Groundwater contamination by nitrate and sulfate in mining areas is a significant challenge. Consequently, the inputs sources of these contaminants and their evolution have received considerable ...attention, with the knowledge gained critical for improved management of water quality. This study integrated data on multiple stable isotopes and water chemistry data and a Bayesian isotope mixing model to investigate the relative contributions of inputs sources of sulfate and nitrate sources to bodies of water in a karst mining area in southwest China. The outcomes indicated that hydrochemical component in the water bodies of the study area is mainly derived from the dissolution of silicate rocks, carbonate rocks and sulfate minerals as well as the oxidation of sulfides. The human and agricultural wastewater, soil nitrogen, and fertilizers were the predominant inputs sources of nitrate to the mine water environment; the predominant inputs sources of sulfide were mineral oxidation, evaporite dissolution, atmospheric deposition, and sewage. Groundwater is mainly recharged from atmospheric precipitation, and surface water is closely hydraulically connected to groundwater. Nitrogen and oxygen isotope composition and water chemistry indicative of nitrification dominate the nitrogen cycle in the study area. The oxidation of pyrite and bacterial sulfate reduction (SRB) had no significant impact on the stable isotopes of groundwater. The results of this study demonstrate the inputs of different sources to nitrate and sulfate in karst mines and associated transformation processes. The results of this study can assist in the conservation of groundwater quality in mining areas and can act as a reference for future related studies.
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•Groundwater, surface water, mine water nitrate and sulfate sources and transformation were investigated.•Water chemistry, stable isotopes, and a Bayesian mixing model were combined.•Soil nitrogen, sewage and mine water were the most important nitrate sources.•Dissolution of evaporites, sewage and sulfide mineral oxidation were the main sulfate sources.•Anthropogenic factors have changed the circulation pattern of groundwater and accelerated its evolution.
Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form. Markov chain ...Monte Carlo methods solve this problem by sampling from a series of simpler conditional distributions that can be evaluated. The R package MCMCglmm implements such an algorithm for a range of model fitting problems. More than one response variable can be analyzed simultaneously, and these variables are allowed to follow Gaussian, Poisson, multi(bi)nominal, exponential, zero-inflated and censored distributions. A range of variance structures are permitted for the random effects, including interactions with categorical or continuous variables (i.e., random regression), and more complicated variance structures that arise through shared ancestry, either through a pedigree or through a phylogeny. Missing values are permitted in the response variable(s) and data can be known up to some level of measurement error as in meta-analysis. All simu- lation is done in C/ C++ using the CSparse library for sparse linear systems.
Fusarium head blight (FHB) of wheat, caused by the fungus
, is associated with grain contamination with mycotoxins such as deoxynivalenol (DON). Although FHB is often positively correlated with DON, ...this relationship can break down under certain conditions. One possible explanation for this could be the conversion of DON to DON-3-glucoside (D3G), which is typically missed by common DON testing methods. The objective of this study was to quantify the effects of temperature, relative humidity (RH), and preharvest rainfall on DON, D3G, and the D3D/DON relationship. D3G levels were higher in grain from spikes exposed to 100% RH than to 70, 80, or 90% RH at 20 and 25°C across all tested levels of mean FHB index (percentage of diseased spikelets per spike). Mean D3G contamination was higher at 20°C than at 25 or 30°C. There were significantly positive linear relationships between DON and D3G. Rainfall treatments resulted in significantly higher mean D3G than the rain-free check and induced preharvest sprouting, as indicated by low falling numbers (FNs). There were significant positive relationships between the rate of increase in D3G per unit increase in DON (a measure of conversion) and sprouting. As FN decreased, the rate of D3G conversion increased, and this rate of conversion per unit decrease in FN was greater at relatively low than at high mean DON levels. These results provide strong evidence that moisture after FHB visual symptom development was associated with DON-to-D3G conversion and constitute valuable new information for understanding this complex disease-mycotoxin system.
•A big-data-driven analytical framework assessing human mobility during COVID-19.•Location-based evidence from large-scale and privacy-protected samples in the US.•County-level human mobility metrics ...are made publicly available via an open platform.•Comprehensive assessment of human mobility using generalized additive mixed models.•The policy effect of “Stay-at-home” order on restricting travel is found limited.
During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus.
The use of mixed effect models with a specific functional form such as the Sigmoidal Mixed Model and the Piecewise Mixed Model (or Changepoint Mixed Model) with abrupt or smooth random change allows ...the interpretation of the defined parameters to understand longitudinal trajectories. Currently, there are no interface R packages that can easily fit the Sigmoidal Mixed Model allowing the inclusion of covariates or incorporating recent developments to fit the Piecewise Mixed Model with random change.
To facilitate the modeling of the Sigmoidal Mixed Model, and Piecewise Mixed Model with abrupt or smooth random change, we have created an R package called nlive. All needed pieces such as functions, covariance matrices, and initials generation were programmed. The package was implemented with recent developments such as the polynomial smooth transition of the piecewise mixed model with improved properties over Bacon-Watts, and the stochastic approximation expectation-maximization (SAEM) for efficient estimation. It was designed to help interpretation of the output by providing features such as annotated output, warnings, and graphs. Functionality, including time and convergence, was tested using simulations. We provided a data example to illustrate the package use and output features and interpretation. The package implemented in the R software is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=nlive .
The nlive package for R fits the Sigmoidal Mixed Model and the Piecewise Mixed: abrupt and smooth. The nlive allows fitting these models with only five mandatory arguments that are intuitive enough to the less sophisticated users.
The impact of Gibberella ear rot (GER; caused by
) on deoxynivalenol (DON) contamination of grain and yield components in maize were investigated using data from 30 environments in Ohio (3 years by ...10 locations). Fifteen hybrids, later classified as susceptible (SU), moderately susceptible (MS), or moderately resistant (MR), based on the magnitude of differences in mean arcsine square-root-transformed GER severity (arcSEV) and log-transformed DON (logDON) relative to a reference SU check, were planted in each environment, and 10 ears per hybrid were inoculated with a spore suspension of
. Relationships between GER severity and DON were well described by a Kono-Sugino-type nonlinear equation. Estimated parameters representing height (
) and steepness (β) of the curves were significantly higher for SU than MS and MR hybrids but
was not significantly different between MS and MR. Results from a surrogacy analysis showed that GER was a moderate trial- and individual-level surrogate for DON. Both grain weight per ear and ear diameter decreased with increasing arcSEV but the regression slopes varied among resistance classes. The rates of reduction in both yield components per unit increase in arcSEV were significantly greater for SU than for MS and MR. An estimated 50% reduction in grain weight occurred at 62% GER severity for SU, compared with 77% severity for MS and 83% for MR. These results show that GER severity can be used as a surrogate for early estimation of DON contamination and yield loss to help guide grain handling and marketing decisions.
Timeline followback (TLFB) is often used in addiction research to monitor recent substance use, such as the number of abstinent days in the past week. TLFB data usually take the form of binomial ...counts that exhibit overdispersion and zero inflation. Motivated by a 12‐week randomized trial evaluating the efficacy of varenicline tartrate for smoking cessation among adolescents, we propose a Bayesian zero‐inflated beta‐binomial model for the analysis of longitudinal, bounded TLFB data. The model comprises a mixture of a point mass that accounts for zero inflation and a beta‐binomial distribution for the number of days abstinent in the past week. Because treatment effects appear to level off during the study, we introduce random changepoints for each study group to reflect group‐specific changes in treatment efficacy over time. The model also includes fixed and random effects that capture group‐ and subject‐level slopes before and after the changepoints. Using the model, we can accurately estimate the mean trend for each study group, test whether the groups experience changepoints simultaneously, and identify critical windows of treatment efficacy. For posterior computation, we propose an efficient Markov chain Monte Carlo algorithm that relies on easily sampled Gibbs and Metropolis–Hastings steps. Our application shows that the varenicline group has a short‐term positive effect on abstinence that tapers off after week 9.
•Data collected from strategic river temperature monitoring network•Novel spatio-temporal model of maximum daily river temperature developed•Models include air temperature, location, day and ...landscape characteristics•Model predictions show spatial temperature variability and climate sensitivity.•Maps provide tools for fisheries and river managers.
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The thermal suitability of riverine habitats for cold water adapted species may be reduced under climate change. Riparian tree planting is a practical climate change mitigation measure, but it is often unclear where to focus effort for maximum benefit. Recent developments in data collection, monitoring and statistical methods have facilitated the development of increasingly sophisticated river temperature models capable of predicting spatial variability at large scales appropriate to management. In parallel, improvements in temporal river temperature models have increased the accuracy of temperature predictions at individual sites. This study developed a novel large scale spatio-temporal model of maximum daily river temperature (Twmax) for Scotland that predicts variability in both river temperature and climate sensitivity. Twmax was modelled as a linear function of maximum daily air temperature (Tamax), with the slope and intercept allowed to vary as a smooth function of day of the year (DoY) and further modified by landscape covariates including elevation, channel orientation and riparian woodland. Spatial correlation in Twmax was modelled at two scales; (1) river network (2) regional. Temporal correlation was addressed through an autoregressive (AR1) error structure for observations within sites. Additional site level variability was modelled with random effects. The resulting model was used to map (1) spatial variability in predicted Twmax under current (but extreme) climate conditions (2) the sensitivity of rivers to climate variability and (3) the effects of riparian tree planting. These visualisations provide innovative tools for informing fisheries and land-use management under current and future climate.
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