Browse Articles

Supercell-enhanced multimodal plasmonic sensor for high-fidelity antigen detection via refractive index modulation

Basketball team optimization algorithm (BTOA): a novel sport-inspired meta-heuristic optimizer for engineering applications

Abstract Real-world optimisation problems are increasingly high-dimensional, nonlinear and constrained. The No Free Lunch theorem implies that no single optimiser dominates across all problem classes, making domain-specific metaheuristics indispensable. Yet mainstream population-based methods often converge prematurely and fail to balance exploration and exploitation under such complexity. To address these limitations, we propose the Basketball Team Optimisation Algorithm (BTOA), a sports-inspired metaheuristic. BTOA maps four basketball concepts-high-intensity training, fast breaks, dynamic positioning and coordinated passing-onto cooperative search operators. In addition, we introduce two extensible modules: (i)a dynamic positioning strategy guided by diagonal structures, significantly improving global exploration capabilities, and (ii) a VariableAttributes to manage the distribution of individual diversity. These modules can be embedded into other population-based optimisers, enriching the heuristic design space. Extensive experiments on the CEC2005 and CEC2017 benchmark suites with 30, 50 and 100 dimensions show that BTOA attains the lowest mean error on 82.61% of the CEC2005 functions and on 66.67%, 63.3% and 66.67% of the CEC2017 functions, respectively. Wilcoxon signed-rank and Friedman tests confirm the statistical significance of these gains. Additional comparisons against several recently proposed algorithms and competition-winning algorithms further highlight BTOA’s consistent advantage. Beyond benchmark tests, BTOA performs well on real-world problems with complex constraints and large decision spaces, such as UAV path planning. Its principled design alleviates key shortcomings of existing metaheuristics and offers a scalable, reliable tool for contemporary engineering optimisation tasks.

Investigating the diversity and stylization of contemporary user generated visual arts in the complexity entropy plane

Surface oxygen concentration differs between dry and ice-snow melting bare lands on the Tibetan Plateau

Surface keratin 1, a tumor-selective peptide target in human triple-negative breast cancer

Mitochondria-related parameters of lymphocyte subsets can distinguish different disease stages in patients with HBV infection

Single-cell RNA-seq analysis of mouse carotid artery under disturbed flow and human carotid plaques identifies key cell populations in atherosclerosis development

Abstract Atherosclerosis tends to occur in regions of disturbed blood flow. This study explored how disturbed flow aggravates atherosclerosis using single-cell RNA-seq (scRNA-seq) datasets from mouse carotid arteries under disturbed flow and human carotid artery plaques. The scRNA-seq datasets were obtained from the GEO (GSE159677, GSE43292, GSE163154, and GSE41571) and SRA (PRJNA722117) databases and were processed using Seurat. Functional enrichment analysis was conducted using Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO). Single-cell Flux Estimation Analysis (scFEA) was used to analyze cell type-specific changes in metabolism and “transcriptomic noise analysis” to examine senescence. GWAS-significant cardiovascular disease (CVD) risk genes were used to calculate risk gene scores for main cell populations. CellChat and Cytosig were used to analyze cell communication and cytokines. scRNA-seq identified seven cell clusters in mouse arteries: endothelial cells (ECs), vascular smooth muscle cells (VSMCs), fibroblasts, pericytes, macrophages, neutrophils, and T cells. Fibroblasts showed the most pronounced changes, particularly in inflammation and TGF-β signaling pathways. ECs, VSMCs, and fibroblasts had the highest enrichment of CVD risk gene scores, with fibroblasts showing most significant increases in gene risk scores after disturbed flow stimulation. A distinct fibroblast subgroup displayed high enrichment in inflammation and ossification-related pathways. CD36 + positive ECs exhibited significant senescence phenotypes following disturbed flow stimulation. Notable increases in VEGFA+ macrophages were discovered in the disturbed flow stimulation group, displaying a pronounced M1 pro-inflammatory phenotype associated with the severity of atherosclerosis and plaque stability. This study systematically elucidated functional changes of cell populations under disturbed flow. CD36+ ECs, VEGFA+ macrophages, and adventitial fibroblasts play critical roles in atherosclerosis.

Optimal sizing of hybrid renewable energy systems relying on the black winged kite algorithm for performance evaluation

Effect of substrate temperature on properties of WS2 thin films

Clinical characteristics and outcomes of critically ill elderly patients aged 90 years and older

Illusion of control and passion mediate the relationship between winning exposure and problem lottery gambling in a multigroup analysis

Role of shielding gas in tailoring microstructure and anisotropy in wire arc additive manufactured Inconel 686

Stromal modifying CHST15 siRNA enhances antitumor effect synergistically with anti-PD-1 immune checkpoint antibody in murine pancreatic cancer

Diagnostic value of neutrophil to lymphocyte ratio and serum biomarkers in chronic osteomyelitis

Abstract Chronic osteomyelitis (COM) is a persistent bone infection associated with severe complications, making early and accurate diagnosis essential. Traditional diagnostic methods, including imaging and bacterial cultures, are often limited by low sensitivity, long processing times, and the inability to detect infections in certain clinical scenarios. In this study, we evaluated the diagnostic utility of inflammatory biomarkers for COM, including neutrophil to lymphocyte ratio (NLR), C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), and interleukin-6 (IL-6). A total of 200 participants, including 100 COM patients and 100 healthy controls, were enrolled. Our results showed that Gram-positive bacteria were more prevalent (59%), with Staphylococcus aureus being the most frequently isolated pathogen. Antibiotic resistance profiling revealed that Gram-positive bacteria exhibited high resistance to Penicillins but remained sensitive to Vancomycin and Linezolid. In contrast, Gram-negative bacteria showed high resistance to certain Penicillins, while sensitive to Carbapenems. Inflammatory marker levels (NLR, CRP, TNF-α, and IL-6) were significantly elevated in COM patients, with higher levels in Gram-negative infections. Multivariate logistic regression analysis and ROC curve analysis demonstrated that these inflammatory markers were significant predictors of COM, and the combination of these biomarkers showed superior diagnostic performance. Our findings suggest that NLR, CRP, TNF-α, and IL-6 are valuable diagnostic biomarkers for COM, and their combination enhances diagnostic precision, offering a promising tool for clinical management.

Foliar application of yeast extract mitigates water deficit stress and elicits hypericin and phenolic production in Hypericum perforatum L

Abstract Yeast extract has emerged as a bio-elicitor capable of modulating secondary metabolism and stress tolerance in plants, but its impact on St John’s Wort (Hypericum perforatum L.) remains unexplored. Therefore, the interactive effects of yeast extract (0, 3, and 6 g L−1) and irrigation intervals (7, 10, and 13 days) on hypericin and phenolic production in this medicinal herb were investigated in a field experiment. The prolonged irrigation intervals decreased biomass during both seasons. Hypericin content peaked under the 10-day irrigation interval but declined in the 13-day irrigation interval. Foliar spraying of yeast extract improved biomass, chlorophyll a, b, carotenoids, relative water content, and hypericin concentration across all water regimes. Yeast extract application reduced hydrogen peroxide and malondialdehyde contents in water deficit-subjected plants due to increased activity of superoxide dismutase and catalase, and elevated levels of total phenol and flavonoid contents in the leaves. The highest contents of hypericin and phenolics were recorded with applying 6 g L−1 yeast extract under the 10-day irrigation interval, corresponding with the strongest 2,2-diphenylpicrylhydrazyl scavenging activity and ferric-reducing power in the leaves. These findings suggest that yeast extract spraying might be a promising approach for enhancing the productivity and quality of medicinal plants under water deficit.

Electric load forecasting based on kernel extreme learning machine optimized by improved sparrow search algorithm

A comprehensive screening on the effect of Bacillus cereus and Vibrio natriegens bacterial consortia in cement mortar

Supplementation of silicon oxide nanoparticles mitigates the damaging effects of arsenic stress on photosynthesis, antioxidant mechanism and nitrogen metabolism in Brassica juncea

Supervised aerobic-strength exercise reduces postural sway and improves dual-task gait in Parkinson’s disease

Compression strength and damage model of frozen silty clay in Xing’an Baikal permafrost under temperature effects

Abstract The Daxing’anling, situated within the high-latitude transition zone between continuous and sporadic permafrost, mark the southern boundary of the Northern Hemisphere’s permafrost distribution. The thermally sensitive Xing’an Baikal permafrost in this region was investigated through uniaxial compression tests on remolded silty clay under controlled freezing temperatures (− 7.5 to − 0.5 °C). Results revealed a triphasic strength-temperature relationship: strength increased at 79.99 kPa/°C between − 0.5 and − 2.0 °C, surged to 1842.00 kPa/°C from − 2.0 to − 3.0 °C, then declined to 316.20 kPa/°C below − 3.0 °C. A brittle-ductile transition occurred at − 3.0 °C, shifting failure modes from plastic to brittle deformation. Building on Lemaitre’s strain equivalence principle and Weibull statistics, we developed a dual-variable damage model integrating thermal and mechanical damage, enabling quantitative cryogenic damage assessment, coupled damage evolution equations, and full temperature-regime stress–strain predictions. This work advances theoretical tools for engineering stability evaluation in the Xing’an Baikal permafrost environments.