Using Domain Knowledge to Facilitate Cyber Security Analysis

Network attack classification is an essential component in intrusion detection in that it can improve the performance of intrusion detection system. Several machine-learning methods have been applied in correlating attacks. There is one inherent limitation with these approaches that they strongly rely on datasets, and consequently their models for attack classification can hardly generalize beyond the training data. To address the above limitation, we propose to utilize domain knowledge in form of taxonomy and ontology to improve attack correlation in cyber security. In addition, we expect that the attack correlation results of machine-learning techniques can be used to refine the original attack taxonomy. The proposed methods are evaluated with several experiments. The findings of the experiments suggest that domain knowledge and machine-learning technique should be used together on attack classification tasks

Defining the chromatin signature of inducible genes in T cells

BACKGROUND Specific chromatin characteristics, especially the modification status of the core histone proteins, are associated with active and inactive genes. There is growing evidence that genes that respond to environmental or developmental signals may possess distinct chromatin marks. Using a T cell model and both genome-wide and gene-focused approaches, we examined the chromatin characteristics of genes that respond to T cell activation. RESULTS To facilitate comparison of genes with similar basal expression levels, we used expression-profiling data to bin genes according to their basal expression levels. We found that inducible genes in the lower basal expression bins, especially rapidly induced primary response genes, were more likely than their non-responsive counterparts to display the histone modifications of active genes, have RNA polymerase II (Pol II) at their promoters and show evidence of ongoing basal elongation. There was little or no evidence for the presence of active chromatin marks in the absence of promoter Pol II on these inducible genes. In addition, we identified a subgroup of genes with active promoter chromatin marks and promoter Pol II but no evidence of elongation. Following T cell activation, we find little evidence for a major shift in the active chromatin signature around inducible gene promoters but many genes recruit more Pol II and show increased evidence of elongation. CONCLUSIONS These results suggest that the majority of inducible genes are primed for activation by having an active chromatin signature and promoter Pol II with or without ongoing elongation

Translational selection in human: more pronounced in housekeeping genes

Background: Translational selection is a ubiquitous and significant mechanism to regulate protein expression in prokaryotes and unicellular eukaryotes. Recent evidence has shown that translational selection is weakly operative in highly expressed genes in human and other vertebrates. However, it remains unclear whether translational selection acts differentially on human genes depending on their expression patterns. Results: Here we report that human housekeeping (HK) genes that are strictly defined as genes that are expressed ubiquitously and consistently in most or all tissues, are under stronger translational selection. Conclusions: These observations clearly show that translational selection is also closely associated with expression pattern. Our results suggest that human HK genes are more efficiently and/or accurately translated into proteins, which will inevitably open up a new understanding of HK genes and the regulation of gene expression. Reviewers This article was reviewed by Yuan Yuan, Baylor College of Medicine; Han Liang, University of Texas MD Anderson Cancer Center (nominated by Dr Laura Landweber) Eugene Koonin, NCBI, NLM, NIH, United States of America Sandor Pongor, International Centre for Genetic Engineering and biotechnology (ICGEB), Italy

A finite control set model predictive control method for matrix converter with zero common-mode voltage

In this paper a finite control set model predictive control method is presented that eliminates the common-mode voltage at the output of a matrix converter. In the predictive control process only the rotating vectors are selected to generate the output voltage and the input current in order to remove the common mode voltage. In addition, a modified four-step commutation strategy is proposed to eliminate common-mode voltage spikes caused by the conventional four-step commutation strategy based on the current direction. The proposed method reduces the computational complexity greatly compared with the enhanced space vector modulation with rotating vectors. The feasibility and operation of the proposed method are verified using experimental results. The resulting common-mode voltage is near to zero with good quality input and output converter waveforms

CREB1 and ATF1 Negatively Regulate Glutathione Biosynthesis Sensitizing Cells to Oxidative Stress

The cAMP response element binding protein (CREB) family activating transcription factor 1 (ATF1) and cAMP response element binding protein 1 (CREB1) have been reported in a diverse group of tumors, however, the mechanistic basis for this remains unclear. Here we found that CREB1 and ATF1 unexpectedly regulate glutathione (GSH) biosynthesis by suppressing the expression of glutamate-cysteine ligase modifier subunit (GCLM) and glutathione synthase (GSS), two key enzymes of GSH biosynthesis pathway. Mechanistic studies reveal that GCLM and GSS are direct transcriptional targets of CREB1 and ATF1. Through repressing the expression of these two enzymes, CREB1 and ATF1 reduce the GSH biosynthesis and the capability of cells to detoxicate reactive oxygen species (ROS), thereby increasing cellular susceptibility to oxidative stress. Therefore, our findings link CREB1 family to cellular metabolism, and uncover a potential therapeutic approach by targeting GCLM or oxidative stress for the treatment of tumors with relatively high expression of CREB1 family proteins