Search Results (7,177)

Bovine parainfluenza virus type 3 (BPIV3) is a significant pathogen implicated in bovine respiratory disease complex (BRDC), leading to lung tissue destruction, immunosuppression, and subsequent bacterial infections in cattle, hence incurring considerable economic losses globally. Notwithstanding its importance, a limited number of commercial vaccinations are presently accessible. The fusion (F) protein and hemagglutinin-neuraminidase (HN) protein, as protective antigens of the Paramyxoviridae family, can elicit neutralizing antibodies and are regarded as optimal candidates for the creation of genetically modified vaccines. A multi-epitope-based peptide vaccine (MEBPV) was developed by immunoinformatics methodologies by choosing epitopes from the F and HN proteins characterized by high antigenicity, moderate toxicity, and limited allergenic potential. The epitopes were combined with suitable linkers and adjuvants to produce the vaccine, whose physicochemical qualities, immunological attributes, solubility, and structural stability were improved and evaluated using computational methods. Molecular docking and molecular dynamics simulations demonstrated the strong potential binding affinity and stability of the vaccination with TLR2, TLR3, and especially TLR4 receptors. Immune simulations forecasted strong humoral and cellular responses, accompanied by a significant elevation in interferon-γ (IFN-γ) production. The vaccine sequence was later cloned into the pET-28a (+) vector for possible expression in Escherichia coli. Despite in silico predictions suggesting a favorable immunogenic potential, additional in vitro and in vivo studies are necessary to confirm its protective efficacy and safety. This research establishes a solid foundation for the creation of safe and efficacious subunit vaccines targeting BPIV3 and presents novel perspectives for the formulation of vaccinations against additional viral infections. Full article

Background/Objectives: miR5200 is miRNA unique to Poaceae plants. Induced under short-day conditions, it modulates flowering time by regulating the florigen FT gene expression. However, to date, the genetic locus responsible for mature miR5200 formation remains experimentally unvalidated, and its biological function in abiotic stress responses remains unknown. This has hindered systematic elucidation of miR5200’s physiological role and molecular mechanisms. Methods: This study utilized wheat as the research material. First, through bioinformatics analysis at the genomic level, 13 potential candidate tae-miR5200 gene loci were screened. Subsequently, the authenticity of these gene loci was systematically validated by combining tobacco transient transfection-based GUS staining assay and quantitative real-time PCR (qRT-PCR) to detect expression levels. Building upon this foundation, the expression patterns of tae-miR5200 under abiotic stresses such as low temperature, drought, and salinity, as well as SA, ABA, IAA, GA₃, and MeJA treatments, were further investigated. Results: Experimental validation confirmed that 7 out of 13 potential gene loci are authentic and functional, and tae-miR5200 exhibited specific expression changes under different types of abiotic stress. Conclusions: This study confirms the authenticity of tae-miR5200 gene loci, effectively eliminating interference from bioinformatics-predicted false-positive loci in subsequent functional studies. It provides an experimental foundation for further investigation into the molecular mechanisms of tae-miR5200 in wheat responses to abiotic stress. Full article

Platax teira is a marine fish species with both ornamental and economic value, but it faces challenges in aquaculture due to environmental stress and disease. Genetic research on P. teira has been limited due to the limitations of the partially incomplete reference genome and the lack of a complete transcriptome. In this study, we utilized PacBio SMRT sequencing to generate a full-length transcriptome for P. teira, obtaining 39,770 isoforms, including 32,265 known gene-related transcripts and 4730 novel transcripts from 3455 new genes. All novel genes were annotated, and enrichment analysis revealed significant associations between immune-related pathways, such as cAMP, MAPK, PI3K-Akt, and Wnt. We also identified 14,398 alternative splicing events, 2754 alternative polyadenylation events, 42,250 SSRs, 1569 transcription factors, and 2067 long non-coding RNAs. Additionally, protein–protein interaction (PPI) analysis of immune-related pathways predicted chemokines as key immune factors among novel genes. Domain prediction analysis highlighted the diverse functional potential of immune factors such as NLRC3, tyrosine kinase 2, and A2M in different alternative splicing events. Overall, the characterization of the full-length transcriptome dataset of P. teira lays the foundation for further studies on its genetic analysis and immune regulation. Full article

Woody plants, comprising forest and fruit tree species, provide essential ecological and economic benefits to society. Their genetic improvement is challenging due to long generation intervals and high heterozygosity. Genetic transformation, which combines targeted DNA delivery with plant regeneration from transformed cells, offers a powerful alternative to accelerating their domestication and improvement. Agrobacterium tumefaciens, Rhizobium rhizogenes, and particle bombardment have been widely used for DNA delivery into a wide variety of explants, including leaves, stems, hypocotyls, roots, and embryos, with regeneration occurring via direct organogenesis, callus-mediated organogenesis, somatic embryogenesis, or hairy root formation. Despite successes, conventional approaches are hampered by low efficiency, genotype dependency, and a reliance on challenging tissue culture. This review provides a critical analysis of the current landscape in woody plant transformation, moving beyond a simple summary of techniques to evaluate the co-evolution of established platforms with disruptive technologies. Key advances among these include the use of developmental regulators to engineer regeneration, the rise in in planta systems to bypass tissue culture, and the imperative for DNA-free genome editing to meet regulatory and public expectations. By examining species-specific breakthroughs in key genera, including Populus, Malus, Citrus, and Pinus, this review highlights a paradigm shift from empirical optimization towards rational, predictable engineering of woody plants for a sustainable future. Full article

Background: Thalassemia has been recognized as a critical public health issue in Bangladesh, especially among pregnant women, due to its hereditary nature and the lack of early screening infrastructure. Early identification of at-risk individuals is essential to prevent the transmission of this genetic disorder to future generations and to reduce the burden on an already strained healthcare system. Methods: In this study, an innovative framework for thalassemia risk assessment has been developed by integrating Multi-Criteria Decision-Making (MCDM) methods—specifically AHP-TOPSIS—with machine learning algorithms including Random Forest, XGBoost, and CatBoost. Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME have also been incorporated to improve model transparency and trustworthiness. Real-world clinical and demographic data, consisting of 16 features and 1200 samples, have been collected through a structured survey and processed using rigorous feature selection and ranking methods. Risk stratification has been performed to classify patients into high, medium, and low categories, enabling targeted intervention. Results: Among all models, the XGBoost classifier trained on AHP–TOPSIS–prioritized features achieved a consistent accuracy of 99.28% under stratified 20-fold cross-validation, demonstrating robust diagnostic classification performance. The model predominantly captures hematologic patterns characteristic of thalassemia manifestations, functioning as an assistive diagnostic framework rather than a causal risk predictor. The explainability of predictions, ensured through comprehensive visual and statistical analyses, further enhances the model’s clinical transparency and reliability. Conclusion: The proposed MCDM–machine learning framework demonstrates strong potential for improving thalassemia risk assessment, enabling early detection and informed decision-making in maternal healthcare. The proposed framework should be regarded as a preliminary proof-of-concept system that demonstrates the feasibility of integrating Multi-Criteria Decision-Making (AHP–TOPSIS) with advanced machine learning and explainable-AI techniques for thalassemia assessment. Although the model achieved strong diagnostic performance under nested cross-validation, additional external validation and inclusion of causal predictors are required before clinical deployment. Full article

Investment in equity assets is characterized by high volatility, both in prices and returns, which poses a constant challenge for the efficient management of risk and profitability. In this context, investors continuously seek innovative strategies that enable them to maximize their returns within acceptable risk levels, in accordance with their investment profile. The purpose of this research is to develop a model with a high predictive capacity for equity asset returns through the application of artificial intelligence techniques that integrate genetic algorithms and neural networks. The methodology is framed within a technical analysis-based investment approach, using the Relative Strength Index as the main indicator. The results show that more than 58% of the predictions generated with the proposed methodology outperformed the results obtained through the traditional technical analysis approach. These findings suggest that the incorporation of genetic algorithms and neural networks constitutes an effective alternative for optimizing investment strategies in equity assets, by providing superior returns and more accurate predictions in most of the analyzed cases. Full article

Aiming at the problems of low production efficiency and high manual dependence in the heat-sealing process of the pillow packaging machine, the existing optimization methods of process parameters were improved, and an intelligent decision-making model of the longitudinal sealing heat-sealing process based on the radial basis function neural network and orthogonal least square method was proposed to realize the efficient and accurate optimization of heat-sealing process parameters. By analyzing the fracture yield strength of the composite, the target heat-sealing strength range was determined. The heat-sealing temperature, heat-sealing speed, and heat-sealing plate distance were selected as key process variables, and the actual production data were used to train the model to accurately construct the nonlinear mapping relationship between heat-sealing process parameters and heat-sealing strength. On this basis, the genetic algorithm optimization framework with the model predictive output as the fitness function is designed to realize the rapid search of the optimal combination of process parameters. The optimization results were introduced into the pillow packaging machine for a verification test. The measured heat-sealing strength was stable within the target range. The maximum error of the optimization group was less than 10%, and the average error was less than 5%, which was significantly better than the effect of manual experience. The experimental results show that the proposed method can effectively improve the efficiency and consistency of process optimization under the premise of ensuring the quality of heat-sealing, meet the requirements of automatic production for high precision and low consumption of the heat-sealing process optimization, and realize the comprehensive improvement of efficiency, accuracy, and intelligent level in the longitudinal heat-sealing process of the pillow packaging machine. Full article

Dengue virus (DENV) is a major global health concern, with pathogenesis driven by complex interactions between the virus, host genetics, and immune responses. Key determinants of disease severity include antibody-dependent enhancement (ADE), cross-reactive T cells, anti-NS1 antibodies, autoimmunity, and genetic predisposition, with the NS1 protein and its antibodies strongly implicated in severe dengue. This review highlights recent advances in our understanding of how DENV impacts host immune responses at cellular, molecular, and genetic levels. We particularly focus on how the virus interacts with the host, alters immune responses, and escapes immune detection. These factors are crucial for disease progression and immune dysfunction. The host mounts both innate and adaptive immune responses involving interferon signalling, cytokine production, antigen presentation, and T-cell activation. However, DENV evades immunity by suppressing interferon pathways, disrupting antigen presentation, and leveraging antibody-dependent enhancement (ADE), leading to immune dysregulation, prolonged viremia, and severe dengue. Gaining insight into these host-pathogen interactions is essential for understanding dengue pathogenesis for designing safer and more effective therapeutics. Furthermore, integrating omics approaches with immune response models shows promise for identifying early, reliable markers that can predict disease severity and guide treatment. A deeper understanding of these processes will support the development of personalised treatment strategies and enhance preparedness for future dengue outbreaks. Full article

Background: Intellectual disability (ID) is a heterogeneous condition caused by diverse genetic factors, including single-nucleotide variants (SNVs) and copy number variants (CNVs). Whole-exome sequencing (WES) and clinical exome sequencing (CES) have become essential tools for identifying pathogenic variants; however, their relative diagnostic performance in ID has not been fully characterized. Methods: Children diagnosed with ID or related neurodevelopmental disorders underwent WES or CES. Identified variants were classified according to ACMG/AMP and ClinGen guidelines, with segregation analysis performed when parental samples were available. Diagnostic yields were compared across demographic, prenatal, and phenotypic subgroups. A multidimensional semi-quantitative scoring system encompassing 15 clinical domains (e.g., age at onset, neuro-motor function, seizures, MRI findings, vision, and dysmorphic features) was developed. Z-scores were calculated for each parameter, followed by hierarchical cluster analysis (HCA) and correlation modeling to define genotype–phenotype associations and pathway-level clustering. Results: A broad spectrum of pathogenic and likely pathogenic variants across multiple genes and biological pathways was identified in our study. CNV-associated cases frequently exhibited prenatal anomalies or multisystem phenotypes associated with large chromosomal rearrangements. Monogenic variants and their corresponding phenotypic profiles were identified through clinical exome sequencing (CES) and whole-exome sequencing (WES). Phenotypic HCA based on Z-scores revealed three major biological groups of patients with coherent genotype–phenotype relationships: Group 1, severe multisystem neurodevelopmental disorders dominated by transcriptional and RNA-processing genes (POLR1C, TCF4, HNRNPU, NIPBL, ACTG1); Group 2, intermediate epileptic and metabolic forms associated with ion-channel and excitability-related genes (SCN2A, PAH, IQSEC2, GNPAT); and Group 3, milder or focal neurodevelopmental phenotypes involving myelination and signaling-related genes (NKX6-2, PLP1, PGAP3, SMAD6, ATP1A3). Gene distribution significantly differed among these biological categories (χ² = 54.566, df = 34, p = 0.0141), confirming non-random, biologically consistent grouping. Higher Z-scores correlated with earlier onset and greater neurological severity, underscoring the clinical relevance of the multidimensional analytical framework. Conclusions: This study highlights the genetic complexity and clinical heterogeneity of intellectual disability and demonstrates the superior diagnostic resolution of WES and CES. Integrating multidimensional phenotypic profiling with genomic analysis enhances genotype–phenotype integration and enables data-driven phenotype stratification and pathway-based re-analysis. This combined diagnostic and analytical framework offers a more comprehensive approach to diagnosing monogenic ID and provides a foundation for future predictive and functional studies. Full article

Recycling water treatment sludge (WTS) offers a sustainable solution to reduce environmental waste and enhance soil stabilisation in geotechnical applications. This study investigates the mechanical performance of soil-sludge-cement-lime mixtures through an extensive experimental program and focuses on compaction characteristics and California Bearing Ratio (CBR) values. Mixtures containing 40% soil, 50% sludge, and 10% lime achieved a CBR value of 58.7% and represented a 550% increase compared to untreated soil. Additionally, advanced predictive modelling using symbolic metaheuristic-based genetic programming (GP) techniques, including the Dingo Optimisation Algorithm (DOA), Osprey Optimisation Algorithm (OOA), and Rime-Ice Optimisation Algorithm (RIME), demonstrated exceptional accuracy in predicting CBR values. The GP-RIME model achieved an R² of 0.991 and a mean absolute error (MAE) of 1.02 in predicting CBR values, significantly outperforming traditional regression methods. Four formulas are proposed to predict CBR values. This research highlights the dual benefits of sustainable WTS recycling and advanced modelling techniques, providing scalable solutions for environmentally friendly infrastructure development. This research aligns with global sustainability goals by valorising waste streams from water treatment plants. The reuse of sludge not only reduces landfill disposal but also lowers demand for energy-intensive binders, contributing to circular economy practice and sustainable infrastructure development. Full article

To discover new probiotics that can protect mammary glands from mastitis, 40 Lactobacillus (Ligilactobacillus) spp. isolates from bovine milk were subjected to a preliminary series of in vitro subtractive analyses. Antibiotic susceptibility testing was performed according to the ISO norm 10932. Many lactobacilli had elevated MIC values for kanamycin (35%), but fewer were resistant to chloramphenicol (15%), streptomycin (7.5%) and tetracycline (5%). The enzymic activities of lactobacilli were tested using an API ZYM system. Nearly 27% exhibited undesirable activities (β-glucuronidase, β-glucosidase and N-acetyl-β-glucosaminidase). The safe strains were monitored for antimicrobial activity against Staphylococcus aureus, Escherichia coli, Salmonella enteritidis, and Bacillus cereus using microtiter plates and for their ability to form biofilms using the crystal violet assay. The antimicrobial activity of lactobacilli against indicator bacteria ranged from 29 to 89% and the isolates exhibited moderate-to-high biofilm formation. Suitable strains were selected for whole-genome sequencing analysis. Antibiotic-resistance genes and putative virulence genes were not predicted in the genomic analysis. Moreover, the isolate Ligilactobacillus salivarius 48 carries genetic information responsible for bacteriocin production that is similar to that encoding salivaricin CRL1328. Our study demonstrates the safety of the above mentioned isolate, which has potential to be used as a probiotic, exerting health benefits through production of antimicrobial substances. Full article

The pervasive spread of Android malware poses significant threats to users and systems worldwide. In most existing studies, differences in feature importance are often overlooked, and the calculation of feature weights is conducted independently of the classification model. In this paper, we propose an Android malware detection method, Leveraging Extraction Method and Soft Voting classification (LEMSOFT). This approach includes a novel preprocessing module, lexical occurrence ratio-based filtering (LORF), and an improved Soft Voting mechanism optimized through genetic algorithms. We introduce LORF to evaluate and enhance the significance of permissions, API calls, and opcodes. Each type of feature is then independently classified using tailored machine learning models. To integrate the outputs of these classifiers, this paper proposes an innovative soft voting mechanism that improves prediction accuracy for encountered applications by assigning weights through a genetic algorithm. Our solution outperforms the baseline methods we studied, as evidenced by the evaluation of 5560 malicious and 8340 benign applications, with an average accuracy of 99.89%. The efficacy of our methodology is demonstrated through extensive experiments, showcasing significant improvements in detection rates compared to state-of-the-art (SOTA) methods. Full article

The small brown planthopper (SBPH), Laodelphax striatellus, transmits rice stripe virus (RSV), a devastating pathogen that causes significant yield losses in rice. The components of the gut microbiota in SBPH and the effects of RSV infection on gut microorganisms are unclear. In this study, high-throughput sequencing of 16S rRNA was utilized to evaluate the composition of gut microorganisms in SBPH. The gut microbiota of SBPH was primarily composed of Proteobacteria, Firmicutes and Bacteroidetes at ratios of 94.79%, 3.04% and 1.39%, respectively; furthermore, the composition of bacteria in the gut microbiota was relatively conserved with differences at the genus level. To elucidate the response of the SBPH gut microbiota to RSV infection, we compared its composition and abundance in viruliferous and naïve SBPH. Interestingly, RSV infection was associated with increased diversity in the SBPH gut microbiota. Comparative analysis demonstrated that RSV infection elevated the relative abundance of Proteobacteria while reducing that of Firmicutes. Population counts demonstrated that RSV infection reduced the gut loads of Stenotrophomonas, Brevundimonas, and Brevibacillus, whereas the gut load of Staphylococcus was significantly increased. Further functional predictive assays revealed that RSV infection enhanced the functions of the SBPH gut microbiota in terms of metabolism, cellular processes, genetic and environmental information processing, and organismal systems. Our results indicate that RSV reshapes the composition, abundance, and functions of the SBPH gut microbiota, offering insights into virus–host–microbiome interactions. Full article

This study systematically investigates the complex nonlinear rheological behavior of magnetorheological fluids (MRFs) and greases (MRGs) through comparative experiments under two shear modes (steady-state shear and large-amplitude oscillatory shear) at room temperature. Results demonstrate that during steady-state shear tests, the apparent viscosity of both materials decreases with the increasing shear rate, exhibiting shear-thinning behavior at high shear rates that aligns with the Herschel–Bulkley constitutive model. Throughout the logarithmically increasing shear rate range, the viscosity and shear stress of MRF consistently exceed those of MRG. Under low-frequency, large-amplitude oscillatory shear (LAOS) conditions, both materials display pronounced viscoelasticity and hysteresis. At higher current levels, the maximum shear stress of MRF surpasses MRG, but its hysteresis loops exhibit reduced smoothness. The Bouc–Wen model accurately characterizes the nonlinear hysteresis of both materials, with model parameters successfully identified via a genetic algorithm. This work establishes a universal framework for the dynamic mechanical response mechanisms of magnetorheological materials, providing theoretical guidance for designing and predicting the performance of smart damping devices. Full article

Background: Morphological similarities among Angelicae Sinensis Radix and related species often lead to market substitution. This study develops a DNA barcoding method to authenticate these herbs and identify adulterants derived from Angelica, Heracleum, and Peucedanum genera. Methods: Phylogenetic analysis was conducted using MEGA 11.0 software with ITS2 sequences from Angelicae Sinensis Radix, Ligusticopsis Pubescens Radix, Angelicae Pubescens Radix, and their related species within the genera Angelica, Peucedanum, and Heracleum. Additionally, ITS2 secondary structures were predicted for the three herbs to provide supplementary evidence for identification. Results: The amplification success rate was 86.67%, and the interspecific genetic distance, ranging from 0.009~0.220, was significantly greater than the intraspecific genetic distance range from 0.000~0.062, indicating that the ITS2 sequence is suitable for differentiating three herbs and their related species. Additionally, their ITS2 secondary structures exhibited significant differences, which can also serve as a reliable criterion for their identification. Conclusions: This study not only validated the identification efficacy of ITS2 sequences and their secondary structures for the three herbs, but more importantly, enabled precise traceability of adulterants through the construction of a comprehensive phylogenetic framework. Full article