IL-1 beta-converting enzyme (ICE) cleaves pro-IL-1 beta to generate mature IL-1 beta. ICE is homologous to other proteins that have been implicated in apoptosis, including CED-3 and Nedd-2/lch-1. We generated ICE-deficient mice and observed that they are overtly normal but have a major defect in the production of mature IL-1 beta after stimulation with lipopolysaccharide. IL-1 alpha production is also impaired. ICE-deficient mice are resistant to endotoxic shock. Thymocytes and macrophages from the ICE-deficient animals undergo apoptosis normally. ICE therefore plays a dominant role in the generation of mature IL-1 beta, a previously unsuspected role in production of IL-1 alpha, but has no autonomous function in apoptosis.

The mathematical theory of linear systems has been used successfully to describe responding on variable-interval (VI) schedules. In the simplest extension of the theory to the variable-ratio (VR) case, VR schedules are treated as if they were VI schedules with linear feedback loops. The assumption entailed by this approach, namely, that VR and VI-plus-linear-feedback schedules are equivalent, was tested by comparing responding on the two types of schedule. Four human subjects' lever pressing produced monetary reinforcers on five VR schedules, and on five VI schedules with linear feedback loops that reproduced the feedback properties of the VR schedules. Pressing was initiated by instructions in 2 subjects, and was shaped by successive approximation in the other 2. The different methods of response initiation did not have differential effects on behavior. For each of the 4 subjects, the VR and the comparable VI-plus-linear-feedback schedules generated similar average response rates and similar response patterns. The subjects' behavior on both types of schedule was similar to that of avian and rodent species on VR schedules. These results indicate that the assumption entailed by the VI-plus-linear-feedback approach to the VR case is valid and, consequently, that the approach is worth pursuing. The results also confute interresponse-time theories of schedule performance, which require interval and ratio contingencies to produce different response rates.

Matching theory is a mathematical account of behavior, many aspects of which have been confirmed in laboratory experiments with nonhuman and human subjects. The theory asserts that behavior is distributed across concurrently available response alternatives in the same proportion that reinforcement is distributed across those alternatives. The theory also asserts that behavior on a single response alternative is a function not only of reinforcement contingent on that behavior, but also of reinforcement contingent on other behaviors and of reinforcement delivered independently of behavior. These assertions constitute important advances in our understanding of the effects of reinforcement on behavior. Evidence from the applied literature suggests that matching theory holds not only in laboratory environments, but also in natural human environments. In addition, the theory has important therapeutic implications. For example, it suggests four new intervention strategies, and it can be used to improve treatment planning and management. Research on matching theory illustrates the progression from laboratory experimentation with nonhuman subjects to therapeutic applications in natural human environments.Matching theory (Herrnstein, 1970) is a mathematical account of behavior. It has been a part ofbasic behavior analysis for over a quarter of a century, and it has been a topic of considerable interest for at least the last 18 years. Most basic scientists would agree that matching theory has advanced significantly our understanding of how the environment governs behavior.

Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of computational experiments that arranged reinforcement according to random-interval (RI) schedules. The quantitative features of the model were varied over wide ranges in these experiments, and many of the qualitative features of the model also were varied. The digital organism consistently showed a hyperbolic relation between response and reinforcement rates, and this hyperbolic description of the data was consistently better than the description provided by other, similar, function forms. In addition, the parameters of the hyperbola varied systematically with the quantitative, and some of the qualitative, properties of the model in ways that were consistent with findings from biological organisms. These results suggest that the material events responsible for an organism's responding on RI schedules are computationally equivalent to Darwinian selection by consequences. They also suggest that the computational model developed here is worth pursuing further as a possible dynamic account of behavior.

Classic matching theory, which is based on Herrnstein's (1961) original matching equation and includes the well-known quantitative law of effect, is almost certainly false. The theory is logically inconsistent with known experimental findings, and experiments have shown that its central constant-k assumption is not tenable. Modern matching theory, which is based on the power function version of the original matching equation, remains tenable, although it has not been discussed or studied extensively. The modern theory is logically consistent with known experimental findings, it predicts the fact and details of the violation of the classic theory's constant-k assumption, and it accurately describes at least some data that are inconsistent with the classic theory.Key words: matching theory, matching law, quantitative law of effect, Herrnstein's hyperbola _______________________________________________________________________________ Thirty-five years ago, Richard J. Herrnstein (Herrnstein, 1970) proposed the theory of matching, according to which all behavior can be conceptualized as choice, governed by the matching equation,In this equation the Rs represent response rates or counts, the r s represent reinforcement rates or counts, and the numerical subscripts refer to the two components of a concurrent schedule (Herrnstein, 1961). The equation asserts that organisms allocate their behavior across concurrently available response alternatives in the same proportion that reinforcements are allocated across those alternatives. Herrnstein (1970) turned the purely descriptive matching equation into a theory by applying it to single-alternative schedules. He conceptualized these as two-component concurrent schedules consisting of instrumental responding as one alternative, and background or extraneous responding as the other alternative. For this conceptualization of the single-alternative case, the matching equation can be written R RzR e~r r zr e , where R and r refer to the instrumental response and reinforcement rates or counts, and R e and r e refer to the extraneous response and reinforcement rates or counts. Assuming a constant total amount of behavior to allocate, k, then R + R e 5 k, and the equation can be written R~k r r zr e :