Abstract Comprehensive bioinformatics analysis was used to identify the differentially expressed genes (DEGs) between neuroblastoma samples and normal samples in GSE54720 and GSE78061 datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on common DEGs. The protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. The top 15 hub genes were screened out. TAGLN3, KIF5C and SNAP91 were identified by alignment in the PubMed, OMIM, DisGeNET and GeneCards databases and validated by quantitative real-time polymerase chain reaction (qPCR). These three are have never been previously reported in the literature and experimentally validated. We identified a total of 37 commom DEGs from the two microarray databases. The KEGG pathway analysis showed that these DEGs were primarily involved in pathway related to dopaminergic synapses, motor proteins and phenylalanine metabolism related pathways. GO enrichment analysis showed that TAGLN3, KIF5C, and SNAP91 related pathway were mainly concentrated in axon guidance, axon genesis, axon development, distal axon, neuronal cell body, and synaptic vesicle transport, suggesting that they may be involved in biological functions such as protein binding, plasma membrane, membrane composition and nucleus. OMIM, DisGeNET, GeneCards databases, and PubMed have identified that TAGLN3, KIF5C, and SNAP91 were linked to proliferation, migration, and invasion of other tumors. Finally, the expression levels of TAGLN3, KIF5C and SNAP91 were significantly increased in SH-SY5Y cells compared with ARPE-19 cells as verified by qPCR, consistent with our bioinformatics analysis, suggesting that TAGLN3, KIF5C and SNAP91 may be involved in the occurrence and development of neuroblastoma. In this study, some key genes and molecules were identified by bioinformatics methods, revealing the potential pathogenic mechanism of neuroblastoma. These genes can serve as diagnostic indicators and therapeutic biomarkers for neuroblastoma, thereby enhancing our understanding of the molecular mechanisms underlying this disease.
In this paper, we develop the theory of Sobolev spaces on locally finite graphs, including completeness, reflexivity, separability, and Sobolev inequalities. Since there is no exact concept of dimension on graphs, classical methods that work on Euclidean spaces or Riemannian manifolds can not be directly applied to graphs. To overcome this obstacle, we introduce a new linear space composed of vector-valued functions with variable dimensions, which is highly applicable for this issue on graphs and is uncommon when we consider to apply the standard proofs on Euclidean spaces to Sobolev spaces on graphs. The gradients of functions on graphs happen to fit into such a space and we can get the desired properties of various Sobolev spaces along this line. Moreover, we also derive several Sobolev inequalities under certain assumptions on measures or weights of graphs. As fundamental analytical tools, all these results would be extremely useful for partial differential equations on locally finite graphs.
In distributed intelligent computing environment, user information is vulnerable to plaintext intrusion, resulting in information leakage. In order to ensure the security of user information, a user information intrusion prediction method based on empirical mode decomposition and spectrum feature detection in distributed intelligent computing is proposed in this paper. Firstly, a model of user information and intrusion signal in distributed intelligent computing is established; then an intrusion detection model is established with signal processing method; finally, time-frequency analysis and feature decomposition are conducted for intrusion information in distributed intelligent computing with empirical mode decomposition method, and accurate prediction of user intrusion information is achieved based on joint probability density distribution of spectrum feature, so as to improve the algorithm design. The simulation results show that when the signal to noise ratio is 12.4 dB, the detection probability of the method proposed in this paper is 1, and then the false alarm probability can be 0, which indicates that this method can provide good intrusion detection probability and low false alarm probability even at relatively low signal to noise ratio. Therefore, the method proposed in this paper has good intrusion interception and prediction ability.
<p>Liver vessels generated from computed tomography are usually pretty small, which poses big challenges for satisfactory vessel segmentation, including 1) the scarcity of high-quality and large-volume vessel masks, 2) the difficulty in capturing vessel-specific features, and 3) the heavily imbalanced distribution of vessels and liver tissues. To advance, a sophisticated model and an elaborated dataset have been built. The model has a newly conceived Laplacian salience filter, highlighting vessel-like regions and suppressing other liver regions, to shape the vessel-specific feature learning and to balance vessels against others. It is further coupled with a pyramid deep learning architecture to capture various levels of features, hence the enhancement of feature formulation. Experiments show that this model markedly outperforms the state-of-the-art approaches, achieving a relative improvement of Dice score by at least 1.63% compared to the existing best model on available datasets. More promisingly, the averaged Dice score produced by existing models on the newly con- structed dataset is as high as 0.728 ± 0.067, which is at least 19.1% higher than that obtained from the existing best dataset under the same settings. These observations suggest that the proposed Laplacian salience, along with the elaborated dataset, can be helpful for liver vessel seg- mentation. </p>
A multi-interface domain is a domain that can shape multiple and distinctive binding sites to contact with many other domains, forming a hub in domain-domain interaction networks. The functions played by the multiple interfaces are usually different, but there is no strict bijection between the functions and interfaces as some subsets of the interfaces play the same function. This work applies graph theory and algorithms to discover fingerprints for the multiple interfaces of a domain and to establish associations between the interfaces and functions, based on a huge set of multi-interface proteins from PDB. We found that about 40% of proteins have the multi-interface property, however the involved multi-interface domains account for only a tiny fraction (1.8%) of the total number of domains. The interfaces of these domains are distinguishable in terms of their fingerprints, indicating the functional specificity of the multiple interfaces in a domain. Furthermore, we observed that both cooperative and distinctive structural patterns, which will be useful for protein engineering, exist in the multiple interfaces of a domain.
With two corruption cases in Hunan in the ninth and tenth years of Jiaqing period as an example,the article is designed to analyze the weaknesses of local bureaucracy and sort out the changes of punishing rules as well as their corresponding influence on the bureaucracy at that time.In this way,the factors causing the decline in Jiaqing and Daoguang periods will be further discussed.
In distributed intelligent computing environment, user information is vulnerable to plaintext intrusion, resulting in information leakage. In order to ensure the security of user information, a user information intrusion prediction method based on empirical mode decomposition and spectrum feature detection in distributed intelligent computing is proposed in this paper. Firstly, a model of user information and intrusion signal in distributed intelligent computing is established; then an intrusion detection model is established with signal processing method; finally, time-frequency analysis and feature decomposition are conducted for intrusion information in distributed intelligent computing with empirical mode decomposition method, and accurate prediction of user intrusion information is achieved based on joint probability density distribution of spectrum feature, so as to improve the algorithm design. The simulation results show that when the signal to noise ratio is 12.4 dB, the detection probability of the method proposed in this paper is 1, and then the false alarm probability can be 0, which indicates that this method can provide good intrusion detection probability and low false alarm probability even at relatively low signal to noise ratio. Therefore, the method proposed in this paper has good intrusion interception and prediction ability.
The traditional knowledge discovery is based on the database.As the data in the database is lack of semantic,the knowledge discovery is not complete.Introducing ontology and agent to the knowledge discovery,firstly,the semantic information of the data is found and it is saved to the knowledgebase together with knowledge.Then one new kind of knowledge discovery system based on the knowledgebase is proposed.
B-cell secreted antibodies play a critical role in fighting against the invaders and abnormal self tissues. Identifying the epitope on antigens recognized by the paratope on antibodies can enlighten the understanding of this important immune mechanism. Predicting B-cell epitope can also pave the way for vaccine design and disease therapy. However, due to the high complexity of this problem, previous prediction methods that focus on linear and conformational epitope are both unsatisfactory. In this work, we propose a novel method to predict B-cell epitopes, when a pair of sequences is given, by using associations and cooperativity patterns from a relatively small antigen-antibody structural data set. More exactly, our classifier is trained on only PDB protein complexes, but it can be applied to any sequence data. Our evaluation results show that the accuracy of our method is very competitive to, sometimes even much better than, previous structure-based prediction methods which have a smaller applicability scope than ours.
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