We treated two children who had adenosine deaminase deficiency and severe combined immunodeficiency disease by injecting bovine adenosine deaminase modified by conjugation with polyethylene glycol. The modified enzyme was rapidly absorbed after intramuscular injection and had a half-life in plasma of 48 to 72 hours. Weekly doses of approximately 15 U per kilogram of body weight maintained plasma adenosine deaminase activity at two to three times the level of erythrocyte adenosine deaminase activity in normal subjects. The principal biochemical consequences of adenosine deaminase deficiency were almost completely reversed. In erythrocytes, adenosine nucleotides increased and deoxyadenosine nucleotides decreased to less than 0.5 percent of total adenine nucleotides. The activity of S-adenosylhomocysteine hydrolase, which is inactivated by deoxyadenosine, increased to normal in red cells and nucleated marrow cells. Neither toxic effects nor hypersensitivity reactions were observed. In vitro tests of the cellular immune function of each patient showed marked improvement, along with an increase in circulating T lymphocytes. Clinical improvement was indicated by absence of infection and resumption of weight gain. We conclude that from the standpoints of efficacy, convenience, and safety, polyethylene glycol-modified adenosine deaminase is preferable to red-cell transfusion as a treatment for adenosine deaminase deficiency. Patients with other inherited metabolic diseases in which accumulated metabolites equilibrate with plasma could benefit from treatment with the appropriate polyethylene glycol-modified enzyme.

This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant communications and computation bandwidth to capture and process high-resolution, high-rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kinodynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three-point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed-loop simulation in a rapidly exploring randomized trees algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in global positioning system-denied and highly dynamic environments with poor a priori information. C 2008 Wiley Periodicals, Inc.

This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant communications and computation bandwidth to capture and process high-resolution, high-rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kino-dynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three-point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed-loop simulation in a Rapidly-exploring Randomized Trees (RRT) algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message-passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in GPS-denied and highly dynamic environments with poor a priori information.