Abstract: DNA encoding a therapeutically suitable glutaminase has been molecularly cloned. This allows one to obtain a polypeptide which is a therapeutically suitable glutaminase free of contaminating endotoxin. It has been found that this polypeptide is a potent anti-viral agent and when coupled to an anti-tumor monoclonal antibody is a potent anticancer agent. The glutaminase of the present invention is particularly useful for treating lung, breast and colon cancer cells and in the treatment of HIV-infected cells.

Abstract: A server system for multi-level vending of any electronically transferable product through a communications network directly to a customer's computer. This server system (herein called a Sales Support Server) integrates the collection of a payment via the network and the automatic distribution of the product with the calculation of commissions using a multi-level marketing commission structure and the distribution of commissions and fees via the network. The preferred configuration includes a client application (herein called a Sales App) which runs as a plug-in to a network browser on the customer's computer and which provides a purchase request and registration data to a Sales Support Server and performs the installation of the product on the customer's computer. The Sales Support Server acquires the payment, transfers the product, calculates and pays the commissions, and adds the purchaser's registration information to the multi-level sales database for the product.

Abstract: A system of software components is constructed which includes multiple recommendation agents, each having: a list of recommended items for each user; a numerical predicted rating representing the predicted extent of each user's preference for the item recommended and numerical value information representing confidence; and a user profile for holding basic information on each user and influential power information of each recommendation agent with respect to each user. A recommendation manager is constructed to: select one or more recommendation agents using the basic user information and the influential power information; generate a final list of recommended items; and adjust the influential power of each recommendation agent stored in said user agent according to the predicted score and the belief provided by each recommendation agent, and the extent of a user's actual preference for the recommendation.