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Prednisone, not vamorolone, suppresses novel serum bone and cartilage biomarkers associated with growth failure in children with Duchenne muscular dystrophy
Rearing history, larval density, and larval developmental stage affect volatile- and light-mediated diel hiding behavior in Mythimna unipuncta
Effects of bone-anchored maxillary protraction in cleft lip and palate - a single-arm prospective observational study
Abstract To describe changes in maxillofacial morphology observed during bone-anchored maxillary protraction (BAMP) treatment in children with cleft lip and palate (CLP). This prospective, single-arm observational study included 20 consecutively treated patients with CLP (60% male; mean age at baseline (T1), 11.7 ± 1.0 years) who underwent BAMP therapy as part of a treatment protocol. Lateral cephalograms were obtained at baseline (T1) and at the end of maxillary protraction (T2; mean age, 15.3 ± 1.6 years). Protraction was considered complete when a positive overjet with adequate incisor overlap had been achieved and judged stable by the treating orthodontist, or when no further sagittal improvement was observed over two consecutive annual reviews, after which the miniplates were removed. Skeletal and dental changes were assessed using cephalometric measurements. Method error was evaluated using intraclass correlation coefficients. Statistical analyses included paired comparisons and exploratory univariable regression analyses. Significant increases were observed in the SNPg angle, midfacial length, mandibular length, and maxillomandibular difference, accompanied by a significant reduction in the SN/GoMe angle. The SNA angle increased by 1.3° from T1 to T2; however, this change did not reach statistical significance (95% CI: -0.17 to 2.66). Exploratory univariable analyses suggested that baseline age and observation time were associated with selected skeletal changes; these analyses were hypothesis-generating only. BAMP therapy was not associated with a statistically significant change in maxillary sagittal position (SNA), and overall skeletal changes were modest; clinicians should therefore maintain realistic expectations regarding the extent of skeletal correction achievable. Because the primary outcome (SNA) was not statistically significant, these findings are hypothesis-generating; whether BAMP can stabilise maxillary position during growth in milder discrepancies, and thereby reduce the likelihood of later orthognathic surgery, requires confirmation in controlled studies. Because the study had no control group, the observed T1-T2 changes cannot be separated from normal growth, and no causal effect of BAMP can be inferred.
Active-passive RRT based multi-arm frame disassembly robot planning for belt conveyors in coal mine
Abstract Belt conveyors are vital in coal mine production and need disassembly per mining progress. To improve production safety, a kinematic-based disassembly planning method for multi-arm frame disassembly robots is proposed. Firstly, D-H parameters are applied to the robot’s kinematic models and disassembly process. Secondly, an active-passive extended rapidly-exploring random trees (APRRT) algorithm is proposed, using Sobol sequences, node weight strategies and an active-passive dual tree extension strategy for dual-arm collaborative planning. Additionally, a collision warning method based on multi-geometry envelope is introduced to enhance safety. Simulation and physical experiments verify the method’s feasibility that APRRT reduces path length by over 23% and exploration time by over 14% compared with traditional algorithms, with stable performance meeting expected requirements.
Evaluation of patients’ anxiety and perceptions toward dental imaging modalities: a cross-sectional study
Experimental validation of a Ku-band cross-polarization converter and detailed numerical RCS investigation across multiple geometries
A multi-objective particle swarm optimization algorithm with two-stage archive maintenance and auxiliary archive guidance
Abstract In Multi-objective particle swarm optimization (MOPSO), the external archive largely determines how well convergence and diversity are balanced. Ineffective archive maintenance may lead to uneven solution distributions, inaccurate convergence, and premature trapping in local regions.To overcome these limitations, this paper proposes TAMOPSO, a Two-stage Archive Maintenance-based Multi-Objective Particle Swarm Optimization algorithm. In the first stage, adaptive grids with dynamic boundary expansion are used to locate high-density regions. In the second stage, solutions in these regions are evaluated by integrating angle-based diversity assessment and a dual-distance convergence metric, with selection preferences adaptively adjusted according to the evolutionary stage, thereby improving the distribution and Pareto-front coverage of the obtained solution set while controlling archive size. To further enhance particle guidance, a bounded auxiliary archive is introduced to reuse historical high-quality non-dominated solutions discarded during archive maintenance and assist personal best updates. In addition, a stagnation detection-based particle reconstruction strategy is designed, using sparsely distributed elite solutions from the external archive as reconstruction templates to guide stagnant particles back to promising search regions and enhance global exploration. Tests on representative benchmark suites indicate that TAMOPSO produces higher-quality approximation sets than the compared mainstream algorithms in most cases.
Application of gamma and electron-beam irradiations for aflatoxin B1 decontamination in peanut: effects on physicochemical properties and food safety
A surrogate model for capturing wave power farm dynamics using spatial–temporal attention
Abstract Wave energy converters deployed in farms can experience intense hydrodynamic interactions due to the scattered and radiated waves on the free surface, making farm modeling challenging in realistic sea states. This study introduces a spatial–temporal surrogate model based on a transformer encoder architecture to predict the motion of multiple interacting wave energy converters in various sea states. The framework leverages experimental data from the SWELL dataset, predicting array responses in a previously unseen layout, i.e., a farm configuration, not available during the model’s training phase. The model embeds incident wave time series together with device coordinates into a unified spatial–temporal representation. Self-attention then jointly captures the temporal evolution of motion dynamics and inter-device spatial dependencies. Across three irregular sea states, the model predicts device responses with high accuracy, showing close agreement with experimental measurements. These findings provide an initial proof-of-concept, highlighting the potential of an attention-based spatial–temporal surrogate model as a building block for predicting the dynamics of several interacting wave energy converters in previously unseen array configurations.
Multi-omics integrated with machine learning identifies LPS-related genes potentially associated with iron metabolism-Immune axis imbalance in PCOS: a bioinformatics-based exploration of mechanisms and diagnostic markers
Abstract Chronic low-grade inflammation induced by bacterial lipopolysaccharide has been implicated in the pathogenesis of polycystic ovary syndrome; however, the genetic mechanisms linking lipopolysaccharide signaling to immune and metabolic dysregulation remain insufficiently elucidated. In the present study, transcriptomic datasets and single-cell sequencing data related to polycystic ovary syndrome were analyzed in combination with lipopolysaccharide-related genes retrieved from a toxicogenomics database. Differential expression analysis, weighted gene co-expression network analysis, clustering analysis, and machine learning algorithms were integrated to identify candidate biomarkers. Subsequently, functional enrichment analysis, immune cell infiltration analysis, regulatory network construction, and drug prediction analyses were conducted, while single-cell sequencing analysis was employed to identify key cellular populations and characterize gene expression dynamics. Two genes, C11orf68 and EVI5L, were identified as potential biomarkers and were significantly downregulated in patients with polycystic ovary syndrome. Functional analyses associated these genes with iron metabolism and immune regulation, whereas immune infiltration profiling identified T lymphocytes as key effector cells involved in disease progression. These findings suggest a potentially previously unrecognized association among lipopolysaccharide-related genes, iron metabolism imbalance, and immune dysregulation in polycystic ovary syndrome, thereby providing a potential framework for future mechanistic investigations and the development of diagnostic and therapeutic targets.
A proof-of-concept study associating artificial intelligence surveillance of surgical site infections with antibiotic prophylaxis from 765,962 surgeries
A real-time framework for mapping subsea cable burial state using Poincaréspectral coherence of DAS measurements
Abstract Distributed acoustic sensing (DAS) on subsea fibre-optic cables is emerging as a powerful tool for underwater acoustics, providing dense, kilometre-scale measurements of sound propagation through the water column, the seabed, and the cable’s ambient environment. These observations enable new approaches to environmental acoustic monitoring and subsea-infrastructure assessment, including the detection of oceanographic processes, anthropogenic noise, and geophysical wavefields. However, a central challenge remains: fidelity of DAS measurements depends critically on acoustic coupling between the cable and its surroundings, i.e., variations in burial, exposure, and suspension alter the incident acoustic energy coupling into the fibre, introducing inconsistencies or artefacts in environmental and structural interpretations. Detecting these coupling states directly from DAS data is difficult because the signatures are subtle and datasets are exceptionally large. We introduce a simple, scalable method based on Poincaré spectral coherence. It quantifies the consistency of neighbouring channels across selected acoustic frequency bands. Buried segments show smooth, coherent spectral behaviour, whereas exposed or suspended sections exhibit sharp spatial variability. Applied to two shallow-water deployments, including a 5.8-km coastal cable with diver-verified burial, the method reliably identifies major coupling transitions. Its unsupervised, computationally efficient, real-time compatibility strengthens the case for DAS as a next-generation underwater vibrations sensing technology.
Coupled evolution of the stress concentration shell and fracture field during mining-induced overburden failure
Influence of alkaline and sodium bicarbonate treatments on the machinability of sisal jute epoxy hybrid biocomposites using RSM and ANN modeling
Precision pharmacology: deep learning infused ontological framework with E-GRU enhancement for tailored medicine prescriptions
Abstract Advanced Clinical Decision Support Systems significantly influence patient care, with medicine prescriptions being a vital area of research. Ontology, a growing discipline in the semantic web, enables hierarchical domain representation, thereby allowing finer data access to be achieved. Deep Learning (DL) supports pattern recognition in Electronic Health Records (EHR), which include patient demographics and diagnosis histories. Prescribing medications with minimal adverse effects is crucial, particularly for patients who require multiple drugs, as drug interactions can result in more complex conditions. This study introduces an integrated approach that combines Ontology with DL neural networks to improve prescription accuracy. This study proposes NexusOpti, a model featuring an Enhanced Gated Recurrent Unit (E-GRU) layer. To understand drug–disease interactions, hierarchical data were extracted from the International Classification of Diseases (ICD) and Anatomical Therapeutic Chemical (ATC) ontologies. These structured data were processed using a self-attention mechanism to enhance the recommendation precision. This integration not only addresses data security concerns but also improves the accuracy of the medicine recommendations. The model was evaluated using key metrics such as the hit ratio and normalised discounted cumulative gain (NDCG). The NexusOpti model, incorporating the Enhanced Gated Recurrent Unit (E-GRU) layer, outperforms the existing GRU model in terms of NDCG and Hit Ratio metrics. 13% of improvement in performance was oberved to the comparison between NexusOpti with the E-GRU and the GRAM baseline model. These findings highlight the effectiveness of the model in advancing personalised, safer, and data-driven medication prescriptions.