ASU Scholarship Showcase

Despite increasing interest in the effects of triclosan and triclocarban on human biology, current knowledge is still limited on the impact of these additives to antimicrobial personal care products on the human microbiome. A carefully designed recent study published in mSphere by Poole and colleagues [A. C. Poole et al., mSphere 1(3):e00056-15, 2016, http://dx.doi.org/10.1128/mSphere.00056-15] highlights both the power of novel methodologies for microbiome elucidation and the longstanding challenge of employing small-cohort studies to inform risk assessment for chemicals of ubiquitous use in modern society.

Sequential affect dynamics generated during the interaction of intimate dyads, such as married couples, are associated with a cascade of effects - some good and some bad - on each partner, close family members, and other social contacts. Although the effects are well documented, the probabilistic structures associated with micro-social processes connected to the varied outcomes remain enigmatic. Using extant data we developed a method of classifying and subsequently generating couple dynamics using a Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM). Our findings indicate that several key aspects of existing models of marital interaction are inadequate: affect state emissions and their durations, along with the expected variability differences between distressed and nondistressed couples are present but highly nuanced; and most surprisingly, heterogeneity among highly satisfied couples necessitate that they be divided into subgroups. We review how this unsupervised learning technique generates plausible dyadic sequences that are sensitive to relationship quality and provide a natural mechanism for computational models of behavioral and affective micro-social processes.

Traditionally, hazardous chemicals have been regulated in the U.S. on a one-by-one basis, an approach that is slow, expensive and can be inefficient, as illustrated by a decades-long succession of replacing one type of organohalogen flame retardants (OHFRs) with another one, without addressing the root cause of toxicity and associated public health threats posed. The present article expounds on the need for efficient monitoring strategies and pragmatic steps in reducing environmental pollution and adverse human health impacts. A promising approach is to combine specific bioassays with state-of-the-art chemical screening to identify chemicals and chemical mixtures sharing specific modes of action (MOAs) and pathways of toxicity (PoTs). This approach could be used to identify and regulate hazardous chemicals as classes or compound families, featuring similar biological end-points, such as endocrine disruption and mutagenicity. Opportunities and potential obstacles of implementing this approach are discussed.

Processed municipal sewage sludges (MSS) are an abundant, unwanted by-product of wastewater treatment, increasingly applied to agriculture and forestry for inexpensive disposal and soil conditioning. Due to their high organic carbon and lipid contents, MSS not only is rich in carbon and nutrients but also represents a “sink” for recalcitrant, hydrophobic, and potentially bioaccumulative compounds. Indeed, many organics sequestered and concentrated in MSS meet the US Environmental Protection Agency’s definition of being persistent, bioaccumulative, and toxic (PBT). In a strategic effort, our research team at the Biodesign Institute has created the National Sewage Sludge Repository (NSSR), a large repository of digested MSSs from 164 wastewater treatment plants from across the USA, as part of the Human Health Observatory (H2O) at Arizona State University (ASU). The NSSR likely represents the largest archive of digested MSS specimens in the USA. The present study summarizes key findings gleaned thus far from analysis of NSSR samples. For example, we evaluated the content of toxicants in MSS and computed estimates of nationwide inventories of mass produced chemicals that become sequestrated in sludge and later are released into the environment during sludge disposal on land. Ongoing efforts document co-occurrence of a variety of PBT compounds in both MSS and human samples, while also identifying a large number of potentially harmful MSS constituents for which human exposure data are still lacking. Finally, we summarize future opportunities and invite collaborative use of the NSSR by the research community. The H2O at ASU represents a new resource and research tool for environmental scientists and the larger research community. As illustrated in this work, this repository can serve to (i) identify and prioritize emerging contaminants, (ii) provide spatial and temporal trends of contaminants, (iii) inform and evaluate the effectiveness of environmental policy-making and regulations, and (iv) approximate, ongoing exposures and body burdens of mass-produced chemicals in human society.

This study extended the findings of Tighe and Schatschneider (2015) by investigating the predictive utility of separate dimensions of morphological awareness as well as vocabulary knowledge to reading comprehension in adult basic education (ABE) students. We competed two- and three-factor structural equation models of reading comprehension. A three-factor model of real word morphological awareness, pseudoword morphological awareness, and vocabulary knowledge emerged as the best fit and accounted for 79% of the reading comprehension variance. The results indicated that the constructs contributed jointly to reading comprehension; however, vocabulary knowledge was the only potentially unique predictor (p = 0.052), accounting for an additional 5.6% of the variance. This study demonstrates the feasibility of applying a latent variable modeling approach to examine individual differences in the reading comprehension skills of ABE students. Further, this study replicates the findings of Tighe and Schatschneider (2015) on the importance of differentiating among dimensions of morphological awareness in this population.

Recent advances in nonequilibrium statistical physics have provided unprecedented insight into the thermodynamics of dynamic processes. The author recently used these advances to extend Landauer’s semi-formal reasoning concerning the thermodynamics of bit erasure, to derive the minimal free energy required to implement an arbitrary computation. Here, I extend this analysis, deriving the minimal free energy required by an organism to run a given (stochastic) map π from its sensor inputs to its actuator outputs. I use this result to calculate the input-output map π of an organism that optimally trades off the free energy needed to run π with the phenotypic fitness that results from implementing π. I end with a general discussion of the limits imposed on the rate of the terrestrial biosphere’s information processing by the flux of sunlight on the Earth.

The known occurrence of pharmaceuticals in the built and natural water environment, including in drinking water supplies, continues to raise concerns over inadvertent exposures and associated potential health risks in humans and aquatic organisms. At the same time, the number and concentrations of new and existing pharmaceuticals in the water environment are destined to increase further in the future as a result of increased consumption of pharmaceuticals by a growing and aging population and ongoing measures to decrease per-capita water consumption. This review examines the occurrence and movement of pharmaceuticals in the built and natural water environment, with special emphasis on contamination of the drinking water supply, and opportunities for sustainable pollution control. We surveyed peer-reviewed publications dealing with quantitative measurements of pharmaceuticals in U.S. drinking water, surface water, groundwater, raw and treated wastewater as well as municipal biosolids. Pharmaceuticals have been observed to reenter the built water environment contained in raw drinking water, and they remain detectable in finished drinking water at concentrations in the ng/L to μg/L range. The greatest promises for minimizing pharmaceutical contamination include source control (for example, inputs from intentional flushing of medications for safe disposal, and sewer overflows), and improving efficiency of treatment facilities.