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Cancer cells ‘poison’ the immune system with tainted mitochondria
Author Correction: An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients
Comparison of the efficacy and adverse effects of long pulsed 1064 nm Nd: YAG laser and sclerotherapy in the treatment of pyogenic granuloma in children: a retrospective study
What’s the best way to become a professor? The answer depends on where you are
A new method for recognizing geometric parameters of industrial robots
Catalytic efficiency of GO-PANI nanocomposite in the synthesis of N-Aryl-1,4-Dihydropyridine and hydroquinoline derivatives
Abstract In this research, graphene oxide-polyaniline (GO-PANI) nanocomposite was successfully synthesized and its catalytic performance was evaluated for the synthesis of N-aryl-1,4-dihydropyridine (1,4-DHP) and hydroquinoline derivatives. The GO nanosheets were prepared using the Hummers’ method, and in-situ polymerization of aniline was conducted with ammonium persulfate (APS) serving as the polymerization initiator. The synthesized nanocomposite demonstrated notable efficiency, achieving yields of 80–94% for 1,4-DHP derivatives and 84–96% for hydroquinoline derivatives. The GO-PANI nanocomposite was thoroughly characterized by various techniques, including Fourier Transforms Infrared spectroscopy (FT-IR), Field Emission Scanning Electron Microscopy (FE-SEM), X-ray Diffraction analysis (XRD), Thermogravimetric analysis (TGA), and Energy Dispersive X-ray spectroscopy (EDS), all of which confirmed the successful synthesis of the nanocomposite. Furthermore, after ten cycles of reusability testing, the nanocomposite retained its high catalytic performance with no significant degradation. This findings indicate that the GO-PANI nanocomposite is a promising non-metal catalyst for the synthesis of N-aryl-1,4-dihydropyridine and hydroquinoline derivatives.
Azo dye adsorption on ZrO2 and natural organic material doped ZrO2
Spinal cord injury induces transient activation of hepatic stellate cells in rat liver
Research on the improvement method of imbalance of ground penetrating radar image data
Abstract Ground Penetrating Radar (GPR) has been widely used to detect highway pavement structures. In recent years, deep learning techniques have achieved significant success in image recognition, which is potentially relevant for interpreting ground-penetrating radar data. This is because the various types of damage develop at different levels and in different quantities. So the number of datasets of various types of road injuries is not balanced. This leads to poor accuracy of deep learning for injury classification. And the cost of collecting a large amount of data in the field is higher. The aim of this paper is to improve classification accuracy at a lower cost relative to field collection, we propose a damage data expansion method based on generative adversarial network, which consists of encoder and a generative adversarial network. We have made a number of improvements to the generator and discriminator, as well as to the newly added encoder. All of these improvements have improved the generation results in terms of metrics. So that the network can stably generate damage samples with a small number of samples to improve the classification network’s accuracy. The effect on accuracy by varying the proportions of different kinds of samples and traditional expansion methods is also explored. The improvement of the classification network accuracy and FlD metrics illustrates the better performance of the proposed method.
Regulation of photosynthetic characteristics carbon and nitrogen metabolism and growth of maize seedlings by graphene oxide coating
Biosynthetic pathway for leukotrienes is stimulated by lipopolysaccharide and cytokines in pig endometrial stromal cells
Abstract An inflammatory response is related to different inflammatory mediators generated by immune and endometrial cells. The links between lipopolysaccharide (LPS), cytokines, and leukotrienes (LTs) in endometrial stromal cells remain unclear. This study aimed to examine the influence of LPS, tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-4 and IL-10 on 5-lipooxygenase (5-LO), LTA4 hydrolase (LTAH) and LTC4 synthase (LTCS) mRNA and protein abundances, and LTB4 and cysteinyl (cys)-LTs release including LTC4, by the cultured pig endometrial stromal cells, as well as on cell viability. 24-hour exposure to LPS, TNF-α, IL-4 and IL-10 up-regulated 5-LO mRNA and protein abundances. LPS increased LTAH mRNA abundance, while TNF-α, IL-1β and IL-10 augmented LTAH mRNA and protein abundances. TNF-α and IL-4 increased LTCS mRNA and protein abundances. In addition, LTCS mRNA abundance was enhanced by LPS and IL-4, while LTCS protein abundance was increased by IL-1β. Cells responded to LPS, TNF-α, IL-1β and IL-10 with increased LTB4 release. TNF-α, IL-1β and IL-4 stimulated LTC4 release. Cys-LTs release was up-regulated by LPS, TNF-α, IL-1β and IL-4. All studied cytokines augmented cell viability. In summary, LPS, TNF-α, IL-1β, IL-4 and IL-10 are potential LTs immunomodulatory agents in endometrial stromal cells. These functional interactions could be one of the mechanisms responsible for local orchestrating events in inflamed and healthy endometrium.
A map of parental-DNA exchanges charts course for studies of human evolution
The effect of plasticizers on rheological, physical and mechanical properties of low cement high alumina gunning refractories
Abstract In this research, the effect of different plasticizers with different amounts on the properties of monolithic alumina-based refractories has been investigated. All samples were fired at 1100 °C and 1550 °C. In order to evaluate the desired properties, first the rheological properties of the samples were examined, and then for further investigations, loss on ignition (LOI), percentage of permanent linear changes (PLC), apparent porosity (AP), bulk density (BD) and cold crushing strength (CCS) tests were used. In addition, scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX) and X-ray diffractometry (XRD) were used to characterize the samples. 1 and 2 wt% of CMC, H19, bentonite and ballclay were added to the mixtures as plasticizers. The results of this research showed that the sample containing 1 wt% of ball clay can be the most appropriate one due to its highest strength, highest density and lowest apparent porosity. Moreover, the sample containing 2 wt% of H19 (a commercially available binder) has the optimum properties because of its highest strength for the samples fired at 1100 °C. For the mixtures fired at 1550 °C, the more amount of silica has caused higher cold crushing strength due to the presence of the low melting point phases which are not desired and therefore, the mixtures with less silica can be used.
Large study broadens view of the genetic landscape of bipolar disorder
Health state assessment method for complex system based on multiexpert joint belief rule base
Gaming time and impulsivity as independent yet complementary predictors of gaming disorder risk
Abstract Prolonged gaming time, along with increased impulsivity—a key element of poor self-regulation—has been identified as linked to gaming disorder. Despite existing studies in this field, the relationship between impulsivity and gaming time remains poorly understood. The present study explored the connections between impulsivity, measured both by self-report and behavioral assessments, gaming time and gaming disorder within a cohort of 82 participants. While gaming time exhibited a significant correlation with gaming disorder, only self-reported measures of impulsivity and one behavioral metric showed a correlation with gaming disorder. Self-report measures of impulsivity exclusively predicted gaming disorder when included in a regression model with gaming time. The interaction between gaming time and impulsivity, aside from one behavioral metric was deemed insignificant. These findings suggest that impulsivity and gaming time, although associated with gaming disorder risk, are independent variables. Further research should aim to clarify these relationships and explore potential interventions targeting both DGI and impulsivity to mitigate gaming disorder risk.
Enhanced streamflow forecasting using hybrid modelling integrating glacio-hydrological outputs, deep learning and wavelet transformation
Abstract Understanding snow and ice melt dynamics is vital for flood risk assessment and effective water resource management in populated river basins sourced in inaccessible high-mountains. This study provides an AI-enabled hybrid approach integrating glacio-hydrological model outputs (GSM-SOCONT), with different machine learning and deep learning techniques framed as alternative ‘computational scenarios, leveraging both physical processes and data-driven insights for enhanced predictive capabilities. The standalone deep learning model (CNN-LSTM), relying solely on meteorological data, outperformed its counterpart machine learning and glacio-hydrological model equivalents. Hybrid models (CNN-LSTM1 to CNN-LSTM15) were trained using meteorological data augmented with glacio-hydrological model outputs representing ice and snow-melt contributions to streamflow. The hybrid model (CNN-LSTM14), using only glacier-derived features, performed best with high NSE (0.86), KGE (0.80), and R (0.93) values during calibration, and the highest NSE (0.83), KGE (0.88), R (0.91), and lowest RMSE (892) and MAE (544) during validation. Finally, a multi-scale analysis using different feature permutations was explored using wavelet transformation theory, integrating these into the final hybrid model (CNN-LSTM19), which significantly enhances predictive accuracy, particularly for high-flow events, as evidenced by improved NSE (from 0.83 to 0.97) and reduced RMSE (from 892 to 442) during validation. The comparative analysis illustrates how AI-enhanced hydrological models improve the accuracy of runoff forecasting and provide more reliable and actionable insights for managing water resources and mitigating flood risks - despite the paucity of direct measurements.