Design of operational application system of cruise missile supported by meteorological and marine information

In information war, the concept of “digital battlefield” is put forward, and battlefield meteorological and marine information has become an important part of digital battlefield. Based on the in-depth analysis of the impact of meteorological marine information on cruise missile, this paper puts forward the multi-dimensional demand analysis method of meteorological marine information supporting cruise missile combat application and the architecture design method based on MBSE, and gives the conceptual design framework of combat application system to provide information support for cruise missile combat application

A Deep Learning Semantic Segmentation Method for Landslide Scene Based on Transformer Architecture

Semantic segmentation technology based on deep learning has developed rapidly. It is widely used in remote sensing image recognition, but is rarely used in natural disaster scenes, especially in landslide disasters. After a landslide disaster occurs, it is necessary to quickly carry out rescue and ecological restoration work, using satellite data or aerial photography data to quickly analyze the landslide area. However, the precise location and area estimation of the landslide area is still a difficult problem. Therefore, we propose a deep learning semantic segmentation method based on Encoder-Decoder architecture for landslide recognition, called the Separable Channel Attention Network (SCANet). The SCANet consists of a Poolformer encoder and a Separable Channel Attention Feature Pyramid Network (SCA-FPN) decoder. Firstly, the Poolformer can extract global semantic information at different levels with the help of transformer architecture, and it greatly reduces computational complexity of the network by using pooling operations instead of a self-attention mechanism. Secondly, the SCA-FPN we designed can fuse multi-scale semantic information and complete pixel-level prediction of remote sensing images. Without bells and whistles, our proposed SCANet outperformed the mainstream semantic segmentation networks with fewer model parameters on our self-built landslide dataset. The mIoU scores of SCANet are 1.95% higher than ResNet50-Unet, especially

SAR image quality assessment of satellite TH-2-01

The satellite TH-2-01 was successfully launched on April 30, 2019, equipped with the high-resolution synthetic aperture radar(SAR) and the high-precision inter-satellite relative state measurement equipment and other payloads, which was able to obtain all-day and all-weather InSAR image data and auxiliary measurement data for scientific experimental research, land and resources census, geographic information mapping and other fields. In order to measure the imaging capability of the satellite, we comprehensively evaluated and analyzed the satellite A and satellite B of TH-2-01 by employing objective quality evaluation indexes based on point target and area target. The analysis results prove that the imaging quality of both satellite A and satellite B are able to meet the requirements of the satellite design index, which means that the images of satellite TH-2-01 can meet high-accuracy in the application of target area selection and analysis, target identification, target feature extraction, etc. Most of the objective indexes of satellite A and satellite B are similar, and the spatial resolution in the range direction is better than that in the azimuth direction, and the values of the range direction integral side lobe ratio and the peak side lobe ratio are better than that in the azimuth direction, that is, the more concentrated the energy of the point targets is in the range direction, the better ability to detect the dark target and the weak target from the radar image. The experimental results show, the images of satellite A has lower peak side lobe ratio in the range direction, that is, satellite A is slightly better than satellite B in range direction imaging quality

Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library

Abstract Spectrum matching is the most common method for compound identification in mass spectrometry (MS). However, some challenges limit its efficiency, including the coverage of spectral libraries, the accuracy, and the speed of matching. In this study, a million-scale in-silico EI-MS library is established. Furthermore, an ultra-fast and accurate spectrum matching (FastEI) method is proposed to substantially improve accuracy using Word2vec spectral embedding and boost the speed using the hierarchical navigable small-world graph (HNSW). It achieves 80.4% recall@10 accuracy (88.3% with 5 Da mass filter) with a speedup of two orders of magnitude compared with the weighted cosine similarity method (WCS). When FastEI is applied to identify the molecules beyond NIST 2017 library, it achieves 50% recall@1 accuracy. FastEI is packaged as a standalone and user-friendly software for common users with limited computational backgrounds. Overall, FastEI combined with a million-scale in-silico library facilitates compound identification as an accurate and ultra-fast tool

Tranilast directly targets NLRP3 to treat inflammasome‐driven diseases

Abstract The dysregulation of NLRP3 inflammasome can cause uncontrolled inflammation and drive the development of a wide variety of human diseases, but the medications targeting NLRP3 inflammasome are not available in clinic. Here, we show that tranilast (TR), an old anti‐allergic clinical drug, is a direct NLRP3 inhibitor. TR inhibits NLRP3 inflammasome activation in macrophages, but has no effects on AIM2 or NLRC4 inflammasome activation. Mechanismly, TR directly binds to the NACHT domain of NLRP3 and suppresses the assembly of NLRP3 inflammasome by blocking NLRP3 oligomerization. In vivo experiments show that TR has remarkable preventive or therapeutic effects on the mouse models of NLRP3 inflammasome‐related human diseases, including gouty arthritis, cryopyrin‐associated autoinflammatory syndromes, and type 2 diabetes. Furthermore, TR is active ex vivo for synovial fluid mononuclear cells from patients with gout. Thus, our study identifies the old drug TR as a direct NLRP3 inhibitor and provides a potentially practical pharmacological approach for treating NLRP3‐driven diseases

Fusion of Quality Evaluation Metrics and Convolutional Neural Network Representations for ROI Filtering in LC–MS

Region of interest (ROI) extraction is a fundamental step in analyzing metabolomic datasets acquired by liquid chromatography–mass spectrometry (LC–MS). However, noises and backgrounds in LC–MS data often affect the quality of extracted ROIs. Therefore, developing effective ROI evaluation algorithms is necessary to eliminate false positives meanwhile keep the false-negative rate as low as possible. In this study, a deep fused filter of ROIs (dffROI) was proposed to improve the accuracy of ROI extraction by combining the handcrafted evaluation metrics with convolutional neural network (CNN)-learned representations. To evaluate the performance of dffROI, dffROI was compared with peakonly (CNN-learned representation) and five handcrafted metrics on three LC–MS datasets and a gas chromatography–mass spectrometry (GC–MS) dataset. Results show that dffROI can achieve higher accuracy, better true-positive rate, and lower false-positive rate. Its accuracy, true-positive rate, and false-positive rate are 0.9841, 0.9869, and 0.0186 on the test set, respectively. The classification error rate of dffROI (1.59%) is significantly reduced compared with peakonly (2.73%). The model-agnostic feature importance demonstrates the necessity of fusing handcrafted evaluation metrics with the convolutional neural network representations. dffROI is an automatic, robust, and universal method for ROI filtering by virtue of information fusion and end-to-end learning. It is implemented in Python programming language and open-sourced at https://github.com/zhanghailiangcsu/dffROI under BSD License. Furthermore, it has been integrated into the KPIC2 framework previously proposed by our group to facilitate real metabolomic LC–MS dataset analysis

Recognition of gut microbiota by NOD2 is essential for the homeostasis of intestinal intraepithelial lymphocytes.

NOD2 functions as an intracellular sensor for microbial pathogen and plays an important role in epithelial defense. The loss-of-function mutation of NOD2 is strongly associated with human Crohn's disease (CD). However, the mechanisms of how NOD2 maintains the intestinal homeostasis and regulates the susceptibility of CD are still unclear. Here we found that the numbers of intestinal intraepithelial lymphocytes (IELs) were reduced significantly in Nod2(-/-) mice and the residual IELs displayed reduced proliferation and increased apoptosis. Further study showed that NOD2 signaling maintained IELs via recognition of gut microbiota and IL-15 production. Notably, recovery of IELs by adoptive transfer could reduce the susceptibility of Nod2(-/-) mice to the 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis. Our results demonstrate that recognition of gut microbiota by NOD2 is important to maintain the homeostasis of IELs and provide a clue that may link NOD2 variation to the impaired innate immunity and higher susceptibility in CD