Establishing the precise evolutionary history of a gene improves prediction of disease-causing missense mutations

PURPOSE: Predicting the phenotypic effects of mutations has become an important application in clinical genetic diagnostics. Computational tools evaluate the behavior of the variant over evolutionary time and assume that variations seen during the course of evolution are probably benign in humans. However, current tools do not take into account orthologous/paralogous relationships. Paralogs have dramatically different roles in Mendelian diseases. For example, whereas inactivating mutations in the NPC1 gene cause the neurodegenerative disorder Niemann-Pick C, inactivating mutations in its paralog NPC1L1 are not disease-causing and, moreover, are implicated in protection from coronary heart disease. METHODS: We identified major events in NPC1 evolution and revealed and compared orthologs and paralogs of the human NPC1 gene through phylogenetic and protein sequence analyses. We predicted whether an amino acid substitution affects protein function by reducing the organism’s fitness. RESULTS: Removing the paralogs and distant homologs improved the overall performance of categorizing disease-causing and benign amino acid substitutions. CONCLUSION: The results show that a thorough evolutionary analysis followed by identification of orthologs improves the accuracy in predicting disease-causing missense mutations. We anticipate that this approach will be used as a reference in the interpretation of variants in other genetic diseases as well. Genet Med 18 10, 1029–1036

Context-aware Authorization in Highly Dynamic Environments

Highly dynamic computing environments, like ubiquitous and pervasive computing environments, require frequent adaptation of applications. Context is a key to adapt suiting user needs. On the other hand, standard access control trusts users once they have authenticated, despite the fact that they may reach unauthorized contexts. We analyse how taking into account dynamic information like context in the authorization subsystem can improve security, and how this new access control applies to interaction patterns, like messaging or eventing. We experiment and validate our approach using context as an authorization factor for eventing in Web service for device (like UPnP or DPWS), in smart home security

A log mining approach for process monitoring in SCADA

SCADA (Supervisory Control and Data Acquisition) systems are used for controlling and monitoring industrial processes. We propose a methodology to systematically identify potential process-related threats in SCADA. Process-related threats take place when an attacker gains user access rights and performs actions, which look legitimate, but which are intended to disrupt the SCADA process. To detect such threats, we propose a semi-automated approach of log processing. We conduct experiments on a real-life water treatment facility. A preliminary case study suggests that our approach is effective in detecting anomalous events that might alter the regular process workflow

On consistency maintenance in service discovery

Communication and node failures degrade the ability of a service discovery protocol to ensure Users receive the correct service information when the service changes. We propose that service discovery protocols employ a set of recovery techniques to recover from failures and regain consistency. We use simulations to show that the type of recovery technique a protocol uses significantly impacts the performance. We benchmark the performance of our own service discovery protocol, FRODO against the performance of first generation service discovery protocols, Jini and UPnP during increasing communication and node failures. The results show that FRODO has the best overall consistency maintenance performance

Evaluation and extracting factual software architecture of distributed system by process mining techniques

The factual software architectures that are actually implemented of distributed systems do not conform the planned software architectures (Beck 2010). It happens due to the complexity of distributed systems. This problem begets two main challenges; First, how to extract the factual software architectures with the proper techniques and second, how to compare the planned software architecture with the extracted factual architecture. This study aims to use process mining to discover factual software architecture from codes and represents software architecture model in Petri Net to evaluate model by the linear temporal logic and process mining. In this paper, the applicability of process mining techniques, implemented in the ProM6.7 framework is shown to extract and evaluate factual software architectures. Furthermore, capabilities of Hierarchical Colored Petri Net implemented in CPN4.0 are exploited to model and simulate software architectures. The proposed approach has been conducted on a case study to indicate applicability of the approach in the distributed data base system. The final result of the case study indicates process mining is able to extract factual software architectures and also to check its conformance