Spatial variation in soil inorganic nitrogen across an arid urban ecosystem

We explored variations in inorganic soil nitrogen (N) concentrations across metropolitan Phoenix, Arizona, and the surrounding desert using a probability-based synoptic survey. Data were examined using spatial statistics on the entire region, as well as for the desert and urban sites separately. Concentrations of both NO₃-N and NH₄-N were markedly higher and more heterogeneous amongst urban compared to desert soils. Regional variation in soil NO₃-N concentration was best explained by latitude, land use history, population density, along with percent cover of impervious surfaces and lawn, whereas soil NH₄-N concentrations were related to only latitude and population density. Within the urban area, patterns in both soil NO₃-N and NH₄-N were best predicted by elevation, population density and type of irrigation in the surrounding neighborhood. Spatial autocorrelation of soil NO₃-N concentrations explained 49% of variation among desert sites but was absent between urban sites. We suggest that inorganic soil N concentrations are controlled by a number of ‘local’ or ‘neighborhood’ human-related drivers in the city, rather than factors related to an urban-rural gradient.     

Statistics for weighing benefits and harms in a proposed genetic substudy of a randomized cancer prevention trial

Summary.  When evaluating potential interventions for cancer prevention, it is necessary to compare benefits and harms. With new study designs, new statistical approaches may be needed to facilitate this comparison. A case in point arose in a proposed genetic substudy of a randomized trial of tamoxifen versus placebo in asymptomatic women who were at high risk for breast cancer. Although the randomized trial showed that tamoxifen substantially reduced the risk of breast cancer, the harms from tamoxifen were serious and some were life threaten-ing. In hopes of finding a subset of women with inherited risk genes who derive greater bene-fits from tamoxifen, we proposed a nested case–control study to test some trial subjects for various genes and new statistical methods to extrapolate benefits and harms to the general population. An important design question is whether or not the study should target common low penetrance genes. Our calculations show that useful results are only likely with rare high penetrance genes.     

Glass ceilings or sticky floors? A model of high-income law graduates

Minorities and females are underrepresented in the top-income quintile of law school graduates. Employing a binary logistic regression model, I examine whether this is due to a“glass ceiling” (an invisible barrier erected by third parties) or a“sticky floor” (self-imposed limitations regarding employment). My major finding is that being female, a minority, or disabled did not significantly reduce one's probability of making the top-income quintile once hours of work, experience, and other factors are taken into account. My findings directly contradict the large body of glass-ceiling literature and support the sticky-floor model. I thank the Law School Admission Council for funding this research. Helpful comments and suggestions were received from Robert Nelson of Northwestern University and the American Bar Foundation, Steven Conroy of the University of West Florida, and R. Kim Craft and Douglas Bonzo of Southern Utah University. The views expressed here are solely those of the author and do not necessarily reflect those of the institutions or persons listed above.     

An empirical Bayes' procedure for ranking players in Ryder Cup golf

We describe a model to obtain strengths and rankings of players appearing in golf's Ryder Cup. Obtaining rankings is complicated because of two reasons. First, competitors do not compete on an equal number of occasions, with some competitors appearing too infrequently for their ranking to be estimated with any degree of certainty, and second, different competitors experience different levels of volatility in results. Our approach is to assume the competitor strengths are drawn from some common distribution. For small numbers of competitors, as is the case here, we fit the model using Monte-Carlo integration. Results suggest there is very little difference between the top performing players, though Scotland's Colin Montgomerie is estimated as the strongest Ryder Cup player.     

Beyond the urban stream syndrome: organic matter budget for diagnostics and restoration of an impaired urban river

In response to water quality standard violations linked to excessive organic matter (OM) and a lack of sampling data informing the Total Maximum Daily Load (TMDL), an organic matter budget was created to quantify and identify sources of OM in the lower Jordan River (Salt Lake City, UT). By sampling dissolved, fine and coarse particulate OM, as well as measuring ecosystem metabolism at seven different sites, the researchers aimed to identify the origin of excess OM, and understand pathways by which different size classes of the OM pool are generated. The dissolved fraction (DOM; 94 %) was found to be the dominant form of OM transported within the river with fine particulate organic matter (FPOM; 6 %) the second most abundant, and coarse particulate organic matter (CPOM; 1 %) transport relatively insignificant in the overall OM budget. Primary production exceeded respiratory losses in the upper river, and this, along with OM inputs from two tributaries (where water reclamation facilities discharge into the river) delivered excess OM to the impaired lower reaches. Increasing stream metabolism index (SMI) with distance downstream (>1 in the lower river) further demonstrated that transport of excessive organic matter into the lower river was from upstream sources and not due to lateral inputs. This simple approach to characterizing the organic matter budget as it relates to water quality in the Jordan River was effective and could serve as a model for future studies attempting to quantify and identify sources of OM in urban ecosystems.     

Using In Vitro High‐Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high‐throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline‐based meta‐regression can be used to integrate data across multiple assay replicates to generate a concentration–response curve. We used this analysis to calculate an in vitro point of departure of 0.751 μM and risk‐specific in vitro concentrations of 0.29 μM and 0.28 μM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta‐regression, may allow risk assessors to identify points of departure and risk‐specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.     

State Immigration Policies: The Role of State Compacts and Interest Groups on Immigration Legislation

US states are active in enacting immigration policies, which vary widely and have substantial impact on the lives of immigrants. Our understanding of what produces these divergent state laws remains limited. Qualitative research demonstrates the importance of a 2010 immigration compact, supported by a powerful religious organization, in shaping immigration policies in Utah, and the Utah Compact was held up as a model for other states. But is the experience of Utah applicable across other states? We test the effects of compacts and interest groups on immigration policy adoption across all 50 states between 2005 and 2013. Our findings suggest that compacts are actually associated with more restrictive immigration policy. Although states with compacts are more likely to pass inclusive immigration laws, these are counterbalanced by higher numbers of exclusive laws. Both religious and non‐religious interests groups are associated with policy, but they do not explain the effects of compacts.     

Information Seeking about Global Climate Change among Adolescents: The Role of Risk Perceptions, Efficacy Beliefs and Parental Influences

Global climate change is likely to have significant impacts on public health. Effective communication is critical to informing public decision making and behavior to mitigate climate change. An effective method of audience segmentation, the risk perception attitude (RPA) framework has been previously tested with other health behaviors and classifies people into 4 groups on the basis of their perceptions of risk and beliefs about personal efficacy. The 4 groups - indifference (low risk, weak efficacy), proactive (low risk, strong efficacy), avoidance (high risk, weak efficacy), and responsive (high risk, strong efficacy) - are hypothesized to differ in their self-protective behaviors and in their motivations to seek information. In this paper, we extend the RPA framework in two ways. First, we use it at the household level to determine whether parental classifications into the 4 groups are associated with their teenage children's classification into the same 4 groups. Second, we predict adolescent information-seeking behaviors on the basis of their and their parents' membership in the 4 RPA groups. Results (N = 523 parent-adolescent pairs) indicated that parental membership in the 4 RPA groups was significantly associated with children's membership in the same 4 groups. Furthermore, the RPA framework was a significant predictor of adolescent information-seeking: those in the responsive and avoidance groups sought more information on climate change than the indifference group. Family communication on global warming was positively associated with adolescents' information-seeking. Implications for interventions are discussed.