Urinary Excretion of Chromium Following Ingestion of Chromite-Ore Processing Residues in Humans: Implications for Biomonitoring

Biomonitoring programs for urinary chromium (Cr) typically attempt to evaluate occupational exposure via the inhalation route. This study investigated whether Cr can be detected in the urine of people following the ingestion of soils that contain relatively high concentrations of chromium in chromite ore processing residue (COPR). To evaluate the reasonableness of using urinary monitoring to assess environmental exposure, six volunteers ingested 400 mg of soil/day (low-dose group), two others ingested 2.0 g of soil/day (high-dose group) for 3 consecutive days, and one person ingested a placebo on each of 3 days. The soil and COPR mixture contained concentrations of total chromium (Cr) and hexavalent chromium [Cr(VI)] of 103 ± 20 and 9.3 ± 3.8 mg/kg, respectively. Therefore, the low-dose group ingested 41 μg Cr/day [including 3.7 μg Cr(VI)] and the high-dose group ingested 206 μg Cr/day [including 18.6 μg Cr(VI)] on each of 3 consecutive days. All urine samples were collected and analyzed individually for total Cr on the day prior to dosing, during the 3 days of dosing, and up to the first void 48 h after the last dose. No significant increases in urinary Cr excretion were found when background excretion data were compared with data following each of the 3 days of dosing or in daily mean urine concentrations of the high- vs the low-dose groups. It appears that Cr present in a soil and COPR mixture at Cr doses up to 200 μg/day is not sufficiently bioavailable for biomonitoring of urine to be informative. These results are consistent with previously published findings suggesting that incidental exposure to dusts and soils containing comparable levels of Cr will not result in increased concentrations of Cr in urine.     

An Application of Markov Chain Analysis to the Game of Squash*

If the score in a squash game is tied late in the game, one player has a choice of how many additional points (from a prespecified set of possibilities) are to be played to determine the winner. This paper constructs a Markov chain model of the situation and solves for the optimal strategy. Expressions for the optimal strategy are obtained with a symbolic algebra computer package. Results are given for both international and American scoring systems. The model and analysis are very suitable for educational purposes. The resulting Markov chain is small enough that it can be easily presented in a classroom setting, yet the model is sufficiently complex that algebraic manipulation is nearly hopeless. The final results illustrate the power of the combination of mathematical and computer modeling applied to a problem of practical interest.     

Temporary Employment Contracts and Household Income

Social Indicators Research - It is widely accepted that temporary jobs tend to be associated with low pay which, in turn, will have negative consequences for household income. Evidence in support...     

Generalized Reflexive Responding and Cross-Modal Tactile Transfer of Stimulus Function in Children with Autism

We sought to evaluate the efficacy of successive matching training for establishing generalized reflexive matching across 4 children with autism. In Experiment 1, differential reinforcement with delay fading was efficacious in establishing “yes” and “no” matching and nonmatching responses in 2 participants when 2 identical or nonidentical picture stimuli were presented. In addition, emergent visual–visual reflexive relational responses were observed using novel picture stimuli in a transfer test phase. In Experiment 2, differential reinforcement alone was efficacious in establishing matching and nonmatching responses in the other 2 participants when 2 identical or nonidentical objects were presented. Transfer to identical objects presented through touch (i.e., tactile discrimination) was additionally observed for both participants. Procedures in the study were adapted from the PEAK Relational Training System to aid in clinical replication, and the translational results have implications for language training with individuals with autism.     

Explaining individual differences in infant visual sensory seeking

Individual differences in infants’ engagement with their environment manifest early in development and are noticed by parents. Three views have been advanced to explain differences in seeking novel stimulation. The optimal stimulation hypothesis suggests that individuals seek further stimulation when they are under‐responsive to current sensory input. The processing speed hypothesis proposes that those capable of processing information faster are driven to seek stimulation more frequently. The information prioritization hypothesis suggests the differences in stimulation seeking index variation in the prioritization of incoming relative to ongoing information processing. Ten‐month‐old infants saw 10 repetitions of a video clip and changes in frontal theta oscillatory amplitude were measured as an index of information processing speed. Stimulus‐locked P1 peak amplitude in response to checkerboards briefly overlaid on the video at random points during its presentation indexed processing of incoming stimulation. Parental report of higher visual seeking did not relate to reduced P1 peak amplitude or to a stronger decrease in frontal theta amplitude with repetition, thus not supporting either the optimal stimulation or the processing speed hypotheses. Higher visual seeking occurred in those infants whose P1 peak amplitude was greater than expected based on their theta amplitude. These findings indicate that visual sensory seeking in infancy is explained by a bias toward novel stimulation, thus supporting the information prioritization hypothesis.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

Predatory purchasing practices in global apparel supply chains and the employment relations squeeze in the Indian garment export industry

While the literature has long acknowledged worker rights abuses in global apparel supply chains, less attention has been paid to the recent worsening of certain decent work deficits and its causes, which this article links to predatory purchasing practices arising from growing power asymmetries. These practices, illustrated here by the garment export industry in India, exert downward pressure on wages and incite non‐standard forms of employment and greater verbal and physical abuse as productivity demands rise. They can be addressed through pricing mechanisms that cover the cost of decent work, appropriate State regulation and worker involvement.