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Combined impact of jet stream and turbulence on long-term trans-oceanic flight routes over North Atlantic Ocean using ERA5 reanalysis
Anthelmintic medication with focus on first-trimester exposure: an evaluation of pregnancy outcomes based on the Embryotox cohort
Abstract The present study aims to contribute to the available experience on the systemic use of five different anthelmintics during pregnancy (albendazole, mebendazole, praziquantel, pyrantel and pyrvinium). Particular focus was placed on the occurrence of major birth defects and pregnancy loss following first-trimester exposure. As the assessed agents differ considerably in terms of their chemical structure and mechanism of action, they were analyzed separately. A total of 282 pregnancies with exposure to the study medication were recorded in the Embryotox database (January 1, 2000 – February 28, 2023) and analyzed descriptively. Among 75 live-born infants with first-trimester exposure to mebendazole, five major malformations were reported, three of which were heart defects. Of the 21 live-born infants exposed to pyrantel in the first trimester, two were affected by aplasia cutis congenita of the scalp, one of whom was co-exposed to thiamazole. Among 47 live-born children with first-trimester pyrvinium exposure, one major birth defect was reported. No major malformations were observed among 4 and 10 live-born children exposed to praziquantel and albendazole, respectively. Given the limitations, the findings should be interpreted as descriptive observations and possible signals only. Further investigations with larger cohorts are required.
A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer’s disease
Abstract We previously proposed an MRI-based machine learning model to describe the mesoscopic architecture of the human brain to aid in classifying subjects as having non-AD related pathology (nADrp) or AD related pathology (ADrp), including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The method, developed on data from patients scanned at 1.5T showed high performance, but did not generalise well to scans obtained from 3T MRI. In the current work we overcome the problem and extend the approach to patients scanned longitudinally, and at different field strengths. Retrospective T1-MRI data from 1592 subjects scanned at 3T were included to develop the machine learning models. Three additional longitudinal datasets (n = 211) at different magnetic field strengths—1.5 and 3T—were adopted to evaluate the models. Radiomic features were extracted from each brain region. A logistic regression method with least absolute shrinkage and selection operator (LASSO) model selection was employed to classify nADrp from ADrp (classifier 1) or MCI from AD (classifier 2). Classifier 1 that discriminates nADrp from ADrp achieves high performance, with area under the curve (AUC) of the receiver operating characteristics (ROC) of 0.84 in the independent hold-out cross-sectional dataset. High performance was also seen in external testing datasets for classifier 1 (AUC of 0.70 to 0.96). Classifier 2 that discriminates MCI from AD achieves AUC of 0.79 in the independent hold-out dataset and moderate to good performance in the external testing datasets (AUC of 0.56 to 0.93). The new data analysis methods, trained on 3T data, demonstrate potential for aiding AD early detection and disease progression on both 3T and 1.5T scanners.
Construct validation of a portable virtual reality simulator for $$30^\circ$$ laparoscopic camera navigation via machine learning and latent behavioral modeling
Association between methylated fatty acids and early pregnancy outcomes in dairy cows: a plasma GC‒MS assay
Symptom-linked disruption of the vaginal Lactobacillus-IL-2/IFN-γ axis in genitourinary syndrome of menopause related symptoms
Syphilis epidemiology in Jordan: prevalence, incidence, and seroreversion from 15 years of laboratory-based data
Investigation of the molecular network underlying PET-MPs-induced inflammatory bowel disease via integrated machine learning and molecular docking approaches
Abstract Polyethylene terephthalate microplastics (PET-MPs), as environmental contaminants, have raised significant concerns due to their potential toxicity, leading to serious environmental pollution and health issues. However, their impact on inflammatory bowel disease (IBD) remains poorly elucidated. This study aims to investigate the potential molecular mechanisms linking PET-MPs to IBD pathogenesis. Multiple datasets were employed to identify Crohn’s disease (CD)- and ulcerative colitis (UC)-associated targets. Multi-machine learning approaches were combined with molecular docking of a PET-related compound to evaluate potential binding interactions with the identified hub targets. This study delineated 9 putative targets associated with PET-MPs-related CD pathogenesis and 17 potential targets associated with UC pathogenesis. For CD, GBM showed the best performance among all models, with a mean ROC-AUC of 0.862; for UC, RF performed best, with a mean ROC-AUC of 0.806, based on both training and validation sets. Multi-machine learning analysis identified 14 hub genes as candidate key regulatory factors. SHAP analysis highlighted their significant contributions to the model predictions. Molecular docking simulations suggested favorable binding affinities between a PET-related compound and the hub targets. This study suggests that PET-MPs may contribute to IBD pathogenesis by potentially interacting with 14 machine learning-prioritized hub genes. Molecular docking analyses indicated predicted high-affinity binding between a PET-related compound and these targets. Collectively, these findings provide a hypothesis-generating framework for investigating the potential role of PET-MPs in IBD progression.
Lip reading systems for Urdu alphabets in diverse environments
Abstract Lip reading technology has potential use across various fields, significantly enhancing communication for the deaf, aiding in noisy settings, and supporting information security through silent password entry. Although, notable progress has been made in constructing datasets of different types like digits, alphabets, words, phrases, and sentences levels for lip reading in various languages. However, developing a robust Urdu lip reading model remains a challenge due to the lack of a suitable dataset. Moreover, difficulties in adapting previous models, such as the LipNet model to Urdu. To address these barriers, we present the ULRA (Urdu lip reading alphabets) dataset, leverage advanced data augmentation techniques, and evaluate three cutting-edge DNN models: a LipNet-based 2D-CNN model, a Hybrid 2D_3D-CNN model, and a baseline 3D-CNN model. Each model undergoes rigorous testing in diverse environments, with both familiar and unfamiliar data. The results reveal that the LipNet-based 2D-CNN model achieves an impressive 81.97% accuracy on unknown data across diverse environments, while the Hybrid model excels in generalization, reaching 69.45% accuracy on unfamiliar data, thanks to its superior spatiotemporal feature extraction capabilities. Additionally, precision, recall, and F1-Score values of LipNet-Based 2D CNN are 0.83, 0.82, and 0.82 respectively. All three values of this model are also higher than the other two models. These findings underscore the strengths of various DNN architectures and the critical advancements made possible by the ULRA dataset, paving the way for future breakthroughs in Urdu lip reading research.
Inbred strains of Xenopus tropicalis show morphological and genetic variation
Structural insights into selective recognition of ATP-mimicking inhibitors by the atypical kinase HASPIN
Design of a slotted bowtie dual-polarized antenna for Sub-6 GHz multi-service wireless applications
A pilot study of the effect of norepinephrine dose on left ventricular-arterial coupling in patients with septic shock
A comparative study of machine learning models for microbiome-based diagnosis and multi-class staging of colorectal cancer
Saltation-consistent event-aware digital twins for uncertainty transport in non-smooth dynamical systems
Replacing fish oil with Tetraselmis chui microalgae biomass does not compromise rainbow trout health: Biochemical, histologic, antioxidant and immune gene expression
Abstract Microalgae offer a nutritionally robust alternative to fishmeal and fish oil, helping reduce pressure on wild stocks and supporting more sustainable aquafeed production. This study explored the potential of replacing fish oil with Tetraselmis ( Tetraselmis chui ) microalgae biomass in the diet of juvenile rainbow trout (89.0 ± 1.10 g) ( Oncorhynchus mykiss ), assessing its effects on the fish’s health. A control diet containing 53% crude protein and fish oil (FO) was modified by replacing FO with Tetraselmis at three graded inclusion levels: 33% (Tetra33), 66% (Tetra66), and 100% (Tetra100). The 84-day feeding trial evaluated key growth parameters, biochemistry, liver and intestinal histo-architectures, and immune-antioxidant gene expression profiles of the experimental fish. Time-series analyses of growth performance revealed no significant treatment effects from day 14 to day 70, except at the 84-day biomass sampling. The FO (7321.65 ± 60.03g) attained a significantly greater final weight (FW) than Tetra33 (6984.70 ± 86.15g) and Tetra100 (6823.93 ± 160.42g), while remaining statistically similar to Tetra66 (7051.77 ± 107.30g). Likewise, weight gain (WG) of the FO group (5519.65 ± 57.16g) exceeded that of the Tetra100 group (5043.93 ± 142.09g) but did not differ significantly from the Tetra33 (5220.70 ± 73.95g) and Tetra66 (5281.77 ± 110.86g). The feed conversion ratios (FCRs) and specific growth rates (SGRs) of the Tetra groups were comparable to the FO control. Dietary variation did not elicit significant changes in leukocyte distribution, biochemical indices, or gene expression patterns across Tetra groups relative to the FO. Similarly, the histological analysis revealed that Tetraselmis dietary inclusions did not trigger inflammatory reactions in hepatic or intestinal tissues in the Tetra groups compared to the FO. Minor but inconsequential histological modifications were noted, such as moderated sinusoid dilation in the liver and slight changes in intestinal villi of Tetra33 fish. Health biomarker analyses indicated that replacing fish oil with Tetraselmis preserved physiological homeostasis, whereas 66% replacement (Tetra66) yielded the best growth performance compared to FO. However, longer feeding trials are necessary to confirm long-term health and nutritional outcomes.
Accurate alpha-particle stopping power measurements in graphenic carbon foils and their application to high-precision, non-destructive areal density determination
Abstract A precise, non-destructive method for determining the areal density of thin graphenic carbon (GC) foils via alpha-particle energy loss is presented. Two types of GC foils — sourced from KETEK GmbH and Applied Nanotech Inc. — were investigated using a three-isotope mixed alpha source emitting particles in the 5.0–5.8 $$\textrm{MeV}$$ range. Both foils have similar nominal areal densities of approximately $$0.2\,\mathrm {mg\,cm^{-2}}$$ , but differ slightly in chemical composition and microstructure. High-resolution alpha spectroscopy yielded energy-loss measurements with relative uncertainties below 1%. The uncertainty of the extracted areal densities and stopping powers is dominated by the determination of foil mass, area and composition metrology, rather than by the alpha-energy-loss measurement itself. Experimental stopping powers were obtained by combining the measured energy loss with independently determined foil masses and areas, and were compared with established stopping-power models. A modified Bethe formalism incorporating Barkas and Bloch corrections, together with an empirically adjusted mean excitation energy $$I_\textrm{adj}$$ , provided the most consistent description of the data across the investigated energy range. The resulting values were $$(73 \pm 2)\,\textrm{eV}$$ for the KETEK foil and $$(85 \pm 3)\,\textrm{eV}$$ for the Applied Nanotech foil. The fitted stopping-power curves indicate a systematic difference between the two GC foils, consistent with their differing compositions and microstructures. Because the stopping-power model is calibrated against the same reference foils, however, this interpretation is model-dependent and requires further validation using independently characterised samples. While the method is well suited to thin foils, angular straggling and the non-linear energy dependence of the stopping power may limit its applicability beyond the thin-target approximation. The reported stopping-power data are relevant for benchmarking Monte Carlo simulations and modelling energy deposition in carbon-based materials, with applications in accelerator technology and radiopharmaceutical research. In medical physics, stopping power is closely related to linear energy transfer, which governs the biological effectiveness of alpha-emitting isotopes in targeted therapies.