Space-air-ground-sea integrated network (SAGSIN), which integrates satellite communication networks, aerial networks, terrestrial networks, and marine communication networks, has been widely ...envisioned as a promising network architecture for 6G. In consideration of its cooperation characteristics of multi-layer networks, open communication environment, and time-varying topologies, SAGSIN faces many unprecedented security challenges, and there have been a number of researches related to SAGSIN security performed over the past few years. Based on such observation, we provide in this paper a detailed survey of recent progress and ongoing research works on SAGSIN security in the aspects of security threats, attack methodologies, and defense countermeasures. To the best of our knowledge, we are the first to present the state-of-the-art of security for SAGSIN, since existing surveys focused either on a certain segment or on several segments of the integrated network, and little can be found on the full coverage network. In addition to reviewing existing works on SAGSIN security, we also present some discussions on cross-layer attacks and security countermeasure in SAGSIN, and identify new challenges ahead and future research directions.
Abstract
Background
Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the ...particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time.
Results
We propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI-CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state-of-the-art methods.
Conclusions
All the experimental results show that the performance of DTI-CNN is better than that of the three existing methods and the proposed method is appropriately designed.
The emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at the single-cell resolution. ScRNA-seq data analysis is complicated by excess ...zero counts, the so-called dropouts due to low amounts of mRNA sequenced within individual cells. We introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute automatically identifies likely dropouts, and only perform imputation on these values without introducing new biases to the rest data. scImpute also detects outlier cells and excludes them from imputation. Evaluation based on both simulated and real human and mouse scRNA-seq data suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropouts. scImpute is shown to identify likely dropouts, enhance the clustering of cell subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics.
The Chinese novelist and playwright Li Yu 李漁 (1610~1680) enjoyed great fame in Japan since the 1690s when he was introduced to Japanese readers of the Tokugawa period. Particularly important in the ...reception history of Li Yu in Japan was Jieziyuan huazhuan, the Mustard Seed Garden Manual of Painting. The reproduction and reinterpretation of Jieziyuan huazhuan in Tokugawa Japan shaped Li Yu’s reputation as a literatus ideal among his Japanese readers in spite of his obscure reputation among his Chinese contemporaries. Through a wide range of primary materials, this article examines the idolization of Li Yu in the middle and late Tokugawa period and argues that it was a result of the misrepresentation of Li Yu as a literati painting master, as well as a hermit fiction writer. The close connection established between him and Jieziyuan huazhuan led to the recognition of him in Tokugawa Japan as one of the greatest literati painting artists. Meanwhile, the imagination of him as a hermit further established his image as the ideal of literati spirit among his Japanese admirers. Such idolization in turn contributed to his reputation in early modern China when his works were re-introduced to Chinese readers in the 1930s.
We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning ...interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.
Abstract
MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein ...sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download. This database will be updated every three months. The MusiteDeep server is available at https://www.musite.net. The stand-alone tools for locally using MusiteDeep are available at https://github.com/duolinwang/MusiteDeep_web.
Histone modifications have critical roles in regulating the expression of developmental genes during embryo development in mammals. However, genome-wide analyses of histone modifications in ...pre-implantation embryos have been impeded by the scarcity of the required materials. Here, by using a small-scale chromatin immunoprecipitation followed by sequencing (ChIP-seq) method, we map the genome-wide profiles of histone H3 lysine 4 trimethylation (H3K4me3) and histone H3 lysine 27 trimethylation (H3K27me3), which are associated with gene activation and repression, respectively, in mouse pre-implantation embryos. We find that the re-establishment of H3K4me3, especially on promoter regions, occurs much more rapidly than that of H3K27me3 following fertilization, which is consistent with the major wave of zygotic genome activation at the two-cell stage. Furthermore, H3K4me3 and H3K27me3 possess distinct features of sequence preference and dynamics in pre-implantation embryos. Although H3K4me3 modifications occur consistently at transcription start sites, the breadth of the H3K4me3 domain is a highly dynamic feature. Notably, the broad H3K4me3 domain (wider than 5 kb) is associated with higher transcription activity and cell identity not only in pre-implantation development but also in the process of deriving embryonic stem cells from the inner cell mass and trophoblast stem cells from the trophectoderm. Compared to embryonic stem cells, we found that the bivalency (that is, co-occurrence of H3K4me3 and H3K27me3) in early embryos is relatively infrequent and unstable. Taken together, our results provide a genome-wide map of H3K4me3 and H3K27me3 modifications in pre-implantation embryos, facilitating further exploration of the mechanism for epigenetic regulation in early embryos.
Controversial and inconsistent results on the eco-toxicity of TiO2 nanoparticles (NPs) are commonly found in recorded studies and more experimental works are therefore warranted to elucidate the ...nanotoxicity and its underlying precise mechanisms. Toxicities of five types of TiO2 NPs with different particle sizes (10∼50 nm) and crystal phases were investigated using Escherichia coli as a test organism. The effect of water chemistry on the nanotoxicity was also examined. The antibacterial effects of TiO2 NPs as revealed by dose-effect experiments decreased with increasing particle size and rutile content of the TiO2 NPs. More bacteria could survive at higher solution pH (5.0-10.0) and ionic strength (50-200 mg L(-1) NaCl) as affected by the anatase TiO2 NPs. The TiO2 NPs with anatase crystal structure and smaller particle size produced higher content of intracellular reactive oxygen species and malondialdehyde, in line with their greater antibacterial effect. Transmission electron microscopic observations showed the concentration buildup of the anatase TiO2 NPs especially those with smaller particle sizes on the cell surfaces, leading to membrane damage and internalization. These research results will shed new light on the understanding of ecological effects of TiO2 NPs.
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