Proteases are an important class of enzymes, whose activity is central to many physiologic and pathologic processes. Detailed knowledge of protease specificity is key to understanding their function. Although many methods have been developed to profile specificities of proteases, few have the diversity and quantitative grasp necessary to fully define specificity of a protease, both in terms of substrate numbers and their catalytic efficiencies. We have developed a concept of "selectome"; the set of substrate amino acid sequences that uniquely represent the specificity of a protease. We applied it to two closely related members of the Matrixin family-MMP-2 and MMP-9 by using substrate phage display coupled with Next Generation Sequencing and information theory-based data analysis. We have also derived a quantitative measure of substrate specificity, which accounts for both the number of substrates and their relative catalytic efficiencies. Using these advances greatly facilitates elucidation of substrate selectivity between closely related members of a protease family. The study also provides insight into the degree to which the catalytic cleft defines substrate recognition, thus providing basis for overcoming two of the major challenges in the field of proteolysis: 1) development of highly selective activity probes for studying proteases with overlapping specificities, and 2) distinguishing targeted proteolysis from bystander proteolytic events.

In this paper we describe a reconfigurable architecture for image processing and computer vision based on a multi-ring network which we call a Reconfigurable Multi-Ring System (RMRS). We describe the reconfiguration switch for the RMRS and also describe its VLSI implementation. The RMRS topology is shown to be regular and scalable and hence well-suited for VLSI implementation. We prove some important properties of the RMRS topology and show that a broad class of algorithms for the n-cube can be mapped to the RMRS in a simple and elegant manner. We design and analyze a class of procedural primitives for the SIMD RMRS and show how these primitives can be used as building blocks for more complex parallel operations. We demonstrate the usefulness of the RMRS for problems in image processing and computer vision by considering two important operations—the Fast Fourier Transform (FFT) and the Hough transform for detection of linear features in an image. Parallel algorithms for the FFT and the Hough transform on the SIMD RMRS are designed using the aforementioned procedural primitives. The analysis of the complexity of these algorithms shows that the SIMD RMRS is a viable architecture for problems in computer vision and image processing.

We describe the quadratic sieve factoring algorithm and a pipeline architecture on which it could be efficiently implemented. Such a device would be of moderate cost to build and would be able to factor 100-digit numbers in less than a month. This represents an order of magnitude speed-up over current implementations on supercomputers. Using a distributed network of many such devices, it is predicted much larger numbers could be practically factored.

This correspondence describes a microcomputer system, called ¿BE (for microprocessor-based English), for processing natural languages. Its techniques and facilities, however, should be extendible to other languages. By using the microprocessor as special purpose hardware for several functions of a natural language processor (in particular, hashing and parsing), the system aids computational linguists by increasing the computational power available for natural language investigations. An overview of ¿BE is given followed by a more detailed discussion of the hasher and parser. The paper concludes with specific applications of the microprocessor system to computational linguistics. ¿BE is not based on a particular theory of language so that the system's user may define his own theory and investigate its consequences. ¿BE contains facilities and capabilities to assist such investigations.

A classroom method for programming instruction which emphasizes the evolution of correct algorithms by try, test, repeat is presented. The method is simple, reasonably obvious, and serves to illustrate many of the concepts of introductory programming. No special equipment or background is needed.