An Airfield Minimum Operating Strip Selection Method Based on the TOPSIS Method

With Multiple Attribute Decision Making (MADM) method, the selection of airfield Minimum Operating Strip (MOS) schemes is researched. The influencing factors of the selection decision making are analyzed. The weight values of these factors are determined. Then the decision making optimization method based on TOPSIS is presented. At last, the feasibility of the method is validated with a calculation example

DPSA: Dense pixelwise spatial attention network for hatching egg fertility detection

© 2020 SPIE and IS & T. Deep convolutional neural networks show a good prospect in the fertility detection and classification of specific pathogen-free hatching egg embryos in the production of avian influenza vaccine, and our previous work has mainly investigated three factors of networks to push performance: depth, width, and cardinality. However, an important problem that feeble embryos with weak blood vessels interfering with the classification of resilient fertile ones remains. Inspired by fine-grained classification, we introduce the attention mechanism into our model by proposing a dense pixelwise spatial attention module combined with the existing channel attention through depthwise separable convolutions to further enhance the network class-discriminative ability. In our fused attention module, depthwise convolutions are used for channel-specific features learning, and dilated convolutions with different sampling rates are adopted to capture spatial multiscale context and preserve rich detail, which can maintain high resolution and increase receptive fields simultaneously. The attention mask with strong semantic information generated by aggregating outputs of the spatial pyramid dilated convolution is broadcasted to low-level features via elementwise multiplications, serving as a feature selector to emphasize informative features and suppress less useful ones. A series of experiments conducted on our hatching egg dataset show that our attention network achieves a lower misjudgment rate on weak embryos and a more stable accuracy, which is up to 98.3% and 99.1% on 5-day and 9-day old eggs, respectively

Urban PM2.5 concentration prediction via attention-based CNN–LSTM.

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN-LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coecient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance

Growth of single-walled carbon nanotubes from well-defined POSS nanoclusters structure

High-quality single-walled carbon nanotubes (SWNTs) with narrow diameter distribution can be generated from well-defined Si8O12 nanoclusters structure which form from thermal decomposition of chemically modified polyhedral oligomeric silsesquioxane (POSS). The nanosized SixOy particles were proved to be responsible for the SWNT growth and believed to be the reason for the narrow diameter distribution of the as-grown SWNTs. This could be extended to other POSS. The SWNTs grown from the nanosized SixOy particles were found to be semiconducting enriched SWNTs (s-SWNTs). A facile patterning technology, direct photolithography, was developed for generating SWNT pattern, which is compatible to industrial-level fabrication of SWNTs pattern for device applications. The metal-free growth together with preferential growth of s-SWNTs and patterning in large scale from the structure-defined silicon oxide nanoclusters not only represent a big step toward the control growth of SWNTs and fabrication of devices for applications particularly in nanoelectronics and biomedicine but also provide a system for further studying and understanding the growth mechanism of SWNTs from nanosized materials and the relationship between the structure of SWNT and nonmetal catalysts

Comprehensive Network Analysis Reveals Alternative Splicing-Related lncRNAs in Hepatocellular Carcinoma

© Copyright © 2020 Wang, Wang, Bhat, Chen, Xu, Mo, Yi and Zhou. It is increasingly appreciated that long non-coding RNAs (lncRNAs) associated with alternative splicing (AS) could be involved in aggressive hepatocellular carcinoma. Although many recent studies show the alteration of RNA alternative splicing by deregulated lncRNAs in cancer, the extent to which and how lncRNAs impact alternative splicing at the genome scale remains largely elusive. We analyzed RNA-seq data obtained from 369 hepatocellular carcinomas (HCCs) and 160 normal liver tissues, quantified 198,619 isoform transcripts, and identified a total of 1,375 significant AS events in liver cancer. In order to predict novel AS-associated lncRNAs, we performed an integration of co-expression, protein-protein interaction (PPI) and epigenetic interaction networks that links lncRNA modulators (such as splicing factors, transcript factors, and miRNAs) along with their targeted AS genes in HCC. We developed a random walk-based multi-graphic (RWMG) model algorithm that prioritizes functional lncRNAs with their associated AS targets to computationally model the heterogeneous networks in HCC. RWMG shows a good performance evaluated by the ROC curve based on cross-validation and bootstrapping strategies. As a conclusion, our robust network-based framework has derived 31 AS-related lncRNAs that not only validates known cancer-associated cases MALAT1 and HOXA11-AS, but also reveals new players such as DNM1P35 and DLX6-AS1with potential functional implications. Survival analysis further provides insights into the clinical significance of identified lncRNAs

B7H3 ameliorates LPS-induced acute lung injury via attenuation of neutrophil migration and infiltration

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are characterized by an excessive inflammatory response within the lungs and severely impaired gas exchange resulting from alveolar-capillary barrier disruption and pulmonary edema. The costimulatory protein B7H3 functions as both a costimulator and coinhibitor to regulate the adaptive and innate immune response, thus participating in the development of microbial sepsis and pneumococcal meningitis. However, it is unclear whether B7H3 exerts a beneficial or detrimental role during ALI. In the present study we examined the impact of B7H3 on pulmonary inflammatory response, polymorphonuclear neutrophil (PMN) influx, and lung tissue damage in a murine model of lipopolysaccharide (LPS)-induced direct ALI. Treatment with B7H3 protected mice against LPS-induced ALI, with significantly attenuated pulmonary PMN infiltration, decreased lung myeloperoxidase (MPO) activity, reduced bronchoalveolar lavage fluid (BALF) protein content, and ameliorated lung pathological changes. In addition, B7H3 significantly diminished LPS-stimulated PMN chemoattractant CXCL2 production by inhibiting NF-kappa B p65 phosphorylation, and substantially attenuated LPS-induced PMN chemotaxis and transendothelial migration by down-regulating CXCR2 and Mac-1 expression. These results demonstrate that B7H3 substantially ameliorates LPS-induced ALI and this protection afforded by B7H3 is predominantly associated with its inhibitory effect on pulmonary PMN migration and infiltration

Construction of lncRNA and mRNA co-expression network associated with nasopharyngeal carcinoma progression

Nasopharyngeal carcinoma is a type of head and neck cancer with a high incidence in men. In the past decades, the survival rate of NPC has remained around 70%, but it often leads to treatment failure due to its distant metastasis or recurrence. The lncRNA-mRNA regulatory network has not been fully elucidated. We downloaded the NPC-related gene expression datasets GSE53819 and GSE12452 from the Gene Expression Omnibus database; GSE53819 included 18 NPC tissues and 18 normal tissues, and GSE12452 included 31 NPC tissues and 10 normal tissues. Weighted gene co-expression network analysis was performed on mRNA and lncRNA to screen out modules that were highly correlated with tumor progression. The two datasets were subjected to differential analysis after removing batch effects, and then Venn diagrams were used to screen for overlapping genes in the module genes and differential genes. The lncRNA-mRNA co-expression network was then constructed, and key mRNAs were identified by MCODE analysis and expression analysis. GSEA analysis and qRT-PCR were performed on key mRNAs. Through a series of analyses, we speculated that BTK, CD72, PTPN6, and VAV1 may be independent predictors of the prognosis of NPC patients.Taken together, our study provides potential candidate biomarkers for NPC diagnosis, prognosis, or precise treatment