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Correction: iTRAQ-based quantitative proteomics reveals reduced expression of KRT19, KRT7, and PTGDS in cutaneous specimens after kidney transplantation
Identification of potential PI3Kγ inhibitors among FDA approved drugs using integrated computational and quantum chemical approaches
Effect of hepatitis B virus infection in males on pregnancy outcomes of intrauterine insemination
Author Correction: Synergistic effects of cold atmospheric plasma and doxorubicin on melanoma: A systematic review and meta-analysis
An optimized Arabic cyberbullying detection approach based on genetic algorithms
Abstract The rise of cyberbullying in digital communication platforms has triggered widespread concern, not just for its reach but for the lasting psychological harm caused. Identifying such harmful behavior online is difficult in general, but when the target language is Arabic, the task becomes more complicated. The issue is not just that Arabic is written in multiple dialects, each with its own informal vocabulary, spelling variations, and structure. What complicates matters further is that meaning often shifts based on region, tone, and the social context, making abusive content harder to catch using conventional tools. This study aims to improve Arabic cyberbullying detection mechanisms by introducing a feature-selection strategy. Its main contribution involves utilizing a Genetic Algorithm (GA)-based feature selector to pinpoint harmful language patterns in a corpus of 46k Arabic Instagram comments. The GA effectively reduced the feature space by approximately half, preserving essential semantic structures while removing noise and redundancy. Four classifiers were evaluated, and GA-driven selection improved F1-scores by (3.45–14.96%) and reduced classification time by a factor of 2.32–12. These findings suggest that genetic-feature optimization enhances model precision while significantly improving runtime and reducing complexity, thereby enabling scalable, context-sensitive cyberbullying detection for Arabic and morphologically rich languages.
Pesticide identification and quantification in honey produced and marketed in Rio de Janeiro Brazil by the UPLC MSMS technique
Barium lanthanum sulfide ceramics as new far-infrared transparent materials
A novel model for cultural-based classification of liberal arts using deep reinforcement learning
Mitofusin 1 in mitochondrial quality control and anti-inflammatory responses in nucleus pulposus cells during disc degeneration
Transcriptome-conditioned molecule generation via gene interaction-aware fragment modeling with a GPT-based architecture
A deep hybrid inception network model with entropy based attention for automated iron ore image characterization
Dispositional mindfulness in climbers with different levels of experience
Abstract This cross-sectional study investigated whether climbing experience is associated with higher levels of dispositional mindfulness and its related key mechanisms. A total of N = 203 climbers—comprising 33 leisure and novice climbers, 85 moderately experienced climbers, and 85 experienced climbers—primarily from Germany (113 women, 86 men, and three non-binary individuals, aged between 20 and 61 years) completed five self-report questionnaires to test the hypotheses concerning mindfulness (measured with the Five Facet Mindfulness Questionnaire) , attention regulation (measured with the Attention Control Scale), and emotion regulation (measured with the Brief Version of the Difficulties in Emotion Regulation Scale ) across varying levels of climbing experience. In addition, the study examined the relationship between climbing experience and body awareness (measured with the Embodied Mindfulness Questionnaire), and non-attachment (measured with the Short Form of the Non-Attachment Scale). Experienced climbers scored higher than moderately experienced climbers on the non-judging facet of dispositional mindfulness. In contrast, moderately experienced climbers reported significantly higher overall values of emotion regulation in the sub-scales of clarity , strategies, and non-acceptance compared to experienced climbers. Furthermore, the climbing and meditation experience was associated with attention to and awareness of bodily sensations. To conclude, the experience of climbing is only related to the non-objective facet of dispositional mindfulness. The observed advantages in emotion regulation among experienced climbers suggest that sustained engagement in climbing is associated with an agentic emotional experience.
Federated asynchronous graph attention network with structural semantic embedding for multi-label graph classification
Abstract Federated Learning (FL) provides a privacy-preserving framework for training graph neural networks (GNNs) in privacy-sensitive scenarios. However, traditional FL-GNN approaches often focus on addressing data distribution inconsistencies across clients from a purely data-centric viewpoint, overlooking the critical role of label semantics. Incorporating label semantic information, however, significantly enhances a model’s performance in multi-label classification tasks. Moreover, the performance of FL-GNNs is constrained by two key challenges related to heterogeneity: intra-client heterogeneity in graph representations and inter-client heterogeneity across distributed graphs. Unfortunately, few FL methods effectively handle discrepancies in both label distributions and graph heterogeneity across different clients. To address this gap, we introduce the federated asynchronous graph attention network with structural semantic embedding for multi-label classification (FasSGAT). FasSGAT primarily contains: (1) client-specific label semantic embedding modules that learn feature encodings from constructed label-semantic distribution graphs, (2) the integration of these encodings into the backbone multi-label classifier, along with specially designed structure-sensitive spectral features to mitigate client-side heterogeneity, and (3) a novel structure-sensitive asynchronous aggregation mechanism at the server level that uses the graph spectral features to construct a global model and address graph heterogeneity. Our experimental results on multi-label benchmarks show that FasSGAT outperforms traditional FL methods across various evaluation metrics.
Deep bayesian neural networks for UWB phase error correction in positioning systems
Construction and validation of a nomogram for screening for sarcopenia in patients with osteoporotic vertebral compression fracture
Portable mixed reality navigation system for neurosurgery: a clinical feasibility study
Correction: Comparing neutralizing antibody activity over time between naïve and convalesced COVID-19 vaccinated individuals
Integrating portable qPCR and image recognition to combat illegal trade in sharks and rays
Abstract Illegal trade in sharks and rays continues to undermine global conservation efforts, with enforcement often hampered by the inability to identify products to the species level. Here, we present a portable, cost-effective High-Resolution melt (HRM) assay for rapid DNA-based identification of elasmobranch species in trade. Using a reference library of 669 vouchered tissue samples collected from field operations and international market surveys, we validated the assay’s capacity to accurately differentiate at least 55 shark and ray species based on melt curve profiles, including 38 species listed under the Convention on International Trade in Endangered Species of Wild Fauna and Flora. Automated image classification enabled high-throughput identification with 99.2% accuracy. The assay yields results within two hours at a per-sample cost of $1.50, and is compatible with portable qPCR platforms, making it suitable for on-site applications. This approach represents a scalable molecular enforcement tool that can empower local authorities to monitor trade more effectively, support compliance with international regulations, and enhance global efforts to combat wildlife trafficking.
Stabilisation of waterlogged archaeological wood: the analysis of structural and dimensional changes of different conservation methods using magnetic resonance imaging and X-ray micro-computed tomography
Abstract Waterlogged archaeological wood can be preserved for many years in the absence of air, as decomposition is substantially slowed down. After excavation, conservation is necessary to prevent damage of objects due to uncontrolled drying. In this study, the following conservation methods were tested to investigate their ability to stabilise the objects: alcohol-ether resin, melamine-formaldehyde (Kauramin 800), lactitol/trehalose, saccharose, polyethylene glycol (PEG 2000 with air-drying and PEG 2000 or 400 and 4000 with subsequent freeze-drying). In order to precisely understand the changes caused by conservation and drying, 40 samples each of pine wood and oak wood were documented using magnetic resonance imaging and X-ray micro-computed tomography before and after conservation. This imaging made it possible to quantitatively record changes in the wood structure, for example due to shrinkage, collapse and cracks, which could not be prevented by conservation. The alcohol-ether-resin method with solvent drying had the best stabilizing effect and no damage of the wood structure was visible. The two PEG treatments followed by freeze drying showed effective volume stabilisation. In both cases, however, the treatment led to cracks in the wood structure, which occurred less frequently when the cryoprotectant PEG 400 was used. In comparison, the other methods with air drying did not show consistently good results in stabilizing the volume or wood structure.