Role of GM-CSF in lung balance and disease

Granulocyte-macrophage colony-stimulating factor (GM-CSF) is a hematopoietic growth factor originally identified as a stimulus that induces the differentiation of bone marrow progenitor cells into granulocytes and macrophages. GM-CSF is now considered to be a multi-origin and pleiotropic cytokine. GM-CSF receptor signals activate JAK2 and induce nuclear signals through the JAK-STAT, MAPK, PI3K, and other pathways. In addition to promoting the metabolism of pulmonary surfactant and the maturation and differentiation of alveolar macrophages, GM-CSF plays a key role in interstitial lung disease, allergic lung disease, alcoholic lung disease, and pulmonary bacterial, fungal, and viral infections. This article reviews the latest knowledge on the relationship between GM-CSF and lung balance and lung disease, and indicates that there is much more to GM-CSF than its name suggests

Correlation between microwave properties and compressive strength of engineered cementitious mortar

This article presents an application of microwave near-field technique into the cement-based material, which is designated as engineered cementitious mortar in civil material engineering. Parallel measurements of microwave near-field probing and conventional compression test were undertaken. The objective of this interdisciplinary research is to develop correlations of compressive strengths with electrical properties at microwave frequencies. First-order exponential regressions were applied to the measurement data. Simple yet accurate correlation models have been developed for strength prediction

Low-frequency metamaterial absorber with small-size unit cell based on corrugated surface

In this paper, we report the design, analysis, and simulation of the low-frequency perfect metamaterial absorber (MMA) based on corrugated surface, which has very small unit-cell size. The proposed MMA consist of a regular square-array and a metallic background plane, separated by a corrugated surface with periodic square-pillar-array. Through the optimized design, the ratios between lattice constant and resonance wavelength for nearly-perfect and high absorption MMA are close to 1/15 and 1/21, respectively. To explain the absorption mechanism of suggested structures, the surface current and electromagnetic field distributions are given. Moreover, the absorption peaks remain high with large angles of incidence for both transverse electric and transverse magnetic polarizations, which provide more efficient absorptions for oblique incident electromagnetic wave

Interaction of lncRNA Gm2044 and EEF2 promotes estradiol synthesis in ovarian follicular granulosa cells

Abstract The functions and molecular mechanisms of long noncoding RNA (lncRNA) in reproduction have been widely studied at present. However, lncRNA regulating hormone synthesis in ovarian follicular granulosa cells has not been sufficiently studied. Our previous research demonstrated that lncRNA Gm2044 could promote estradiol synthesis in follicular granulosa cells. In this study, we identified 21 binding proteins of lncRNA Gm2044 in ovarian follicles using comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS). RNA immunoprecipitation (RNA IP) and reverse transcription PCR (RT-PCR) confirmed that lncRNA Gm2044 can interact with eukaryotic translation elongation factor 2 (EEF2) protein. Furthermore, we constructed lncRNA Gm2044 knockout mice using the CRISPR/Cas9 method. Although the follicular development and fertility of female lncRNA Gm2044 knockout mice were not affected, the serum estradiol concentration in female lncRNA Gm2044 knockout mice significantly decreased. Western blotting and ELISA revealed that lncRNA Gm2044 may promote the binding of EEF2 to Nr5a1 mRNA and then enhance the Nr5a1 mRNA translation, and the upregulated NR5A1 protein can strengthen estradiol synthesis. To determine the potential signaling pathway of lncRNA Gm2044 regulating estradiol synthesis, transcriptome sequencing was performed for ovaries of adult lncRNA Gm2044 knockout mice, which identified 565 significant up-regulated genes and 303 significant down-regulated genes, which were then analyzed with Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and validated by molecular experiments. Understanding how lncRNA Gm2044/EEF2 protein regulates estradiol synthesis will help treat estrogen-related reproductive diseases

Research on Blast Furnace Tuyere Image Anomaly Detection, Based on the Local Channel Attention Residual Mechanism

The channel attention mechanism is widely used in deep learning. However, the existing channel attention mechanism directly performs the global average pooling and then full connection for all channels, which causes the local information to be ignored and the feature information cannot be reasonably assigned with the proper weights. This paper proposed a local channel attention module, based on the channel attention. This module focuses on the local information of the feature image, obtains the weight of each regional channel through convolution, and then integrates the information, so that the regional information can be fully utilized. Moreover, the local channel attention module is combined with the residual module, and the local channel attention residual network LSERNet is constructed to detect the abnormal state of the blast furnace tuyere image. With sufficient experiments on the collected datasets of the blast furnace tuyere, the results show that the proposed method can efficiently extract the feature information, and the recognition accuracy of the LSERNet model reached 98.59%. Further, our model achieved the highest accuracy, compared with SE-ResNet50, ResNet50, LSE-ResNeXt, SE-ResNeXt, and ResNeXt models

Application of Efficient Channel Attention Residual Mechanism in Blast Furnace Tuyere Image Anomaly Detection

In the steelmaking industry, the state of the blast furnace tuyere is an important basis for obtaining the internal information of the blast furnace. Traditional detections mainly rely on manual experience judgment, which is a time-consuming and tiring procedure for a human. In order to improve the efficiency of the detection, this paper is devoted to applying artificial intelligence methods to blast furnace anomaly detection. However, because of the low imaging degree of the abnormal state monitoring of the furnace mouth, the difference in the abnormal category is inconspicuous, and it is difficulty to extract the features with the existing intelligent models. To solve these problems, a novel and stable method is proposed in this paper to classify the image recognition of the abnormal state of the tuyere into one category; this is a new architecture that combines multiple technologies. For the fine-grained image classification task, an improved abnormal state recognition algorithm of the blast furnace tuyere based on the channel attention residual mechanism is proposed. In the model, the dataset is augmented by rotating it at random angles to balance the amount of data in each category; then, the residual module is used to integrate high- and low-order feature information and optimize the network; then, the multi-layer channel attention module is added based on the channel attention residual mechanism, and it obtains the optimal parameter combination of the model through k-fold cross-validation. Moreover, the number of channels was reduced by half after channel fusion, which could effectively reduce the model parameters and model complexity. It is shown in our experiments that the proposed method has an accuracy rate of 97.10% in identifying the abnormal state of the tuyere in our collection of blast furnace tuyere datasets. In order to test the performance of the proposed method, some existing models, such as SERNet, ResNeXt, and repVGG, are involved for comparison, and the proposed method has a better classification effect in comparison to them

Application of Efficient Channel Attention Residual Mechanism in Blast Furnace Tuyere Image Anomaly Detection

In the steelmaking industry, the state of the blast furnace tuyere is an important basis for obtaining the internal information of the blast furnace. Traditional detections mainly rely on manual experience judgment, which is a time-consuming and tiring procedure for a human. In order to improve the efficiency of the detection, this paper is devoted to applying artificial intelligence methods to blast furnace anomaly detection. However, because of the low imaging degree of the abnormal state monitoring of the furnace mouth, the difference in the abnormal category is inconspicuous, and it is difficulty to extract the features with the existing intelligent models. To solve these problems, a novel and stable method is proposed in this paper to classify the image recognition of the abnormal state of the tuyere into one category; this is a new architecture that combines multiple technologies. For the fine-grained image classification task, an improved abnormal state recognition algorithm of the blast furnace tuyere based on the channel attention residual mechanism is proposed. In the model, the dataset is augmented by rotating it at random angles to balance the amount of data in each category; then, the residual module is used to integrate high- and low-order feature information and optimize the network; then, the multi-layer channel attention module is added based on the channel attention residual mechanism, and it obtains the optimal parameter combination of the model through k-fold cross-validation. Moreover, the number of channels was reduced by half after channel fusion, which could effectively reduce the model parameters and model complexity. It is shown in our experiments that the proposed method has an accuracy rate of 97.10% in identifying the abnormal state of the tuyere in our collection of blast furnace tuyere datasets. In order to test the performance of the proposed method, some existing models, such as SERNet, ResNeXt, and repVGG, are involved for comparison, and the proposed method has a better classification effect in comparison to them

MicroRNA-20b promotes the accumulation of CD11b+Ly6G+Ly6C low myeloid-derived suppressor cells in asthmatic mice

miR-20b is a member of the miR-106a-363 gene cluster, which has been shown to play an important role in a variety of diseases, including cancer, inflammation, and autoimmune diseases. Our previous study indicated that miR-20b has an inhibitory effect on airway inflammation in asthmatic mice, but the exact mechanism is unclear. In this study, we report that the ratio of CD11b+Ly6G+Ly6C low cells, but not the amount of CD11b+Ly6C+Ly6G– cells, was increased in the lung tissue of asthmatic mice after intranasal instillation with miR-20b mimics, while Th2-type cytokines (interleukin (IL)-4 and IL-13) were significantly decreased in the bronchoalveolar lavage fluid. In addition, the transcription factor CREB regulated the expression of miR-20b. Our findings suggest that miR-20b can induce the accumulation of myeloid-derived suppressor cells in the lungs of asthmatic mice, which may be a mechanism by which miR-20b inhibits airway inflammation in asthmatic mice. Thus, miR-20b may be used as a target for the effective treatment of asthma in the future

Additional file 1 of LINC02086 promotes cell viability and inhibits cell apoptosis in breast cancer by sponging miR-6757-5p and up-regulating EPHA2

Additional file 1: Figure S1. LINC02086 overexpression promotes cell viability and prohibits cell apoptosis. A, lentivirus-mediated LINC02086 overexpression in MDA-MB-231 cells; B, cell viability; C, cell apoptosis detected by flow cytometery. ***P<0.001 vs. vector