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Aiming at the complex network security situation prediction problem,a network security situation prediction model based on improved particle swarm optimization bidirectional long-short term memory(IPSO-BILSTM) network is proposed to improve the convergence speed and prediction accuracy.Firstly,in view of the lack of real situation value in the data set,a situation value calculation method based on attack influence is adopted for situation prediction.Secondly,to address the problems that particle swarm optimization(PSO) algorithm is prone to fall into local optima and unbalanced search capability,the inertia weights and acceleration factors are improved,and the improved particle swarm optimization(IPSO) algorithm has balanced global and local search capability and faster convergence speed.Finally,IPSO is used to optimize the parameters of bidirectional long short term memory(BiLSTM) network,so as to improve the prediction ability.Experimental results show that the fitting degree of IPSO-BiLSTM can reach 0.994 6,and the fitting effect and convergence speed are better than other models.

Gene expression profiling studies have shown the pathogenetic role of oncogenic pathways in extranodal natural killer/T-cell lymphoma (ENKL). In this study, we aimed to identify the microRNAs (miRNAs) playing potential roles in ENKL, and to evaluate the genes and biological pathways associated to them. Gene expression profiles of ENKL patients were acquired from the gene expression omnibus (GEO) database. Most differentially expressed (DE)-miRNAs were identified in ENKL patients using limma package. Gene targets of the DE-miRNAs were collected from online databases (miRDB, miRWalk, miRDIP, and TargetScan), and used in Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses on Database for annotation, visualization, and integrated discovery database, and then used in protein–protein interaction (PPI) analysis on STRING database. Hub genes of the PPI network were identified in cytoHubba, and were evaluated in Biological networks gene ontology. According to the series GSE31377 and GSE43958 from GEO database, four DE-miRNAs were screened out: hsa-miR-363-3p, hsa-miR-296-5p, hsa-miR-155-5p, and hsa-miR-221-3p. Totally 164 gene targets were collected from the online databases, and used in the GO and KEGG pathway analyses and PPI network analysis. Ten hub genes of the PPI network were identified: AURKA, TP53, CDK1, CDK2, CCNB1, PLK1, CUL1, ESR1, CDC20, and PIK3CA. Those hub genes, as well as their correlative pathways, may be of diagnostic or therapeutic potential for ENKL, but further clinical evidence is still expected.

In order to solve the problem of low accuracy of traditional network security situation awareness research methods in the case of complex network information,combined with deep learning,this paper proposes a network security situation assessment model based on time factor and composite CNN structure,which combines volume integral solution technology and deep separable technology to form a four layer series composite optimal unit structure.The one-dimensional network data are transformed into two-dimensional matrix and loaded into the neural network model in the form of gray value,so as to give full play to the advantages of convolution neural network.In order to make full use of the time-series relationship between data,time factor is introduced to form fusion data,which makes the network to learn the original data and fusion data with time-series relationship at the same time,the feature extraction ability of the model is increased,the spatial mapping of time-series data is established by using time factor and point convolution,and the integrity of the model structure is increased.Experimental results show that the accuracy of the proposed model on two datasets is 92.89% and 92.60% respectively,which is 2%～6% higher than randomfo-rest and LSTM algorithm.

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Abstract Background A robust bacterial artificial chromosome (BAC)-based physical map is essential for many aspects of genomics research, including an understanding of chromosome evolution, high-resolution genome mapping, marker-assisted breeding, positional cloning of genes, and quantitative trait analysis. To facilitate turkey genetics research and better understand avian genome evolution, a BAC-based integrated physical, genetic, and comparative map was developed for this important agricultural species. Results The turkey genome physical map was constructed based on 74,013 BAC fingerprints (11.9 × coverage) from two independent libraries, and it was integrated with the turkey genetic map and chicken genome sequence using over 41,400 BAC assignments identified by 3,499 overgo hybridization probes along with > 43,000 BAC end sequences. The physical-comparative map consists of 74 BAC contigs, with an average contig size of 13.6 Mb. All but four of the turkey chromosomes were spanned on this map by three or fewer contigs, with 14 chromosomes spanned by a single contig and nine chromosomes spanned by two contigs. This map predicts 20 to 27 major rearrangements distinguishing turkey and chicken chromosomes, despite up to 40 million years of separate evolution between the two species. These data elucidate the chromosomal evolutionary pattern within the Phasianidae that led to the modern turkey and chicken karyotypes. The predominant rearrangement mode involves intra-chromosomal inversions, and there is a clear bias for these to result in centromere locations at or near telomeres in turkey chromosomes, in comparison to interstitial centromeres in the orthologous chicken chromosomes. Conclusion The BAC-based turkey-chicken comparative map provides novel insights into the evolution of avian genomes, a framework for assembly of turkey whole genome shotgun sequencing data, and tools for enhanced genetic improvement of these important agricultural and model species.