Physiologically Based Liver Modeling and Risk Assessment

Because of the inherent complexity of biological systems, there is often a choice between a number of apparently equally applicable physiologically based models to describe uptake and metabolism processes in toxicology or risk assessment. These models may fit the particular data sets of interest equally well, but may give quite different parameter estimates or predictions under different (extrapolated) conditions. Such competing models can be discriminated by a number of methods, including potential refutation by means of strategic experiments, and their ability to suitably incorporate all relevant physiological processes. For illustration, three currently used models for steady-state hepatic elimination--the venous equilibration model, the parallel tube model, and the distributed sinusoidal perfusion model--are reviewed and compared with particular reference to their application in the area of risk assessment. The ability of each of the models to describe and incorporate such physiological processes as protein binding, precursor-metabolite relations and hepatic zones of elimination, capillary recruitment, capillary heterogeneity, and intrahepatic shunting is discussed. Differences between the models in hepatic parameter estimation, extrapolation to different conditions, and interspecies scaling are discussed, and criteria for choosing one model over the others are presented. In this case, the distributed model provides the most general framework for describing physiological processes taking place in the liver, and has so far not been experimentally refuted, as have the other two models. These simpler models may, however, provide useful bounds on parameter estimates and on extrapolations and risk assessments.     

Mining the Data: Analyzing the Economic Implications of Mining for Nonmetropolitan Regions

Extractive industries such as logging and mining are generally expected to bring significant economic benefits to rural regions, but a growing number of findings have now challenged that common expectation. Still, it is not clear whether the findings of less–than–desirable economic outcomes are isolated or representative. In this article, we assemble literally all of the relevant quantitative findings on mining that we have been able to identify in published and/or technical literature from the United States. In the interest of rigor, we limit the assessment to cases in which strictly nonmetropolitan mining regions are compared against other nonmetropolitan regions and/or against those regions’ own experiences over time. Overall, 301 findings meet the criteria for inclusion. Contrary to the long–established assumptions, but consistent with more recent critiques, roughly half of all published findings indicate negative economic outcomes in mining communities, with the remaining findings being split roughly evenly between favorable and neutral/indeterminate ones. Positive findings are more likely to be associated with incomes than with poverty or (especially) unemployment rates, and they are more likely to come from the western United States, where much of the mining involves relatively large, new coal strip mines. Over half of all positive findings come from the years prior to 1982. In virtually all other categories, the plurality or majority of findings have been negative. When the patterns of findings are subjected to one–sample means tests, the only way to produce a significantly positive outcome is by combining all neutral/indeterminate findings with the positive ones, while focusing exclusively on incomes; by contrast, in the case of poverty or unemployment rates—as well as for the overall body of findings—the results are consistently and significantly negative, whether the neutral/indeterminate findings are combined with negative ones or omitted from the equations altogether. Until or unless future studies produce dramatically different findings, there appears to be no scientific basis for accepting the widespread, “obvious” assumption that mining will lead to economic improvement.     

Labor relations and technological change at Canadian Pacific Railway

There was a time in this fair land when the railroad did not run When the wild majestic mountains stood alone against the sun Long before the white man and long before the wheel When the green dark forest was too silent to be real For they looked into the future and what did they see They saw an iron road running from the sea to the sea Bringing the goods to a young growing land All up through the seaports and into their hands From “Canadian Railroad Trilogy” by Gordon Lightfoot The authors thank CPR managers and union officials representing CPR employees for their cooperation and patience in explaining the intricacies of the railroad industry. Special appreciation is extended to Jason Copping, Labour Relations Manager, for his facilitating our initial access to the company.     

In Whose Backyard?: Concern About Siting a Nuclear Waste Facility*

Proponents of hazardous and nuclear waste depositories label opponents to local siting of such facilities “NIMBYs” (Not In My Backyard). This study assesses the extent to which the NIMBY label and the strategies of industry proponents to reduce opposition function on a reasonable set of assumptions. Using survey data and multiple regression techniques, the levels of concern of residents living in the county selected as the site of a low level radioactive waste disposal facility (imminent threat condition) are compared with a statewide sample (hypothetical threat condition). Consistent with proponents' theoretical assumptions, the levels of concern are greater for respondents under conditions of imminent threat than of hypothetical threat. However, within the host county, levels of concern are lowest, albeit most polarized, in the community closest to the proposed site. A conflict theory approach enhances an understanding of these findings by suggesting that within the most proximate community levels of concern are lowest for citizens who stand to gain the most economic benefits from the facility but highest for those citizens who are least likely to derive tangible gains.     

The Texaco racial discrimination case and shareholder wealth

We analyze the wealth ejfects of the Texaco racial discrimination lawsuit both on the shareholders of Texaco and its major U.S. competitors. Employing a comprehensive data set which included every case docket entry and every Wall Street Journal article on the case as an experimental stimulus, our findings suggest that the overall cost of the case to Texaco shareholders exceeded $500 million, that Texaco's tribulations had little, if any, impact on the share prices of its major competitors, and that Wall Street Journal coverage of the case was highly correlated with significant changes in Texaco stock prices. This last finding provides significant support for Hite 's suppostion that newspaper editors “key ” on ex post stock price changes in selecting the events to be covered in the next day's edition. The authors are grateful to Kee Chung for helpful comments on earlier drafts and also acknowlege the help-ful assistance of the staff of the law library at the Cecil C. Humphreys School of Law at The University of Memphis.     

Review Essay: What Is Left of the Left?

Sociological Forum -     

The Boston Area Diary Study and the Moral Citizenship of Care

This paper describes the theoretical foundations, empirical findings, and practical and philosophical implications of the Boston Area Diary Study (BADS), a study of the caring behavior of 44 participants over one calendar year. In particular, the paper presents an identification theory of care and discusses how it shaped the conceptualization, collection, and analysis of the data in a year-long diary study of daily voluntary assistance. The findings from the BADS (1) theoretically confirm the identification theory of care; (2) methodologically capture how individuals perceive and carry out caring behavior as a unity; and (3rpar; empirically document the existence of a moral citizenship in America that is substantially more vigorous than is implied by the usual indicators of civic and political citizenship.     

Enabling a Powerful Marine and Offshore Decision-Support Solution Through Bayesian Network Technique

A powerful practical solution is by far the most desired output when making decisions under the realm of uncertainty on any safety-critical marine or offshore units and their systems. With data and information typically being obtained incrementally, adopting Bayesian network (BN) is shown to realistically deal with the random uncertainties while at the same time making risk assessments easier to build and to check. A well-matched methodology is proposed to formalize the reasoning in which the focal mechanism of inference processing relies on the sound Bayes's rule/theorem that permits the logic. Expanding one or more influencing nodal parameters with decision and utility node(s) also yields an influence diagram (ID). BN and ID feasibility is shown in a marine evacuation scenario and that of authorized vessels to floating, production, storage, and offloading collision, developed via a commercial computer tool. Sensitivity analysis and validation of the produced results are also presented.     

A Comparison of Partially Adaptive and Reweighted Least Squares Estimation

The small sample performance of least median of squares, reweighted least squares, least squares, least absolute deviations, and three partially adaptive estimators are compared using Monte Carlo simulations. Two data problems are addressed in the paper: (1) data generated from non-normal error distributions and (2) contaminated data. Breakdown plots are used to investigate the sensitivity of partially adaptive estimators to data contamination relative to RLS. One partially adaptive estimator performs especially well when the errors are skewed, while another partially adaptive estimator and RLS perform particularly well when the errors are extremely leptokur-totic. In comparison with RLS, partially adaptive estimators are only moderately effective in resisting data contamination; however, they outperform least squares and least absolute deviation estimators.