As a general strategy to selectively target antibody activity in vivo, a molecular architecture was designed to render binding activity dependent upon proteases in disease tissues. A protease-activated antibody (pro-antibody) targeting vascular cell adhesion molecule 1 (VCAM-1), a marker of atherosclerotic plaques, was constructed by tethering a binding site-masking peptide to the antibody via a matrix metalloprotease (MMP) susceptible linker. Pro-antibody activation in vitro by MMP-1 yielded a 200-fold increase in binding affinity and restored anti-VCAM-1 binding in tissue sections from ApoE(−/−) mice ex vivo. The pro-antibody was efficiently activated by native proteases in aorta tissue extracts from ApoE(−/−), but not from normal mice, and accumulated in aortic plaques in vivo with enhanced selectivity when compared to the unmodified antibody. Pro-antibody accumulation in aortic plaques was MMP-dependant, and significantly inhibited by a broad-spectrum MMP inhibitor. These results demonstrate that the activity of disease-associated proteases can be exploited to site-specifically target antibody activity in vivo.

We present a study of the simultaneous observation of protease reaction and surface diffusion as the enzyme interacts with a model substrate surface. We use micro-fluidic patterning to decorate a bovine serum albumin substrate surface with stripes of adsorbed enzyme in the absence of physical barriers. Spreading of the enzyme from the initial striped region indicates surface diffusion, while removal of the substrate provides a measure of reactivity. Microfluidic patterning provides a means to determine the relative importance of enzyme adsorption, surface diffusion, and reaction on the rate of substrate removal.

A general method was developed for the discovery of protease-activated binding ligands, or proligands, from combinatorial prodomain libraries displayed on the surface of E. coli. Peptide libraries of candidate prodomains were fused with a matrix metalloprotease-2 substrate linker to a vascular endothelial growth factor-binding peptide and sorted using a two-stage flow cytometry screening procedure to isolate proligands that required protease treatment for binding activity. Prodomains that imparted protease-mediated switching activity were identified after three sorting cycles using two unique library design strategies. The best performing proligand exhibited a 100-fold improvement in apparent binding affinity after exposure to protease. This method may prove useful for developing therapeutic and diagnostic ligands with improved systemic targeting specificity.

As part of Turkey's efforts to position itself to join the European Economic Community before the end of the century, the Turkish Petroleum Refineries Corporation (TUPRAS) is seeking to upgrade the specifications of her domestically refined petroleum products to approach and ultimately match the specifications that will be in effect throughout Europe. TUPRAS also seeks to increase production of refined products to try to keep pace with increasing domestic demand. Significant capital investment over the next 15 years will be required to attain these goals. TUPRAS senior management commissioned us to develop mathematical programming models for studying their strategic investment options, to exercise the models in performing an extensive analysis of the options, and to transfer to TUPRAS a decision support system based on the models for continuing analysis. The system is used by TUPRAS to analyze capital investments worth tens of millions of dollars.

To meet the growing demands of the changing business and regulatory environments, local distribution companies are compelled to take a more active role in forecasting demand and in managing their respective gas-supply ponfolios. This article addresses the application of linear programing to LDCs' strategic management of longterm gas-supply portfolios.Linear programming can be defined as a linear mathematical method for solvingpracricalproblems through the determination of values of relevant variables (called decision variables) that are subject to a variety of identifiable constraints such that profits are kept at a maximum or costs are kept at a minimum. Linear programming answers questions such as "What's the best alternative?" or"What if?" The strength of linear programming is its ability to analyze a number of possible alternatives and combinations of alternatives interactively. Role of Load DurationBefore an LDC can use linear programming effectively, gas demand and available gas supply must be analyzed in detail. The use of the load-duration concept is very useful in analyzing available means for satisfying gas demand. The typical load-duration curve (Figure 1) orders or sorts daily gas demand from peak day to lowest day over a 365-day period. The area below the load-duration curve can be broken down into at least three distinct components of gas demand:core-market base load core-market temperature-sensitive load non-core market (interruptible load)To begin, the LDC must obtain good weather data, including, but not limited to, temperature, wind velocity, and cloud coverage data, in order to characterize coremarket, temperature-sensitive load in terms of these respective variables. Using these data, a hear factor and base heating load can be determined by linear regression. The heat factor, usually measured in millions of Btu's for a degree-day, is defined as the net increase or decrease in weather-sensitive load attributable to a given decrease or increase in mean temperature, adjusted for wind velocity, cloud coverage, etc. To analyze demand properly, three correlative load-duration curves are required, corresponding to extremely cold weather, normal weather, and warm weather. Load-duration curves corresponding to future periods might be constructed based on anticipated peakday and annual-load growth in various customer classes in given districts.