A Novel Isoform of the Hepatic Antimicrobial Peptide, Hepcidin (Hepc-CB1), from a Deep-Sea Fish, the Spinyjaw Greeneye Chlorophthalmus bicornis (Norman, 1939): Molecular Characterisation and Phylogeny

Hepcidin is cysteine-rich short peptide of innate immune system of fishes, equipped to perform prevention and proliferation of invading pathogens like bacteria and viruses by limiting iron availability and activating intracellular cascades. Hepcidins are diverse in teleost fishes, due to the varied aquatic environments including exposure to pathogens, oxygenation and iron concentration. In the present study, we report a 87-amino acid (aa) preprohepcidin (Hepc-CB1) with a signal peptide of 24 aa, a prodomain of 39 aa and a bioactive mature peptide of 24 aa from the gill mRNA transcripts of the deep-sea fish spinyjaw greeneye, Chlorophthalmus bicornis. Molecular characterisation and phylogenetic analysis categorised the peptide to HAMP2-like group with a mature peptide of 2.53 kDa; a net positive charge (?3) and capacity to form b-hairpin-like structure configured by 8 conserved cysteines. The present work provides new insight into the mass gene duplication events and adaptive evolution of hepcidin isoforms with respect to environmental influences and positive Darwinian selection. This work reports a novel hepcidin isoform under the group HAMP2 from a nonacanthopterygian deep-sea fish, C. bicornis

An Alternative Approach To Computer System Security Monitoring And Enhancement Through System Call Sequence Analysis

Modern computer systems are plagued with stability and security problems: applications lose data, web servers are hacked, and systems crash under heavy load. Many of these problems or anomalies arise from rare program behavior caused by attacks or errors. A substantial percentage of the web-based attacks are due to buffer overflows. Many methods have been devised to detect and prevent anomalous situations that arise from buffer overflows. The current state-of-art of anomaly detection systems is relatively primitive and mainly depend on static code checking to take care of buffer overflow attacks. For protection, Stack Guards and I-leap Guards are also used in wide varieties.This dissertation proposes an anomaly detection system, based on frequencies of system calls in the system call trace. System call traces represented as frequency sequences are profiled using sequence sets. A sequence set is identified by the starting sequence and frequencies of specific system calls. The deviations of the current input sequence from the corresponding normal profile in the frequency pattern of system calls is computed and expressed as an anomaly score. A simple Bayesian model is used for an accurate detection.Experimental results are reported which show that frequency of system calls represented using sequence sets, captures the normal behavior of programs under normal conditions of usage. This captured behavior allows the system to detect anomalies with a low rate of false positives. Data are presented which show that Bayesian Network on frequency variations responds effectively to induced buffer overflows. It can also help administrators to detect deviations in program flow introduced due to errors.