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The Dongba manuscripts are a unique primitive pictographic writing system that originated among the Naxi people of Lijiang, China, boasting over a thousand years of history. The uniqueness of the Dongba manuscripts stems from their pronounced pictorial and ideographic characteristics. However, the digital preservation and inheritance of Dongba manu manuscripts face multiple challenges, including extracting its rich semantic information, recognizing individual characters, retrieving Dongba manuscripts, and automatically interpreting the meanings of Dongba manuscripts. Developing efficient Dongba character detection technology has become a key research focus, wherein establishing a standardized Dongba detection dataset is crucial for training and evaluating techniques. In this study, we have created a comprehensive Dongba manuscripts detection dataset covering various commonly used Dongba characters and vocabularies. Additionally, we propose a model named STEF. Firstly, the Swin Transformer extracts the complex structures and diverse shapes of Dongba manuscripts' features. Then, by introducing a Feature Pyramid Enhancement Module, features of different sizes are cascaded to preserve multi-scale information. Subsequently, all features are fused in a FUSION module, resulting in features of various Dongba manuscript styles. Each pixel's binarisation threshold is dynamically adjusted through a differentiable binarisation operation, accurately distinguishing between foreground Dongba manuscripts and background. Lastly, deformable convolution is introduced, allowing the model to dynamically adjust the convolution kernel's size and shape based on the Dongba manuscripts' size, thereby better capturing the detailed information of Dongba characters of different sizes. Experimental results show that STEF achieves a recall rate of 88.88%, a precision rate of 88.65%, and an F-measure of 88.76%, outperforming other text detection algorithms. Visualization experiments demonstrate that STEF performs well in detecting Dongba manuscripts of various sizes, shapes, and styles, especially in blurred handwriting and complex backgrounds. [ABSTRACT FROM AUTHOR]

Objective: Liver fibrosis is a chronic liver disease caused by a variety of pathophysiological. However, there are no effective treatments to combat it. HSCs are a major source of fibrotic cells and exploring the mechanisms of HSC activation may provide new strategies for the treatment of liver fibrosis. Objectives: To explore the role and underlying mechanism of SLIT3 in HSCs fibrosis. Results: GSE163211 dataset analysis identified aberrant expression of SLIT3 in NASH F1-F4 tissues and SLIT3 expression level was positively correlated with fibrosis-related proteins. In vitro experiments showed that TGF-β induced upregulation of SLIT3 in LX-2 cells. Knockdown of SLIT3 significantly inhibited TGF-β-induced α-SMA, COL1A2, and COL1A1 expression, inhibited excessive cell proliferation and migration, and suppressed YAP activity. Conclusion: Collectively, our findings suggest that SLIT3 deficiency alleviates TGF-β-induced HSCs activation by inhibiting YAP activity. [ABSTRACT FROM AUTHOR]

Font classification of oracle bone inscriptions serves as a crucial basis for determining the historical period to which they belong and holds significant importance in reconstructing significant historical events. However, conventional methods for font classification in oracle bone inscriptions heavily rely on expert knowledge, resulting in low efficiency and time-consuming procedures. In this paper, we proposed a novel recurrent graph neural network (R-GNN) for the automatic recognition of oracle bone inscription fonts. The proposed method used convolutional neural networks (CNNs) to perform local feature extraction and downsampling on oracle bone inscriptions. Furthermore, it employed graph neural networks (GNNs) to model the complex topologiure and global contextual information of oracle bone inscriptions. Finally, we used recurrent neural networks (RNNs) to effectively combine the extracted local features and global contextual information, thereby enhancing the discriminative power of the R-GNN. Extensive experiments on our benchmark dataset demonstrate that the proposed method achieves a Top-1 accuracy of 88.2%, significantly outperforming the competing approaches. The method presented in this paper further advances the integration of oracle bone inscriptions research and artificial intelligence. The code is publicly available at: https://github.com/yj3214/oracle-font-classification. [ABSTRACT FROM AUTHOR]

Environmental-friendly and high-performance biopolymer-based dielectrics have attracted great attention in light weight and flexible electronics. In this work, the biodegradable TEMPO-oxidized cellulose nanofibrils (TOCNs) were mixed with inorganic mica platelets through vacuum-assisted filtration and subsequent hot-pressing method. It was found that mica platelets could be well aligned within TOCN matrix to form a nacre-like multilayer structure due to good compatibility and hydrogen bonding interaction between mica and TOCNs. The TOCN/mica composite paper shows good flexibility, high mechanical strength (9.8 MPa) and enhanced dielectric breakdown strength (17.85 kV/mm). TOCN/mica papers were further dip-treated by epoxidized soybean oil (ESO) and cured at 170 °C for 2 h. The ESO-treated composite papers show greatly enhanced breakdown strength and reach as high as 175.65 kV/mm for TN-M50/ESO sample. Therefore, these cellulose nanofibrils-based composite papers have shown great potentials in paper insulator in power transformers. [ABSTRACT FROM AUTHOR]

Background: The inflammatory response caused by microglia in the central nervous system plays an important role in Alzheimer's disease. Neuregulin-1 (NRG1) is a member of the neuregulin family and has been demonstrated to have anti-inflammatory properties. The relationship between NRG1, microglia phenotype and neuroinflammation remains unclear. Materials and methods: BV2 cells were used to examine the mechanism of NRG1 in regulating microglia polarization. Neuronal apoptosis, inflammatory factors TNF-α and iNOS, microglia polarization, ErbB4 and NF-κB p65 expression were assessed. Results: We found that exogenous NRG1 treatment or overexpression improved microglial activity and reduced the secretion of the inflammatory factors TNF-α and iNOS in vitro. The expression of Bax in SH-SY5Y neuron cells incubated with medium collected from the NRG1 treatment group decreased. Additionally, our study showed that NRG1 treatment reduced the levels of the M1 microglia markers CD120 and iNOS and increased the levels of the M2 microglia markers CD206 and Arg-1. Furthermore, we observed that NRG1 treatment attenuated Aβ-induced NF-κB activation and promoted the expression of p-ErbB4 and that knockdown of ErbB4 abrogated the effects of NRG1 on NF-κB, Bax levels and M2 microglial polarization. Conclusion: NRG1 inhibits the release of inflammatory factors in microglia and regulates the switching of the M1/M2 microglia phenotype, most likely via ErbB4-dependent inhibition of the NF-κB pathway. [ABSTRACT FROM AUTHOR]

Microalloying is an effective method to improve the mechanical properties of 6063 Al–Mg–Si alloy. Here, in this work, the effects of trace Cu (0.14 wt.%) and Cr (0.10 wt.%.) additions on the microstructure and mechanical properties of 6063 Al–Mg–Si alloy were studied. The results show that the additions of two elements can cause significant age-hardening effects, and the yield and tensile strengths of the alloys are significantly increased after T6 heat treatment. Further, it is found that the Cu and Cr additions show different strengthening mechanisms in the alloy. Cu addition leads to a finer and denser distribution of the main strengthening phases in the peak aging of the alloy, as well as a large number density of the precipitated phases, resulting in an increase in the dislocation density of the alloy during strain and hindering the dislocation movement. However, Cr addition acts as an inhibitor of grain growth and results in grain refinement, which also increases the hindrance of dislocation movement during the plastic deformation of the alloy, resulting in an increase in the dislocation density, thereby strengthening the alloy. [ABSTRACT FROM AUTHOR]