We describe a system for accurate photometry and analysis of two-dimensional (2-D) gels, and its application to both high and low resolution gels. Image acquisition is video-based and includes methods of data correction necessary for accurate photometry. The analysis is based upon a method described previously by Lutin et al. [SI, which we have modified for improved stability and adapted to run on our PDP 11/60 computer. We illustrate analysis of a typical gel with a sequence of images showing the data while Gaussian components are estimated, removed, added and fitted as the iterative algorithm proceeds. We conclude by demonstrating the use of a mathematical curve-of-growth model for calibrating peak volumes vs. protein loading.
Large networks of disparate chemical/biological (CB) sensors, MET sensors, and intelligence, surveillance, and reconnaissance (ISR) sensors reporting to various command/display locations can lead to conflicting threat information, questions of alarm confidence, and a confused situational awareness. Sensor netting algorithms (SNA) are being developed to resolve these conflicts and to report high confidence consensus threat map data products on a common operating picture (COP) display. A phase I SBIR study to develop a conceptual design for a SNA was recently completed. Mathematical approaches for assigning uncertainty to incoming data streams, doing spatial/temporal correlation of point and standoff sensor data (via vector translation based tomography), estimating uncertainty for threat maps, and consistency checking between the consensus threat map result and the individual input data streams were developed. A set of simulation environment tools for testing the SNA, including a simple threat model, sensor models, and fused and un-fused COPs, were also prototyped during phase I. The SNA development and simulation based testing will continue during the phase II effort, which was just awarded.
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