Matthew J. Heaton

Characterizing urban vulnerability to heat stress using a spatially varying coefficient model

Identifying and characterizing urban vulnerability to heat is a key step in designing intervention strategies to combat negative consequences of extreme heat on human health. This study combines excess non-accidental mortality counts, numerical weather simulations, US Census and parcel data into an assessment of vulnerability to heat in Houston, Texas. Specifically, a hierarchical model with spatially varying coefficients is used to account for differences in vulnerability among census block groups. Socio-economic and demographic variables from census and parcel data are selected via a forward selection algorithm where at each step the remaining variables are orthogonalized with respect to the chosen variables to account for collinearity. Daily minimum temperatures and composite heat indices (e.g. discomfort index) provide a better model fit than other ambient temperature measurements (e.g. maximum temperature, relative humidity). Positive interactions between elderly populations and heat exposure were found suggesting these populations are more responsive to increases in heat.

A novel method for predicting antioxidant activity based on amino acid structure.

Epidemiological studies show a positive correlation between oxidative stress and chronic disease development such as heart disease and cancer. While several antioxidant compounds with varying physical and chemical characteristics are able to reduce oxidative stress in biological systems, relatively few studies have been performed to examine the structural characteristics that produce potent antioxidants. We examined 20 essential and non-essential amino acids using the ORAC assay and used a simplest-case amino acid model to gather data to make predictions regarding the antioxidant activity of non-amino acid compounds; we also tested our findings on chalcone and nitrone data from the current literature. We observed that the sp(2)-hybridized carbons were the most consistent predictors of antioxidant activity in all groups. Valence electron to carbon ratio and length of conjugated double bond groups also emerged as important structural characteristics. Further testing may help to elucidate more accurate trends, as well as nonlinear relationships.

Nonstationary Gaussian Process Models Using Spatial Hierarchical Clustering from Finite Differences

Modern digital data production methods, such as computer simulation and remote sensing, have vastly increased the size and complexity of data collected over spatial domains. Analysis of these large spatial datasets for scientific inquiry is typically carried out using the Gaussian process. However, nonstationary behavior and computational requirements for large spatial datasets can prohibit efficient implementation of Gaussian process models. To perform computationally feasible inference for large spatial data, we consider partitioning a spatial region into disjoint sets using hierarchical clustering of observations and finite differences as a measure of dissimilarity. Intuitively, directions with large finite differences indicate directions of rapid increase or decrease and are, therefore, appropriate for partitioning the spatial region. Spatial contiguity of the resulting clusters is enforced by only clustering Voronoi neighbors. Following spatial clustering, we propose a nonstationary Gaussian process model across the clusters, which allows the computational burden of model fitting to be distributed across multiple cores and nodes. The methodology is primarily motivated and illustrated by an application to the validation of digital temperature data over the city of Houston as well as simulated datasets. Supplementary materials for this article are available online.

Incorporating Time-Dependent source Profiles Using the Dirichlet Distribution in Multivariate Receptor Models.

Multivariate receptor modeling is used to estimate profiles and contributions of pollution sources from concentrations of pollutants such as particulate matter in the air. The majority of previous approaches to multivariate receptor modeling assume pollution source profiles are constant through time. In an effort to relax this assumption, this article uses the Dirichlet distribution in a dynamic linear receptor model for pollution source profiles. The receptor model developed herein is evaluated using simulated datasets and then applied to a physical dataset of chemical species concentrations measured at the U.S. Environmental Protection Agency’s St. Louis–Midwest supersite. Supplemental materials to this articles are available online.

Evaluation of various glyphosate concentrations on DNA damage in human Raji cells and its impact on cytotoxicity

Abstract Glyphosate is a highly used active compound in agriculturally based pesticides. The literature regarding the toxicity of glyphosate to human cells has been highly inconsistent. We studied the resulting DNA damage and cytotoxicity of various glyphosate concentrations on human cells to evaluate DNA damaging potential. Utilizing human Raji cells, DNA damage was quantified using the comet assay, while cytotoxicity was further analyzed using MTT viability assays. Several glyphosate concentrations were assessed, ranging from 15 mM to 0.1 &mgr;M. We found that glyphosate treatment is lethal to Raji cells at concentrations above 10 mM, yet has no cytotoxic effects at concentrations at or below 100 &mgr;M. Treatment concentrations of 1 mM and 5 mM induce statistically significant DNA damage to Raji cells following 30–60 min of treatment, however, cells show a slow recovery from initial damage and cell viability is unaffected after 2 h. At these same concentrations, cells treated with additional compound did not recover and maintained high levels of DNA damage. While the cytotoxicity of glyphosate appears to be minimal for physiologically relevant concentrations, the compound has a definitive cytotoxic nature in human cells at high concentrations. Our data also suggests a mammalian metabolic pathway for the degradation of glyphosate may be present. HighlightsSignificant DNA damage and cellular death occurs when Raji cells are exposed to glyphosate concentrations at or above 10 mM.Glyphosate concentrations below 10 &mgr;M do not induce significant DNA damage and cells maintain full long term viability.Intermediate glyphosate concentrations induce initial damage, but undergo repair that leads to eventual viability.Discrepancies in regards to cytotoxicity may be due to inconsistent time points that show alternative damage.

Extending distributed lag models to higher degrees

Distributed lag (DL) models relate lagged covariates to a response and are a popular statistical model used in a wide variety of disciplines to analyze exposure-response data. However, classical DL models do not account for possible interactions between lagged predictors. In the presence of interactions between lagged covariates, the total effect of a change on the response is not merely a sum of lagged effects as is typically assumed. This article proposes a new class of models, called high-degree DL models, that extend basic DL models to incorporate hypothesized interactions between lagged predictors. The modeling strategy utilizes Gaussian processes to counterbalance predictor collinearity and as a dimension reduction tool. To choose the degree and maximum lags used within the models, a computationally manageable model comparison method is proposed based on maximum a posteriori estimators. The models and methods are illustrated via simulation and application to investigating the effect of heat exposure on mortality in Los Angeles and New York.

Parameter tuning for a multi-fidelity dynamical model of the magnetosphere

Geomagnetic storms play a critical role in space weather physics with the potential for far reaching economic impacts including power grid outages, air traffic rerouting, satellite damage and GPS disruption. The LFM-MIX is a state-of-the-art coupled magnetospheric-ionospheric model capable of simulating geomagnetic storms. Imbedded in this model are physical equations for turning the magnetohydrodynamic state parameters into energy and flux of electrons entering the ionosphere, involving a set of input parameters. The exact values of these input parameters in the model are unknown, and we seek to quantify the uncertainty about these parameters when model output is compared to observations. The model is available at different fidelities: a lower fidelity which is faster to run, and a higher fidelity but more computationally intense version. Model output and observational data are large spatiotemporal systems; the traditional design and analysis of computer experiments is unable to cope with such large data sets that involve multiple fidelities of model output. We develop an approach to this inverse problem for large spatiotemporal data sets that incorporates two different versions of the physical model. After an initial design, we propose a sequential design based on expected improvement. For the LFM-MIX, the additional run suggested by expected improvement diminishes posterior uncertainty by ruling out a posterior mode and shrinking the width of the posterior distribution. We also illustrate our approach using the Lorenz `96 system of equations for a simplified atmosphere, using known input parameters. For the Lorenz `96 system, after performing sequential runs based on expected improvement, the posterior mode converges to the true value and the posterior variability is reduced.

Wombling Analysis of Childhood Tumor Rates in Florida

The Florida Association of Pediatric Tumor Program (FAPTP) is a statewide network charged with the responsibility to monitor and evaluate children’s cancer care in Florida. As part of this responsibility, the FAPTP collects data about the race, gender, age, ZIP code tabulation area of residence, and year of diagnosis for cancer cases across Florida. In accord with the goals of the FAPTP, this article seeks to identify spatial, temporal, and covariate regions of rapid change in the rate of cancer occurrence with the goal of understanding important spatial and demographic factors that determine the occurrence of childhood cancer. Herein, the FAPTP data are modeled as a marked point pattern (process) with an unknown intensity function. By estimating the intensity function from data, regions of high cancer occurrences and boundaries denoting rapid increases (or decreases) in the corresponding cancer rates can be identified. Results indicate that regions of high cancer risk vary with race. Furthermore, younger populations are found to have the highest risk.

A spatio‐temporal absorbing state model for disease and syndromic surveillance

Reliable surveillance models are an important tool in public health because they aid in mitigating disease outbreaks, identify where and when disease outbreaks occur, and predict future occurrences. Although many statistical models have been devised for surveillance purposes, none are able to simultaneously achieve the important practical goals of good sensitivity and specificity, proper use of covariate information, inclusion of spatio-temporal dynamics, and transparent support to decision-makers. In an effort to achieve these goals, this paper proposes a spatio-temporal conditional autoregressive hidden Markov model with an absorbing state. The model performs well in both a large simulation study and in an application to influenza/pneumonia fatality data.

An Analysis of an Incomplete Marked Point Pattern of Heat-Related 911 Calls

We analyze an incomplete marked point pattern of heat-related 911 calls between the years 2006–2010 in Houston, TX, to primarily investigate conditions that are associated with increased vulnerability to heat-related morbidity and, secondarily, build a statistical model that can be used as a public health tool to predict the volume of 911 calls given a time frame and heat exposure. We model the calls as arising from a nonhomogenous Cox process with unknown intensity measure. By using the kernel convolution construction of a Gaussian process, the intensity surface is modeled using a low-dimensional representation and properly adheres to circular domain constraints. We account for the incomplete observations by marginalizing the joint intensity measure over the domain of the missing marks and also demonstrate model based imputation. We find that spatial regions of high risk for heat-related 911 calls are temporally dynamic with the highest risk occurring in urban areas during the day. We also find that elderly populations have an increased probability of calling 911 with heat-related issues than younger populations. Finally, the age of individuals and hour of the day with the highest intensity of heat-related 911 calls varies by race/ethnicity. Supplementary materials are included with this article.