Host-Virus Interactions in Dengue Infection indicate Targets for Detection and Therapeutic Interventions

Dengue is a mosquito-transmitted viral infectious disease that is endemic to 110 countries spanning tropical and subtropical regions. While infection is typically asymptomatic, symptoms of the estimated 50-100 million clinical cases are often debilitating and occasionally life-threatening, resulting in as many as 5 million hospitalisations annually. Consequently, the World Health Organization regards dengue as a significant global public health concern. Immense challenges exist in both formulating and constructing an efficacious vaccine for prophylaxis and in developing therapeutics for cure. Although there have been numerous molecular studies of the interaction between host and virus, and the metabolic pathways of several proteins are implicated in dengue virus replication, their biological significance remains unclear. It is important to consider clinical, immunopathological and epidemiological features to decipher the complexity of disease and to unravel the mechanisms attributed to its progression. This editorial emphasises the critical events causing vascular endothelial permeability, which underpins the manifestations in humans of dengue haemorrhagic fever and dengue shock syndrome

Adaptive neuro-fuzzy intrusion detection systems

The Intrusion Detection System architecture commonly used in commercial and research systems have a number of problems that limit their configurability, scalability or efficiency. In this paper, two machine-learning paradigms, Artificial Neural Networks and Fuzzy Inference System, are used to design an Intrusion Detection System. SNORT is used to perform real time traffic analysis and packet logging on IP network during the training phase of the system. Then a signature pattern database is constructed using protocol analysis and Neuro-Fuzzy learning method. Using 1998 DARPA Intrusion Detection Evaluation Data and TCP dump raw data, the experiments are deployed and discussed