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Role of ethnicity in the determination of the transverse sigmoid sinus Junction (TSSJ): A prospective hospital-based radiological study in adult Sabah, East Malaysia
PrivEdge: a hybrid split–federated learning framework for real-time electricity theft detection on edge nodes
Abstract Electricity theft is one of the primary contributors of non-technical losses in contemporary power grids, and traditional centralized methods of detection are limited in scale, feature a large communication cost, and create privacy issues. The presented paper introduces PrivEdge, a deployment-friendly hybrid Split–Federated Learning (SL–FL) system to detect real-time electricity theft on resource-constrained edge devices. PrivatEdge uses a Raspberry Pi 4-based smart meter gateway to do localized preprocessing with the Raspberry Pi 4 smart meter gateway and run a lightweight LSTM-based FrontNet; server-side functionality does more in-depth model inference, collaborative coordination, ensemble stacking, and score-level fusion. Split Learning allows conveying small intermediate activations as opposed to raw consumption data, which significantly lowers communication costs and minimizes privacy loss. Federated Learning supports distributed learning between highly non-IID clients who are geographically well-spread. Privacy maintenance is realized by secure aggregation and Laplace differential privacy, where ε = 3 is used as a uniform operation compromise due to practical consideration. As a high-security deployment mode, homomorphic encryption is supported. Extensive experiments on the SGCC smart meter data with IID and non-IID conditions reveal that PrivEdge would perform better in terms of detection accuracy and F1-score than both centralized and FL-only or SL-only baseline frameworks, especially in non-IID conditions. The software-level assessment using Raspberry Pi 4 hardware establishes a low inference time, consistent resource consumption, and endurance at that rate using sustained load. Ablation experiments also confirm the importance of localized preprocessing, time expression, ensemble-based aggregation of data, and their privacy-conscious learning. In general, PrivEdge helps in closing the gap between hybrid concepts of SL–FL learning and the practical needs of deployment in privacy-aware electricity theft detection at the network edge.
Analysis of the NLRP3 inflammasome components expression in triple-negative breast cancer patients with and without BRCA1 mutations
Abstract Triple-negative breast cancer (TNBC) is an aggressive and immunogenic breast cancer subtype frequently associated with BRCA1 alterations. Inflammation and innate immune sensing pathways, including inflammasomes, play complex and context-dependent roles in tumor progression and anti-tumor immunity. However, the prognostic significance of inflammasome components in TNBC remains poorly defined. We evaluated the protein expression of key inflammasome components including NLR family pyrin domain-containing 3 (NLRP3), PYD and CARD domain-containing protein (PYCARD), caspase-1 (CASP1), and interleukin-18 (IL-18) by immunohistochemistry in tumor samples from 88 TNBC patients stratified by BRCA1 status, including pathogenic germline mutations, promoter hypermethylation, and wild-type tumors. Survival analyses were performed using Kaplan–Meier estimates and Cox proportional hazards models. Lower CASP1 expression was significantly associated with smaller tumor size ( p = 0.005), whereas lower NLRP3 expression was associated with axillary lymph node metastasis ( p = 0.003). No significant association was observed between inflammasome protein expression and BRCA1 mutation or promoter hypermethylation status. Importantly, low NLRP3 expression was independently associated with worse disease-free survival (DFS) (hazard ratio (HR) = 3.15, 95% confidence interval (CI) = 1.36 to 7.30, p = 0.007) and overall survival (OS) (HR = 2.63, 95% CI = 1.19 to 5.79, p = 0.01). These findings indicate that reduced NLRP3 expression is associated with unfavorable prognosis in TNBC. Although exploratory in nature, this study highlights the potential relevance of inflammasome components as prognostic biomarkers in this aggressive breast cancer subtype and warrants further validation in independent cohorts.
Nitrogen-doped carbon dot-based dual-emission ratiometric probe for smartphone-assisted ultrasensitive detection of moxifloxacin
Capsular polysaccharides of Acinetobacter baumannii modulate antimicrobial resistance and innate immune response
Abstract Acinetobacter baumannii is an opportunistic pathogen characterised by multidrug resistance and is among the leading causes of nosocomial infections. This study investigated the role of capsular polysaccharides (CPS) in antimicrobial resistance and host-pathogen interaction. CPS-deficient mutant (∆ galU ) displayed increased susceptibility to gentamicin, tetracycline, colistin, and sodium dodecyl sulfate (SDS). In the absence of CPS, bacteria adapted by forming biofilm, characterised by a thicker extracellular matrix. These biofilms displayed increased resistance to colistin, chlorhexidine, and hydrogen peroxide, but remained sensitive to tetracycline and SDS. CPS were also essential for the resistance to photodynamic therapy induced by blue light and chlorophyllin. Gene expression analysis revealed upregulation of galU gene under the exposure to antibiotics, blue light, fetal bovine serum, and during the contact with macrophages. CPS-deficient mutant and its derived outer membrane vesicles elicited a stronger pro-inflammatory response, compared to wild-type. CPS shielding A. baumannii demonstrated the ability to induce caspase-3 activation to a greater extent compared to the mutant strain. Moreover, purified CPS induced pro-inflammatory cytokine expression in sterile infection and promoted neutrophil chemotaxis. Altogether, this study demonstrates that CPS not only mask A. baumannii virulence surface structures, but also actively modulate host immune response and antimicrobial resistance.
Performance evaluation and codal assessment of double-skinned solid-core CFST columns with varying steel configurations
Abstract This experimental study explores the behaviour of columns with double-skinned solid core concrete-filled steel tubular (DS-CFST) sections. Many experimental studies have been conducted on double-skinned hollow CFST sections so far, and this research has involved evaluating the performance of various configurations of steel tube geometry (square and circular combinations) of solid-core CFST specimens. Eight CFST short columns were subjected to axial compression, each measuring 410 mm in height (H). The study investigated the effects of various parameters, including concrete strength, steel area, width-to-thickness ratio, steel and concrete core percentage, and inner steel embedment position, on CFST short columns with slenderness ratios (λ) ranging from 9.4 to 10.9. The test results showed improvements in DS-CFST column axial compression, ductility, stiffness, failure modes, and structural behaviour due to adequate steel inner tube embedment. The CFSC specimens exhibited 6.64 times higher results than the steel tubes and 2.33 times greater strength than the CFST specimens, while other steel tube embedded specimens demonstrated significant strength improvements. The results are checked with the current codal provisions, ANSI/AISC-360, EC-4 and modelled by an artificial neural network (ANN).
A digital mindfulness intervention improves sleep efficiency and heart rate variability in healthy adults
Abstract To test whether a 10-day mindfulness program delivered through the ŌURA app improves sleep and stress in healthy adults, eighty-one adults were randomized to mindfulness (n = 49) or waitlist control (n = 32). Participants wore an Ōura ring during baseline, intervention/wait-list, and 4-week follow-up, recording sleep efficiency, total, deep, and light sleep, plus sleep-onset time. Questionnaires—Pittsburgh sleep quality index (PSQI), perceived stress scale (PSS), Copenhagen burnout inventory (CBI), and mindful attention awareness scale (MAAS)—were completed pre- and post-intervention and at follow-up. Mixed-model ANOVAs revealed significant group × time interactions for sleep efficiency, total sleep, deep and light sleep, and sleep-onset time (all p s < 0.031). Results showed that the mindfulness group improved after 10 days (all p s < 0.021); gains persisted at follow-up except for deep sleep. The mindfulness group exhibited increased personal burnout ( p = 0.021) immediately post-intervention, though this returned toward baseline at follow-up. In addition, the mindfulness showed higher MAAS scores ( p = 0.017). During mindfulness sessions heart rate fell ( p = 0.011) and heart-rate variability rose ( p = 0.029). A brief, app-based mindfulness program produced sustained improvements in sleep efficiency and enhanced HRV, demonstrating that digital mindfulness can favorably influence biobehavioral sleep-stress metrics. Trial Registration : ClinicalTrials.gov: NCT07469644
Multifractal evolution of shale fracture and pore structures under uniaxial compression
A novel spatiotemporal decomposition and identification of sparse equations for human brain deformation
Quasi-elastic neutron scattering studies on bacterial spores and their hydration water
Analysis of influencing factors on the plastic zone width and stability of aeolian sand-based backfill strips
Investigating the mechanistic link between pesticide DDT and breast cancer through network toxicology, molecular docking, and molecular dynamics simulation
Honey bee genetic resistance outperforms a cold-storage induced halt in brood production to control mites and viruses
Abstract Varroa destructor mites seriously threaten honey bees by spreading viruses like Deformed Wing Virus (DWV) and contributing to global colony losses. Growing resistance to widely-applied miticides highlights the urgent need for sustainable mite control methods. This study evaluated the impact of a cold storage strategy to decrease bee brood production, and increase mite treatment efficacy, in a commercial Italian bee stock and mite-resistant Russian and Pol-line bee stocks, from prior to cold storage in August until the start of the commercial pollination season the following February. For each year of two years, thirty new bee colonies (10 colonies per stock) were either placed in cold storage (5 °C, darkness, 18 days) starting mid August or left outdoors, and all hives subsequently treated with a thymol-based varroacide. Colony brood area, adult bee mass, hive weight, internal temperature and CO₂ levels were monitored during the experiments using periodic hive assessments as well as sensors. Honey bee workers were sampled at different points and evaluated for bee health biomarker gene expression ( vitellogenin ) as well as virus levels of DWV-A and DWV-B . Cold storage effectively halted brood production but differences in brood levels between groups disappeared within two months, with no long-term impact on population size, mite levels, virus loads, or daily hive weight change. Bee stock was the dominant factor influencing outcomes: mite susceptible Italian colonies had higher mite densities, higher DWV loads, lower vitellogenin expression and higher rates of hive weight loss than Russian or Pol-line colonies. In this study mite-resistant honey bee stocks offered more effective control, reducing mite loads by over 65% compared to the susceptible stock, across both years and both treatment groups of the study, and they have the potential to support honey bee health by reducing reliance on chemical treatments in beehives.
Comparative analysis of physiological and psychological effects of viewing and drinking flower tea
Abstract Flower tea has gained attention for its potential health benefits, yet empirical evidence regarding its comprehensive effects on human physiology and psychology remains limited. This study aimed to investigate how both visual stimulation by flowers and the act of drinking flower tea influence physiological and psychological relaxation. Twenty-nine university students (mean age: 21.0 ± 2.0 years) participated in a within-subject experimental design consisting of four stages: (1) resting with their eyes closed for 1 min (rest (before) period), (2) viewing tea with flowers, tea without flowers, or water (control) for 1 min after opening their eyes (viewing period), (3) drinking it for 3 min (drinking period), (4) closing their eyes again and resting for 1 min (rest (after) period), (5) responding to the questionnaire, and (6) taking a break for 5 min. After completing this sequence, the same process was repeated using a different stimulus. Heart rate variability and heart rate were measured as physiological indicators, while the Profile of Mood States and semantic differential scales were used as psychological indicators. Results showed that viewing tea with flowers significantly enhanced parasympathetic nervous system activity compared to other stimuli. Further, tea with and without flowers enhanced vigor alleviated total mood disturbances, and induced positive feelings throughout the entire process. These findings demonstrate that the combined visual and olfactory–gustatory experience of flower tea promotes physiological and psychological relaxation, suggesting its potential as an effective everyday method for stress relief and emotional well-being.
Combined PLGA scaffolds and therapeutic stem cells for locomotion recovery in spinal cord-injured rats: a systematic review and meta-analysis
Development and validation of an interpretable prediction model for the risk of unplanned reoperation in patients underwent intracranial tumor surgery
Biometeorological regulation of male and female fertility traits in banana (Musa spp.) across contrasting flowering environments
Baicalein inhibits human neutrophil myeloperoxidase and protects mice from LPS-induced lung inflammation
An effective detection model based on YOLO for pore defects in additive manufacturing
Abstract Microscopic imaging serves as a crucial method for assessing the quality of selective laser melting (SLM). Traditional approaches rely on manual inspection, which limits their efficiency and reproducibility. To address the demand for defect detection and analysis, this paper proposes a synergistic method for analyzing pore defects in microscopic images, integrating image segmentation with polynomial fitting. We designed a high-performance image segmentation model. Its capabilities are enhanced through an adaptive curved learning rate adjustment strategy, an attention-based feature extraction module, and a lightweight feature fusion network. Additionally, the model automatically calculates and quantifies the pixel proportion of pore defects within micrographs. Experiments conducted on a constructed SLM pore defect microscopic image dataset demonstrated excellent performance, enabling effective calibration and quantification of defect information. Chebyshev polynomials are employed to fit the nonlinear relationship between key process parameters and porosity. Based on these results, we conducted an in-depth analysis of how different process parameters influence pore defect formation, revealing the intrinsic correlation between process parameters and defects. This study provides an effective automated detection and analysis tool for SLM quality assessment and analysis.