gains in Duolang sheep. This integrated approach promises to improve reproductive outcomes, with implications for sustainable sheep production in Xinjiang and similar environments across the globe. Full article

Multispectral satellite image segmentation constitutes a challenging optimization problem due to high dimensionality and complex inter-band correlation structures. As the number of thresholds increases, the search space grows exponentially, causing metaheuristic methods to suffer from convergence instability by getting trapped in local optima on highly multimodal landscapes. In this study, a hybrid optimization method is proposed by integrating the Marine Predators Algorithm (MPA) with the Runge–Kutta (RUN) approach. The proposed framework enhances global exploration through Cauchy-based perturbation, while improving exploitation capability via a mutation-based local refinement mechanism, and reduces spectral redundancy using Principal Component Analysis (PCA). The MPA-RUN hybrid structure, combined with a Cauchy-driven exploration strategy and an adaptive local search mechanism, significantly improves the exploration–exploitation balance in multispectral image thresholding problems. Experiments are conducted on Sentinel-2 multispectral images, and the proposed method is evaluated against conventional metaheuristic algorithms over a wide threshold range (2–26), encompassing both low- and high-dimensional configurations. At high threshold levels, the proposed method achieves Peak Signal-to-Noise Ratio (PSNR) = 23.66, Structural Similarity Index Measure (SSIM) = 0.863, and Feature Similarity Index Measure (FSIM) = 0.797, while providing approximately 35% lower computational time at moderate levels, demonstrating superior efficiency. These results demonstrate that a balanced trade-off between accuracy and computational cost is achieved. The proposed approach offers a fast and reliable solution for processing high-dimensional data by effectively balancing segmentation quality and computational complexity. Full article