Aloe-emodin alleviates cerebral ischemia-reperfusion injury by regulating microglial polarization and pyroptosis through inhibition of NLRP3 inflammasome activation.

BACKGROUND: Microglial activation plays a crucial role in injury and repair after cerebral ischemia, and microglial pyroptosis exacerbates ischemic injury. NOD-like receptor protein 3 (NLRP3) inflammasome activation has an important role in microglial polarization and pyroptosis. Aloe-emodin (AE) is a natural anthraquinone compound originated from rhubarb and aloe. It exerts antioxidative and anti-apoptotic effects during cerebral ischemia/reperfusion (I/R) injury. However, whether AE affects microglial polarization, pyroptosis, and NLRP3 inflammasome activation remains unknown. PURPOSE: This study aimed to explore the effects of AE on microglial polarization, pyroptosis, and NLRP3 inflammasome activation in the cerebral infarction area after I/R. METHODS: The transient middle cerebral artery occlusion (tMCAO) and oxygen-glucose deprivation/re-oxygenation (OGD/R) methods were used to create cerebral I/R models in vivo and in vitro, respectively. Neurological scores and triphenyl tetrazolium chloride and Nissl staining were used to assess the neuroprotective effects of AE. Immunofluorescence staining, quantitative polymerase chain reaction and western blot were applied to detect NLRP3 inflammasome activation and microglial polarization and pyroptosis levels after tMCAO or OGD/R. Cell viability and levels of interleukin (IL)-18 and IL-1ß were measured. Finally, MCC950 (an NLRP3-specific inhibitor) was used to evaluate whether AE affected microglial polarization and pyroptosis by regulating the activation of the NLRP3 inflammasome. RESULTS: AE improved neurological function scores and reduced the infarct area, brain edema rate, and Nissl-positive cell rate following I/R injury. It also showed a protective effect on BV-2 cells after OGD/R. AE inhibited microglial pyroptosis and induced M1 to M2 phenotype transformation and suppressed microglial NLRP3 inflammasome activation after tMCAO or OGD/R. The combined administration of AE and MCC950 had a synergistic effect on the inhibition of tMCAO- or OGD/R-induced NLRP3 inflammasome activation, which subsequently suppressed microglial pyroptosis and induced microglial phenotype transformation. CONCLUSION: AE exerts neuroprotective effects by regulating microglial polarization and pyroptosis through the inhibition of NLRP3 inflammasome activation after tMCAO or OGD/R. These findings provide new evidence of the molecular mechanisms underlying the neuroprotective effects of AE and may support the exploration of novel therapeutic strategies for cerebral ischemia.

A lightweight method for maize seed defects identification based on Convolutional Block Attention Module.

Maize is widely cultivated and planted all over the world, which is one of the main food resources. Accurately identifying the defect of maize seeds is of great significance in both food safety and agricultural production. In recent years, methods based on deep learning have performed well in image processing, but their potential in the identification of maize seed defects has not been fully realized. Therefore, in this paper, a lightweight and effective network for maize seed defect identification is proposed. In the proposed network, the Convolutional Block Attention Module (CBAM) was integrated into the pretrained MobileNetv3 network for extracting important features in the channel and spatial domain. In this way, the network can be focused on useful feature information, and making it easier to converge. To verify the effectiveness of the proposed network, a total of 12784 images was collected, and 7 defect types were defined. Compared with other popular pretrained models, the proposed network converges with the least number of iterations and achieves the true positive rate is 93.14% and the false positive rate is 1.14%.

Respiratory Prediction Based on Multi-Scale Temporal Convolutional Network for Tracking Thoracic Tumor Movement.

Radiotherapy is one of the important treatments for malignant tumors. The precision of radiotherapy is affected by the respiratory motion of human body, so real-time motion tracking for thoracoabdominal tumors is of great significance to improve the efficacy of radiotherapy. This paper aims to establish a highly precise and efficient prediction model, thus proposing to apply a depth prediction model composed of multi-scale enhanced convolution neural network and temporal convolutional network based on empirical mode decomposition (EMD) in respiratory prediction with different delay times. First, to enhance the precision, the unstable original sequence is decomposed into several intrinsic mode functions (IMFs) by EMD, and then, a depth prediction model of parallel enhanced convolution structure and temporal convolutional network with the characteristics specific to IMFs is built, and finally training on the respiratory motion dataset of 103 patients with malignant tumors is conducted. The prediction precision and time efficiency of the model are compared at different levels with those of the other three depth prediction models so as to evaluate the performance of the model. The result shows that the respiratory motion prediction model determined in this paper has superior prediction performance under different lengths of input data and delay time, and, furthermore, the network update time is shortened by about 60%. The method proposed in this paper will greatly improve the precision of radiotherapy and shorten the radiotherapy time, which is of great application value.

Continuous Flat Pressing of MDF Quality Control Model Framework and Collaborative Programming Approach Based on Wood Fiber Hot Pressing Mechanism.

The increasing demand for forestry resources is driving the need for smarter systems capable of saving and protecting forests that can optimize agile forestry production. This study uses the continuous hot-pressing process of wooden medium-density fiberboard (MDF) to investigate the possibility of automatic quality control of the continuous flat pressing process. For this purpose, conceptual digital twin modeling for mechanism and sequence parameter control was conducted based on the cellular automata (CA) theory. A distributed coordination mode framework was constructed, and a craft control programming method was proposed for the quality control of MDF continuous flat pressing. Based on the MDF continuous flat press craft mechanism and control standards, a framework of five distributed flat press cooperative control mode elements for the cylinder array of the continuous panel system (CPS) was defined. To satisfy the distributed distance servo and pressure servo demands of the multi-stage hot pressing craft design, five kinds of synergy collaborative control modes of multiple rack groups were constructed using mode elements: For the four types of typical deviations in slab production, i.e., thickness, slope, depression, and bulge, a multi-zone mutual cooperative mode craft control sequence was programmed. According to the type and intensity of real-time deviation, the corresponding regulation sequence was applied. This effectively counteracts the deviation caused by the uncertainty interference due to the multi-field coupling effect in actual production. The application tests demonstrate that the adjustment and response time of the continuous flat press were greatly improved, and the quality superiority rate is controlled above 95%, thereby confirming the effectiveness of the control strategy.

CACPU-Net: Channel attention U-net constrained by point features for crop type mapping.

Crop type mapping is an indispensable topic in the agricultural field and plays an important role in agricultural intelligence. In crop type mapping, most studies focus on time series models. However, in our experimental area, the images of the crop harvest stage can be obtained from single temporal remote sensing images. Only using single temporal data for crop type mapping can reduce the difficulty of dataset production. In addition, the model of single temporal crop type mapping can also extract the spatial features of crops more effectively. In this work, we linked crop type mapping with 2D semantic segmentation and designed CACPU-Net based on single-source and single-temporal autumn Sentinel-2 satellite images. First, we used a shallow convolutional neural network, U-Net, and introduced channel attention mechanism to improve the model's ability to extract spectral features. Second, we presented the Dice to compute loss together with cross-entropy to mitigate the effects of crop class imbalance. In addition, we designed the CP module to additionally focus on hard-to-classify pixels. Our experiment was conducted on BeiDaHuang YouYi of Heilongjiang Province, which mainly grows rice, corn, soybean, and other economic crops. On the dataset we collected, through the 10-fold cross-validation experiment under the 8:1:1 dataset splitting scheme, our method achieved 93.74% overall accuracy, higher than state-of-the-art models. Compared with the previous model, our improved model has higher classification accuracy on the parcel boundary. This study provides an effective end-to-end method and a new research idea for crop type mapping. The code and the trained model are available on https://github.com/mooneed/CACPU-Net.

Sparse attention double-channel FCN network for numerical analysis tracheid features in larch.

Understanding the macro-mechanical behavior of wood at the micro-scale is of great significance for the design of cell-wall-like composite materials and pulp papermaking. In order to predict tracheid mechanical properties and analyze its relationship with tracheid features, based on the FCN network model, a double-channel FCN network with sparse attention (D-SA-FCN) was designed by introducing the double-channel mechanism and the sparse attention mechanism. The features of tracheid of larch were extracted numerically and the data set was established by using the compression strength data, the gray level co-occurrence matrix, cell segmentation and geometric analysis. A feature analysis algorithm based on PCA and random forest was established to optimize the feature values. The training set accuracy of the D-SA-FCN network model reached 85.75% with the five-level mechanical property level according to the classification standard. The accuracy of the training model is 71.48% and 79.52% when the morphological and texture features are input respectively. The results show that texture features had a more significant impact on mechanics to a certain extent and the D-SA-FCN could reduce the computational complexity and improve the prediction accuracy.

NAS-HRIS: Automatic Design and Architecture Search of Neural Network for Semantic Segmentation in Remote Sensing Images.

The segmentation of high-resolution (HR) remote sensing images is very important in modern society, especially in the fields of industry, agriculture and urban modelling. Through the neural network, the machine can effectively and accurately extract the surface feature information. However, using the traditional deep learning methods requires plentiful efforts in order to find a robust architecture. In this paper, we introduce a neural network architecture search (NAS) method, called NAS-HRIS, which can automatically search neural network architecture on the dataset. The proposed method embeds a directed acyclic graph (DAG) into the search space and designs the differentiable searching process, which enables it to learn an end-to-end searching rule by using gradient descent optimization. It uses the Gumbel-Max trick to provide an efficient way when drawing samples from a non-continuous probability distribution, and it improves the efficiency of searching and reduces the memory consumption. Compared with other NAS, NAS-HRIS consumes less GPU memory without reducing the accuracy, which corresponds to a large amount of HR remote sensing imagery data. We have carried out experiments on the WHUBuilding dataset and achieved 90.44% MIoU. In order to fully demonstrate the feasibility of the method, we made a new urban Beijing Building dataset, and conducted experiments on satellite images and non-single source images, achieving better results than SegNet, U-Net and Deeplab v3+ models, while the computational complexity of our network architecture is much smaller.