Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs

Motivation: In bottom-up proteomics, proteins are enzymatically digested before measurement with mass spectrometry. The relationship between proteins and peptides can be represented by bipartite graphs. This representation is useful to aid protein inference and quantification, which is complex due to the occurrence of shared peptides. We conducted a comprehensive analysis of bipartite graphs using theoretical peptides from in silico digestion of protein databases as well as quantified peptides quantified from real data sets. Results: The graphs based on quantified peptides are smaller and have less complex structures compared to graphs using theoretical peptides. The proportion of protein nodes without unique peptides and of graphs that contain such proteins are considerably greater for real data. Large differences between the two analyzed organisms (mouse and yeast) on database as well as quantitative level have been observed. Insights of this analysis may be useful for the development of protein inference and quantification algorithms.

A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits.

System-Level BITs Fault Diagnosis and Isolation Based on Diagnostic Tree and Bayesian Network

Large systems have complex structures and functions, which makes it more difficult in fault isolation. Given the design features of BITs (built-in test) of large complex systems, a method, based on the diagnostic tree and Bayesian network, has been proposed for fault diagnosis and isolation of System-level BITs. First, faults are rapidly isolated in accordance with the diagnostic tree as well as the information of periodic BITs and maintenance BITs. Then, a diagnostic method based on the Bayesian network has been put forward to solve the problems of ambiguity groups of three LRUs or more and to accomplish further diagnosis and isolation. Finally, a certain subsystem of radar has been exemplified to verify the effectiveness of the method proposed by this study.

F-22 Lessons Learned: Line Replaceable Module (LRM) Electrical Connector—An Investigation of Vibration-Induced Contact Wear and Electro Static Discharge Hardening

Flight Line Replaceable Modules (LRMs) are the foundation for modern day architectures in fighters such as the F-22. The 2-level maintenance concept (flight line to depot) reduces costs for spares and base level repair of circuit cards. However, this maintenance concept is predicated on the ability of the software to isolate failures down to the LRM or circuit card level. The types of failures that result from worn and corroded connectors result in large ambiguity groups; thus, making isolation to the circuit card level difficult. The vibration, thermal, humidity and corrosion environments of the F-22 are severe. Similarly, the ESD environment (for F-22 flight line maintenance) is severe because maintenance is performed without grounding straps or any special handling requirements. Hardware designed for use in this environment must be easily installed, capable of performing in a severe environment while maintaining high data rates without interruption. This paper identifies the F-22 risk reduction approach taken to mitigate the effects of the severe environments. The design & analytical approach taken during the program development phase offers important insight into the methods needed for predicting and analyzing hardware installation within the severe vibration and thermal environments commonly found in today’s fighter aircraft.