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The role of PE teacher exchanges rotation (TER) in teaching quality: moderated mediation of job satisfaction and cross cultural competence
QRS fragmentation and long-term cardiovascular outcomes and mortality: a population-based cohort study
Machine learning-based forecasting of CO2-related economic growth and agricultural land change in IORA countries
Charge based boundary element method with residual driven adaptive mesh refinement for high resolution electrical stimulation modeling
Enzymatic and chemical glycosylation of sugarcane-derived sucrose yields glycosides with moderate anti-inflammatory activity in vitro
Abstract Chronic inflammation is a key driver of many non-communicable diseases, yet current pharmacological therapies are often limited by adverse effects and poor accessibility. Engineered sucrose-derived glycosides from sugarcane offer a sustainable, food-derived candidate with the potential to modulate inflammatory pathways safely and effectively. We developed a dual synthetic approach combining enzymatic and acid-catalyzed glycosylation to generate targeted glycosides. Products were purified and characterized using HPLC, LC-MS, and NMR, achieving high yields and analytical reproducibility. Anti-inflammatory efficacy was evaluated in LPS-stimulated THP-1 macrophages via cytokine assays, while bioavailability was assessed using Caco-2 monolayers. Stability testing in simulated gastric and intestinal fluids and cytotoxicity profiling were also performed. All experiments were conducted in triplicate and analyzed using one-way ANOVA with Tukey’s post hoc test. All glycosides demonstrated high yields (84.9–86.4%), significant cytokine suppression (TNF-α: −35.2% ± 1.8%; IL-6: −33.1% ± 1.5%; p < 0.001), and favorable permeability (2.3–2.5 × 10⁻⁶ cm/s) with > 90% stability under gastrointestinal conditions. No cytotoxicity was observed up to 200 µM. While sucrose-derived glycosides have been reported in related literature to influence NF-κB and MAPK signaling, the present study did not directly evaluate these pathways. Therefore, any mechanistic interpretation remains hypothetical and requires targeted validation. This is the first integrated demonstration of scalable, high-purity, sugarcane-derived glycosides with moderate but statistically significant in vitro anti-inflammatory activity, measurable intestinal permeability in the Caco-2 model, and food-grade stability. These findings support their development as next-generation nutraceuticals, supporting further investigation toward food-grade applications.
Trajectories of anxiety and depressive symptoms during hospitalization for hematopoietic stem cell transplantation
Oo oo, ha ha: why humans and great apes giggle alike when tickled
A novel reflective intelligence optimizer with machine learning (RIO-ML) for parameter estimation of photovoltaic models
Abstract This paper presents a new Reflective Intelligence Optimizer with Machine Learning (RIO-ML) approach to estimate the parameters of solar photovoltaic (PV) equivalent circuit models, which are highly nonlinear and multimodal, and hence require efficient handling by conventional optimizers. RIO-ML combines three major components: a multi-leader social learning algorithm with personal-best reflective memory, machine learning-driven adaptive control of important parameters using Multi-Layer Perceptron models, and progressive Gaussian refinement with reflective boundary treatment for improved convergence and robustness. The performance of RIO-ML is tested on the standard RTC France solar cell model with three different model settings: Single Diode Model (SDM) with 5 parameters, Double Diode Model (DDM) with 7 parameters, and Triple Diode Model (TDM) with 9 parameters, for 30 independent runs for each scenario. RIO-ML obtains the minimum RMSE of 8.739710 × 10 − 4 A, 8.456760 × 10 − 4 , and 7.7546980 × 10 − 4 A for SDM, DDM, and TDM models, respectively, with corresponding low mean RMSE values of 2.223109 × 10 − 3 A, 2.282687 × 10 − 3 A, and 1.717649 × 10 − 3 A. Comparative studies reveal that RIO-ML performs better than some of the best metaheuristic algorithms available in the literature with respect to solution quality, convergence rate, and robustness, while maintaining the maximum absolute current errors less than 1.6 × 10 − 3 A for all models. These findings clearly indicate that the developed RIO-ML approach is an effective and efficient tool for accurate estimation of PV parameter values.
Circulating carbohydrate antigen Ca10H predicts favorable prognosis in colorectal cancer
A theoretical analysis of PhysioChem-K-mer features for protein classification using controlled synthetic benchmarks
Abstract Standard k-mer methods treat amino acids as categorical tokens without directly encoding physicochemical properties. Although physicochemical properties have been incorporated into various bioinformatics tasks, their potential as a direct, systematic alternative for the k-mer counting paradigm has not been fully evaluated. We present PhysioChem-K-mer, a framework that transforms protein sequences into physicochemical property-based feature spaces, serving as an alternative to conventional amino-acid-identity k-mer representations. Our main hypothesis is that property-based representations capture functional constraints more effectively than traditional amino acid-based methods. To test this hypothesis, we created a controlled benchmark comprising 1500 synthetic sequences spanning 10 diverse protein families. The dataset retained core functional motifs while deliberately excluding evolutionary patterns typically found in natural biological sequences. Notably, our hydropathy-based PhysioChem-K-mer achieved a classification accuracy of 81.33% on a controlled synthetic benchmark, representing an absolute gain of 44.33% points over standard 3-mer methods (37.00%). The framework was further evaluated using real UniProt/Swiss-Prot data, comprising 11,620 sequences across 10 families, to ensure practical generalizability. Based on real data, PhysioChem-Hydropathy achieved 64.63%, an absolute gain of 47.68% points over the standard 3-mer baseline (16.95%), while reducing features by 73.9% and training time by 81.6%. By directly integrating biochemical knowledge into feature representations as a primary design principle, PhysioChem-K-mer combines interpretability with computational efficiency. These results suggest that physicochemical properties offer a vital source of information for protein classification, validated here on both synthetic and real-world data.
Experimental evidence of male–male interaction in laboratory swarms of Anopheles gambiae mosquitoes
Abstract Mosquitoes mostly mate in the context of swarms: to facilitate encounters with females, males form disordered aggregations over a visual marker, which serves as a positional reference. While the relevance of this visual marker for swarming activity has been largely addressed, it is still poorly understood whether, in addition to an individual’s response to environmental stimuli, insects in a swarm interact with each other, giving rise to a collective behavior. Here, with a dataset comprising three-dimensional trajectories of 30 laboratory swarms of different sizes (ranging from 80 to 400 mosquitoes), we investigate swarming behavior of Anopheles gambiae mosquitoes. We find that individual speed fluctuations are strongly correlated in space, meaning that mosquitoes in close proximity tend to display similar deviations from the group average, effectively flying at a similar speed, although no such correlation is observed in the flight direction. With a series of targeted tests, we prove that this correlation is not compatible with a random arrangement of individuals, nor with random fluctuations of individual speeds, thereby providing empirical evidence of an effective male-male interaction at play in our swarms.
Trimester-specific reference intervals for coagulation biomarkers (TAT, PIC, TM, tPAI-C) and their clinical associations in healthy Chinese pregnant women
Integrated framework for pediatric height assessment: X-ray-based height extreme cases classification and machine learning for multivariate height prediction
Sulfoxaflor modulates diet-dependent effects on bumble bee development but shows no detectable effects on adult respiration rate and patterns
Abstract Pollinators face stressors, including pesticide exposure and poor nutrition, yet their combined effects on developing brood remain poorly understood. In this study, we experimentally reared bumble bee ( Bombus terrestris ) larvae on pollen diets differing in protein-to-lipid ratios and exposed them to the insecticide sulfoxaflor via their food to test how these factors jointly affect survival, development, and adult traits. Larvae fed multifloral pollen exhibited higher survival and more consistent growth than monofloral (oilseed rape, faba bean) diets. Under the oilseed rape, sulfoxaflor reduced mortality and increased adult emergence despite poorer baseline performance in controls. In contrast, under the multifloral diet, sulfoxaflor prolonged development, reduced larval growth, and lowered adult body mass. Responses under faba bean varied among traits, with prolonged development at lower exposure and reduced growth at higher exposure. Despite these developmental effects, larval exposure to sulfoxaflor did not significantly affect adult respiration rate, and respiration patterns showed no significant treatment effects, although diet-dependent trends were observed. These findings demonstrate that sulfoxaflor effects are strongly context-dependent and mediated by nutritional conditions. They indicate that sulfoxaflor shifted diet-dependent trade-offs between survival and development, improving survival under nutritionally limiting diets while imposing developmental delays and growth costs when nutrition was favourable.
Laser light switches on heat flow in ultra-thin structures
Effect of pressure-controlled tourniquet use on peripheral intravenous catheterization in adult patients: a randomized controlled clinical trial
Longitudinal trajectories of the TyG-WHtR index and the risk of cardiovascular-metabolic multimorbidity: evidence from the CHARLS prospective cohort
A Streptomyces megacluster encodes synergistic biotin-targeting antibiotics
Repeatability of cerebral arteriovenous pulse wave propagation in flow-related enhancement MRI
Abstract Flow Related Enhancement (FREE) MRI is a non-contrast technique for temporally resolved cerebral pulse-wave analysis. Clinical implementation requires proven repeatability. We aimed to evaluate the intra- and inter-scan repeatability of FREE-MRI measurements in healthy volunteers. Twenty-four healthy volunteers were scanned on a 3T MRI using a balanced steady-state free precession (bSSFP) sequence. A test-retest protocol was performed: two scans, a break with repositioning, and two more scans. From the resulting pulse-wave delay maps, the arteriovenous delay (AVD) was computed for the anterior (ACA), middle (MCA), and posterior (PCA) cerebral arteries. Repeatability was assessed using Bland-Altman analysis and Spearman correlation. Mean AVDs were 366 ± 51 ms (ACA), 371 ± 55 ms (MCA), and 376 ± 53 ms (PCA). Intra-scan repeatability was variable; the first session (Run 1 vs. 2) showed no significant differences, whereas the second session (Run 3 vs. 4, post-repositioning) showed significant deviation. However, inter-scan repeatability (comparing pooled pre- vs. post-break acquisitions) showed no significant differences after Bonferroni correction. Bland-Altman analysis confirmed that averaging measurements (Before vs. After) narrowed the limits of agreement compared to single-run comparisons, indicating improved stability. FREE-MRI provides quantitative assessments of cerebral pulse-wave dynamics, though single-acquisition precision is sensitive to physiological state. While intra-scan repeatability was high at rest, immediate post-repositioning scans showed significant variability, highlighting the need for a settling period. Crucially, while averaging measurements across sessions (Before vs. After) helps mitigate this noise, inter-scan reproducibility remains limited and protocol-dependent. Despite these limitations, this study provides a foundational step toward establishing a practical protocol for future clinical and longitudinal applications.