A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species.

Per- and polyfluoroalkyl substances (PFAS) are a diverse group of man-made chemicals that are commonly found in body tissues. The toxicokinetics of most PFAS are currently uncharacterized, but long half-lives (t½) have been observed in some cases. Knowledge of chemical-specific t½ is necessary for exposure reconstruction and extrapolation from toxicological studies. We used an ensemble machine learning method, random forest, to model the existing in vivo measured t½ across four species (human, monkey, rat, mouse) and eleven PFAS. Mechanistically motivated descriptors were examined, including two types of surrogates for renal transporters: (1) physiological descriptors, including kidney geometry, for renal transporter expression and (2) structural similarity of defluorinated PFAS to endogenous chemicals for transporter affinity. We developed a classification model for t½ (Bin 1: <12 h; Bin 2: <1 week; Bin 3: <2 months; Bin 4: >2 months). The model had an accuracy of 86.1% in contrast to 32.2% for a y-randomized null model. A total of 3890 compounds were within domain of the model, and t½ was predicted using the bin medians: 4.9 h, 2.2 days, 33 days, and 3.3 years. For human t½, 56% of PFAS were classified in Bin 4, 7% were classified in Bin 3, and 37% were classified in Bin 2. This model synthesizes the limited available data to allow tentative extrapolation and prioritization.

A Scoping Assessment of Implemented Toxicokinetic Models of Per- and Polyfluoro-Alkyl Substances, with a Focus on One-Compartment Models.

Toxicokinetic (TK) models have been used for decades to estimate concentrations of per-and polyfluoroalkyl substances (PFAS) in serum. However, model complexity has varied across studies depending on the application and the state of the science. This scoping effort seeks to systematically map the current landscape of PFAS TK models by categorizing different trends and similarities across model type, PFAS, and use scenario. A literature review using Web of Science and SWIFT-Review was used to identify TK models used for PFAS. The assessment covered publications from 2005-2020. PFOA, the PFAS for which most models were designed, was included in 69 of the 92 papers, followed by PFOS with 60, PFHxS with 22, and PFNA with 15. Only 4 of the 92 papers did not include analysis of PFOA, PFOS, PFNA, or PFHxS. Within the corpus, 50 papers contained a one-compartment model, 17 two-compartment models were found, and 33 used physiologically based pharmacokinetic (PBTK) models. The scoping assessment suggests that scientific interest has centered around two chemicals-PFOA and PFOS-and most analyses use one-compartment models in human exposure scenarios.

Combining epidemiological and ecological methods to quantify social effects on Escherichia coli transmission.

Enteric microparasites like Escherichia coli use multiple transmission pathways to propagate within and between host populations. Characterizing the relative transmission risk attributable to host social relationships and direct physical contact between individuals is paramount for understanding how microparasites like E. coli spread within affected communities and estimating colonization rates. To measure these effects, we carried out commensal E. coli transmission experiments in two cattle (Bos taurus) herds, wherein all individuals were equipped with real-time location tracking devices. Following transmission experiments in this model system, we derived temporally dynamic social and contact networks from location data. Estimated social affiliations and dyadic contact frequencies during transmission experiments informed pairwise accelerated failure time models that we used to quantify effects of these sociobehavioural variables on weekly E. coli colonization risk in these populations. We found that sociobehavioural variables alone were ultimately poor predictors of E. coli colonization in feedlot cattle, but can have significant effects on colonization hazard rates (p ≤ 0.05). We show, however, that observed effects were not consistent between similar populations. This work demonstrates that transmission experiments can be combined with real-time location data collection and processing procedures to create an effective framework for quantifying sociobehavioural effects on microparasite transmission.

Correction to Designing QSARs for Parameters of High Throughput Toxicokinetic Models Using Open-Source Descriptors.

The intrinsic metabolic clearance rate (Clint) and fraction of chemical unbound in plasma (fup) serve as important parameters for high throughput toxicokinetic models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on Bioactivity: Exposure Ratios (BER), in which a BER < 1 indicates exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6631 chemicals) we found that the proportion of chemicals with BER < 1 was similar using either in silico (1337/6631; 20.16%) or in vitro (151/850; 17.76%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER < 1 or >1 using either in silico or in vitro parameters (776/850, 91.30%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors.

The intrinsic metabolic clearance rate (Clint) and the fraction of the chemical unbound in plasma (fup) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

Accounting for space and uncertainty in real-time location system-derived contact networks.

Point data obtained from real-time location systems (RTLSs) can be processed into animal contact networks, describing instances of interaction between tracked individuals. Proximity-based definitions of interanimal "contact," however, may be inadequate for describing epidemiologically and sociologically relevant interactions involving body parts or other physical spaces relatively far from tracking devices. This weakness can be overcome by using polygons, rather than points, to represent tracked individuals and defining "contact" as polygon intersections.We present novel procedures for deriving polygons from RTLS point data while maintaining distances and orientations associated with individuals' relocation events. We demonstrate the versatility of this methodology for network modeling using two contact network creation examples, wherein we use this procedure to create (a) interanimal physical contact networks and (b) a visual contact network. Additionally, in creating our networks, we establish another procedure to adjust definitions of "contact" to account for RTLS positional accuracy, ensuring all true contacts are likely captured and represented in our networks.Using the methods described herein and the associated R package we have developed, called contact, researchers can derive polygons from RTLS points. Furthermore, we show that these polygons are highly versatile for contact network creation and can be used to answer a wide variety of epidemiological, ethological, and sociological research questions.By introducing these methodologies and providing the means to easily apply them through the contact R package, we hope to vastly improve network-model realism and researchers' ability to draw inferences from RTLS data.

A Model of Culex quinquefasciatus Abundance Constructed Using Routine Surveillance and Treatment Data in Tarrant County, Texas.

Mosquito surveillance and pesticide treatment data can be combined in statistical models to provide insight into drivers of mosquito population dynamics. In cooperation with the county-based public health authority, multiple municipalities in Tarrant County, Texas, supplied surveillance and pesticide treatment data available from the 2014 mosquito season for analysis. With these data, general linear mixed modeling was used to model population dynamics of Culex quinquefasciatus, the primary vector for West Nile virus. Temporally lagged pesticide treatment information, weather data, and habitat variables were used as predictors of log + 1 transformed mosquito count data, and Akaike information criteria corrected for small sample sizes (AICc)-based model selection and multimodel averaging was used to produce a final model of mosquito abundance. The model revealed that mosquito counts were driven mainly by seasonally fluctuating temperature, precipitation, human population density, and treatment. In particular, interactions between temperature and treatment, and precipitation and human population density significantly contributed to the interpretation of the effects of the nonweather variables.

Ecological and Socioeconomic Factors Associated with Bartonella henselae Exposure in Dogs Tested for Vector-Borne Diseases in North Carolina.

Bartonella henselae is a zoonotic vector-borne pathogen affecting both humans and dogs. Little is known about the epidemiology of B. henselae in dogs, including risk factors associated with exposure. The objectives of this study were to map the current distribution of B. henselae in dogs in North Carolina (NC) and to identify ecological and socioeconomic factors influencing B. henselae seroreactivity. Results from 4446 B. henselae serology samples from dogs in NC submitted by veterinarians for clinical diagnostic testing to the North Carolina State University College of Veterinary Medicine Vector Borne Disease Diagnostic Laboratory between January 1, 2004 and December 31, 2015 were retrospectively reviewed. These results were used to generate a map of B. henselae seroreactivity. To account for sparsely sampled areas, statistical smoothing using head banging and areal interpolation kriging was performed. Using previously described risk factors for exposure to canine tick-borne diseases, eight multivariable logistic regression models based on biologically plausible hypotheses were tested, and a final model was selected using an Akaike's Information Criterion weighted-average approach. Seroreactivity among dogs tested for vector-borne disease was variable across the state: higher along the southern/eastern coastal plains and eastern Piedmont, and lower in the western mountains. Of 25 explanatory factors considered, the model combining demographic, socioeconomic, climatic, and land use variables fits best. Based on this model, female intact sex and increasing percentage of the county with low-intensity development and evergreen forest were associated with higher seroreactivity. Conversely, moderate development, increasing median household income, and higher temperature range and relative humidity were associated with lower seroreactivity. This model could be improved, however, by including local and host-scale factors that may play a significant role in dogs' exposure.

Transmission on empirical dynamic contact networks is influenced by data processing decisions.

Dynamic contact data can be used to inform disease transmission models, providing insight into the dynamics of infectious diseases. Such data often requires extensive processing for use in models or analysis. Therefore, processing decisions can potentially influence the topology of the contact network and the simulated disease transmission dynamics on the network. In this study, we examine how four processing decisions, including temporal sampling window (TSW), spatial threshold of contact (SpTh), minimum contact duration (MCD), and temporal aggregation (daily or hourly) influence the information content of contact data (indicated by changes in entropy) as well as disease transmission model dynamics. We found that changes made to information content by processing decisions translated to significant impacts to the transmission dynamics of disease models using the contact data. In particular, we found that SpTh had the largest independent influence on information content, and that some output metrics (R0, time to peak infection) were more sensitive to changes in information than others (epidemic extent). These findings suggest that insights gained from transmission modeling using dynamic contact data can be influenced by processing decisions alone, emphasizing the need to carefully consideration them prior to using contact-based models to conduct analyses, compare different datasets, or inform policy decisions.

Investigating behavioral drivers of seasonal Shiga-Toxigenic Escherichia Coli (STEC) patterns in grazing cattle using an agent-based model.

The causes of seasonal variability in pathogen transmission are not well understood, and have not been comprehensively investigated. In an example for enteric pathogens, incidence of Escherichia coli O157 (STEC) colonization in cattle is consistently higher during warmer months compared to cooler months in various cattle production systems. However, actual mechanisms for this seasonality remain elusive. In addition, the influence of host (cattle) behavior on this pattern has not been thoroughly considered. To that end, we constructed a spatially explicit agent-based model that accounted for the effect of temperature fluctuations on cattle behavior (direct contact among cattle and indirect between cattle and environment), as well as its effect on pathogen survival in the environment. We then simulated the model in a factorial approach to evaluate the hypothesis that temperature fluctuations can lead to seasonal STEC transmission dynamics by influencing cattle aggregation, grazing, and drinking behaviors. Simulation results showed that higher temperatures increased the frequency at which cattle aggregated under shade in pasture, resulting in increased direct contact and transmission of STEC between individual cattle, and hence higher incidence over model simulations in the warm season. In contrast, increased drinking behavior during warm season was not an important transmission pathway. Although sensitivity analyses suggested that the relative importance of direct vs. indirect (environmental) pathways depend to upon model parameterization, model simulations indicated that factors influencing cattle aggregation, such as temperature, were likely strong drivers of transmission dynamics of enteric pathogens.

Predictive Modeling for West Nile Virus and Mosquito Surveillance in Lubbock, Texas.

West Nile virus (WNV) was first detected in North America during 1999, and has since spread throughout the contiguous USA. West Nile virus causes West Nile fever and the more severe West Nile neuroinvasive disease. As part of a WNV vector surveillance program, we collected mosquitoes in Lubbock, Texas, using CO2-baited encephalitic vector survey (EVS) traps. During 219 wk from 2009 through 2017, EVS traps were operated for 1,748 trap nights, resulting in more than 101,000 mosquitoes captured. Weekly, selected female mosquito specimens were pooled by species and trap site, and screened for WNV using reverse transcription-polymerase chain reaction assay. Mosquitoes positive for WNV were detected during 16.9% (37/219) of the weeks. Using this information, we constructed a statistical model to predict the probability of detecting an infection within a mosquito pool as a factor of weather variables. The final model indicated that detection of WNV in mosquitoes was negatively associated with the week of year squared and average wind from 3 wk prior to sampling, and was positively associated with week of year, average visibility, average humidity from 2 wk prior to sampling, and average dew point from 4 wk prior to sampling. The model developed in this study may aid public health and vector control programs in swift and effective decision making relative to city-wide mosquito control efforts by predicting when the chances of mosquitoes having WNV are at their greatest.

Transgenerational endpoints provide increased sensitivity and insight into multigenerational responses of Lymnaea stagnalis exposed to cadmium.

Ecotoxicology provides data to inform environmental management. Many testing protocols do not consider offspring fitness and toxicant sensitivity. Cadmium (Cd) is a well-studied and ubiquitous toxicant but little is known about the effects on offspring of exposed parents (transgenerational effects). This study had three objectives: to identify endpoints related to offspring performance; to determine whether parental effects would manifest as a change in Cd tolerance in offspring and how parental exposure duration influenced the manifestation of parental effects. Adult snails were exposed to Cd 0, 25, 50, 100, 200 and 400 µg Cd/L for eight weeks. There were effects on adult endpoints (e.g., growth, reproduction) but only at the highest concentrations (>100 µg/L). Alternatively, we observed significant transgenerational effects at all Cd concentrations. Surprisingly, we found increased Cd tolerance in hatchlings from all parental Cd exposure concentrations even though eggs and hatchlings were in Cd-free conditions for 6 weeks. Explicit consideration of offspring performance adds value to current toxicity testing protocols. Parental exposure duration has important implications for offspring effects and that contaminant concentrations that are not directly toxic to parents can cause transgenerational changes in resistance that have significant implications for toxicity testing and adaptive responses.