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Synthesis of Macrocyclic Peptides via Photochemical Radical Thiol–yne Reaction

SERPINA3 and NDRG1 are critical diagnostic immune genes associated with macrophages in preeclampsia

Cultivar dependent responses of olive (Olea europaea L.) to foliar fertilization in fruit quality and mineral composition of leaves and fruits

RT-DETR-FCES: a lightweight ship detection algorithm from remote sensing perspective

Energy-efficient wireless sensor networks using unequal clustering and cluster head rotation optimization

Abstract The energy hole problem is a significant challenge in wireless sensor networks (WSN) that use multi-hop routing protocols. Nodes near the base station (BS) typically experience higher energy consumption due to higher data traffic, resulting in faster network energy depletion and creating an energy hole near the BS. To address this issue, the paper proposes a solution involving a mobile data collector (MDC) in an unequal grid cluster. The number and size of the clusters are determined based on the radio energy model’s threshold transmission value, which provides balanced data traffic distribution in the network. The cluster head (CH) is elected based on the node’s distance from the cluster centroid and its residual energy. Additionally, the frequency of CH rotation is optimized through an energy-efficient CH change mechanism. The inclusion of an MDC enables data collection from the CHs along the vertical boundaries, effectively reducing the occurrence of energy holes and extending the network’s overall lifespan. Simulation results demonstrate the superior performance of our protocol compared to similar existing schemes. Our proposed work was simulated using OMNeT++, and the results indicate that it achieves approximately 21% less energy consumption than similar existing works.

Bilateral cerebellar intermittent theta-burst stimulation combined with laryngeal elevation training promotes neurorestoration in post-stroke dysphagia

Organic acids induce plant defense responses and suppress root-knot nematodes in tomato via molecular mechanisms and molecular docking insights

Abstract Root-knot nematodes ( Meloidogyne spp. ) are among the most destructive pathogens affecting tomato production worldwide, leading to severe yield losses and compromised plant health. This study evaluated the efficacy of six organic acids—benzoic, salicylic, citric, lactic, malic, and acetic acids—applied at three concentrations each, in managing Meloidogyne incognita infection in tomato cultivar “023.”Among all treatments, salicylic acid (SA) exhibited the strongest nematicidal activity, achieving 100% juvenile mortality within 72 hours and the highest inhibition of egg hatchability, even at the lowest concentration (0.01%). Benzoic and malic acid followed, showing strong nematocidal effects with moderate suppression of egg hatching, while citric and lactic acids displayed intermediate efficacy. Acetic acid was the least effective. Under greenhouse conditions, Salicylic acid alleviated nematode-induced growth loss through a significant reduction in root galling, egg masses, larvae, and egg counts, resulting in marked improvements in shoot and root biomass compared to the infected untreated control and other treatments. Citric and benzoic acids moderately improved plant growth, especially in root development, whereas lactic and acetic acids had minimal effects. At the molecular level, salicylic acid markedly upregulated the expression of peroxidase (POD) and polyphenol oxidase (PPO) genes, peaking at day 4 and remaining elevated through day 8. Citric, benzoic, and malic acids caused moderate induction, while lactic and acetic acids induced weak or transient expression. The expression pattern suggests dynamic and transient defense activation in response to organic acid treatments. Computational insights obtained through molecular docking simulations predicted that salicylic and benzoic acids may interfere with essential biological processes in Meloidogyne incognita . Both compounds exhibited the highest binding affinities toward several key pathogenicity-related proteins, including NAD(P)H oxidase, putative aspartyl protease, cytochrome c oxidase subunit 1, prefoldin-2, venom allergen-like protein Mi-vap-2, and protein disulfide-isomerase, complementing their in vivo nematicidal effects. Overall, salicylic acid at 0.01% emerged as the most potent and eco-friendly alternative for managing root-knot nematodes in tomato plants, combining strong nematicidal activity, enhanced plant defense responses, and molecular-level evidence of protein inhibition.

Protective effects of rutin and quercetin against diazinon-induced toxicity in Wistar Rat Liver

Hybrid vision transformer and ensemble machine learning framework for automated atherosclerotic plaque classification in intravascular ultrasound imaging

β-Cyclodextrin–chitosan based carriers enhance the solubility and dissolution of baicalein through inclusion complex formation

Solitons, bifurcation, chaos, and stability of the fractional Schrödinger-Hirota equation with beta derivative

Effects of monoglucosyl hesperidin on human lymphatic circulatory function: A randomized placebo-controlled, double-blind, crossover trial

Abstract Monoglucosyl hesperidin promotes nitric oxide production in vascular endothelial cells and contributes to blood flow improvement and edema reduction. While preclinical studies in animal subjects have also suggested a role in promoting lymphatic drainage, its effect on human lymphatic function remains unexplored. Here, we examined the effect of monoglucosyl hesperidin intake (300 mg/day) on the lymphatic system in a controlled trial in healthy human adults ( n  = 30). Thoracic duct diameter was assessed by high-frequency ultrasonography as a surrogate marker of central lymphatic flow. While no effect was detected in the full cohort, in the subgroup analysis of participants with no exercise habit, monoglucosyl hesperidin intake significantly increased or showed a trend toward an increase in the thoracic duct diameter (Variation maximum value in the amount of change and the rate of change were p  < 0.01 and p  < 0.01, respectively; Variation minimum value in the amount of change and the rate of change were p  = 0.06 and p  < 0.01 respectively; ). This was consistent with the increase in lymph flow rate observed in previous animal studies, highlighting, for the first time, monoglucosyl hesperidin influencing the human lymphatic circulation function. In addition, the results suggested that thoracic duct ultrasonography had potential as a noninvasive biomarker for future interventional studies targeting lymphatic circulation function.

A network-based approach to model volcanic repose durations

Abstract The global distributions of volcanic eruption durations and of repose times between two eruptions have broad and heterogeneous shapes. Statistical analyses indicate that both distributions may exhibit a power-law tail for medium-to-long timescales. This scale-free behaviour could be symptomatic of volcanic systems that self-organize into a critical state. In this paper, we build on a former model for magma ascent through a pipe of stacked cells, which successfully reproduces the trend of the global distribution of volcanic eruption durations, but does not account for the inter-event times between eruptive episodes. By adapting and implementing this model within tree-like graphs instead of a linear structure, we are able to retain the behaviour of the eruption duration distribution, while obtaining a broad distribution for the repose times. In contrast with the pipe linear structure, the tree-like structure accounts for the fact that a large part of the magma that ascends through the crust does not reach the surface. This result highlights the importance of modelling volcanic plumbing systems as networks that reflect their complexity and variety.

SIFA: A two-stage adaptive ensemble framework for solar irradiance forecasting using a wrapper-based feature selection and chaotic manta ray optimization

Abstract Accurate solar irradiance forecasting is increasingly crucial for managing solar energy systems effectively, as their power output is directly dependent on solar irradiance (SI). Several models in the literature have been presented for SI forecasting; however, they still face at least one of these limitations: difficulties in modelling nonlinear data, demanding high computational resources, and often struggling to identify the best feature subsets for higher accuracy. To address these challenges, this study proposes a new multi-stage forecasting approach, termed SIFA, for accurate SI prediction, aiming to enhance the stability and efficiency of PV power plants. This approach comprises two main stages. The first stage employs a hybrid feature selection strategy combining random forest (RF) and sequential forward selection (SFS) to identify the most informative features. Specifically, SFS explores candidate feature subsets, while RF evaluates various subsets to select the most effective one. To further improve the RF performance, the number of estimators is tuned using an enhanced manta ray foraging optimizer, called IWMRFO, which employs chaotic maps instead of random generators to better balance exploration and exploitation, thereby avoiding local optima and accelerating convergence. The second stage combines three effective ML models—Huber Regressor (HR), Extra Trees (ET), and Extreme Gradient Boosting (XGB)—using a weight vector that is used to control the contribution of each base model in the hybrid ensemble approach (SIFA). This vector is optimized by the proposed IWMRFO, resulting in an adaptive ensemble that enhances predictive accuracy while maintaining stable generalization capability. This approach is tested on three popular datasets: the San Diego dataset, the Islamabad dataset, and the NASA SI dataset. Its performance is compared with several other models using several performance metrics, including RMSE, MAE, MAPE, MSE, and R². The numerical results demonstrate that SIFA achieved lower average forecasting errors than the competing models across the three evaluated datasets under repeated experiments, indicating that it is a strong alternative for predicting SI with higher accuracy.

Comparison of techniques and markers to distinguish Plasmodium falciparum recrudescence from new infection in Rwanda

Transformer-based emotion recognition in interactive art: A multimodal neural approach

Understanding how interactive digital art affects emotional states is essential to advance research into the interface between affective neuroscience and human–computer interaction. Although previous studies have used either EEG or self-reported measures to evaluate emotional responses, only a few have integrated both to investigate the temporal dynamics of affective change. This study addresses this gap by adopting a multimodal approach combining neural oscillatory features with subjective affective assessment. We analysed a publicly available dataset containing pre- and post-interaction EEG recordings across five canonical frequency bands (Delta–Gamma) together with affect scores derived from the Positive and Negative Affect Scale (PANAS). EEG signals were pre-processed using independent component analysis, bandpass filtering, and z-score normalisation. A multi-output Transformer-based regression model was trained to predict affective shifts (ΔPositive, ΔNegative) from EEG band-wise change features. Statistical analyses included paired t-tests, ordinary least squares and Lasso regression, and permutation-based feature importance estimation. The Transformer outperformed LSTM and Random Forest baselines, achieving an R² of 0.162 with an MSE of 36.2 and MAE of 5.42. Delta-band oscillations showed the strongest association with affective recovery, while changes in beta and gamma activity were significantly associated with increases in positive affect (p < 0.01). Negative affect decreased significantly following interaction (p = 0.0014, d = 0.867). The dual-output structure of the model enabled the simultaneous modelling of positive and negative affective change. Overall, these findings demonstrate the utility of EEG band-change features for modelling affective variation in interactive art settings. The study integrates perspectives from emotion regulation theory, affective aesthetics, and deep learning, and provides methodological implications for multimodal affective modelling in interactive digital environments.

Psychological safety and perceived risk are associated with emergency nurses’ intention to use AI-augmented triage systems

Abstract Emergency department overcrowding places sustained pressure on triage workflows and patient prioritization. Artificial intelligence (AI)-augmented triage systems have been introduced to support emergency decision-making, but frontline adoption may depend on both technology-related perceptions and human-organizational conditions. This study examined factors associated with emergency nurses’ attitudes and intention to use AI-augmented triage systems, with particular attention to psychological safety and perceived risk. A multi-hospital cross-sectional survey was conducted among 162 frontline triage nurses across nine pilot hospitals in Shanghai between June and August 2025. All participants had at least six months of emergency triage experience and at least three months of actual experience using the AI-augmented triage system. Partial least squares structural equation modelling was used to assess the measurement and structural models. The model explained 57.2% of the variance in attitude and 41.0% of the variance in intention to use. Task-technology fit (β = 0.483, 95% CI [0.387, 0.574]), perceived explainability (β = 0.385, 95% CI [0.280, 0.484]), and psychological safety (β = 0.401, 95% CI [0.294, 0.512]) were positively associated with attitude. Attitude was positively associated with intention to use (β = 0.629, 95% CI [0.526, 0.710]). Perceived risk showed a small negative moderating effect on the association between attitude and intention to use (β = − 0.139, p  = 0.039, f 2  = 0.029), although the 95% confidence interval included zero. Common method bias was assessed using procedural and statistical checks; however, same-source bias could not be fully ruled out. Emergency nurses’ intention to use AI-augmented triage systems was associated with both technology-related perceptions and human-organizational conditions. Psychological safety was positively associated with attitudes toward AI use, while perceived risk may modestly weaken the translation of favourable attitudes into intention to use. Given the cross-sectional and self-reported design, the findings should be interpreted as associations rather than causal evidence. Implementation strategies should address task fit, explainability, accountability boundaries, and psychologically safe human–AI collaboration.

Type 2 diabetes and cancer: A retrospective longitudinal comparative cohort study of disease sequence and comorbidity profiles

Type 2 Diabetes Mellitus (T2DM) and cancer are major non-communicable diseases that impose a substantial dual burden when they co-occur. However, this dual burden remains understudied and the majority of existing studies investigate T2DM as a comorbidity without distinguishing whether it is pre-existing at the time of cancer diagnosis or newly-onset following diagnosis. This study aimed to investigate the co-occurrence of T2DM and cancer and to assess differences in laboratory variables, comorbidities, complications and overall survival according to the sequence of diagnosis. This retrospective cohort study with longitudinal follow-up included patients diagnosed with both T2DM and cancer. A total of 213 adult patients who meet the inclusion criteria were categorized based on the sequence of diagnosis: T2DM prior to cancer (n = 131) or cancer prior to T2DM (n = 82). The prevalence of T2DM among in-patients with cancer was 27.4%. Statistical analyses were performed to compare clinical and laboratory characteristics between groups. Breast cancer and colon cancers were the most common cancer types. Most patients were diagnosed with T2DM prior to cancer onset and were significantly older than those diagnosed with cancer first ( p  = 0.02). Male patients had a higher risk of mortality compared to females (HR = 1.96, p  = 0.03). Among patients diagnosed with cancer prior to T2DM, males had significantly higher diastolic blood pressure ( p  = 0.02), while females had significantly higher total cholesterol levels ( p  = 0.04). No significant differences were observed in comorbidities or complications between groups (all p >  0.05). There were no significant differences in overall survival between the two diagnostic-order groups ( p  = 0.03). Moreover, separate survival analysis for breast, colon, lung and gastric cancers showed no significant differences between patients diagnosed with T2DM before cancer and those diagnosed with cancer before T2DM (all p >  0.05). Future studies are warranted to confirm these findings and to better understand the burden of the co-occurrences of T2DM and cancer at national level.

Enhancing healthcare explainability through multiobjective counterfactual explanations

The association between upper limb function, physical exercise, and cognitive ability among empty-nest elderly in China: A cross-sectional study based on CLHLS

Background The rapid aging of China’s population poses serious challenges to the cognitive health of older adults living without co-resident children (empty-nest older adults). Currently, the role of upper limb function on the cognitive function of this group and its potential pathway of effect through physical exercise remain unclear. Objective This study aimed to explore the associations among upper limb function, physical exercise, and cognitive ability in the Chinese empty-nest elderly population, and to explore the potential mediating role of physical exercise in the relationship between upper limb function and cognition. Due to the cross-sectional design, no causal relationships can be established; all reported associations are statistical in nature. Methods This study employed a cross-sectional design using data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). Based on household structure information, older adults aged 60 and above who did not live with their children were defined as “empty-nest elderly”. A total of 5,060 empty-nest elderly were included in the final analysis. Upper limb function (normal/restricted), physical exercise (yes/no), and cognitive ability (normal/impaired) were assessed via questionnaire. Analyses included multivariable logistic regression, restricted cubic spline curves, Bootstrap-based mediation effect analysis, and subgroup analysis. Results Among the 5,060 participants (mean age 78.60 ± 10.31 years), 755 (14.92%) had cognitive impairment. Multivariable-adjusted analysis showed that restricted upper limb function was significantly positively associated with the risk of cognitive impairment (OR=2.55, 95% CI: 1.95–3.29), while regular physical exercise was significantly negatively associated with this risk (OR=0.77, 95% CI: 0.62–0.95). Mediation analysis indicated that physical exercise accounted for 5.95% of the total effect (ACME = 0.0121, 95% CI: 0.0074–0.0172). Given the small effect size, this pathway should be interpreted as a minor statistical contributor rather than a dominant mechanism. Subgroup analysis showed that the association of upper limb function and physical exercise with cognitive impairment was significantly modified by drinking status (both P for interaction < 0.05), with stronger effects in non-drinkers. Conclusion Among Chinese empty-nest elderly, restricted upper limb function is positively associated with the risk of cognitive impairment, while regular physical exercise is negatively associated with this risk. Physical exercise accounts for a minor portion of the statistical association between upper limb function and cognitive ability.