Sodium chloride promotes macrophage pyroptosis and aggravates rheumatoid arthritis by activating SGK1 through GABA receptors Slc6a12.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by synovial inflammation and the production of autoantibodies. Previous studies have indicated an association between high-salt diets (HSD) and an increased risk of RA, yet the underlying mechanisms remain unclear. Macrophage pyroptosis, a pro-inflammatory form of cell death, plays a pivotal role in RA. In this study, we demonstrate that HSD exacerbates the severity of arthritis in collagen-induced arthritis (CIA) mice, correlating with macrophage infiltration and inflammatory lesions. Given the significant alterations observed in macrophages from CIA mice subjected to HSD, we specifically investigate the impact of HSD on macrophage responses in the inflammatory milieu of RA. In our in vitro experiments, pretreatment with NaCl enhances LPS-induced pyroptosis in RAW.264.7 and THP-1 cells through the p38 MAPK/NF-κB signaling pathway. Subsequent experiments reveal that Slc6a12 inhibitors and SGK1 silencing inhibit sodium-induced activation of macrophage pyroptosis and the p38 MAPK/NF-κB signaling pathway, whereas overexpression of the SGK1 gene counteracts the effect of sodium on macrophages. In conclusion, our findings verified that high salt intake promotes the progression of RA and provided a detailed elucidation of the activation of macrophage pyroptosis induced by sodium transportation through the Slc6a12 channel.

Effective Signal Extraction Algorithm for Cerebral Blood Oxygen Based on Dual Detectors.

Functional near-infrared spectroscopy (fNIRS) can dynamically respond to the relevant state of brain activity based on the hemodynamic information of brain tissue. The cerebral cortex and gray matter are the main regions reflecting brain activity. As they are far from the scalp surface, the accuracy of brain activity detection will be significantly affected by a series of physiological activities. In this paper, an effective algorithm for extracting brain activity information is designed based on the measurement method of dual detectors so as to obtain real brain activity information. The principle of this algorithm is to take the measurement results of short-distance channels as reference signals to eliminate the physiological interference information in the measurement results of long-distance channels. In this paper, the performance of the proposed method is tested using both simulated and measured signals and compared with the extraction results of EEMD-RLS, RLS and fast-ICA, and their extraction effects are quantified by correlation coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE). The test results show that even under low SNR conditions, the proposed method can still effectively suppress physiological interference and improve the detection accuracy of brain activity signals.

Research on Emotion Recognition Method of Cerebral Blood Oxygen Signal Based on CNN-Transformer Network.

In recent years, research on emotion recognition has become more and more popular, but there are few studies on emotion recognition based on cerebral blood oxygen signals. Since the electroencephalogram (EEG) is easily disturbed by eye movement and the portability is not high, this study uses a more comfortable and convenient functional near-infrared spectroscopy (fNIRS) system to record brain signals from participants while watching three different types of video clips. During the experiment, the changes in cerebral blood oxygen concentration in the 8 channels of the prefrontal cortex of the brain were collected and analyzed. We processed and divided the collected cerebral blood oxygen data, and used multiple classifiers to realize the identification of the three emotional states of joy, neutrality, and sadness. Since the classification accuracy of the convolutional neural network (CNN) in this research is not significantly superior to that of the XGBoost algorithm, this paper proposes a CNN-Transformer network based on the characteristics of time series data to improve the classification accuracy of ternary emotions. The network first uses convolution operations to extract channel features from multi-channel time series, then the features and the output information of the fully connected layer are input to the Transformer netork structure, and its multi-head attention mechanism is used to focus on different channel domain information, which has better spatiality. The experimental results show that the CNN-Transformer network can achieve 86.7% classification accuracy for ternary emotions, which is about 5% higher than the accuracy of CNN, and this provides some help for other research in the field of emotion recognition based on time series data such as fNIRS.

Differences in pathogenic community assembly processes and their interactions with bacterial communities in river and lake ecosystems.

Pathogenic bacterial infections caused by water quality degradation are one of the most widespread environmental problems. Clarifying the structure of pathogens and their assembly mechanisms in lake ecosystems is vital to prevent the infestation of waterborne pathogens and maintain human health. However, the composition and assembly mechanisms of pathogenic bacterial communities in river and lake ecosystems are still poorly understood. In this study, we collected 17 water and 17 sediment samples from Lake Chaohu and its 11 inflow rivers. Sequencing of 16S rRNA genes was used to study bacterial pathogen communities. The results of the study showed that there was a significant difference (P < 0.05) in the composition of the pathogen community between riverine and lake habitats. Acinetobacter (36.49%) was the dominant bacterium in the river, whereas Flavobacterium (21.6%) was the most abundant bacterium in the lake. Deterministic processes (i.e., environmental filtering and species interaction) drove the assembly of pathogenic bacterial communities in the lake habitat, while stochastic processes shaped river pathogenic bacterial communities. Spearman correlation analysis showed that the α-diversity of bacterial communities was linearly and negatively linked to the relative abundance of pathogens. Having a higher bacterial community diversity had a suppressive effect on pathogen abundance. In addition, co-occurrence network analysis showed that bacterial communities were tightly linked to pathogenic bacteria. Pseudomonas aeruginosa and Salmonella enterica were identified as keystone species in an inflow water sampling network (W_FR), reducing the complexity of the network. These results provide a reference for assessments of water quality safety and pathogenic bacteria posing risks to human health in large freshwater lakes.

Improved Mask R-CNN Multi-Target Detection and Segmentation for Autonomous Driving in Complex Scenes.

Vision-based target detection and segmentation has been an important research content for environment perception in autonomous driving, but the mainstream target detection and segmentation algorithms have the problems of low detection accuracy and poor mask segmentation quality for multi-target detection and segmentation in complex traffic scenes. To address this problem, this paper improved the Mask R-CNN by replacing the backbone network ResNet with the ResNeXt network with group convolution to further improve the feature extraction capability of the model. Furthermore, a bottom-up path enhancement strategy was added to the Feature Pyramid Network (FPN) to achieve feature fusion, while an efficient channel attention module (ECA) was added to the backbone feature extraction network to optimize the high-level low resolution semantic information graph. Finally, the bounding box regression loss function smooth L1 loss was replaced by CIoU loss to speed up the model convergence and minimize the error. The experimental results showed that the improved Mask R-CNN algorithm achieved 62.62% mAP for target detection and 57.58% mAP for segmentation accuracy on the publicly available CityScapes autonomous driving dataset, which were 4.73% and 3.96%% better than the original Mask R-CNN algorithm, respectively. The migration experiments showed that it has good detection and segmentation effects in each traffic scenario of the publicly available BDD autonomous driving dataset.

Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO.

In order to overcome the problems of object detection in complex scenes based on the YOLOv4-tiny algorithm, such as insufficient feature extraction, low accuracy, and low recall rate, an improved YOLOv4-tiny safety helmet-wearing detection algorithm SCM-YOLO is proposed. Firstly, the Spatial Pyramid Pooling (SPP) structure is added after the backbone network of the YOLOv4-tiny model to improve its adaptability of different scale features and increase its effective features extraction capability. Secondly, Convolutional Block Attention Module (CBAM), Mish activation function, K-Means++ clustering algorithm, label smoothing, and Mosaic data enhancement are introduced to improve the detection accuracy of small objects while ensuring the detection speed. After a large number of experiments, the proposed SCM-YOLO algorithm achieves a mAP of 93.19%, which is 4.76% higher than the YOLOv4-tiny algorithm. Its inference speed reaches 22.9FPS (GeForce GTX 1050Ti), which meets the needs of the real-time and accurate detection of safety helmets in complex scenes.

Corncob-derived activated carbon for roxarsone removal from aqueous solution: isotherms, kinetics, and mechanism.

In this study, the adsorption of roxarsone (ROX) onto corncob-derived activated carbon (AC) was optimized using response surface methodology (RSM). Following this, the AC was comprehensively characterized by FT-IR, SEM, and EDS analysis. The results showed that the highest ROX adsorption efficiency of 304.34 mg/g was obtained at the contact time of 262 min, initial pH of 2.5, adsorbent dosage of 0.4 g/L, and initial concentration of 240 mg/L. Besides, it was found that the adsorption equilibrium data was fitted well to the Langmuir and Sips isotherm models. The thermodynamic parameters (e.g., ΔG, ΔH, and ΔS) revealed the spontaneous and exothermic nature of ROX adsorption. As indicated by pseudo second-order kinetics model, the adsorption of ROX onto AC could be achieved through the hydrogen bond, π-π adsorbate-adsorbent interaction, and electrostatic interaction between AC surface functional group and molecular species variations of ROX at different pH values. Overall, it can be concluded that corncob-derived AC is an alternative option for removing ROX from aqueous solution.

Thermal decomposition and kinetics of coal and fermented cornstalk using thermogravimetric analysis.

The thermal behavior and kinetics of Yiluo coal (YC) and the residues of fermented cornstalk (FC) were investigated in this study. The Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO) methods were used for the kinetic analysis of the pyrolysis process. The results showed that the activation energy (Eα) was increased with the increase of the thermal conversion rate (α), and the average values of Eα of YC, FC and the blend (mYC/mFC = 6/4) were 304.26, 224.94 and 233.46 kJ/mol, respectively. The order reaction model function for the blend was also developed by the master-plots method. By comparing the Ea and the enthalpy, it was found that the blend was favored to format activated complex due to the lower potential energy barrier. Meanwhile, the average value of Gibbs free energy of the blend was 169.83 kJ/mol, and the changes of entropies indicated that the pyrolysis process was evolved from ordered-state to disordered-state.