STOCHASTIC KINETIC MODELS: DYNAMIC INDEPENDENCE, MODULARITY AND GRAPHS

The dynamic properties and independence structure of stochastic kinetic models (SKMs) are analyzed. An SKM is a highly multivariate jump process used to model chemical reaction networks, particularly those in biochemical and cellular systems. We identify SKM subprocesses with the corresponding counting processes and propose a directed, cyclic graph (the kinetic independence graph or KIG) that encodes the local independence structure of their conditional intensities. Given a partition [A, D, B] of the vertices, the graphical separation A ⊥ B|D in the undirected KIG has an intuitive chemical interpretation and implies that A is locally independent of B given A ∪ D. It is proved that this separation also results in global independence of the internal histories of A and B conditional on a history of the jumps in D which, under conditions we derive, corresponds to the internal history of D. The results enable mathematical definition of a modularization of an SKM using its implied dynamics. Graphical decomposition methods are developed for the identification and efficient computation of nested modularizations. Application to an SKM of the red blood cell advances understanding of this biochemical system.     

Exposure Assessment: Then, Now, and Quantum Leaps in the Future

Health risk assessments have become so widely accepted in the United States that their conclusions are a major factor in many environmental decisions. Although the risk assessment paradigm is 10 years old, the basic risk assessment process has been used by certain regulatory agencies for nearly 40 years. Each of the four components of the paradigm has undergone significant refinements, particularly during the last 5 years. A recent step in the development of the exposure assessment component can be found in the 1992 EPA Guidelines for Exposure Assessment. Rather than assuming worst-case or hypothetical maximum exposures, these guidelines are designed to lead to an accurate characterization, making use of a number of scientific advances. Many exposure parameters have become better defined, and more sensitive techniques now exist for measuring concentrations of contaminants in the environnment. Statistical procedures for characterizing variability, using Monte Carlo or similar approaches, eliminate the need to select point estimates for all individual exposure parameters. These probabilistic models can more accurately characterize the full range of exposures that may potentially be encountered by a given population at a particular site, reducing the need to select highly conservative values to account for this form of uncertainty in the exposure estimate. Lastly, our awareness of the uncertainties in the exposure assessment as well as our knowledge as to how best to characterize them will almost certainly provide evaluations that will be more credible and, therein, more useful to risk managers. If these refinements are incorporated into future exposure assessments, it is likely that our resources will be devoted to problems that, when resolved, will yield the largest improvement in public health.     

Labor markets and sunday closing laws

This paper puts forth arguments regarding the occurrence of state Sunday closing laws and reports statistical estimates that predict the occurrence of those laws in 1970 and 1984. Along with other socio-economic variables, two labor market activities are found to be significant determinants of Blue laws. Membership in labor unions is found to have a positive declining influence on the laws; female labor market participation is found to have a negative but growing influence.     

Determination of Less-Than-Lifetime Exposures to Point Source Emissions

This paper presents a method of estimating long-term exposures to point source emissions. The method consists of a Monte Carlo exposure model (PSEM or Point Source Exposure Model) that combines data on population mobility and mortality with information on daily activity patterns. The approach behind the model can be applied to a wide variety of exposure scenarios. In this paper, PSEM is used to characterize the range and distribution of lifetime equivalent doses received by inhalation of air contaminated by the emissions of a point source. The output of the model provides quantitative information on the dose, age, and gender of highly exposed individuals. The model is then used in an example risk assessment. Finally, future uses of the model's approach are discussed.     

Co-dependency and myth

Timmen Cernak’s use of Ovid’s mythological account of Narcissus and Echo as a classic case of co-dependence is critically analyzed against other interpretations. Simultaneous consideration is given to whether and to what extent the clinical concept of co-dependency has been confounded with the psychology of women in a male-oriented society. The complex and ambiguous symbolism of myth demands separation of myth from reality in describing the symptoms, etiology, and treatment of clinical co-dependence.