Search Results (5,453)

Avian infectious bronchitis virus (IBV) is widely distributed worldwide and causes substantial economic losses to the poultry industry. Because IBV undergoes frequent mutation, prevention and control of infection remain challenging. Immunization is an important measure for the prevention and control of IB. Therefore, there is an urgent need for a rapid, sensitive, specific, and convenient method for the detection of antibodies against IBV. In this study, we firstly developed an indirect colloidal gold immunochromatographic strip for the rapid detection of antibodies against IBV based on a conserved epitope peptide. The recombinant epitope peptide recognized by N2D5 monoclonal antibody (mAb) against the N protein of IBV was expressed as a GST fusion protein (GST-N2D5) based on the conserved antigenic epitope previously identified in our laboratory. Colloidal gold-labeled GST-N2D5 was used as the detection reagent to generate visual signals. Rabbit anti-chicken IgY and mouse anti-GST mAb were immobilized on the nitrocellulose membrane as the test line (T line) and control line (C line), respectively. The optimal pH and optimal protein concentration for conjugation of gold nanoparticles (AuNPs) with GST-N2D5 were pH 8.5 and 72 µg/mL, respectively. Specificity was evaluated using common avian pathogens, and no cross-reactivity was observed. The detection limit of the strip for IBV-positive serum was 1:180. In addition, the assay showed good reproducibility and stability, and results could be observed within 5 min without any specialized equipment. Clinical chicken serum samples were tested using both the developed strip and an enzyme-linked immunosorbent assay (ELISA), and the strip showed high agreement with the ELISA. In conclusion, the established immunochromatographic strip is rapid, sensitive, specific, and easy to operate, and therefore has potential as an on-site tool for the rapid detection of antibodies against IBV, particularly in resource-limited settings. Full article

Background/Objectives: Antimicrobial peptides (AMPs) are considered alternatives to classical antibiotics due to limited resistance development in bacteria. However, bacteria can develop resistance to AMPs through evolutionary adaptation, including oligosaccharide modifications and multidrug efflux pumps. Further research is needed to elucidate the defense mechanisms employed against AMPs to address the emerging resistance problem. Pexiganan is a cationic peptide with effective broad-spectrum antimicrobial activity. The aim of this study is to elucidate the genomic and transcriptomic basis of E. coli’s evolutionary adaptation to pexiganan. Methods: The E. coli ATCC BAA-2523 strain became resistant to pexiganan via evolutionary adaptation methodologies. Whole-genome and transcriptome analyses of resistant and susceptible populations were conducted using Nanopore sequencing and Illumina RNA sequencing, respectively. Results: Resistance development became particularly evident after 15 µg/mL, and the bacteria demonstrated the ability to grow even at high doses (up to 1000 µg/mL). Increases in expression levels of classical ARGs such as emrB, acrF, OXA, sul2, and dfrA14 in the pexiganan-resistant strain indicate that bacteria have the potential for broad-spectrum resistance to other antibiotics alongside pexiganan. Missense mutations have been identified in the phosphatidylserine synthase and cardiolipin synthase genes, which are involved in membrane biosynthesis. Increased expression was observed in the membrane-bound lytic murein transglucosylase (mltF), the murein hydrolase activator (EnvC), and the Antigen 43 (Ag43) gene. Conclusions: Pexiganan may not only target the cell membrane but also trigger the bacterium’s overall transcriptional response and cross-resistance and MDR systems. Upregulation of multidrug efflux pumps, lytic murein transglucosylase, the murein hydrolase activator and the Antigen 43 gene might be associated with resistance. In addition, the missense mutation was detected in the membrane biosynthesis genes pssA and clsB. In vivo infection models, targeted functional genomics, and comprehensive phenotypic cross-resistance testing will be required to validate our results. Full article

Traditional particle swarm optimization (PSO) easily falls into premature convergence, while differential evolution (DE) is highly sensitive to fixed control parameters. Existing PSO-DE hybrid frameworks suffer from static population sizes and insufficient cross-population information exchange. This paper proposes PSO-DE-ADP, a hybrid optimizer with sinusoidal adaptive parameters, elite-guided mutation, ring neighborhood-weighted PSO and fitness-driven dynamic dual-population allocation. Four complementary mechanisms are integrated: (i) sine-wave perturbation superimposed on linear decay adaptively adjusts PSO inertia weight, acceleration factors and DE scaling/crossover coefficients to balance search stages; (ii) global elite individuals are embedded into DE mutation to reduce blind random search; (iii) ring topology with weighted learning realizes bidirectional information interaction between PSO and DE subpopulations; (iv) the proportion of PSO/DE individuals is dynamically adjusted according to elite ratio to allocate computing resources. Experiments adopt the CEC2017 30-dimensional benchmark with 30 test functions covering unimodal, multimodal, hybrid and composite landscapes. Compared with 8 state-of-the-art metaheuristics, PSO-DE-ADP achieves the lowest Friedman rank (1.08 vs. 2.23–4.90 for PSO variants; 1.53 vs. 2.07–5.00 for non-PSO algorithms). Ablation tests prove each component significantly boosts accuracy; The algorithm only costs 0.172 s average runtime, superior to all competitors. Statistical Wilcoxon and Friedman tests verify its significant superiority. Future work extends this method to multi-objective, constrained and real engineering optimization tasks. Full article

Adaptive Reinforced Gray Langur Optimization (ARGLO), an enhanced variant of the Gray Langurs Optimizer, is developed for high-dimensional, multimodal, and nonlinear search landscapes susceptible to local trapping. Although the original GLO performs multi-population cooperative search by simulating the social structures of gray langurs, it still suffers from uneven random initialization, insufficient adaptive population partitioning, weak local perturbation, and premature convergence. ARGLO incorporates three strategies: good point set-based oppositional and quasi-oppositional learning initialization, hierarchical equilibrium adaptive population partitioning, and elite-guided hybrid mutation. Collectively, these mechanisms generate a higher-quality starting population, coordinate global search with local refinement, and reduce the risk of entrapment in suboptimal regions. Evidence from component-wise experiments together with the CEC test suite indicates that ARGLO delivers higher solution precision, steadier convergence, as well as more consistent performance, especially as dimensionality increases. Moreover, ARGLO is applied to near-infrared spectral feature selection and SVR parameter optimization for polysaccharide content prediction in Dendrobium huoshanense. Compared with unoptimized SVR, ARGLO-SVR reduces RMSE by 35.35% and improves R² by 21.92%; compared with GLO-SVR, it reduces RMSE by 6.05% and improves R² by 2.30%. These results demonstrate the effectiveness and application potential of ARGLO in complex optimization and rapid nondestructive quality detection of traditional Chinese medicinal materials. Full article

Breast cancer remains highly prevalent, where the lifetime risk before age 75 is one in 13. However, known genetic factors were only identified in 14.7% of cases in our Hong Kong Hereditary Breast Cancer Family Registry. Our current local policy for genetic testing does not cover the detection of beyond BRCA1/2. Here we highlight the clinical value of extending testing to beyond BRCA susceptibility genes for improved prevention, diagnosis, and management. We recruited 6597 hereditary breast and ovarian cancer (HBOC) patients from our registry based on family history and clinical criteria. Germline mutations were identified by multi-gene sequencing analysis using next-generation sequencing (NGS). Clinical–pathological characteristics of BRCA and beyond BRCA carriers were compared and the real-world management and surveillance services adopted in Hong Kong were highlighted. In this multi-gene hereditary cancer cohort, germline mutations were identified in 10.9% of cases for BRCA1/2 and 3.5% for beyond BRCA susceptibility genes. These beyond BRCA mutations constitute a considerable proportion of actionable hereditary risk. Notably, PALB2 emerged as the most prevalent non-BRCA gene, followed by TP53, ATM, and BARD1. New cancers or recurrences were detected during their surveillance; the overall pick up rates were 8.3% (PALB2), 29.4% (TP53), 33.3% (PTEN) and 5.6% (BARD1). This study provides the first comprehensive characterization of the beyond BRCA germline landscape in a Hong Kong hereditary cancer cohort and highlights the current need for implementing multi-gene sequencing analysis and surveillance services for HBOC patients, establishing the predominant non-BRCA drivers and providing a robust empirical basis for expanding public genetic screening frameworks. Full article

This study addresses a large-scale location–allocation problem in a three-echelon automotive supply chain comprising 382 suppliers, candidate distribution centers, and six assembly plants. The planning task is to redesign the inbound consolidation network while minimizing transportation and distribution center operating costs, enforcing a 480 km supplier-to-center service radius, and achieving at least 90% demand-weighted coverage. We formulate a mixed discrete-continuous model with supplier-to-center assignment, center location, throughput, and flow decisions. A feasibility-oriented hybrid algorithm uses a genetic algorithm as the main search engine, ant colony construction to seed solutions near the feasible region, adaptive mutation and simulated annealing to preserve exploration and refine elite solutions, and an online neural surrogate to avoid a subset of costly exact fitness evaluations. The design differs from a simple collection of metaheuristics: all components share one variable-length encoding, the same feasibility metrics, and periodic exact reevaluation of candidate solutions. Using the competition case data, the redesigned network reduces total cost by 27.0% relative to the six-center baseline, decreases the demand-weighted average supplier-to-center distance from 461.3 km to 53.0 km, lowers the maximum distance from 2807.22 km to 441.78 km, and raises coverage from 45.0% to 100%. Across ten independent runs, the hybrid method obtains a mean cost 10.3% below that of a standard genetic algorithm, with lower run-to-run dispersion. The results show that feasibility-aware initialization, adaptive search, and selective surrogate evaluation can support practical redesign of a strongly constrained, national-scale inbound logistics network. The directly attached reproducibility package provides the MATLAB implementation and the seven supplied input workbooks used by the reported model. The evidence is limited to one deterministic competition instance, a fixed cost schedule, and fixed-topology sensitivity calculations; generalization under demand uncertainty, facility disruption, and alternative road conditions remains to be tested. Full article

Accurate carbon footprint accounting is fundamental for urban environmental governance. However, multi-stakeholder transit networks struggle with data manipulation, privacy risks, and labor-intensive manual auditing. To resolve these trust and scalability bottlenecks, this paper introduces a tri-layer hybrid blockchain framework based on an “off-chain storage, on-chain evidence” paradigm. The architecture synergizes a relational database (MySQL) for high-throughput structured data, the InterPlanetary File System (IPFS) for decentralized raw evidence, and Hyperledger Fabric to immutably anchor dual-layer cryptographic hashes. We engineer a smart contract auditing pipeline that autonomously executes deterministic verification of hash consistency, emission thresholds, and physical logic integrity. Empirical evaluations utilizing a large-scale urban transit dataset injected with adversarial mutations demonstrate high robustness, achieving F1-scores of 1.000 across multidimensional anomalies. This replaces manual testing with statistically significant verification. Ultimately, this framework provides environmental regulators and transit authorities with a highly scalable, privacy-preserving, and trust-minimized infrastructure for continuous carbon footprint traceability. Full article

The emergence of resistance-associated mutations in coronavirus main protease (Mpro) poses a significant challenge to the development of broad-spectrum antiviral therapeutics. In this study, we improved and accelerated a mutation-aware machine learning (ML) framework to predict the binding score of Nirmatrelvir analogue ligands against wild-type and mutant MERS-CoV Mpro. A library of 15,889 Nirmatrelvir derivatives generated through systematic scaffold modification was docked against the wild-type and five variants of the Mpro, producing a total of 95,334 structural and docking score datasets of these protein–ligand complexes. During the ML model development phase, ligand effects were learned from RDKit molecular descriptors and graph-based representations, and the mutation-induced effects were captured through delta-encoded physicochemical properties (hydrophobicity, charge, aromaticity, and polarity) of the active-site residues. Among the evaluated models, the CatBoost regressor tree-based algorithm achieved the lowest mean absolute error (MAE) value of 0.23 Kcal/mol and an R² of 0.87. Further improvement was achieved by creating a weighted ensemble model combining the CatBoost regressor, XGBoost and LightGBM regressor, resulting in a prediction accuracy with a MAE of 0.19 Kcal/mol and an R² of 0.90 relative to docking scores. Model robustness was further evaluated through random-, ligand group- and scaffold group- K-fold cross-validation along with their Y-randomization. Moreover, the models were also tested with a new set of 1000 structurally diverse compounds. SHAP analysis was conducted, which identified 20 molecular descriptors critical for accurate predictions. The ensemble model accurately predicted the binding affinities of Nirmatrelvir and its four analogues (E1–E4), reproducing the experimental pIC50 trend and correctly identifying the most potent inhibitors. The ensemble model also showed consistent performance across all MERS-CoV Mpro variants, S147Y, S142G, L144A, S142G/S147Y, and S142G/L144A/S147Y, demonstrating its potential for rapidly discovering mutation-resistant antiviral drugs. Full article

With the increasing complexity of engineering optimization and intelligent decision-making problems, traditional metaheuristic algorithms often suffer from premature convergence, loss of population diversity, and insufficient adaptability to complex fitness landscapes. To address these issues, this paper proposes a Multi-strategy Symmetry-Aware Improved Golden Sine Algorithm (MIGoldSA). The proposed algorithm introduces a symmetry-guided multi-strategy framework in which multiple complementary search operators are organized in a structurally balanced manner. Specifically, a strategy pool consisting of the original golden sine update rule, three differential evolution mutation strategies, and an elite-based quadratic interpolation local search operator is constructed. An adaptive strategy selection mechanism is further developed to dynamically regulate the selection probabilities of different strategies according to their historical success rates, forming a dynamic probabilistic symmetry that balances global exploration and local exploitation throughout the optimization process. The numerical performance of the resulting method is assessed using the CEC2014, 30-dimensional CEC2017, and 20-dimensional CEC2022 test collections. Comparative and statistical findings confirm that MIGoldSA generally delivers more accurate final solutions, more consistent outcomes across independent trials, and stronger convergence behavior than established algorithms and recently developed competitors. Its applicability is further examined in corporate insolvency forecasting by employing MIGoldSA to determine the hyperparameter configuration of a K-nearest neighbors classifier. Tests conducted on the Wieslaw financial database show that the resulting MIGoldSA-KNN system outperforms the selected reference models in classification accuracy, Matthews correlation coefficient, F1-score, and recall. These findings suggest that the proposed symmetry-inspired architecture offers an effective means of coordinating diversified search and intensive refinement, thereby providing a valuable computational approach for challenging global optimization and financial classification tasks. Full article

Recursive evidence fusion gives an intrusion detection system temporal memory, but it also gives unreliable windows and erroneous review outcomes a path to influence later diagnoses. Existing drift-handling, open-set, conformal, continual-learning, and human-in-the-loop methods provide useful signals or update classifiers and memories; they do not, by themselves, specify when a post-classification evidential state may be written or replaced. We present RTEF-IDS, a protocol that separates current action, model-evidence admission, reviewed-feedback admission, and history replacement. The protocol retains the history-relative reliability principle from our previous work, instantiates it for singleton-plus-ignorance IDS evidence, and assigns operation-specific credentials. Reviewed windows make no base-state change, mapped-known feedback may be appended, and replacement requires persistent confirmation. On 33,384 frozen windows, 30% retrospective admission excludes 26.3% of held-out-or-misclassified mass while retaining 94.8% of known-correct evidence. Under paired imperfect feedback, retrospective replacement increases one-window future history-state agreement by 0.107 in the primary block and 0.129 in IoT-23 leave-scenario-out replay. Under a past-only rolling-budget gate within externally supplied frozen partitions, the corresponding increments are 0.001 and 0.000, indicating that the tested gate exposes few qualifying replacement opportunities; bounded external short streams show the same opportunity constraint. Independent second review reduces false authorization from 5.66 to 0.124 per 1000 first-stage reviewed windows under independent errors and from 34.27 to 0.181 under five-window correlated errors, with a corresponding increase in review demand and a reduction in admitted corrective feedback. A shared systematic label alias remains unresolved by the tested review arms. These results support explicit, auditable state-mutation control while identifying the causal-opportunity and feedback-provenance conditions under which it operates. Full article

Background: Wastewater surveillance has emerged as an effective tool for population-level pathogen monitoring. Its application to mutations associated with resistance to antivirals remains comparatively underdeveloped. We assessed the wastewater epidemiology framework using SARS-CoV-2 as a model pathogen to evaluate spatial, temporal, and therapeutic class-specific resistance dynamics. Methods: We analyzed about 10,000 SARS-CoV-2-positive wastewater samples from six Ontario public health regions collected between October 2021 and July 2024. Fifty-five mutations were screened, comprising therapeutic resistance-associated mutations and a biologically distinct group of immune-evasion mutations. Mutations detected in ≥10 samples at ≥1% frequency were retained for spatiotemporal analysis using LOESS smoothing and Kruskal–Wallis testing. Results: Twelve mutations met the inclusion thresholds. S:E340D, associated with reduced susceptibility to sotrovimab was geographically widespread but transient and low-frequency. Five remdesivir-associated polymerase mutations were sporadic with sharp localized peaks, including two mutations exceeding 99% frequency in isolated catchments. Three nirmatrelvir-associated protease mutations were detected, with ORF1a:Q3452K showing significant regional variation. FLiRT and FLuQE immune-evasion mutations were most persistent and abundant. LOESS smoothing showed distinct temporal patterns among mutations, while Kruskal–Wallis testing identified significant regional variation for ORF1a:Q3452K and the three immune-evasion mutations. Conclusions: These findings demonstrate that wastewater surveillance enables population-scale monitoring of antiviral resistance and immune escape-associated mutations and offers a scalable model for broader surveillance. Full article

Fatigue crack propagation prediction plays a critical role in structural health monitoring and remaining useful life (RUL) assessment of engineering structures. However, conventional particle filter (PF) algorithms may suffer from particle impoverishment and insufficient particle diversity, which can adversely affect prediction accuracy and stability. To address these limitations, a hybrid genetic algorithm–particle filter (GA-PF) is developed for fatigue crack propagation prediction by incorporating genetic operations, including selection, crossover, and mutation, into the PF framework to optimize particle distribution and enhance global search capability. The proposed method is evaluated using fatigue crack growth experimental data, and its performance is compared with that of the conventional PF algorithm. The results show that the GA-PF method achieves improved prediction performance for fatigue crack propagation and remaining useful life estimation. At 255,000 cycles, the GA-PF algorithm predicted a median RUL of 22,500 cycles, with a relative RUL error of 9.04%, whereas the conventional PF algorithm resulted in a relative RUL error of 45.41%. These results indicate the potential benefit of introducing genetic optimization into the particle filter framework for fatigue crack propagation prediction. The findings further suggest that the hybrid GA-PF method can improve predictive performance compared with conventional PF on the tested dataset. Full article

Background: The growing awareness of breast cancer’s molecular diversity has not changed the technical foundations of surgery itself, but it has profoundly reshaped how surgeons think about surgery. Rather than molecular biology prescribing specific surgery, it is the surgeon’s interpretation of biological behavior—tumor subtype, genomic risk, treatment responsiveness—that influences surgical timing, extent, and feasibility. This is particularly important in the developing world, where mastectomy continues to be the default surgery, not always because it is required, but because biological nuance is underutilized in surgical planning. This review integrates existing evidence, guidelines, and real-world clinical experience to show how a surgeon who understands tumor biology can meaningfully expand safe breast conservation, de-escalate axillary surgery, and align operative choices with systemic therapy. In essence, molecular biology becomes a lens through which surgeons can practice more personalized, precise, and less invasive surgery, without compromising oncologic safety. Recent findings: We present evidence-based algorithms focusing on Luminal A, Luminal B, HER2-positive, and triple-negative subtypes, while discussing the nuances of multifocal and multicentric disease, metaplastic histologies, and discordant lesion management. The review addresses axillary management in the molecular era, specifying the appropriateness of sentinel lymph node biopsy, targeted axillary dissection, or completion axillary dissection, and how subtype-specific nodal responses to neoadjuvant therapy can guide de-escalation strategies. Through clinical vignettes, we exemplify how molecular integration into surgical planning can modify clinical courses, enabling oncoplastic conservation in downstaged tumors and justifying definitive resection in chemo-resistant cases. We examine the implications of germline and somatic genetic testing on surgical decision-making, particularly in relation to BRCA1/2 and PALB2 mutation carriers, alongside ethical and practical counseling considerations. Additionally, we review emerging biomarkers—such as circulating tumor DNA and immune and radiomic signatures—and propose research priorities for their incorporation into surgical trials. Conclusions: Effective implementation necessitates enhanced surgeon education, standardized assays, and multidisciplinary coordination to promote equitable access, consistent utilization of biology-driven algorithms, and rigorous quality oversight. This review furnishes breast surgeons with a pragmatic framework for translating molecular knowledge into multidisciplinary, patient-centered care pathways that optimize oncological safety, aesthetic outcomes, and overall quality of life. Full article

Gene inactivation in model organisms has identified numerous genes and signaling pathways involved in mammalian cardiac outflow tract (OFT) development. Human genetics data have implicated the VEGFR3 gene in OFT development, but when and where it is required is unknown. In this study we determined the sensitivity of the developing murine heart to reduced Vegfr3 gene dosage, and we tested whether its requirement is dependent upon TBX1, a known regulator of Vegfr3 expression in cardiac and lymphatic endothelial cells. We found that in the mouse, a single copy of the Vegfr3 gene was sufficient for normal heart development in most cases. Mutation of a single copy of the Tbx1 gene greatly enhanced the sensitivity of heart development to Vegfr3 dosage reduction and led to the formation of cardiac defects. In addition, deletion of Vegfr3 in the Tbx1 expression domain also led to severe cardiac OFT abnormalities. We used RNAscope to reveal the location of Vegfr3 and Tbx1 transcripts in midterm mouse embryos. This revealed co-localization of these transcripts in the endothelium of the aortic sac, caudal pharyngeal arch arteries and proximal OFT, suggesting that these are potential sites of genetic interaction between Vegfr3 and Tbx1 that are critical for murine heart development. Full article

Background/Objectives: Malignant hyperthermia (MH) is a life-threatening pharmacogenetic disorder of skeletal muscle primarily associated with pathogenic variants in RYR1; however, a substantial proportion of MH-susceptible individuals lack identifiable variants in known genes. This study aimed to identify novel genetic contributors in Ca²⁺-induced Ca²⁺ release (CICR)-positive patients without RYR1 variants and to evaluate their functional relevance. Methods: Among 29 CICR-positive individuals without pathogenic variants identified by gene panel testing, five patients underwent whole-exome sequencing (WES). In one family, heterozygous variants in ANO5 (p.Arg547Gln) and ORAI3 (p.Arg287Cys) were identified. To evaluate their functional significance, intracellular Ca²⁺ dynamics were analyzed in primary myotubes derived from Case 165, an affected individual carrying both variants, and compared with those from CICR-negative controls and CICR-positive patients harboring RYR1 variants. Results: Myotubes derived from Case 165 demonstrated enhanced sensitivity to caffeine and 4-chloro-m-cresol, elevated resting intracellular Ca²⁺ levels, and greater Ca²⁺ reduction under Ca²⁺-free conditions compared with CICR-negative controls, resembling the phenotype observed in the RYR1 variant group. In contrast, dantrolene-induced Ca²⁺ reduction was significantly greater only in the RYR1 variant group. Conclusions: These findings suggest that ANO5 and ORAI3 variants may contribute to abnormal Ca²⁺ regulation in MH-susceptible individuals without RYR1 mutations and highlight the importance of combining genomic analysis with functional validation to identify novel genetic contributors to MH susceptibility. Full article