Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

Optimal scaling of Metropolis algorithms: Heading toward general target distributions

The authors provide an overview of optimal scaling results for the Metropolis algorithm with Gaussian proposal distribution. They address in more depth the case of high‐dimensional target distributions formed of independent, but not identically distributed components. They attempt to give an intuitive explanation as to why the well‐known optimal acceptance rate of 0.234 is not always suitable. They show how to find the asymptotically optimal acceptance rate when needed, and they explain why it is sometimes necessary to turn to inhomogeneous proposal distributions. Their results are illustrated with a simple example.     

A note on estimating regression coefficients with missing data

In this note, we consider the problem of estimating regression coefficients when there are missing observations of some explanatory variables. Following Dagenais (1973), Gourieroux and Monfort (1981), and Conniffe (1983a, 1983b), we assume auxiliary relationships exist among explanatory varibles. Several estimatprs and their interrelationships are discussed. We begin with the model of Gourieroux and

Monfort (1981)     

The availability of personal and external coping resources: Thier impact on job stress and employee attitudes during organizational restructuring

This study examines the relationships among personal coping resources, social support, external coping resources, job stressors and job strains in a sample of 110 American Telephone and Telegraph employees undergoing a major organizational restructuring. The study expanded on a model suggested by Ashford (1988) by defining another category of coping resources that employees may draw upon to deal with the stressors and strains which occur during major organizational changes. External coping resources were defined as those which provided employees with a sense of 'vicarious control' in stressful situations. Results indicated that personal coping resources, social support and external coping resources had a direct effect upon job stressor and strain levels. No 'buffering' effect of these coplng resources was found. Hierarchical regression analyses indicated that external coping resources added to the prediction of job stressors and strains even when pertonal coping resources and social support were entered first into the prediction questions.     

Making ends meet: perceptions of poverty in Sweden

During the era after the Second World War, Sweden has built a welfare system based on labor market participation and income maintenance. Low unemployment and decent wages are supposed to guarantee people a labor market income or income maintenance, which in turn should provide a proper standard for everyone. However, a rapid increase in unemployment and economic problems have made the future of the Swedish welfare state more uncertain than ever. These circumstances have, among other things, led to the suggestion that Sweden should abandon the income maintenance policy and create a social policy system with the more limited ambition of guaranteeing everyone a minimum income. In that case, one central question must be answered: what constitutes a decent minimum income in today's Sweden? Where should we draw the poverty line under which people will not be forced to live? These questions are central in the current debate. The consensual poverty line method is used in this article to derive a poverty line relevant for today's Sweden. The results shows that more than every fifth household has an income below the consensual poverty line. That is, they have an income that most Swedes would argue is too low to make ends meet. The level of the consensual poverty line was compared with the National Board of Health and Welfare's guidelines for social assistance. The consensual poverty line was shown to be more generous to small households and the norm for social assistance was more generous to larger households. Finally, the expenditure for guaranteeing all Swedish household a minimum income equal to the consensual poverty line was estimated: more than SEK 25 billion per year. The results in the article casts serious doubt on the ability of the Swedish welfare state to secure a decent income to all citizens.     

A modified false discovery rate multiple-comparisons procedure for discrete data, applied to human immunodeficiency virus genetics

Summary.  To help to design vaccines for acquired immune deficiency syndrome that protect broadly against many genetic variants of the human immunodeficiency virus, the mutation rates at 118 positions in HIV amino-acid sequences of subtype C versus those of subtype B were compared. The false discovery rate (FDR) multiple-comparisons procedure can be used to determine statistical significance. When the test statistics have discrete distributions, the FDR procedure can be made more powerful by a simple modification. The paper develops a modified FDR procedure for discrete data and applies it to the human immunodeficiency virus data. The new procedure detects 15 positions with significantly different mutation rates compared with 11 that are detected by the original FDR method. Simulations delineate conditions under which the modified FDR procedure confers large gains in power over the original technique. In general FDR adjustment methods can be improved for discrete data by incorporating the modification proposed.     

Summary of the Workshop on Issues in Risk Assessment: Quantitative Methods for Developmental Toxicology

This report summarizes the proceedings of a conference on quantitative methods for assessing the risks of developmental toxicants. The conference was planned by a subcommittee of the National Research Council's Committee on Risk Assessment Methodology ⁴ in conjunction with staff from several federal agencies, including the U.S. Environmental Protection Agency, U.S. Food and Drug Administration, U.S. Consumer Products Safety Commission, and Health and Welfare Canada. Issues discussed at the workshop included computerized techniques for hazard identification, use of human and animal data for defining risks in a clinical setting, relationships between end points in developmental toxicity testing, reference dose calculations for developmental toxicology, analysis of quantitative dose-response data, mechanisms of developmental toxicity, physiologically based pharmacokinetic models, and structure-activity relationships. Although a formal consensus was not sought, many participants favored the evolution of quantitative techniques for developmental toxicology risk assessment, including the replacement of lowest observed adverse effect levels (LOAELs) and no observed adverse effect levels (NOAELs) with the benchmark dose methodology.