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This thesis will explore N-terminal methionine excision (NME), a co-translational process that occurs on virtually all proteins in all organimsms from bacteria to eukaryotes. NME is an essential process for the funtioning of proteins, with the enzymes responsible for carrying out NME belonging to the minimal bacterial cell (HPG⁺99). This excision is almost exclusively determined by the second amino acid of the nascient protein. The set of seven amino acids that prompt cleavage also correspond to the seven stabilizing amino acids that Arfin and Bradshaw [AB88] connected to protein degradation. Comparative proteogenomics approaches are employed to perform an analysis of NME based on sequence as well as other available data. The ties of NME to protein degradation are questioned, with the results of experiments suggesting that only two of the seven amino acids that prompt cleavage are important and required for post-translational modifications, specifically N-acetylation

Diagnostics and prognostics is particularly challenging in systems with a restricted suite of sensors; e.g., in aircraft engines where harsh operating conditions, weight considerations, and regulatory concerns limit the number of sensors. In this paper, we investigate anomaly detection techniques subject to these constraints. Specifically, we use as input to these techniques only controller-generated, log-data for the system. While such log-data is not designed to carry predictive information related to system health, we show that it is possible to extract early warning signals related to the failure of the system by looking for the presence or onset of anomalous or novel patterns in the log-data. We present preliminary results obtained by the application of this approach to some complex systems. We also provide a roadmap for extending this approach by the incorporation of minimal amount of system-specific knowledge of the kind that is typically available for complex systems. This extension is expected to strengthen the applicability of the approach to diagnostic and prognostic analysis at the level of the system components, as well as to the estimation of the root cause of a detected system anomaly.Copyright © 2007 by General Electric Company