Intelligent Detection of Road Cracks Based on Improved YOLOv5

With the gradual increase of highway coverage, the frequency of road cracks also increases, which brings a series of security risks. It is necessary to detect road cracks, but the traditional detection method is inefficient and unsafe. In this paper, deep learning is used to detect road cracks, and an improved model BiTrans-YOLOv5 is proposed. We add Swin Transformer to YOLOv5s to replace the original C3 module, and explore the performance of Transformer in the field of road crack detection. We also change the original PANet of YOLOv5s into a bidirectional feature pyramid network (BIFPN), which can detect small targets more accurately. Experiments on the data set Road Damage show that BiTrans-YOLOv5 has improved in Precision, Recall, F1 score and [email protected] compared with YOLOv5s, among which [email protected] has improved by 5.4%. It is proved that BiTrans-YOLOv5 has better performance in road detection projects

ABCG2 is associated with HER-2 Expression, lymph node metastasis and clinical stage in breast invasive ductal carcinoma

<p>Abstract</p> <p>Background</p> <p>ABCG2 is an ABC transporter. It has been demonstrated that endogenous ABCG2 expression in certain cancers is a possible reflection of the differentiated phenotype of the cell of origin and likely contributes to intrinsic drug resistance. But little is known about the contribution of ABCG2 to the drug resistance and the clinicopathological characteristics in breast cancer. In the present study, we investigated the expression of ABCG2 and the correlations between ABCG2 expression and patients' clinicopathological and biological characteristics.</p> <p>Methods</p> <p>Immunohistochemistry was employed on the tissue microarray paraffin sections of surgically removed samples from 196 breast cancer patients with clinicopathological data.</p> <p>Results</p> <p>The results showed that ABCG2 was expressed in different intensities and distributions in the tumor cells of the breast invasive ductal carcinoma. A positive stain for ABCG2 was defined as a brown stain observed in the cytoplasm and cytomembrane. A statistically significant correlation was demonstrated between ABCG2 expression and HER-2 expression (p = 0.001), lymph node metastasis (p = 0.049), and clinical stage (p = 0.015) respectively.</p> <p>Conclusion</p> <p>ABCG2 correlated with Her-2 expression, lymph node metastasis and clinical stage in breast invasive ductal carcinoma. It could be a novel potential bio-marker which can predict biological behavior, clinical progression, prognosis and chemotherapy effectiveness.</p

A double-layer Huygens’ metasurface with complete phase coverage and its dual-polarized meta-lens antenna application

A novel double-layer Huygens’ unit with dual-polarized response and complete phase coverage has been proposed for highly efficient meta-lens antenna application. The designed unit is a symmetric structure in which the double-layer metallic patterns not only act as the electric dipoles but also induce the magnetic dipoles. The balance between the initial electric dipole and the induced magnetic dipole makes the impedance of the metasurface match with that of free space, thus allowing efficient transmission of electromagnetic waves. Moreover, full transmission phase shift and near-unit transmission amplitude can be achieved simultaneously by change the structural parameters of the unit. Utilizing such electromagnetic properties, a dual-polarized meta-lens consisting of 35 × 35 units with size of 161 × 161 mm2 is designed, fabricated and measured. The results exhibit its good radiation performance. The maximum gain at a frequency of 28 GHz reaches 30.8 dBi, with an aperture efficiency of 42.3%. The 3-dB gain bandwidth reaches 12.5%, covering the frequency range of 26.7–30.2 GHz. The simple structure of the designed dual-polarized metasurface, high gain and high antenna efficiency make it an important antenna engineering role

Argo Lite: Open-Source Interactive Graph Exploration and Visualization in Browsers

Graph data have become increasingly common. Visualizing them helps people better understand relations among entities. Unfortunately, existing graph visualization tools are primarily designed for single-person desktop use, offering limited support for interactive web-based exploration and online collaborative analysis. To address these issues, we have developed Argo Lite, a new in-browser interactive graph exploration and visualization tool. Argo Lite enables users to publish and share interactive graph visualizations as URLs and embedded web widgets. Users can explore graphs incrementally by adding more related nodes, such as highly cited papers cited by or citing a paper of interest in a citation network. Argo Lite works across devices and platforms, leveraging WebGL for high-performance rendering. Argo Lite has been used by over 1,000 students at Georgia Tech's Data and Visual Analytics class. Argo Lite may serve as a valuable open-source tool for advancing multiple CIKM research areas, from data presentation, to interfaces for information systems and more.Comment: CIKM'20 Resource Track (October 19-23, 2020), 6 pages, 6 figure

Construction and characterization of mice with conditional knockout of Stat3 gene in microglia

Objective·To construct mice with conditional knockout of Stat3 gene in microglia based on the Cre-Loxp system and validate their knockout efficiency.Methods·Cx3cr1creERT2 and Stat3fl/fl genotypic mice were bred for conditional knockout mice (CKO) and Wild Type mice (WT). The mouse genotypes were determined by extracting DNA from mouse tissues through Polymerase Chain Reaction (PCR) combined with the amplification results of cre and flox primers. Stat3 knockdown was induced by intraperitoneal injection of tamoxifen in the CKO and WT mice at 6 weeks of age. The CKO mice (n=4) and WT mice (n=4) were randomly selected for the detection. After two weeks of observation, microglia cells were sorted out by Magnetic Activated Cell Sorting (MACS). Real-time PCR (RT-PCR) was used to detect gene knockout efficiency at the gene level. The expression of STAT3 in microglia was observed by brain immunofluorescence staining. The expression rate of STAT3 in microglia was detected by flow cytometry. The expression rate of STAT3 in macrophages of the spleen was detected by flow cytometry. The condition of neuronal cells was examined by Nissl staining. The condition of the microglia in the cortex and hippocampus was observed by brain immunofluorescence staining. The phenotype of the microglia was detected by flow cytometry.Results·The CKO mice and WT mice were successfully bred. MACS boosted the proportion of microglia in brain cells from 10% to 85%. RT-PCR results showed that mRNA levels of Stat3 were down-regulated in microglia of CKO mice, compared with the WT mice (P=0.001). The relative mRNA expression of Stat3 in microglia of the CKO mice was 0.331 7±0.041 4. Immunofluorescence staining of brain tissues showed that the fluorescence intensity of STAT3 in microglia of the CKO mice was weaker than that of the WT mice. Flow cytometry of brain tissues showed that the STAT3-positive cells in microglia of the WT mice was (85.30±5.69)% and the CKO mice was (39.70±3.88)%. STAT3 expression was decreased in microglia of the CKO mice (P=0.001). Flow cytometry of spleen tissues showed that there was no statistical difference in the percentage of STAT3-positive cells in splenic macrophages between the CKO and WT mice (P>0.05). Nissl staining showed that there were no significant differences between the neuronal cells of the CKO mice and WT mice. Immunofluorescencestaining of brain tissues showed that there was no significant difference in the shape of microglia between the CKO mice and WT mice. Flow cytometry showed that the phenotype of microglia in the CKO mice was not remarkably different from that of the WT mice.Conclusion·We successfully construct the STAT3 gene conditional knockout mice from microglia, which provides the foundation for subsequent related studies

Estimation of the Age and Amount of Brown Rice Plant Hoppers Based on Bionic Electronic Nose Use

The brown rice plant hopper (BRPH), Nilaparvata lugens (Stal), is one of the most important insect pests affecting rice and causes serious damage to the yield and quality of rice plants in Asia. This study used bionic electronic nose technology to sample BRPH volatiles, which vary in age and amount. Principal component analysis (PCA), linear discrimination analysis (LDA), probabilistic neural network (PNN), BP neural network (BPNN) and loading analysis (Loadings) techniques were used to analyze the sampling data. The results indicate that the PCA and LDA classification ability is poor, but the LDA classification displays superior performance relative to PCA. When a PNN was used to evaluate the BRPH age and amount, the classification rates of the training set were 100% and 96.67%, respectively, and the classification rates of the test set were 90.67% and 64.67%, respectively. When BPNN was used for the evaluation of the BRPH age and amount, the classification accuracies of the training set were 100% and 48.93%, respectively, and the classification accuracies of the test set were 96.67% and 47.33%, respectively. Loadings for BRPH volatiles indicate that the main elements of BRPHs’ volatiles are sulfur-containing organics, aromatics, sulfur-and chlorine-containing organics and nitrogen oxides, which provide a reference for sensors chosen when exploited in specialized BRPH identification devices. This research proves the feasibility and broad application prospects of bionic electronic noses for BRPH recognition

Biomimetic three-dimensional glioma model printed in vitro for the studies of glioma cells and neurons interactions

The interactions between glioma cells and neurons are important for glioma progression but are rarely mimicked and recapitulated in in vitro three-dimensional (3D) models, which may affect the success rate of relevant drug research and development. In this study, an in vitro bioprinted 3D glioma model consisting of an outer hemispherical shell with neurons and an inner hemisphere with glioma cells is proposed to simulate the natural glioma. This model was produced by extrusion-based 3D bioprinting technology. The cells survival rate, morphology, and intercellular Ca2+ concentration studies were carried out up to 5 days of culturing. It was found that neurons could promote the proliferation of glioma cells around them, associate the morphological changes of glioma cells to be neuron-like, and increase the expression of intracellular Ca2+ of glioma cells. Conversely, the presence of glioma cells could maintain the neuronal survival rate and promote the neurite outgrowth. The results indicated that glioma cells and neurons facilitated each other implying a symbiotic pattern established between two types of cells during the early stage of glioma development, which were seldom found in the present artificial glioma models. The proposed bioprinted glioma model can mimic the natural microenvironment of glioma tissue, provide an in-depth understanding of cellâ cell interactions, and enable pathological and pharmacological studies of glioma.The work was supported by the Program of the National Natural Science Foundation of China [52275291], [51675411], [81972359], the Fundamental Research Funds for the Central Universities, and the Youth Innovation Team of Shaanxi Universities

Crop pest image classification based on improved densely connected convolutional network

IntroductionCrop pests have a great impact on the quality and yield of crops. The use of deep learning for the identification of crop pests is important for crop precise management.MethodsTo address the lack of data set and poor classification accuracy in current pest research, a large-scale pest data set named HQIP102 is built and the pest identification model named MADN is proposed. There are some problems with the IP102 large crop pest dataset, such as some pest categories are wrong and pest subjects are missing from the images. In this study, the IP102 data set was carefully filtered to obtain the HQIP102 data set, which contains 47,393 images of 102 pest classes on eight crops. The MADN model improves the representation capability of DenseNet in three aspects. Firstly, the Selective Kernel unit is introduced into the DenseNet model, which can adaptively adjust the size of the receptive field according to the input and capture target objects of different sizes more effectively. Secondly, in order to make the features obey a stable distribution, the Representative Batch Normalization module is used in the DenseNet model. In addition, adaptive selection of whether to activate neurons can improve the performance of the network, for which the ACON activation function is used in the DenseNet model. Finally, the MADN model is constituted by ensemble learning.ResultsExperimental results show that MADN achieved an accuracy and F1Score of 75.28% and 65.46% on the HQIP102 data set, an improvement of 5.17 percentage points and 5.20 percentage points compared to the pre-improvement DenseNet-121. Compared with ResNet-101, the accuracy and F1Score of MADN model improved by 10.48 percentage points and 10.56 percentage points, while the parameters size decreased by 35.37%. Deploying models to cloud servers with mobile application provides help in securing crop yield and quality