Administration of interleukin 12 in combination with type II collagen induces severe arthritis in DBA/1 mice.

The induction of arthritis in DBA/1 mice usually requires immunization with the antigen type II collagen emulsified with Mycobacterium tuberculosis in oil. Here we describe that interleukin 12 (IL-12) can replace mycobacteria and cause severe arthritis of DBA/1 mice when administered in combination with type II collagen. Immunization of DBA/1 mice with type II collagen emulsified in oil alone resulted in a weak immune response, and only a few animals (10-30%) developed arthritis. Administration of IL-12 for 5 days simultaneously with each immunization strongly enhanced the anti-type II collagen immune response. Collagen-specific interferon gamma (IFN-gamma) synthesis by ex vivo activated spleen cells was enhanced 3- to 10-fold. IFN-gamma was almost completely produced by CD4+ T cells. Furthermore, the production of collagen-specific IgG2a and IgG2b antibodies was upregulated 10- to 100-fold. As a consequence, the incidence of arthritis in the group of mice immunized with collagen plus IL-12 was very high (80-100%). The developing arthritis was severe, involving approximately 50% of all limbs with strongly increased footpad thickness in most cases. Furthermore, histological examination revealed massive, mainly polymorphonuclear cell infiltration, synovial hyperplasia, cartilage and bone destruction, as well as new bone formation. In many cases, this resulted in the complete loss of joint structure. Neutralization of IFN-gamma in vivo prevented the development of arthritis in collagen-immunized and IL-12-treated mice. In conclusion, our data show that in vivo administered IL-12 can profoundly upregulate a T helper I-type autoimmune response, resulting in severe joint disease in DBA/1 mice.

Integration of speech with natural language understanding.

The integration of speech recognition with natural language understanding raises issues of how to adapt natural language processing to the characteristics of spoken language; how to cope with errorful recognition output, including the use of natural language information to reduce recognition errors; and how to use information from the speech signal, beyond just the sequence of words, as an aid to understanding. This paper reviews current research addressing these questions in the Spoken Language Program sponsored by the Advanced Research Projects Agency (ARPA). I begin by reviewing some of the ways that spontaneous spoken language differs from standard written language and discuss methods of coping with the difficulties of spontaneous speech. I then look at how systems cope with errors in speech recognition and at attempts to use natural language information to reduce recognition errors. Finally, I discuss how prosodic information in the speech signal might be used to improve understanding.