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Artificial intelligence-based method for detecting wrist fractures in children
Exploring reviewer self-assessment in the context of academic peer review
Myoblast paracrine factors induce catabolic remodelling in electrically stimulated myotubes
Effect of hysterectomy on risk of thyroid cancer and benign thyroid disorders in South Korean women
Hybrid MLOps framework for automated lifecycle management of adaptive phishing detection models
Abstract Phishing detection models degrade quickly due to drift, adversarial evasion, and fairness issues. Existing MLOps platforms mainly automate deployment and monitoring. Prior works have examined SHAP-based monitoring, retraining, or fairness audits separately, but lack an integrated theory of resilience for adversarial environments. We introduce the Hybrid MLOps Framework (HAMF), a system designed to embed resilience and ethical governance into the lifecycle of phishing detection models. HAMF is ‘hybrid’ because it unifies proactive and reactive adaptation, combining automation with stakeholder oversight, and embedding resilience with ethical governance. HAMF treats resilience as an integrated lifecycle property, designed to simultaneously preserve model accuracy, fairness, and stakeholder trust amidst concept drift. Methodologically, HAMF implements this through a hybrid control cycle. This cycle fuses four key capabilities: SHAP-guided feature replacement, event-driven retraining, fairness-triggered audits, and structured human feedback. Unlike conventional pipelines where these functions are isolated, HAMF ensures their interdependence as first-class triggers. Empirical evaluations on large-scale phishing streams demonstrate HAMF’s superior performance. The framework detects drift within 18 seconds, restores F1 scores above 0.99 post-attack, reduces subgroup disparities by over 60%, and scales to over 2,300 requests per second with sub-50ms latency. These results validate HAMF’s design, demonstrating that embedding resilience and ethical alignment into the MLOps lifecycle is both effective and scalable.
A robust edge-detection approach for precise delineation of deep structures from potential field data: Structural insights supported by remote sensing
Abstract This study presents a unique Logsigm Function (LSF) filter for edge detection, established to improve the delineation of geological structures from potential field data. The filter’s performance was first validated using synthetic gravity and magnetic models simulating complex geological configurations with varying depths and contrasts. Results confirm the LSF’s high precision in resolving both shallow and deep structural boundaries, even in the presence of noise, while maintaining computational simplicity and ease of implementation. The method was then applied to real gravity and magnetic data from Northern Sinai, Egypt, a geologically complex region affected by extensional and inversion tectonics. To complement and validate the geophysical results, surface lineaments were extracted from enhanced remote sensing datasets, including Landsat8 OLI and ALOS PALSAR DEM imagery. The comparison between surface and subsurface trends revealed systematic vertical variations in structural orientations, highlighting the role of inherited basement faults and deformation decoupling. The LSF results successfully matched known structures and uncovered previously unrecognized lineaments, offering new insights into the tectonic architecture and basin evolution of Northern Sinai. The integrated approach demonstrates the value of combining advanced filtering techniques with remote sensing to achieve robust structural interpretations. The simplicity, stability, and high resolution of the LSF method make it a powerful tool for structural geology, tectonic analysis, and resource exploration in complex geologic terrains.
Sunlight driven NbSe2 photocatalyst for efficient degradation of Janus green and methylene blue dyes
Abstract With the world increasingly facing the challenge of dye pollution in water bodies, niobium diselenide (NbSe 2 ) nanoparticles have emerged as promising photocatalysts for environmental remediation. In this study, NbSe 2 nanoparticles were synthesized at room temperature, 70 °C, and 100 °C, and employed for the photodegradation of organic dyes Janus green (JG) and methylene blue (MB)—under natural sunlight. The synthesized nanoparticles were thoroughly characterized to confirm their phase composition, crystalline structure, morphology, and functional groups. Photocatalytic performance was evaluated through degradation efficiency and kinetic modeling using pseudo-first and pseudo-second-order reaction models. Results demonstrated that higher synthesis temperatures enhanced photocatalytic activity, with nanoparticles synthesized at 100 °C achieving > 99% degradation of JG and up to 98% for MB. The kinetic data fit best with the pseudo-first-order model, indicating a direct relationship between synthesis temperature and reaction rate constants. These findings underscore the pivotal role of synthesis temperature in tuning the photocatalytic properties of NbSe 2 nanoparticles and affirm their potential in combating dye pollution through efficient solar-driven degradation processes.
Modeling and analysis of the risk evolution for coal mine gas explosion from the perspective of safety information loss
A feasibility study of whole genome germline testing as an adjunct screening tool in a UK general private practice
Green polymeric composites based on construction and packaging waste: toward advanced insulating materials
Abstract This study explores the development of sustainable polymeric composites using waste polystyrene (WPS) as the base matrix reinforced with sawdust (SD), red brick waste (RbW), and ceramic waste (CW). Composites were prepared with hybrid filler loadings of 0–40 wt% relative to 100 g of the total composite. Chemical composition and particle size of the solid waste were characterized by X-ray fluorescence (XRF) and Transmission electron microscope (TEM) to evaluate the probability of using these waste powders as novel alternative fillers for polymeric composites. XRF results showed high SiO 2 and Al 2 O 3 content in CW and RbW, while SD exhibited a high loss on ignition, confirming its organic nature. TEM results showed that the particle size of all solid inorganic wastes is in the nano size range, which makes them suitable for use as novel alternative fillers instead of traditional fillers. Mechanical tests and water absorption were measured for prepared blends, and the results revealed that tensile strength increased significantly with the addition of hybrid fillers, reaching a maximum of 25.45 MPa for the WPS/CW composite, while WPS/SD had the lowest tensile strength, which was 17.41 MPa. Elongation at break was slightly decreased with the addition of CW or RbW. Also, water absorption decreased with increasing demolition waste content, showing superior resistance to water absorption for WPS filled with CW after 15 days. Thermo gravimetric analysis, dynamic mechanical analysis (DMA), dielectric, and electrical conductivity measurements were employed to understand the thermal stability and physical properties of the composites under investigation. Dielectric measurements indicated that WPS composites filled with CW had higher permittivity and lower dielectric loss, making them recommended for electrical insulation applications. DMA also illustrated that storage modulus and glass transition temperature (Tg) enhanced with addition CW confirming enhanced thermal–mechanical stability.
Dietary oregano essential oil and sodium butyrate enhance growth, immunity, and gene expression in nile tilapia post-Aeromonas hydrophila infection
Abstract The impacts of oregano essential oil and sodium butyrate on the growth performance, hematological, biochemical, immunological, antioxidative, and gene expression profiles of Nile tilapia ( Oreochromis niloticus ) fingerlings were examined in this study. One hundred eighty healthy fingerlings (16.00 ± 2.00 g) were acclimated and accidentally divided into three groups, each further split into three replicates. The groups were fed a basal diet (control) or diets supplemented with 0.5% and 1% oregano essential oil and sodium butyrate for eight weeks (The feed additives were administered continuously throughout an 8-week trial period, simulating a practical feeding strategy that would be feasible during high-risk periods (e.g., post-handling stress, seasonal disease outbreaks). Fish fed the supplemented diets demonstrated significantly greater growth performance, with the oregano 1% + sodium butyrate 1% group achieving the highest final body weight (36.91 ± 0.66 g) and lowest feed conversion ratio (1.32 ± 0.04). Hematological indices, including RBC count, Hb, and PCV, were substantially improved. Biochemical analysis revealed reduced ALT, AST, urea, and creatinine levels alongside elevated total protein and globulin concentrations in treated groups. Immunological and antioxidative parameters, such as WBC count, phagocytic activity, SOD, and GPX, showed significant enhancements, particularly in the oregano 1% + sodium butyrate 1% group. Gene expression analysis exposed upregulation of immune-related (TNF-α, IL-1β) and antioxidative (SOD, GPX) genes in supplemented groups, highlighting their role in enhancing health and stress responses. Post Aeromonas hydrophila infection, the supplemented groups exhibited improved immunity, antioxidative status, and survival. These findings suggest that dietary supplementation with oregano essential oil and sodium butyrate, particularly at 1% inclusion, effectively enhances growth, health status, immunity, and gene expression in Nile tilapia.
Habitat degradation alters trait-based survival in a coral reef fish
Abstract This study investigates the effect of habitat degradation on the interrelationships between morphological, performance, and behavioural traits and their influence on the survival of juvenile Ambon damselfish ( Pomacentrus amboinensis ). The research combined laboratory measurements with field observations on live or dead-degraded coral reef patches. Fish behavioural traits emerged as key predictors of survival across habitats. Specifically, boldness, bite rate, and maximum distance ventured from the patch reef were key factors. However, the relative importance of specific traits differed between habitats. On live coral, relative lateralization was a significant predictor of survival. In contrast, for fish on dead-degraded coral, routine swimming speed and fast-start response latency were more important. Damselfish in dead-degraded habitats experienced significantly higher mortality. These results indicate that habitat degradation alters the selective pressures on phenotypic traits. This study highlights the importance of considering context-dependent trait selection when predicting how fish communities will respond to changing habitat conditions. Additionally, our findings provide insights for restocking and restoration interventions, suggesting that behavioural conditioning that promotes risk-averse behaviours and predator recognition could improve the survival of reseeded fishes, although further testing within this applied context is required.
Mechanical and dielectric properties of (ZnSn)1-xMxO (M = Co, Cu) quaternary nanocomposites for promising applications
Integrative multi-omics deciphers the potential mechanism and microbial biomarkers for lymph node metastasis in colorectal cancer
Abstract Understanding and accurate diagnosis of lymph node metastasis (LNM) for patients with colorectal cancer (CRC) is essential to determine treatment and follow-up strategies. Therefore, in this study, we aimed to elucidate the biological process and identify the potential biomarker for LNM in CRC. A total of 30 patients who received a histologically confirmed diagnosis of CRC with Stage I to III and a curative surgery between November 2020 and July 2021 at Kyung Hee university hospital at Gangdong were included. We performed multi-omics approach integrating the data on somatic mutation, transcriptomic expression, DNA methylation, and microbiome with tumor and adjacent matched normal tissues of each patient. In total, 12 significant DEGs between the patients with and without LNM were identified, consisting of significantly upregulated S100A8 gene, a proinflammatory gene. The GSEA revealed that gene sets involving “MULTI CANCER INVASIVENESS” in terms related to epithelial-mesenchymal transition was significantly upregulated in the patients with LNM. Integrated functional analysis of DNA methylation with transcriptome profile shows that significantly hypomethylated promoters of the genes are enriched for LNM. The phylum Proteobacteria, unassigned ( p _PU) presented significantly higher proportions in cancer tissues from the adjacent normal tissues. Notably, when compared to the patients without LNM, the gut microbiota of those with LNM appears to exhibit a significantly lower abundance of the p _PU, indicating its potential as promising biomarker for LNM in CRC. We explained the mechanism of tumor spreading using multi-omics analysis and identified the relevant metagenomic biomarker to predict the LNM in CRC by the recognition of host-microbial interaction, thereby can make the cancer surveillance of the patients more individualized and convincing.
Label-free estimation of regulatory T cell activation markers using Raman spectroscopy with machine learning
Abstract Regulatory T cells are a class of T lymphocytes which respond to activation signals by expanding their cell numbers, and whose culturing and expansion are of significant clinical interest. Cellular activation states are used to inform process control decisions such as restimulation and can be probed with experimental measurements of cell surface markers. However, these measurements are expensive, time-consuming, and invasive, and an urgent need exists for devising a non-invasive method for activation state monitoring that could be deployed on-line. Raman spectroscopy is a label-free and information-rich optical method that, when coupled to data analytical methods, can ameliorate these experimental issues. In this work, we quantitatively estimated experimental measurements of regulatory T cell activation markers with high accuracy. We simulated a clinical manufacturing setting by building an $${L}_{1}$$ -regularized least-squares model with spectroscopic data from six regulatory T cell donors. Then, we validated the constructed model by accurately estimating different experimental measurements of biomarker values from two external donors, unseen by the model. We have devised a robust program to effectively estimate the activation state of regulatory T cells. We anticipate our method to be used with on-line Raman probes integrated into cell manufacturing devices for label-free monitoring of these processes.