Environmental pollution caused by plastics has become a public health problem. However, the effect of microplastics on gut microbiota, inflammation development and their underlying mechanisms are not ...well characterized. In the present study, we assessed the effect of exposure to different amounts of polyethylene microplastics (6, 60, and 600 μg/day for 5 consecutive weeks) in a C57BL/6 mice model. Treatment with a high concentration of microplastics increased the numbers of gut microbial species, bacterial abundance, and flora diversity. Feeding groups showed a significant increase in Staphylococcus abundance alongside a significant decrease in Parabacteroides abundance, as compared to the blank (untreated) group. In addition, serum levels of interleukin-1α in all feeding groups were significantly greater than that in the blank group. Of note, treatment with microplastics decreased the percentage of Th17 and Treg cells among CD4+ cells, while no significant difference was observed between the blank and treatment groups with respect to the Th17/Treg cell ratio. The intestine (colon and duodenum) of mice fed high-concentration microplastics showed obvious inflammation and higher TLR4, AP-1, and IRF5 expression. Thus, polyethylene microplastics can induce intestinal dysbacteriosis and inflammation, which provides a theoretical basis for the prevention and treatment of microplastics-related diseases.
•Polyethylene microplastics affected the composition and diversity of gut microbiota.•Polyethylene microplastics increased the secretion of IL-1α in serum.•Polyethylene microplastics decreased the Th17 and Treg cells among CD4+ cells.•High-concentration polyethylene microplastics induced small intestinal inflammation.
Abstract
Computed radiology system (CR) test technology has been applied in the testing of industrial welding defect after the promulgation and implementation of digital radiographic imaging test ...standards. The performance of CR system directly affects the testing rate of welding defects. Therefore, testing and evaluating the performance of CR system becomes the premise and guarantee for the application of CR system. In this paper, based on the testing image of CR phantom and the simulation of scanner dither results, an algorithm combining fault-tolerant voting and improved point-based hoof transform is proposed to test the performance index of scanner dither in CR system. Experimental results show that the proposed algorithm can test not only the row and column dithering, but also the subtle arc dithering. Compared with standard Hough transform algorithm, this algorithm has higher accuracy and can avoid the influence of other image indicators around the T-shaped image quality indicator used in CR phantom.
Traditional pixel-based semantic segmentation methods for road extraction take each pixel as the recognition unit. Therefore, they are constrained by the restricted receptive field, in which pixels ...do not receive global road information. These phenomena greatly affect the accuracy of road extraction. To improve the limited receptive field, a non-local neural network is generated to let each pixel receive global information. However, its spatial complexity is enormous, and this method will lead to considerable information redundancy in road extraction. To optimize the spatial complexity, the Crisscross Network (CCNet), with a crisscross shaped attention area, is applied. The key aspect of CCNet is the Crisscross Attention (CCA) module. Compared with non-local neural networks, CCNet can let each pixel only perceive the correlation information from horizontal and vertical directions. However, when using CCNet in road extraction of remote sensing (RS) images, the directionality of its attention area is insufficient, which is restricted to the horizontal and vertical direction. Due to the recurrent mechanism, the similarity of some pixel pairs in oblique directions cannot be calculated correctly and will be intensely dilated. To address the above problems, we propose a special attention module called the Dual Crisscross Attention (DCCA) module for road extraction, which consists of the CCA module, Rotated Crisscross Attention (RCCA) module and Self-adaptive Attention Fusion (SAF) module. The DCCA module is embedded into the Dual Crisscross Network (DCNet). In the CCA module and RCCA module, the similarities of pixel pairs are represented by an energy map. In order to remove the influence from the heterogeneous part, a heterogeneous filter function (HFF) is used to filter the energy map. Then the SAF module can distribute the weights of the CCA module and RCCA module according to the actual road shape. The DCCA module output is the fusion of the CCA module and RCCA module with the help of the SAF module, which can let pixels perceive local information and eight-direction non-local information. The geometric information of roads improves the accuracy of road extraction. The experimental results show that DCNet with the DCCA module improves the road IOU by 4.66% compared to CCNet with a single CCA module and 3.47% compared to CCNet with a single RCCA module.
Introduction
The N-methyl-D-aspartate receptor (NMDAR) plays a critical role in synaptic transmission and is associated with various neurological and psychiatric disorders. Recently, a novel form of ...postsynaptic plasticity known as NMDAR-based short-term postsynaptic plasticity (STPP) has been identified. It has been suggested that long-lasting glutamate binding to NMDAR allows for the retention of input information in brain slices up to 500 ms, leading to response facilitation. However, the impact of STPP on the dynamics of neuronal populations remains unexplored.
Methods
In this study, we incorporated STPP into a continuous attractor neural network (CANN) model to investigate its effects on neural information encoding in populations of neurons. Unlike short-term facilitation, a form of presynaptic plasticity, the temporally enhanced synaptic efficacy resulting from STPP destabilizes the network state of the CANN by increasing its mobility.
Results
Our findings demonstrate that the inclusion of STPP in the CANN model enables the network state to predictively respond to a moving stimulus. This nontrivial dynamical effect facilitates the tracking of the anticipated stimulus, as the enhanced synaptic efficacy induced by STPP enhances the system's mobility.
Discussion
The discovered STPP-based mechanism for sensory prediction provides valuable insights into the potential development of brain-inspired computational algorithms for prediction. By elucidating the role of STPP in neural population dynamics, this study expands our understanding of the functional implications of NMDAR-related plasticity in information processing within the brain.
Conclusion
The incorporation of STPP into a CANN model highlights its influence on the mobility and predictive capabilities of neural networks. These findings contribute to our knowledge of STPP-based mechanisms and their potential applications in developing computational algorithms for sensory prediction.
Chitin, a major component of the fungal cell wall, triggers plant innate immunity in
a receptor complex including two major lysin motif receptor-like kinases, AtLYK5, and AtCERK1. Although AtLYK5 has ...been proposed to be a major chitin-binding receptor, the pseudokinase domain of AtLYK5 is required to mediate chitin-triggered immune responses in plants. In this study, 48 AtLYK5-interacting proteins were identified using immunoprecipitation and mass spectrometry assay. Among them,
CALCIUM-DEPENDENT PROTEIN KINASE 5 (AtCPK5) is a protein kinase interacting with both AtLYK5 and AtCERK1. Chitin-induced immune responses are inhibited in both
and
mutant plants. AtLYK5 and AtLYK4 but not AtCERK1 are phosphorylated by AtCPK5 and AtCPK6
. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and
kinase assay identified that Ser-323 and Ser-542 of AtLYK5 are important phosphorylation residues by AtCPK5. Transgenic
expressing either AtLYK5-S323A or AtLYK5-S542A in the
mutant only partially rescue the defects in chitin-triggered MPK3/MPK6 phosphorylation. Overexpression of AtCPK5 could increase AtCERK1 protein level after chitin treatment. These data proposed a model in which AtCPK5 directly phosphorylates AtLYK5 and regulates chitin-induced defense responses in
.
The performance of semantic segmentation in remote sensing, based on deep learning models, depends on the training data. A commonly encountered issue is the imbalanced long-tailed distribution of ...data, where the head classes contain the majority of samples while the tail classes have fewer samples. When training with long-tailed data, the head classes dominate the training process, resulting in poorer performance in the tail classes. To address this issue, various strategies have been proposed, such as resampling, reweighting, and transfer learning. However, common resampling methods suffer from overfitting to the tail classes while underfitting the head classes, and reweighting methods are limited in the extreme imbalanced case. Additionally, transfer learning tends to transfer patterns learned from the head classes to the tail classes without rigorously validating its generalizability. These methods often lack additional information to assist in the recognition of tail class objects, thus limiting performance improvements and constraining generalization ability. To tackle the abovementioned issues, a graph neural network based on the graph kernel principle is proposed for the first time. By leveraging the graph kernel, structural information for tail class objects is obtained, serving as additional contextual information beyond basic visual features. This method partially compensates for the imbalance between tail and head class object information without compromising the recognition accuracy of head classes objects. The experimental results demonstrate that this study effectively addresses the poor recognition performance of small and rare targets, partially alleviates the issue of spectral confusion, and enhances the model’s generalization ability.
Staphylococcus aureus, a commensal bacterium, colonizes the skin and mucous membranes of approximately 30% of the human population. Apart from conventional resistance mechanisms, one of the ...pathogenic features of S. aureus is its ability to survive in a biofilm state on both biotic and abiotic surfaces. Due to this characteristic, S. aureus is a major cause of human infections, with Methicillin-Resistant Staphylococcus aureus (MRSA) being a significant contributor to both community-acquired and hospital-acquired infections. Analyzing non-repetitive clinical isolates of MRSA collected from seven provinces and cities in China between 2014 and 2020, it was observed that 53.2% of the MRSA isolates exhibited varying degrees of ability to produce biofilm. The biofilm positivity rate was notably high in MRSA isolates from Guangdong, Jiangxi, and Hubei. The predominant MRSA strains collected in this study were of sequence types ST59, ST5, and ST239, with the biofilm-producing capability mainly distributed among moderate and weak biofilm producers within these ST types. Notably, certain sequence types, such as ST88, exhibited a high prevalence of strong biofilm-producing strains. The study found that SCCmec IV was the predominant type among biofilm-positive MRSA, followed by SCCmec II. Comparing strains with weak and strong biofilm production capabilities, the positive rates of the sdrD and sdrE were higher in strong biofilm producers. The genetic determinants ebp, icaA, icaB, icaC, icaD, icaR, and sdrE were associated with strong biofilm production in MRSA. Additionally, biofilm-negative MRSA isolates showed higher sensitivity rates to cefalotin (94.8%), daptomycin (94.5%), mupirocin (86.5%), teicoplanin (94.5%), fusidic acid (81.0%), and dalbavancin (94.5%) compared to biofilm-positive MRSA isolates. The biofilm positivity rate was consistently above 50% in all collected specimen types. MRSA strains with biofilm production capability warrant increased vigilance.
Accurate and rapid identification of mineral foam flotation states can increase mineral utilization and reduce the consumption of reagents. The traditional flotation process concentrates on ...extracting foam features from a single-modality foam image, and the accuracy is undesirable once problems such as insufficient image clarity or poor foam boundaries are encountered. In this work, a classification method based on multi-modality image fusion and CNN-PCA-SVM is proposed for work condition recognition of visible and infrared gray foam images. Specifically, the visible and infrared gray images are fused in the non-subsampled shearlet transform (NSST) domain using the parameter adaptive pulse coupled neural network (PAPCNN) method and the image quality detection method for high and low frequencies, respectively. The convolution neural network (CNN) is used as a trainable feature extractor to process the fused foam images, the principal component analysis (PCA) reduces feature data, and the support vector machine (SVM) is used as a recognizer to classify the foam flotation condition. After experiments, this model can fuse the foam images and recognize the flotation condition classification with high accuracy.
In deep neural network model training and prediction, due to the limitation of GPU memory and computing resources, massive image data must be cropped into limited-sized samples. Moreover, in order to ...improve the generalization ability of the model, the samples need to be randomly distributed in the experimental area. Thus, the background information is often incomplete or even missing. On this condition, a knowledge graph must be applied to the semantic segmentation of remote sensing. However, although a single sample contains only a limited number of geographic categories, the combinations of geographic objects are diverse and complex in different samples. Additionally, the involved categories of geographic objects often span different classification system branches. Therefore, existing studies often directly regard all the categories involved in the knowledge graph as candidates for specific sample segmentation, which leads to high computation cost and low efficiency. To address the above problems, a parallel walking algorithm based on cross modality information is proposed for the scene graph—knowledge graph matching (PWGM). The algorithm uses a graph neural network to map the visual features of the scene graph into the semantic space of the knowledge graph through anchors and designs a parallel walking algorithm of the knowledge graph that takes into account the visual features of complex scenes. Based on the algorithm, we propose a semantic segmentation model for remote sensing. The experiments demonstrate that our model improves the overall accuracy by 3.7% compared with KGGAT (which is a semantic segmentation model using a knowledge graph and graph attention network (GAT)), by 5.1% compared with GAT and by 13.3% compared with U-Net. Our study not only effectively improves the recognition accuracy and efficiency of remote sensing objects, but also offers useful exploration for the development of deep learning from a data-driven to a data-knowledge dual drive.
Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models ...fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.
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