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Given the increasing demand for efficient technologies in heavy-oil recovery and upgrading, methods that can reduce viscosity and improve oil mobility are of considerable interest. Heavy oils are difficult to produce and process because of their high viscosity and density, as well as their elevated contents of asphaltenes, heteroatoms, and heavy metals. Although thermal and chemical recovery methods can reduce viscosity during production, the oil may regain viscosity after reaching the surface, often requiring diluents or additional upgrading steps. In this context, nanocatalyst-assisted in-situ upgrading has attracted attention as a route to simultaneously improve oil mobility and quality. In this study, a data-driven framework was developed to predict viscosity reduction during NiO nanoparticle-assisted in-situ upgrading. Predictive performance was evaluated using fivefold out-of-fold cross-validation (OOF-CV) to obtain a more reliable estimate of generalization. Conventional models (MLP, RBF, and ANFIS) were compared with modern tree-based ensembles, including Random Forest and Gradient Boosted Decision Trees (GBDT). GBDT delivered the best cross-validated performance, with a pooled OOF [Formula: see text] of 0.925 and a normalized RMSE of 0.1628, demonstrating superior predictive capability over the other examined models. For design-of-experiments (DOE)-based analysis of main effects and two-factor interactions, an MLP surrogate was retained to enable efficient response evaluation across the design space; therefore, DOE findings are interpreted as qualitative, surrogate-dependent trends rather than the most accurate pointwise predictions. Permutation-based importance analysis identified upgrading time as the most consistently influential variable, whereas the secondary driver depended on model family, with temperature contributing more strongly in tree-based ensembles and acidity/catalyst-related descriptors being more prominent in neural/surrogate models. Pressure showed a comparatively smaller contribution within the investigated range. Independent laboratory tests supported the overall predictive trends; however, deviations under some conditions indicate inter-source heterogeneity in the compiled literature data and limited representation of extreme operating regimes. Overall, the proposed framework can serve as a screening and decision-support tool for prioritizing operating conditions and guiding future experimental design in heavy-oil in-situ upgrading studies. The findings of this study can support faster screening of operating conditions, improve understanding of viscosity-reduction behavior, and assist the design of data-driven decision tools for catalytic upgrading of heavy crude oils.

The rapid spread of the competing weed L. arvensis poses a major threat to wheat production; therefore, modern risk assessment methods are necessary for its management. This study developed and compared machine learning models (Random Forest [RF], Boosted Regression Trees [BRT], and Maximum Entropy [MaxEnt]) to evaluate habitat suitability for L. arvensis, a dominant weed in the wheat cropping systems of Pakistan's semi-arid regions. For this purpose, weed data from 402 wheat fields, along with 20 environmental factors, including topography, climate, soil characteristics, anthropogenic factors, and proximity metrics, were analysed. Soil texture (silt and clay), soil chemistry (EC, OM, TDS), and rainfall patterns were identified through a partial least squares (PLS) algorithm as major factors affecting the species distribution. The ROC-AUC results showed that MaxEnt (AUC = 0.93) and RF (AUC = 0.92) performed slightly better than BRT (AUC = 0.86). All models identified the eastern and southeastern regions as the main areas of highly suitable habitat. Although these models are reliable, their predictions may be affected by changes in environmental factors in cropland. These results demonstrate that machine learning methods are effective for mapping weed distribution and provide a scientific foundation for sustainable weed management in these regions.

In the Philippines, community health workers (CHWs) face psychosocial difficulties as they deliver health services to communities with limited access to healthcare, yet they receive no support for their well-being. To maintain their psychological strengths, we adapted the Locus-of-Hope Enhancement Program (LEAP), a positive intervention that strengthens hope of CHWs from urban-poor communities in Metro Manila. Using a double-blind, parallel randomized alternative-treatments experiment, LEAP's effects on locus-of-hope and other psychological resources across three time-points were tested in Manila. Group contrast estimates indicate improvements in external-peer and family locus-of-hope, and psychological well-being. LEAP boosted external-peer and family locus-of-hope after 1 month via peer and family hope at posttreatment. LEAP also increased psychological well-being, civic engagement attitudes, and behaviors posttreatment, and civic engagement attitudes at follow-up through peer locus-of-hope at posttreatment. Findings suggest how LEAP empowers CHWs from urban-poor communities by fortifying hope, thus sustaining their role in community health. Trial Registration: Open Science Framework (OSF) Registries (https://osf.io/egtju).

Nutrient imbalances, soil salinity, and shrinking arable land threaten global food security, driving demand for sustainable biofertilizer alternatives to chemical inputs. Aquatic ecosystem-derived biofertilizers such as Magnetospirillum gryphiswaldense (MSR-1) are promising sustainable substitutes and show strong agricultural potential due to their stress tolerance, adaptability, and plant growth-promoting traits. This study investigated the ability of MSR-1 to enhance the growth and productivity of tomato and paddy under normal, iron-deficient, and saline conditions. MSR-1 was cultured in modified Magnetospirillum Growth Medium (MSGM) under microaerophilic conditions, with SEM confirming its spiral gram-negative morphology and successful, non-destructive colonization on tomato and paddy roots and leaves. Moreover, HR-LCMS profiling of root exudates identified chemoattractant compounds such as quinic acid, tryptophan, quercetin, glucosinolates, and strigolactones, promoting bacterial attachment. Further, Magnetospirillum liquid biofertilizer (MLB) was formulated from MSR-1 cultures (1.5 × 108 cells/mL) and applied at 20-100% concentrations (25 mL/pot). Among the treatments, 20% MLB gave the best results under normal conditions, whereas 60% MLB was more effective under iron-deficient and saline stress conditions. In tomato, 20% MLB increased shoot length (73.5 cm), chlorophyll content (4.5 mg/g), and fruit yield (1066.95 g/plant). Under stress, 60% MLB improved fruit yield (760-800 g/plant) and boosted antioxidant enzymes (SOD 75 U/mg; CAT 15.5 U/mg). In paddy, 20% MLB enhanced shoot and root length (66.0 and 15.13 cm), while 60% MLB under stress increased growth, carbohydrates, proteins, amino acids, phenols, and antioxidant enzymes (SOD/CAT 49.63/19.83 U/mg). Overall, MSR-1 offers a sustainable, effective biofertilizer option for managing soil salinity and iron deficiency.

Live Newcastle disease (ND) vaccines face major limitations: spray vaccination causes adverse vaccinal reactions in day-old chicks, while in ovo vaccination results in high pathogenicity in embryos. We hypothesized that specific host responses contribute to the pathogenicity of live ND vaccines. In this study, lentogenic NDV La Sota vaccination in ovo significantly reduced the hatchability and post-hatch survival, accompanied by robust upregulation of inflammatory and cell death-related genes, notably the receptor-interacting serine/threonine-protein kinase 2 (RIPK2) in chicken lungs. Given the absence of RIPK3 in chickens and the structural homology of chicken RIPK2 to mammalian RIPK3 (sharing the conserved DFG motif), the RIPK3 inhibitor GSK840 targeting the DFG motif was tested as a potential modulator of La Sota pathogenicity. Co-administration of GSK840 with La Sota in ovo markedly improved the hatchability, post-hatch survival and antibody response. Furthermore, GSK840 pre-treatment at 18 days of embryonation prevented tracheal pathology, particularly the loss of cilium and goblet cells, and boosted antibody responses in day-old chicks receiving spray vaccination. Through biotin-conjugated GSK840 pull-down and mass spectrometry, multiple myosins (~200kDa) were identified as its potential targets in chicken trachea, rather than RIPK2. Collectively, our results demonstrate that GSK840 improves the safety and immunogenicity of La Sota in ovo and spray vaccination and that myosins are potential targets of GSK840. These findings present novel insights into the pathogenicity of live ND vaccines and provide important implications for optimization of live ND vaccination.

Asymmetric ion-selective membranes are beneficial for harvesting osmotic energy from salinity gradients such as the seawater-freshwater interface by reverse electrodialysis (RED). Among the various RED membrane technologies, gradient hydrogel membranes can exhibit exceptional performance due to structural features that facilitate ion-selective transport, but often suffer from poor mechanical properties and/or high internal resistance. Here, a high-entanglement slide-ring asymmetric polyelectrolyte double-network (SRAP-DN) hydrogel membrane has been prepared by unilateral photopolymerization for osmotic energy harvesting by RED. The membrane can achieve high output power densities >10 Wm-2 under the neutral 50-fold salinity gradient, which can be boosted to >16 Wm-2 at pH 12 with a 200-fold KCl gradient. Unlike most hydrogel membranes, SRAP-DN is remarkably tough yet extremely high in water content (93%), which lowers internal resistance while benefiting cost and scalability by minimizing polymer content. SRAP-DN was leveraged to prototype miniature and flexible thin-film power supplies made of 24 cells connected in series that can harvest osmotic energy from natural seawater and river water to produce >2 V of stable potential. The excellent performance of these tough, water-rich membranes in blue energy harvesting bodes well for the prospect of low-cost, eco-, and bio-friendly lightweight power supplies for wearable, implantable, or clean energy technologies.

Delayed healing of post-operative anal fistula wounds is driven by a hostile inflammatory microenvironment. While autologous concentrated growth factors (ACGF) show regenerative potential, the underlying immunomolecular mechanisms remain unclear. This study investigates if ACGF accelerates contaminated wound healing by modulating macrophage polarization via the AKT/mTOR pathway. In vitro, RAW264.7 macrophages were treated with ACGF to assess phenotypic switching and AKT/mTOR activation. The paracrine effects of ACGF-primed macrophages on fibroblasts were evaluated using a co-culture system with AKT-siRNA validation. In vivo, a rat fecal-contaminated wound model was established. Therapeutic efficacy, with or without the AKT inhibitor Triciribine, was assessed via laser speckle imaging, histology, and immunofluorescence. ACGF effectively reprogrammed macrophages from a pro-inflammatory M1 phenotype toward a reparative M2 phenotype, significantly upregulating CD206 and Arg-1 expression while activating AKT/mTOR signaling. Mechanistically, ACGF-primed macrophages significantly boosted RSF proliferation, migration, and collagen synthesis, effects that were substantially abrogated by AKT silencing. In vivo, ACGF treatment markedly accelerated wound closure, enhanced microvascular perfusion, and increased the density of CD31 + and Ki67 + cells. These regenerative benefits were accompanied by a significant shift toward M2 infiltration and a reduction in pro-inflammatory cytokines, all of which were reversed by pharmacological inhibition of AKT. ACGF promotes the healing of contaminated wounds by orchestrating an AKT/mTOR-dependent macrophage M2 polarization. This shift initiates a beneficial paracrine relay that enhances fibroblast activity and neoangiogenesis, effectively restoring the regenerative niche. Our findings establish ACGF as a potent immunomodulatory biomaterial and offer a promising therapeutic strategy for complex perianal wounds and chronic recalcitrant defects.

Osteoarthritis (OA) is a common age-associated joint disorder driven not only by mechanical wear but also by progressive intracellular stress, metabolic imbalance, and chronic inflammation that culminate in cartilage degeneration and functional disability. Increasing evidence identifies mitochondrial dysfunction and endoplasmic reticulum stress (ERS) as central pathological hubs regulating chondrocyte survival, extracellular matrix (ECM) integrity, and inflammatory signaling. Mitochondrial impairment promotes excessive reactive oxygen species (ROS) generation, defective ATP production, disturbed mitochondrial dynamics, and inadequate mitophagy, collectively accelerating ECM catabolism and chondrocyte apoptosis. In parallel, ERS activates the unfolded protein response (UPR) to restore proteostasis through the PERK, IRE1α, and ATF6 pathways; however, sustained UPR activation shifts from adaptive signaling to maladaptive outcomes, amplifying inflammation, oxidative injury, and cell death in OA cartilage. Notably, emerging data highlight bidirectional crosstalk between mitochondria and ER, particularly via mitochondria-associated membranes (MAMs), as a key driver of Ca²⁺ dysregulation, inflammasome activation, and degenerative joint remodeling. Therapeutic strategies targeting these stress pathways including mitochondrial antioxidants, NAD⁺-boosting agents, mitophagy modulators, chemical chaperones, and selective UPR regulators have demonstrated potential to attenuate cartilage destruction and restore joint homeostasis. This review synthesizes current mechanistic insights into mitochondrial ERS signaling in OA and critically evaluates evolving disease-modifying interventions aimed at intracellular stress reprogramming. Finally, we discuss translational challenges and future directions for developing precision therapies that exploit organelle stress pathways to improve long-term joint health.

One limitation of clustering algorithms is the manual specification of the number of clusters. Density Peak (DP) clustering can automatically determine the optimal number, but in image segmentation, it may face memory overflow due to large similarity matrices from medium-sized images. this paper introduces the Fuzzy Dissimilarity Histogram (FDH) method, to address this drawback. FDH enhances image contrast using fuzzy background information. FDH improves noise robustness while preserving image details. The image histogram is computed and combined with spatial features to boost segmentation accuracy. This combined histogram better distinguishes different image regions, allowing enhanced identification of specific feature areas. Smoothing the histogram and removing irrelevant data improves processing speed and accuracy. The proposed method applies an Automatic Fuzzy Clustering Framework (AFCF) for image segmentation. It integrates the superpixel concept into the DP algorithm to improve computational efficiency. Fully automated clustering is achieved by initially applying a density equilibrium algorithm followed by fuzzy c-means clustering based on prior entropy, enhancing segmentation quality. Experiments on synthetic and real images demonstrate that the proposed approach outperforms advanced clustering algorithms in segmentation quality and processing time for color images. Additionally, this method significantly improves segmentation results for Multi-scale Morphological Gradient Reconstruction (MMGR)-AFCF, as well as Simple Linear Iterative Clustering (SLIC)-AFCF and Linear Spectral Clustering (LSC)-AFCF algorithms.

Polycystic ovary syndrome (PCOS) is a prevalent endocrine condition in reproductive-aged women, which is associated with adverse maternal and neonatal outcomes in pregnancy. In the present study, pregnancy complications-like gestational diabetes, preterm birth, hypertension, low birth weight and neonatal intensive care unit admission-will be predicted with high accuracy by machine learning-based prediction models. There are three substudies: (1) Developing a researcher-made questionnaire by literature review, data collection from existing medical records in maternal and neonatal systems of women with PCOS who have given birth, collected from hospitals and private clinics of East Azerbaijan Province and Tehran. Approximately 800-1000 women with PCOS will be included in the study and then data preprocessing will be performed. (2) Developing machine learning-based models for predicting adverse pregnancy outcomes in women with PCOS using decision trees, random forests, Extreme Gradient Boosting, support vector machines, k-nearest neighbours and neural network algorithms and (3) Developing a user-friendly application or interface that will operate on various devices. Based on model evaluation metrics, the model with the highest area under the receiver operating characteristic curve for predicting adverse pregnancy outcomes in women with PCOS will be used as the final model. This study was approved by the Ethics Committee of Shahid Beheshti University of Medical Sciences, Tehran (Ethics Code: IR.SBMU.PHARMACY.REC.1404.017). Findings will be disseminated through peer-reviewed journals, conferences and social media and will also be shared with study participants.

During the COVID-19 pandemic, concerns emerged that hospital-based care could increase the risk of SARS-CoV-2 infection among pediatric patients with cancer, especially in resource-limited settings where the capacity to isolate patients would be challenging. Yet, infection rates in this population remain poorly documented, and it is unclear whether prior common human coronavirus (HCoV) exposures influence SARS-CoV-2 antibody responses. Here, we used serology to estimate SARS-CoV-2 exposure in children with and without cancer from Western Kenya and evaluated responses to common HCoVs to explore cross-reactivity. We performed multiplex antibody profiling to assess SARS-CoV-2 and HCoV-specific responses in plasma from children with and without cancer sampled between 2014 and 2022. Unsupervised clustering identified participants with elevated SARS-CoV-2 seroreactivity relative to pre-pandemic samples. Seropositivity was further defined using a multi-antigen approach based on IgG levels to nucleocapsid and receptor-binding domain antigens to improve specificity. Cross-reactivity and cross-boosting by common HCoVs were evaluated through correlation analyses and groupwise comparisons of antibody levels, respectively. Of 564 children, 474 were healthy (184 pre-pandemic; 290 post-pandemic) and 90 had cancer (16 pre-pandemic; 74 post-pandemic). Post-pandemic, 69% (200) of healthy controls exhibited elevated SARS-CoV-2 seroreactivity, with 51% (148) meeting the criteria for seropositivity. Children with cancer showed similar rates of seroreactivity (67%) and seropositivity (57%) after adjusting for sampling year, with incidence increasing annually from 2020 to 2022. Pre-pandemic samples exhibited weak cross-reactivity to HCoV-OC43 which increased following SARS-CoV-2 infection; however, no significant boosting of HCoV antibodies was observed. These findings indicate widespread SARS-CoV-2 exposure among children in Western Kenya and provide no evidence that hospitalization heightened infection risk for pediatric patients with cancer.

Incidental gallbladder cancer (IGBC) is often diagnosed only during or after cholecystectomy, and preoperative identification remains challenging. This study preliminarily explored factors contributing to IGBC missed diagnosis and attempted to develop an ultrasound radiomics&#x2011;based identification model. A retrospective cohort of 62 IGBC and 78 non&#x2011;incidental GBC (NIGBC) patients who were consecutively enrolled between 2016/01-2025/12 was analyzed. Clinical, laboratory, imaging, pathological, and immunohistochemical features were compared. From ultrasound images, 1220 radiomics features were extracted; after stability (ICC&#x2009;>&#x2009;0.75), redundancy removal (Spearman |&#x3c1;|>&#x2009;0.90), and LASSO regression, nine features were retained. Seven machine learning algorithms were used to exploratorily build radiomics&#x2011;only models. Potential clinical predictors were identified by logistic regression, and a clinical&#x2011;only and a combined model were attempted. Performance was preliminarily evaluated using area under the curve (AUC). IGBC showed higher gallstone prevalence (91.9% vs. 53.8%, P&#x2009;<&#x2009;0.001) and a "benign masquerade" laboratory profile (higher albumin (ALB), high-density lipoprotein cholesterol (HDL&#x2011;C); lower ratio of albumin to globulin (RAR); fewer elevated CA19&#x2011;9). Pathologically, IGBC was predominantly infiltrative (77.4% vs. 28.2%, P&#x2009;<&#x2009;0.001), had earlier T stage compared to NIGBC (27.4% vs. 12.8%, P&#x2009;=&#x2009;0.007), and exhibited lower Ki&#x2011;67 high expression (67.7% vs. 83.3%, P&#x2009;=&#x2009;0.031) and weaker Topoisomerase II-alpha (Topo II) staining (P&#x2009;=&#x2009;0.005). The extreme gradient boosting (XGB) radiomics model achieved a validation AUC of 0.865 (accuracy 0.833), suggesting potential discriminative ability. Gallstones (OR&#x2009;=&#x2009;9.484) and growth pattern (OR&#x2009;=&#x2009;0.230) might be independent clinical predictors. The clinical&#x2011;only model had AUC 0.830, and the combined model AUC 0.860, with no significant benefit over radiomics&#x2011;only. Preoperative missed diagnosis of IGBC may be associated with gallstone&#x2011;related inflammation, infiltrative growth, early T stage, seemingly normal laboratory findings, and low proliferation marker expression. Although conventional ultrasound hardly identifies IGBC, its tumor heterogeneity might be quantified by radiomics. The XGB model showed preliminary ability to distinguish IGBC from NIGBC and holds potential as a non&#x2011;invasive tool for preoperative risk stratification, but findings require validation in larger multicenter cohorts.

Efficient trauma assessment is essential for optimal patient care, with imaging playing a critical role in the detection of injuries. Rapid and accurate classification of traumatic spleen injuries is critical for clinical decision-making; however, manual assessment of CT images can be subjective and time-consuming, highlighting the need for objective and automated diagnostic tools. This study aims to evaluate the impact of machine learning models and radiomics features in diagnosing splenic trauma lesions on computed tomography images. A dataset of 600 computed tomography images, including individuals with mild and severe traumatic spleen injuries as well as healthy controls-was collected from the Kaggle database. An experienced radiologist segmented the axial images, and radiomics features were extracted from each designated region of interest for further analysis. Initially, 25 machine learning models were evaluated; ultimately, three-Light Gradient Boosting Machine, Ridge Classifier, and Adaptive Boosting-were selected for detailed assessment. Model performance was measured using accuracy, precision, sensitivity, specificity, area under the receiver operating characteristic curve, F1 score, and misclassification rate. The Light Gradient Boosting Machine model exhibited superior effectiveness in diagnosing mild spleen injuries, achieving an accuracy of 98%, precision, and specificity of 100%. Meanwhile, the Adaptive Boosting model demonstrated acceptable performance in diagnosing severe injuries, achieving an accuracy of 90%, precision of 92.15%, and specificity of 91%. These machine learning models exhibited remarkable capability in automatically detecting traumatic spleen injuries on abdominal computed tomography scans. By integrating radiologist expertise into the analytical framework, our method enables rapid pre-screening of a large number of cases for spleen lesions.

Active hydrogen radicals (&#x2022;H) from water dissociation hold great promise for the defluorination of perfluorooctanoic acid (PFOA). However, the mechanism governing &#x2022;H generation under a local polarization piezoelectric electric field (LPPEF) remains insufficiently explored in borate-based piezocatalysts, which hinders an understanding of the piezocatalysis-driven water dissociation. Herein, we investigated piezocatalyst, Sr2B5O9Cl (SBOC) and its KBH4-impregnated derivative (B-SBOC) in PFOA defluorination. Under ambient conditions, B-SBOC achieves a degradation rate constant of 1.34&#x202f;&#xd7;&#x202f;10-2 min&#x207b;1 for an initial PFOA concentration at 24.2&#x202f;&#x3bc;M, along with a synchronous defluorination ratio of 78% within 120&#x202f;min, representing a 2.5-fold enhancement over pristine SBOC. The incorporated electron-deficient boron atoms serve as Lewis acid sites, which not only enhance surface hydrophilicity and reverse the surface charge to facilitate PFOA adsorption but, more critically, intensify the LPPEF. The strengthened LPPEF promotes the preferential adsorption of &#x2022;OH, thereby effectively suppressing &#x2022;OH/&#x2022;H recombination. This accelerates the kinetics of water dissociation and promotes subsequent &#x2022;H dominated H/F exchange reactions, boosting efficient PFOA defluorination. This work provides fundamental insight into the LPPEF dominated radical separation process and offers a potential strategy for designing highly efficient piezocatalytic systems for recalcitrant fluorinated pollutants remediation.

BRAF V600E mutation, the most prevalent driver alteration in papillary thyroid carcinoma, is associated with aggressive clinicopathological features, including macroscopic extrathyroidal extension, lymph node metastasis, and high-risk histological features. BRAF V600E mutation is determined by tissue biopsy/surgery and gene sequencing, which are invasive and costly. The study aimed to develop an interpretable prediction model based on clinical and ultrasound characteristics via radiomics and deep learning (DL) methods to noninvasively predict the BRAF V600E mutation in patients with thyroid cancer. A total of 6,703 ultrasound images from 1,257 lesions in 1,202 patients with thyroid cancer were retrospectively collected. Since multiple ultrasound images were available for each lesion, the lesion-level prediction was derived as the average of the image-level outputs. Univariate and multivariate logistic regression were adopted to construct the clinical model. Six machine learning models were compared to identify the optimal one. A ResNet50-32x4d model was fine-tuned to build the DL model. The extreme gradient boosting (XGBoost) algorithm was employed to integrate the optimal radiomics score (radscore), DL scores, and clinical factors for combined model construction. The Shapley additive explanations (SHAP) algorithm and gradient-weighted class activation mapping technique were applied for interpretability. Multivariate analysis identified the significant predictive variables to be sex [odds ratio (OR) =0.61; 95% confidence interval (CI): 0.54-0.69; P<0.001], age (OR =1.01; 95% CI: 1.00-1.01; P<0.001), tumor size (OR =0.54; 95% CI: 0.50-0.58; P<0.001), and multifocality (OR =0.66; 95% CI: 0.57-0.75; P<0.001). Among the six machine learning models, the XGBoost model demonstrated the best performance, with an area under the curve (AUC) of 0.809 and 0.745 in the training and test sets at the lesion level, respectively. The DL model outperformed the XGBoost model, achieving an AUC of 0.807 in the test set at the lesion level. The combined model exhibited comparable performance to that of the DL model, with AUCs of 0.845 and 0.814 in training and test sets at the lesion level, respectively. SHAP analysis revealed that DL scores and radscores were key contributors in predicting mutation status. The combined model integrating clinical and ultrasound data can effectively predict BRAF V600E mutation status in patients with thyroid cancer.

The preparation effect (PE) describes enhanced attention and faster responses of dot-probes when stimuli are expected to appear. Prior work portrayed PE as a rigid, mandatory, process-all mechanism that boosts alertness for any upcoming event, largely insensitive to stimulus relevance, valence, or individual differences. The present study tested key boundary conditions of this effect across three experiments. In Experiment 1, we manipulated distractor probability and found a robust PE only under complete certainty (100% distractors), but not under a probabilistic context (50%), indicating that strong temporal expectations are required to trigger preparation. There was no difference between latencies of probe-dot detection under 25 and 75% distractor probability (Exp. 1b). Experiment 2 aimed at testing the PE across time, and distractor presence (0 or 100%) was manipulated between subjects. Dot-probe responses were consistently faster in the distractor group than in the no-distractor group, and this advantage remained stable across blocks, suggesting that the PE constitutes a durable alerting mode that, unlike other proactive effects, does not decay over time. Experiment 3 replaced the dot-probe onset detection with an offset-detection probe and found no significant RT benefit under this condition. Together, these findings demonstrate both the robustness and the limits of the PE. They also highlight the similarities and differences between the PE and other proactive control and phasic alertness effects, and call for a more nuanced explanation that considers both observers' temporal expectations and probe demands.

Abnormal uterine bleeding (AUB) is a primary symptom indicative of endometrial cancer (EC), yet its diagnosis still primarily relies on invasive biopsy. This study aimed to develop and validate a non-invasive, interpretable machine learning (ML) model integrating local epigenetic (exfoliated cell CDO1 methylation) and systemic metabolic indicators for EC triage. CDO1 was identified as a potential biomarker via bioinformatics. Its diagnostic performance was assessed using quantitative methylation-specific PCR (qMSP) in a clinical cohort of 267 women with AUB. Subsequently, a multimodal ML framework was developed that incorporated logistic regression (LR), support vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms to integrate methylation data with clinical metabolic profiles. Model interpretability was ensured by SHapley Additive exPlanations (SHAP) analysis, with final testing in an internal holdout cohort. CDO1 hypermethylation was identified as a significant epigenetic alteration associated with metabolic pathways in EC. Within our clinical cohort, CDO1 methylation emerged as a strong independent risk factor, with an odds ratio (OR) of 12.76 and a 95% confidence interval (CI) ranging from 6.31 to 25.80. Using LASSO regression, four critical predictive variables were identified: CDO1 methylation, menopausal status, diabetes and age. After integrating CDO1 methylation and metabolic profiles, the support vector machine model demonstrated stable performance in the validation set, achieving an area under the curve (AUC) of 0.775 (95% CI: 0.669-0.880), with a sensitivity of 0.800 and a specificity of 0.733. SHAP analysis was used to elucidate the contribution of each feature to the predictive model. The finalized SVM-based model's diagnostic utility was further confirmed in an internal holdout cohort, yielding an AUC of 0.904 (95% CI: 0.838-0.969), with a sensitivity of 0.833 and a specificity of 0.905, demonstrating its preliminary clinical reliability. Using our model with a cutoff of 0.647 in the internal holdout cohort (n&#x2009;=&#x2009;160), 24 patients were triaged for biopsy, with 2 EC cases missed, representing a clinical false-negative rate of 16.7%. The finalized SVM-based predictive model was developed into a free online risk calculator (https://phw1996.shinyapps.io/EC_risk/) for real-time clinical triage. Our SVM-based model offers a non-invasive and highly specific approach for the triage of patients experiencing AUB. By correlating localized molecular changes with systemic metabolic dysregulation, this tool supports personalized risk stratification and clinical triage. It is designed for relative risk stratification, and external recalibration is required for clinical absolute risk estimation.

This study aimed to compare the immunogenicity of three meningococcal vaccines used as booster doses in children primed with MPCV-AC in China. A total of 250 eligible children were enrolled to receive MPSV-AC, MPCV-ACYW135, or MPCV-AC as a booster dose. Participants were subdivided into day 7 and day 28 subgroups based on the timing of sample collection. Blood samples were collected at baseline, day 7, and day 28 to measure IgG antibodies against serogroups A and C by ELISA. Baseline GMCs of IgG were 4.34&#xa0;&#x3bc;g/mL (seropositivity: 79.2%) for serogroup A and 4.36&#xa0;&#x3bc;g/mL (seropositivity: 82.0%) for serogroup C, both negatively correlated with age and time interval (p&#xa0;<&#xa0;0.05). All vaccines significantly increased IgG levels at days 7 and 28 post-booster compared with baseline (p&#xa0;<&#xa0;0.01). In the day 7 group, MPSV-AC yielded a lower GMC (19.14&#xa0;&#x3bc;g/mL) and seroconversion rate (43.8%) for serogroup A compared with MPCV-ACYW135 (44.69&#xa0;&#x3bc;g/mL, 78.9%) and MPCV-AC (43.06&#xa0;&#x3bc;g/mL, 78.1%). In the day 28 group, MPSV-AC showed a comparable GMC for serogroup A (44.42&#xa0;&#x3bc;g/mL vs. 60.39&#xa0;&#x3bc;g/mL and 44.66&#xa0;&#x3bc;g/mL) and a higher GMC for serogroup C (113.40&#xa0;&#x3bc;g/mL) than MPCV-ACYW135 (59.62 &#x3bc;g/mL) and MPCV-AC (72.75&#xa0;&#x3bc;g/mL). Only two mild adverse events were reported during the study period. These findings suggest that boosting with MPSV-AC, MPCV-ACYW135, or MPCV-AC is safe and immunogenic; however, further studies are warranted to assess the long-term persistence of immunity conferred by these booster vaccines.

Non-Line-of-Sight (NLOS)/Line-of-Sight (LOS) identification is crucial to accurate Ultra-Wideband (UWB) positioning. The current Machine Learning solutions to this problem have either too many parameters to tune or too simple features to input, which lead to unsatisfactory performance. To address this issue, this paper proposed a novel binary classifier called LightGBM Ensemble which integrates multiple LightGBMs in parallel with multi-scale patch extraction. The heterogeneous LightGBM ensemble architecture boosts the prediction power of individuals. The multi-scale patch extraction scheme extracts informative features from time-frequency domains. Extensive experiments on an open-source dataset were conducted to evaluate the proposed approach, which proves its superior classification performance and generalization performance with feasible complexity compared to the state-of-the-art Deep Learning and Decision Trees methods.