A Novel Approach to Chemical Mixture Risk Assessment—Linking Data from Population‐Based Epidemiology and Experimental Animal Tests

Humans are continuously exposed to chemicals with suspected or proven endocrine disrupting chemicals (EDCs). Risk management of EDCs presents a major unmet challenge because the available data for adverse health effects are generated by examining one compound at a time, whereas real‐life exposures are to mixtures of chemicals. In this work, we integrate epidemiological and experimental evidence toward a whole mixture strategy for risk assessment. To illustrate, we conduct the following four steps in a case study: (1) identification of single EDCs (“bad actors”)—measured in prenatal blood/urine in the SELMA study—that are associated with a shorter anogenital distance (AGD) in baby boys; (2) definition and construction of a “typical” mixture consisting of the “bad actors” identified in Step 1; (3) experimentally testing this mixture in an in vivo animal model to estimate a dose–response relationship and determine a point of departure (i.e., reference dose [RfD]) associated with an adverse health outcome; and (4) use a statistical measure of “sufficient similarity” to compare the experimental RfD (from Step 3) to the exposure measured in the human population and generate a “similar mixture risk indicator” (SMRI). The objective of this exercise is to generate a proof of concept for the systematic integration of epidemiological and experimental evidence with mixture risk assessment strategies. Using a whole mixture approach, we could find a higher rate of pregnant women under risk (13%) when comparing with the data from more traditional models of additivity (3%), or a compound‐by‐compound strategy (1.6%).     

The Public Sector’s Use of Agencies: A Dynamic Rather than Static Scene

It is an occupational hazard in the study of organizations that we tend to view them as static arrangements, as though what is observed and reported today tells us all we need to know about them. But organizations are dynamic rather than static phenomena, being constantly affected by adjustments to meet the effects of fading past arrangements or approaching new arrangements. Simple “snapshots” taken at a particular moment in history are never likely to reveal all the relevant nuances. This article comments on this problem as it affects non-departmental public bodies often described loosely as “agencies”, by (a) drawing attention to some relevant theoretical contributions to the political science and organization theory literature, and (b) giving some examples of changes in the style of agency formation and operation over the years, mostly drawn from the Australian public sector.

Leadership behavior and employee well-being: An integrated review and a future research agenda

Leadership behavior has a significant impact on employee behavior, performance and well-being. Extant theory and research on leadership behavior, however, has predominantly focused on employee performance, treating employee well-being (typically measured as job satisfaction) as a secondary outcome variable related to performance, rather than as an important outcome in and of itself. This qualitative state of the science review examines the process by which leadership behavior (i.e., change, relational, task, passive) affects employee well-being. We identify five mediator groupings (social-cognitive, motivational, affective, relational, identification), extend the criterion space for conceptualizing employee well-being (i.e., psychological: hedonic, eudaimonic, negative; and physical), examine the limited evidence for differential processes that underlie the leader behavior-employee well-being relationship and discuss theoretical and methodological problems inherent to the literature. We conclude by proposing a theoretical framework to guide a future research agenda on how, why and when leadership behavior impacts employee well-being.     

On Testing Sample Selection Bias Under the Multicollinearity Problem

This paper reviews and extends the literature on the finite sample behavior of tests for sample selection bias. Monte Carlo results show that, when the “multicollinearity problem” identified by Nawata (1993) is severe, (i) the t-test based on the Heckman-Greene variance estimator can be unreliable, (ii) the Likelihood Ratio test remains powerful, and (iii) nonnormality can be interpreted as severe sample selection bias by Maximum Likelihood methods, leading to negative Wald statistics. We also confirm previous findings (Leung and Yu, 1996) that the standard regression-based t-test (Heckman, 1979) and the asymptotically efficient Lagrange Multiplier test (Melino, 1982), are robust to nonnormality but have very little power.     

免费赠送同样可以赚钱!

我每次谈到“免费”二字时，大家就会感到困惑，甚至是怀疑，他们不相信这个世界上会有真正免费的东西，也不相信很多商品和服务的价格会逐渐接近于零。对于他们的困惑，我要解释的很简单，就是“免费是什么，免费不是什么”。     

Who's calling the shots? Decision-makers and the adoption of effective school-based substance use prevention curricula

This study investigates the relative roles of school district and school-level decision-makers in the implementation of effective substance use prevention curricula. Drawing on a "Site-Based Management" approach to effective decision-making, we hypothesized that schools whose personnel played active decision-making roles would be more likely to implement effective curricula than those in which decision-making was the prerogative of school district personnel. Study data comprised 1369 questionnaires completed by a representative national sample of both district-level prevention coordinators and middle school-based lead prevention teachers. From the perspective of the lead prevention teachers, the school district-level prevention coordinator was more influential than school staff in selecting effective prevention curricula. However, we did find some support for our hypothesis from our district-level informants, who indicated that community groups and advisory committees also play a modest role in the selection of such curricula.     

Time series forecasting with neural networks: a comparative study using the air line data

This case-study fits a variety of neural network (NN) models to the well-known air line data and compares the resulting forecasts with those obtained from the Box–Jenkins and Holt–Winters methods. Many potential problems in fitting NN models were revealed such as the possibility that the fitting routine may not converge or may converge to a local minimum. Moreover it was found that an NN model which fits well may give poor out-of-sample forecasts. Thus we think it is unwise to apply NN models blindly in 'black box' mode as has sometimes been suggested. Rather, the wise analyst needs to use traditional modelling skills to select a good NN model, e.g. to select appropriate lagged variables as the 'inputs'. The Bayesian information criterion is preferred to Akaike's information criterion for comparing different models. Methods of examining the response surface implied by an NN model are examined and compared with the results of alternative nonparametric procedures using generalized additive models and projection pursuit regression. The latter imposes less structure on the model and is arguably easier to understand.