A system and method for allowing computer programs to directly access various features of a virus scanning engine is disclosed. In one embodiment of the invention, the system includes a module for instantiating an object to act as an interface between the computer program and the virus scan engine, a module for setting properties of the object that are associated with the desired feature of the virus scan engine to be accessed, a module for invoking a method of the object, the invocation resulting in access to the desired feature of the virus scan engine, and a module for examining properties of the object after the desired feature of the virus scan engine has been accessed.

A phylogenetic algorithm computes a tree of distance relationships on a set, S, of phylogenetic descriptions (which may not be complete), given a phylogenetic-description transformation function, D, defined on S. Maximum Parsimony (MP) is a widely used phylogenetic algorithm that computes the shortest phylogenetic tree that represents the tree distances on S determined by D. To date, the sensitivity of MP to missing/incomplete data has not been systematically investigated. Although a general characterization of this sensitivity is intractable, robust empirical characterizations for typical MP configurations are possible. Here, I present an analysis of the sensitivity of several commonly used tree robustness metrics to missing/incomplete data, for a widely used MP implementation and typical MP problem set-up, applied to randomized mid-sized missing-data sets. The results show a counterintuitive limitation of one of those robustness metrics.