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RT-GalaDet as a real-time model for screening surface-associated health abnormalities in fish
Correction: Mapping spatiotemporal distribution of forest carbon density in Xizang, China
Life cycle assessment of electric and gasoline vehicles considering grid differences and cold climate in China
Geochemical signatures in plastic debris from the Curonian Lagoon, Lithuania
This study examined elemental accumulation on weathered plastic waste as a contributor to environmental pollution. Twenty-five plastic samples collected near the Curonian Lagoon in Lithuania were analyzed for 32 elements using inductively coupled plasma-mass spectrometer (ICP-MS). Five common polymers (polyethylene, polypropylene, Polyethersulfone, polyester, and polyethylene terephthalate) were identified, with polyethylene exhibiting the highest elemental uptake, followed by polypropylene and polystyrene. Correlation analysis suggested relationships between elemental uptake and geochemical behavior, with alkali and alkaline earth elements (REEs) potentially enhancing the uptake of intermediate ions. However, elements such as sulfur, lead, cadmium, and antimony showed limited correlation with other elements. Despite their low mobility, REEs were used to infer sources of pollution, and the aluminum to lanthanum ratio was proposed as a potential indicator of possible anthropogenic pollution from industrial, petroleum, and vehicle emissions.
Residual cholesterol levels are associated with carotid plaque stability in patients with carotid stenosis
Accurate time-series forecasting of floating platform motion via a reinforced fusion CNN–BiLSTM–attention model
Accurate motion prediction of floating platforms is critical for ensuring operational safety in offshore engineering applications or marine equipment testing. However, the strong nonlinearity and non-stationary characteristics induced by complex marine environments pose significant challenges to conventional prediction models. This study proposes a reinforced hybrid neural network (CNN-BiLSTM-Attention) integrated with advanced signal processing techniques to address these challenges. The methodology combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for multi-scale signal analysis, coupled with temporal feature engineering through sliding window optimization. And the architecture innovatively integrates convolutional neural networks for spatial pattern extraction, bidirectional long short-term memory networks for temporal dependency modeling, and attention mechanisms for dynamic feature weighting. By analyzing datasets generated via hydrodynamic simulations, this study elucidates the model’s physical interpretability and establishes a closed-loop validation framework between data-driven methods and physics-based models. Finally, the predictive performance of the model is evaluated using motion datasets of the proportional platform in the water pool test under different working conditions, demonstrating its broad applicability and transferability by assessed using a dual-stage EWMA control line. Overall, the proposed CNN-BiLSTM-Attention model and its data-physics integrated validation method provide a reliable, interpretable and transferable solution for floating platform motion prediction, which can break through the limitations of single analysis methods, and provide a new research idea for integrating data-driven and physics-based methods in the field of ocean engineering.
Retrieval-augmented patch generation for geosynchronous satellite status forecasting
Contemporary trends of witchcraft accusations and resulting violence against children: A scoping review and bibliometric analysis protocol
Objective This review seeks to understand the global trends of contemporary witchcraft accusations and related harms against children and adolescents (0–18 years of age). Introduction Witchcraft-related violence against children and adolescents (children) reflects an alarming and understudied phenomenon of socio-culturally legitimated harm around the globe, particularly in sub-Saharan Africa. ‘Witchcraft’ explains the unexplainable, such as strokes of luck and/or misfortune. Witchcraft accusations are linked to illness, sudden death, financial misfortune, miscarriages, financial windfall, disability, birth abnormalities, or rare conditions. Religious entities also levy witchcraft accusations, referring to black magic, evil, works or malicious spirits, to profit off families while harming the accused. These accusations result in marginalization, alienation, slandered reputation, communal expulsion, and violence, causing disfiguration, disability, and death. Children are especially vulnerable to witchcraft-related violence, including human trafficking, and ceremonial and cultural sacrifice. Inclusion criteria This scoping review will examine witchcraft accusations and related harms against children and adolescents (0–18 years of age) globally from 1946 to 2024. Exclusion criteria This scoping review excludes articles that do not report specifics of the accusation, situation, result, age of the accused, or country of origin. Methods This scoping review will follow the Joanna Briggs Institute’s Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) Statement. Articles published from January 1, 1946 to December 31, 2024 will be collected across academic, grey literature and web-based databases. A systematic search strategy will be applied in each database, and all search results recorded. A bibliometric analysis will also be undertaken to systematically and rigorously review the extant literature. Findings of this review will identify areas of collaboration and gaps for further exploration. The literature analysis can raise awareness and inform resource development across health care, education, social work, government, and community sectors to better support victims of witchcraft-related harms.
Student elective course selection patterns and satisfaction determinants identified through educational data mining
Differences in drug intake levels (high versus low takers) do not necessarily imply distinct drug user types: Insights from a new cluster-based model
Background A current model categorizes drug takers into high versus low takers (HT and LT) based on their drug intake levels, with the assumption that these groups represent different phenotypes. When several drug doses are considered, the inverted u-shaped dose-response curves (IUDR) of HT are shifted upwards and rightward, relative to that of LT. However, these IUDR ‘shifts’ are not quantitative metrics and may be subjective. Also, differences in intake levels do not necessarily imply distinctions in other variables (such as demand elasticity) that are important for drug user phenotypology. With supporting evidence from a recent report, we hypothesized that, contrary to assumptions in the field, HT and LT do not necessarily represent distinct phenotypes. Methods Male Sprague Dawley rats (n = 12) self-administered different doses of cocaine, and we obtained IUDR and demand curves per individual. We developed a new model to quantify the variables that defined the structure of the IUDR and we employed behavioral economic principles to obtain variables that defined the demand curve. We conducted principal component analysis/gaussian mixtures model clustering of variables from both IUDR and demand curves, to identify/compare the clusters that were revealed to HT/LT groups that were distinguished via median split. Results The cluster-based model identified groups more distinct than LT versus HT. LT and HT were composed of mixtures of individuals from these distinct clusters. LT/HT were not very different when several other variables were considered. Conclusions Differences in drug intake levels (HT versus LT) do not necessarily imply distinct phenotypes.
Gut microbiota-derived butyrate primes systemic immunity in honey bees by mediating lipid metabolic reprogramming
Abstract The gut microbiota plays a crucial role in insect immune priming, inducing enhanced immune response that functionally resembles acquired immunity confined to vertebrates. While gut microbiota mediates systemic immune activation in insect hemolymph, the mechanisms underlying remote immunoregulation remain largely unknown. Here we use the honey bee gut microbiota as a model, we identify butyrate as a key microbial metabolite coordinating immune-metabolic crosstalk. Butyrate supplementation restores immune competence in germ-free bees, mirroring the protective effects of microbiota-colonized individuals. Butyrate orchestrates lipid metabolic reprogramming in the fat body by activating glycerolipid and arachidonic acid metabolism through activating the G-protein coupled receptor 41 while inhibiting histone deacetylases. These changes in-turn upregulate prostaglandin E 2 biosynthesis, which is essential for humoral and cellular immune activation. These findings unravel how the intricate integration of immune and metabolic systems in honey bees is driven by gut-host interactions.
Correction: Lablab purpureus phytochemicals demonstrate potential anticancer activity as evidenced through experimental and computational analysis
Construction of the core competencies training system for thoracic surgery specialist nurses: A mixed-methods study
Objective To develop a training system for cultivating the core competencies of thoracic surgery specialist nurses. Methods A mixed-methods study was employed, comprising two stages: (1) A literature review and semi-structured interviews with thoracic surgical healthcare professionals were conducted to develop a preliminary core competencies training system for thoracic surgery specialist nurses; (2) A two-round Delphi expert consultation was conducted to determine the final training system. Results Consensus was reached on the core competency framework (training objectives) (6 first-level, 17 second-level indicators), 92 items of curriculum content along with their corresponding teaching methods, 17 aspects of organizational management, and 8 evaluation methods. The response rate was 100%. The authority coefficient was 0.895. The Kendall’s coefficients for the two rounds of expert inquiry were 0.141 and 0.210, respectively. Conclusion This study developed a scientific, comprehensive specialty training system for thoracic surgery specialist nurses, meeting practical nursing needs, and enriching the field’s evidence base. It delivers substantial clinical and practical value to nursing administrators, practitioners, patients, and the healthcare system. Future research should validate its practical value through larger-scale empirical studies, continuously gather feedback on training outcomes, refine the training system, and further enhance its adaptability and influence.
Promoting sustainable development worldwide in the metacoupled anthropocene
Copy-number amplification drives IFI30 overexpression and coordinated immune activation, identifying a novel diagnostic and therapeutic target in gastric adenocarcinoma
Insights into tea tree oil-mediated transcriptome modulation in Rosa hybrida
In this study, we evaluated the impact of substituting conventional antifungal treatments with a commercial Tea Tree Oil (TTO) formulation in Rosa hybrida crop plants grown under controlled industrial conditions. Using a transcriptomic approach, we analyzed both leaves and petals to assess the molecular responses to TTO application. Our results revealed a pronounced transcriptomic shift in leaves, where 26 genes were significantly upregulated and one was downregulated, whereas petals displayed more subtle changes. The upregulated genes in leaves were enriched in pathways associated with lipid metabolism, cell wall modification, and plant defense, supporting the view that TTO acts as a bio-stimulant by activating stress-response transcriptional programs. In petals, the few upregulated genes included four transcriptional regulators, while the downregulated set encompassed lipase-like enzymes, cytochrome P450s, and a glucoside malonyltransferase. The comparatively diminished response in petals, which are functional specialized in pollination and have a more limited longevity compared to leaves, supports the view that systemic transcriptional adjustments are more evident in vegetative organs. These findings are consistent with previous reports of TTO’s ability to modulate plant stress responses and reinforce its potential as a bio-based alternative to synthetic fungicides in sustainable floriculture.
Acoustic shape-morphing micromachines
Instance-level quantitative saliency in multiple sclerosis lesion segmentation
Abstract In recent years, explainable methods for artificial intelligence (XAI) have tried to reveal and describe models’ decision mechanisms in the case of classification and even for segmentation. However, XAI methods for semantic segmentation and in particular for single specific instances (e.g. one given lesion among others of the same class in medical imaging) have yet to be developed to understand what drove the detection and contouring of the latter, which is crucial for all multi-lesional diseases. We proposed instance-level explanation maps for semantic segmentation extending both SmoothGrad and Grad-CAM++ methods and yielding quantitative instance saliency for the former. The instance-level methods were applied to the segmentation of white matter lesions (WML), a magnetic resonance imaging (MRI) biomarker in multiple sclerosis (MS). 687 patients diagnosed with MS for a total of 4023 FLAIR and MPRAGE MRI scans were collected at the University Hospital of Basel, Switzerland. WM lesion masks were annotated by four expert clinicians on baseline and follow-up imaging. Three deep learning networks—a 3D U-Net, nnU-Net, and Swin UNETR—were trained and tested on these data (test normalized Dice score, respectively of 0.71, 0.78, 0.80; true positive rate of 79%, 78%, and 85%; false discovery rate of 37%, 38%, and 36%; false negative rate of 20%, 22%, and 14%), then saliency maps were computed. Consistent with clinical practice, the proposed instance saliency maps revealed that the model relied more on FLAIR than MPRAGE to segment WMLs, with positive saliency values inside a lesion and negative in its neighborhood. FLAIR hyperintensity combined with healthy WM around the lesion border was required for their detection. Beyond the aforementioned sanity checks, we observed that peak values of the generated saliency maps presented distributions that differ significantly between TP, FN, FP and TN predictions, suggesting that the quantitative nature of the proposed saliency could be used to identify errors. In conclusion, we introduced two XAI methods to generate quantitative instance-level explanations in semantic segmentation. The proposed XAI maps can be applied to any architecture and could serve as a basis to (i) improve model performance (e.g. reducing FPs), (ii) optimize their internal architecture (e.g. patch size), and (iii) justify the model’s decisions to the end users, which are contextualized to a specific lesion instance of interest.
A SNP-based capture and clustering workflow to assess donor-derived cell-free DNA in transplantation
Measurement of donor-derived cell-free DNA (dd-cfDNA) enables early, non-invasive monitoring of transplanted organs, including rejection detection. We developed a method to estimate dd-cfDNA ratios using capture hybridization of 300 SNPs, next-generation sequencing (NGS), and clustering analysis. Validation was conducted using simulated mixtures of fragmented genomic DNA from two individuals (0–100%). dd-cfDNA ratios were estimated via clustering, with and without 0% mixture samples to simulate the presence or absence of pre-transplant recipient plasma. When 0% samples were included, estimation achieved an r² of 0.9987 across the full 0–100% range; without them, r² remained high (0.9973) in the clinically relevant 0–10% range. The robustness of the method was further demonstrated by in silico downsampling. MAEs with 0% samples were 0.823%, 0.766%, and 0.702% at full, 50%, and 25% read depths, respectively (0–100% range). For the 0–10% range, MAEs were 0.333%, 0.300%, and 0.467% with 0% samples, and 0.413%, 0.367%, and 0.503% without them. These results indicate that the method maintains high accuracy even under reduced input and when pre-transplant data are unavailable. We also compared clustering-based estimates with direct calculations from kidney transplant recipients, where donor and recipient SNP genotypes were known. The concordance correlation coefficient (CCC) from day 0 to day 28 post-transplantation was 0.9887 and 0.9316 for unrelated pairs with and without pre-transplant data, respectively. For sibling pairs, CCCs were 0.9923 and 0.9675; for parent–child pairs, the CCC was 0.9831 with pre-transplant data. CCC was not calculated for parent–child pairs without pre-transplant data due to limited samples (<10%, n = 3). These findings demonstrate high concordance, accuracy, and robustness of our clustering-based dd-cfDNA estimation method and support its potential utility in clinical transplantation settings.