Long-term declines in chlorophyll a and variable phenology revealed by a 60-year estuarine plankton time series.

Long-term ecological time series provide a unique perspective on the emergent properties of ecosystems. In aquatic systems, phytoplankton form the base of the food web and their biomass, measured as the concentration of the photosynthetic pigment chlorophyll a (chl a), is an indicator of ecosystem quality. We analyzed temporal trends in chl a from the Long-Term Plankton Time Series in Narragansett Bay, Rhode Island, USA, a temperate estuary experiencing long-term warming and changing anthropogenic nutrient inputs. Dynamic linear models were used to impute and model environmental variables (1959 to 2019) and chl a concentrations (1968 to 2019). A long-term chl a decrease was observed with an average decline in the cumulative annual chl a concentration of 49% and a marked decline of 57% in winter-spring bloom magnitude. The long-term decline in chl a concentration was directly and indirectly associated with multiple environmental factors that are impacted by climate change (e.g., warming temperatures, water column stratification, reduced nutrient concentrations) indicating the importance of accounting for regional climate change effects in ecosystem-based management. Analysis of seasonal phenology revealed that the winter-spring bloom occurred earlier, at a rate of 4.9 ± 2.8 d decade-1. Finally, the high degree of temporal variation in phytoplankton biomass observed in Narragansett Bay appears common among estuaries, coasts, and open oceans. The commonality among these marine ecosystems highlights the need to maintain a robust set of phytoplankton time series in the coming decades to improve signal-to-noise ratios and identify trends in these highly variable environments.

Prenatal Exposure to Opioids and Neurodevelopmental Disorders in Children: A Bayesian Mediation Analysis.

This study explores natural direct and joint natural indirect effects (JNIE) of prenatal opioid exposure on neurodevelopmental disorders (NDDs) in children mediated through pregnancy complications, major and minor congenital malformations, and adverse neonatal outcomes, using Medicaid claims linked to vital statistics in Rhode Island, United States, 2008-2018. A Bayesian mediation analysis with elastic net shrinkage prior was developed to estimate mean time to NDD diagnosis ratio using posterior mean and 95% credible intervals (CrIs) from Markov chain Monte Carlo algorithms. Simulation studies showed desirable model performance. Of 11,176 eligible pregnancies, 332 had ≥2 dispensations of prescription opioids anytime during pregnancy, including 200 (1.8%) having ≥1 dispensation in the first trimester (T1), 169 (1.5%) in the second (T2), and 153 (1.4%) in the third (T3). A significant JNIE of opioid exposure was observed in each trimester (T1, JNIE = 0.97, 95% CrI: 0.95, 0.99; T2, JNIE = 0.97, 95% CrI: 0.95, 0.99; T3, JNIE = 0.96, 95% CrI: 0.94, 0.99). The proportion of JNIE in each trimester was 17.9% (T1), 22.4% (T2), and 56.3% (T3). In conclusion, adverse pregnancy and birth outcomes jointly mediated the association between prenatal opioid exposure and accelerated time to NDD diagnosis. The proportion of JNIE increased as the timing of opioid exposure approached delivery.

Length, width, shape regularity, and chain structure: time series analysis of phytoplankton morphology from imagery.

Functional traits are increasingly used to assess changes in phytoplankton community structure and to link individual characteristics to ecosystem functioning. However, they are usually inferred from taxonomic identification or manually measured for each organism, both time consuming approaches. Instead, we focus on high throughput imaging to describe the main temporal variations of morphological changes of phytoplankton in Narragansett Bay, a coastal time-series station. We analyzed a 2-yr dataset of morphological features automatically extracted from continuous imaging of individual phytoplankton images (~ 105 million images collected by an Imaging FlowCytobot). We identified synthetic morphological traits using multivariate analysis and revealed that morphological variations were mainly due to changes in length, width, shape regularity, and chain structure. Morphological changes were especially important in winter with successive peaks of larger cells with increasing complexity and chains more clearly connected. Small nanophytoplankton were present year-round and constituted the base of the community, especially apparent during the transitions between diatom blooms. High inter-annual variability was also observed. On a weekly timescale, increases in light were associated with more clearly connected chains while more complex shapes occurred at lower nitrogen concentrations. On an hourly timescale, temperature was the determinant variable constraining cell morphology, with a general negative influence on length and a positive one on width, shape regularity, and chain structure. These first insights into the phytoplankton morphology of Narragansett Bay highlight the possible morphological traits driving the phytoplankton succession in response to light, temperature, and nutrient changes.

The P300 event related potential predicts phonological working memory skills in school-aged children.

The P300 event related potential (ERP) has been cited as a marker of phonological working memory (PWM); however, little is known regarding its relationship to behavioral PWM skills in early school-aged children. The current study investigates the P300 ERP recorded in response to native and non-native (English and Spanish) phoneme contrasts as a predictor of PWM skills in monolingual English-speaking first and second grade children. Thirty-three typically developing children, ages 6-9, completed a battery of phonological processing, language, and cognitive assessments. ERPs were recorded within an auditory oddball paradigm in response to both English phoneme contrasts (/ta/, /pa/) and Spanish contrasts (/t̪a/, /d̪a/). The P300 ERP recorded in response to English phoneme contrasts significantly predicted standard scores on the Nonword Repetition subtest of the Comprehensive Test of Phonological Processing, Second Edition. Spanish contrasts did not elicit a P300 response, nor were amplitude or latency values within the P300 timeframe (250-500 ms) recorded in response to Spanish contrasts related to English nonword repetition performance. This study provides further evidence that the P300 ERP in response to native phonemic contrasts indexes PWM skills, specifically nonword repetition performance, in monolingual children. Further work is necessary to determine the extent to which the P300 response to changing phonological stimuli reflects PWM skills in other populations.

A Bayesian latent class model for predicting gestational age in health administrative data.

Health administrative data are oftentimes of limited use in epidemiological study on drug safety in pregnancy, due to lacking information on gestational age at birth (GAB). Although several studies have proposed algorithms to estimate GAB using claims database, failing to incorporate the unique distributional shape of GAB, can introduce bias in estimates and subsequent modeling. Hence, we develop a Bayesian latent class model to predict GAB. The model employs a mixture of Gaussian distributions with linear covariates within each class. This approach allows modeling heterogeneity in the population by identifying latent subgroups and estimating class-specific regression coefficients. We fit this model in a Bayesian framework conducting posterior computation with Markov Chain Monte Carlo methods. The method is illustrated with a dataset of 10,043 Rhode Island Medicaid mother-child pairs. We found that the three-class and six-class mixture specifications maximized prediction accuracy. Based on our results, Medicaid women were partitioned into three classes, featured by extreme preterm or preterm birth, preterm or" early" term birth, and" late" term birth. Obstetrical complications appeared to pose a significant influence on class-membership. Altogether, compared to traditional linear models our approach shows an advantage in predictive accuracy, because of superior flexibility in modeling a skewed response and population heterogeneity.

Evaluating the Effects of Minimal Risk Natural Products for Control of the Tick, Ixodes scapularis (Acari: Ixodidae).

Knockdown and residual activity of 10 minimal risk natural products (MRNPs), one experimental formulation of nootkatone, and two bifenthrin labels were evaluated against host-seeking nymphal Ixodes scapularis Say using a novel micro-plot product screening system placed in a landscape setting similar to a wooded residential property. The MRNPs evaluated included Tick Stop, EcoPCO EC-X, Met52 EC, CedarCide PCO Choice, EcoEXEMPT IC2, EcoSMART Organic Insecticide, Essentria IC3, privately labeled products 1 and 2 (based on EcoEXEMPT IC2 and sold as a professional pest control application), and Tick Killz. Just the nootkatone and 4 of these 10 products tested (EcoPCO EC-X, Met52 EC, EcoEXEMPT IC2, and Essentria IC3) had statistically significant (P < 0.05) knockdown effects (killed ticks while active in the arenas) when compared to water-only controls, but only 2 of these, EcoPCO EC-X and nootkatone, displayed significant residual tick-killing activity after weathering naturally in the landscape for 2 wk prior to tick application/testing. Moreover, botanical oil-based products with the same active ingredients provided inconsistent results when tested multiple times across study years.

Spatiotemporal modeling of dengue fever risk in Puerto Rico.

Dengue Fever (DF) is a mosquito vector transmitted flavivirus and a reemerging global public health threat. Although several studies have addressed the relation between climatic and environmental factors and the epidemiology of DF, or looked at purely spatial or time series analysis, this article presents a joint spatio-temporal epidemiological analysis. Our approach accounts for both temporal and spatial autocorrelation in DF incidence and the effect of temperatures and precipitation by using a hierarchical Bayesian approach. We fitted several space-time areal models to predict relative risk at the municipality level and for each month from 1990 to 2014. Model selection was performed according to several criteria: the preferred models detected significant effects for temperature at time lags of up to four months and for precipitation up to three months. A boundary detection analysis is incorporated in the modeling approach, and it was successful in detecting municipalities with historically anomalous risk.

Developmental trajectories of autonomic functioning in autism from birth to early childhood.

Deficits in social engagement emerge in autism during the infant and toddler period and may be related to emotion regulation and stress response systems. This study examined patterns of growth in autonomic functioning related to autism diagnosis and addresses the hypothesis that there are differences in autonomic functioning related to autism in infancy. Heart rate (HR) and respiratory sinus arrhythmia (RSA) were measured at 8 time points from 1 to 72 months of age in infants later diagnosed with autism (n = 12) and a non-autistic comparison group (n = 106). Multilevel models were used to describe the developmental course of HR and RSA and to test the effect of autism diagnosis on growth trajectories. Both groups showed an expected age-related decrease in HR and increase in RSA. Groups did not differ in the rate of decrease of HR over time. However, infants with autism demonstrated a smaller linear increase in RSA, indicating slower growth in RSA over time in comparison to controls. These results suggest that differences in physiological regulation may develop with age in autism. The slowed RSA growth in autism was most evident after 18 months of age, at a time when behavioral symptoms become prominent. This is consistent with the view that RSA is a marker of functional status in autism rather than a cause of social deficits in autism.

Polycyclic Musks in the Air and Water of the Lower Great Lakes: Spatial Distribution and Volatilization from Surface Waters.

Polycyclic musks (PCMs) are synthetic fragrance compounds used in personal care products and household cleaners. Previous studies have indicated that PCMs are introduced to aquatic environments via wastewater and river discharge. Polyethylene passive samplers (PEs) were deployed in air and water during winter 2011 and summer 2012 to investigate the role of population centers as sources of these contaminants to the Great Lakes and determine whether the lakes were acting as sources of PCMs via volatilization. Average gaseous Σ5PCM ranged from below detection limits (

Spatial Distribution and Air-Water Exchange of Organic Flame Retardants in the Lower Great Lakes.

Organic flame retardants (OFRs) such as polybrominated diphenyl ethers (PBDEs) and novel halogenated flame retardants (NHFRs) are ubiquitous, persistent, and bioaccumulative contaminants that have been used in consumer goods to slow combustion. In this study, polyethylene passive samplers (PEs) were deployed throughout the lower Great Lakes (Lake Erie and Lake Ontario) to measure OFRs in air and water, calculate air-water exchange fluxes, and investigate spatial trends. Dissolved Σ12BDE was greatest in Lake Ontario near Toronto (18 pg/L), whereas gaseous Σ12BDE was greatest on the southern shoreline of Lake Erie (11 pg/m(3)). NHFRs were generally below detection limits. Air-water exchange was dominated by absorption of BDEs 47 and 99, ranging from -964 pg/m(2)/day to -30 pg/m(2)/day. Σ12BDE in air and water was significantly correlated with surrounding population density, suggesting that phased-out PBDEs continued to be emitted from population centers along the Great Lakes shoreline in 2012. Correlation with dissolved Σ12BDE was strongest when considering population within 25 km while correlation with gaseous Σ12BDE was strongest when using population within 3 km to the south of each site. Bayesian kriging was used to predict dissolved Σ12BDE over the lakes, illustrating the utility of relatively highly spatially resolved measurements in identifying potential hot spots for future study.

Structuring targeted surveillance for monitoring disease emergence by mapping observational data onto ecological process.

An efficient surveillance system is a crucial factor in identifying, monitoring and tackling outbreaks of infectious diseases. Scarcity of data and limited amounts of economic resources require a targeted effort from public health authorities. In this paper, we propose a mathematical method to identify areas where surveillance is critical and low reporting rates might leave epidemics undetected. Our approach combines the use of reference-based susceptible-exposed-infectious models and observed reporting data; We propose two different specifications, for constant and time-varying surveillance, respectively. Our case study is centred around the spread of the raccoon rabies epidemic in the state of New York, using data collected between 1990 and 2007. Both methods offer a feasible solution to analyse and identify areas of intervention.

Burden of disease attributed to anthropogenic air pollution in the United Arab Emirates: estimates based on observed air quality data.

This study quantifies the national burden of disease attributed to particulate matter (PM) and ozone (O(3)) in ambient air in the United Arab Emirates (UAE), a rapidly growing nation in which economic development and climatic conditions pose important challenges for air quality management. Estimates of population exposure to these air pollutants are based on observed air quality data from fixed-site monitoring stations. We divide the UAE into small grid cells and use spatial-statistical methods to estimate the ambient pollutant concentrations in each cell based on the observed data. Premature deaths attributed to PM and O(3) are computed for each grid cell and then aggregated across grid cells and over a year to estimate the total number of excess deaths attributable to ambient air pollution. Our best estimate is that approximately 545 (95% CI: 132-1224) excess deaths in the UAE in the year 2007 are attributable to PM in ambient air. These excess deaths represent approximately 7% (95% CI: 2-17%) of the total deaths that year. We attribute approximately 62 premature deaths (95% CI: 17-127) to ground-level O(3) for the year 2007. Uncertainty in the natural background level of PM, due to the frequent dust storms occurring in the region, has significant impacts on the attributed mortality estimates. Despite the uncertainties associated with the integrated assessment framework, we conclude that anthropogenic ambient air pollution, in particular PM, causes a considerable public health impact in the UAE in terms of premature deaths. We discuss important uncertainties and scientific hypotheses to be investigated in future work that might help reduce the uncertainties in the burden of disease estimates.

Joint modeling of sensitivity and specificity.

Sensitivity and specificity are two customary performance measures associated with medical diagnostic tests. Typically, they are modeled independently as a function of risk factors using logistic regression, which provides estimated functions for these probabilities. Change in these probabilities across levels of risk factors is of primary interest and the indirect relationship is often displayed using a receiver operating characteristic curve. We refer to this as analysis of 'first-order' behavior. Here, we consider what we refer to as 'second-order' behavior where we examine the stochastic dependence between the (random) estimates of sensitivity and specificity. To do so, we argue that a model for the four cell probabilities that determine the joint distribution of screening test result and outcome result is needed. Such a modeling induces sensitivity and specificity as functions of these cell probabilities. In turn, this raises the issue of a coherent specification for these cell probabilities, given risk factors, i.e. a specification that ensures that all probabilities calculated under it fall between 0 and 1. This leads to the question of how to provide models that are coherent and mechanistically appropriate as well as computationally feasible to fit, particularly with large data sets. The goal of this article is to illuminate these issues both algebraically and through analysis of a real data set.