Time trends of methylmercury in walleye in northern Wisconsin: a hierarchical Bayesian analysis

Methylmercury data from walleye fillets collected by multiple agencies from northern Wisconsin lakes from 1982 to 2005 were examined for regional time trends. Hierarchical Bayesian methods were used to model dependencies and provide probability statements for parameters pertaining to individual lakes and the region as a whole. A missing data mechanism allowed the sex of the fish to be included as a predictor since the sexes grow at different rates. A slight regional decrease in methylmercury of 0.60% annually was found, consistent with declining atmospheric mercury deposition. Methylmercury was estimated to have decreased in 77% of the 420 lakes from which walleye were sampled, although uncertainty regarding time trends was greater for most individual lakes than for the region as a whole. Methylmercury in walleye varied widely from lake to lake, but generally accumulated in the fish at similar rates by length after accounting for differences in sex. Slower-growing male walleye had higher methylmercury concentrations than females for a given length, and skin-on fillets were 16% lower in methylmercury than skin-off fillets.     

上海市0～6岁儿童血砷暴露水平及饮食影响研究

目的 了解上海市0～6岁儿童血砷水平及饮食相关因素。方法 通过多阶段分层整群随机抽样对上海市6个区抽取的居(村)委会0～6岁儿童进行问卷调查和全血标本采集。通过电感耦合等离子体质谱仪(ICP-MS)对全血砷元素进行检测,并通过多重线性回归模型对饮食因素进行相关性分析。饮食研究因素中,海鱼海虾、淡水鱼淡水虾及贝类分组:≤3次/月、1～3次/周与4～6次/周;主食米、面及牛奶分组:＜50、50～100与≥100 g/d。结果 此次调查儿童总人数为2 263人,血砷浓度范围为0.12～27.94 μg/L,中位数(P25,P75)为2.26(1.63,3.09) μg/L。通过统计分析发现,米、面、海鱼海虾的摄入量越高,儿童血砷水平越高(P<0.05),淡水鱼淡水虾高摄入组血砷水平反而降低(P<0.05),差异均有统计学意义。结论 此次调查92.4%(2 091/2 263)的儿童血砷浓度处于正常范围(<5 μg/L),与对照组比较,米、面、海鱼海虾的摄入量越高,儿童血砷水平越高,而淡水鱼淡水虾的摄入量越高,其血砷水平反而降低。     

Bayesian analysis of longitudinal Johne's disease diagnostic data without a gold standard test

A Bayesian methodology was developed based on a latent change-point model to evaluate the performance of milk ELISA and fecal culture tests for longitudinal Johne's disease diagnostic data. The situation of no perfect reference test was considered; that is, no “gold standard.” A change-point process with a Weibull survival hazard function was used to model the progression of the hidden disease status. The model adjusted for the fixed effects of covariate variables and random effects of subject on the diagnostic testing procedure. Markov chain Monte Carlo methods were used to compute the posterior estimates of the model parameters that provide the basis for inference concerning the accuracy of the diagnostic procedure. Based on the Bayesian approach, the posterior probability distribution of the change-point onset time can be obtained and used as a criterion for infection diagnosis. An application is presented to an analysis of ELISA and fecal culture test outcomes in the diagnostic testing of paratuberculosis (Johne's disease) for a Danish longitudinal study from January 2000 to March 2003. The posterior probability criterion based on the Bayesian model with 4 repeated observations has an area under the receiver operating characteristic curve (AUC) of 0.984, and is superior to the raw ELISA (AUC = 0.911) and fecal culture (sensitivity = 0.358, specificity = 0.980) tests for Johne's disease diagnosis.     

Consumption of brown rice: a potential pathway for arsenic exposure in rural Bengal

This study assesses the arsenic (As) accumulation in different varieties of rice grain, that people in rural Bengal mostly prefer for daily consumption, to estimate the potential risk of dietary As exposure through rice intake. The rice samples have been classified according to their average length (L) and L to breadth (B) ratio into four categories, such as short-bold (SB), medium-slender (MS), long-slender (LS), and extra-long slender (ELS). The brown colored rice samples fall into the SB, MS, or LS categories; while all Indian Basmati (white colored) are classified as ELS. The study indicates that the average accumulation of As in rice grain increases with a decrease of grain size (ELS: 0.04; LS: 0.10; MS: 0.16; and SB: 0.33 mg kg(-1)), however people living in the rural villages mostly prefer brown colored SB type of rice because of its lower cost. For the participants consuming SB type of brown rice, the total daily intake of inorganic As (TDI-iAs) in 29% of the cases exceeds the previous WHO recommended provisional tolerable daily intake value (2.1 μg day(-1) kg(-1) BW), and in more than 90% of cases, the As content in the drinking water equivalent to the inorganic As intake from rice consumption (C(W,eqv)) exceeds the WHO drinking water guideline of 10 μg L(-1). This study further demonstrates that participants in age groups 18-30 and 51-65 yrs are the most vulnerable to the potential health threat of dietary As exposure compared to participants of age group 31-50 yrs, because of higher amounts of brown rice consumption patterns and lower BMI.     

稻米中总砷及无机砷含量的测定与分析

采用氢化物原子荧光光度法测定13份稻米样品中的总砷和无机砷含量,结果表明：所测稻米样品中均检出总砷与无机砷,总砷含量范围为0.06~0.20 mg/kg,无机砷含量范围为0.039~0.150 mg/kg,无机砷占总砷的含量为50.0%~93.1%,无机砷含量在国家限量卫生标准以内;未冲洗大米样品中总砷含量仅为糙米的72.7%~90.0%,无机砷含量为糙米的62.7%~87.7%;多次冲洗的大米样品中总砷含量仅为未冲洗大米的75.0%~91.7%,无机砷含量为83.0%~93.6%。提高加工精度和用水淘洗均能在一定程度上降低总砷与无机砷的含量,减轻人们食用后对身体的危害。     

Development of lead source-specific exposure standards based on aggregate exposure assessment: Bayesian inversion from biomonitoring information to multipathway exposure

Exposure of children to lead is of great concern, and the exposure standards for different media are important for protecting public safety. However, these media-specific standards often fail to ensure the safety of children even when environmental lead levels are lower than the quality standards since humans are often exposed to lead via multiple pathways. To establish exposure standards to protect children from hazards associated with exposure to lead, an analytical tool for assessing aggregate exposure to lead based on Bayesian hierarchical model was developed, and then was used to update the external lead exposure of diet, paint, soil, air and drinking water using the blood lead levels in Chinese children aged 1-6 years. On the basis of updated external exposure, the source allocations for diet, paint, soil, air, and drinking water in China were 65.80 ± 7.92%, 16.98 ± 7.88%, 13.65 ± 5.05%, 3.36 ± 1.75%, and 0.20 ± 0.14%, respectively. Based on the estimated source allocations, the exposure standards were evaluated to be 0.2 μg/m(3), 24.25 mg/kg, 0.027 μg/L, 0.051 μg/mg, 0.042 μg/mg, 38.02 μg/mg for air, soil, water, grains, vegetables, and paint, respectively. Since the standards setting procedure was based on the multipathway aggregate exposure assessment of lead, the newly proposed exposure standards should ensure the safety of children.     

Hierarchical Bayesian analysis of censored microbiological contamination data for use in risk assessment and mitigation

Microbiological contamination data often is censored because of the presence of non-detects or because measurement outcomes are known only to be smaller than, greater than, or between certain boundary values imposed by the laboratory procedures. Therefore, it is not straightforward to fit distributions that summarize contamination data for use in quantitative microbiological risk assessment, especially when variability and uncertainty are to be characterized separately. In this paper, distributions are fit using Bayesian analysis, and results are compared to results obtained with a methodology based on maximum likelihood estimation and the non-parametric bootstrap method. The Bayesian model is also extended hierarchically to estimate the effects of the individual elements of a covariate such as, for example, on a national level, the food processing company where the analyzed food samples were processed, or, on an international level, the geographical origin of contamination data. Including this extra information allows a risk assessor to differentiate between several scenario’s and increase the specificity of the estimate of risk of illness, or compare different scenario’s to each other. Furthermore, inference is made on the predictive importance of several different covariates while taking into account uncertainty, allowing to indicate which covariates are influential factors determining contamination.     

Bayesian analysis of liability of clinical mastitis in Norwegian cattle with a threshold model: effects of data sampling method and model specification.

First-lactation records of Norwegian Cattle were used to infer heritability of liability to clinical mastitis with a threshold sire model. Mastitis was defined as a binary response (presence or absence) in a defined period of first lactation (opportunity period). Length of opportunity period (from 30 d before calving up to 120 or 300 d of lactation) had less effect on heritability estimates than data sampling methods (include or exclude records of cows culled before the end of the opportunity period) whereas sire ranking was more affected by the former. Including all cows, whether culled before the end of the opportunity period or not, gave a sharper and more symmetric posterior distribution of heritability of liability to clinical mastitis. When we analyzed data for all cows, model specification had a small effect on heritability estimates, while sire ranking was affected markedly. Posterior means of heritability range from 0.058 to 0.074. A model regressing on the length of the opportunity period for culled cows without mastitis, was shown favorable for the two opportunity periods using Bayes factors and the deviance information criterion for model comparison. This model, in which liability of mastitis depends on time to culling, may allow utilizing information from all first lactations in genetic evaluation, irrespectively of duration and culling outcome.     

Bayesian analysis of a reduced-form air quality model

Numerical air quality models are being used for assessing emission control strategies for improving ambient pollution levels across the globe. This paper applies probabilistic modeling to evaluate the effectiveness of emission reduction scenarios aimed at lowering ground-level ozone concentrations. A Bayesian hierarchical model is used to combine air quality model output and monitoring data in order to characterize the impact of emissions reductions while accounting for different degrees of uncertainty in the modeled emissions inputs. The probabilistic model predictions are weighted based on population density in order to better quantify the societal benefits/disbenefits of four hypothetical emission reduction scenarios in which domain-wide NO(x) emissions from various sectors are reduced individually and then simultaneously. Cross validation analysis shows the statistical model performs well compared to observed ozone levels. Accounting for the variability and uncertainty in the emissions and atmospheric systems being modeled is shown to impact how emission reduction scenarios would be ranked, compared to standard methodology.     

云南地区茶叶中总砷及无机砷含量调查分析

目的 为云南茶叶的开发利用提供数据支持.方法 利用国家标准方法对云南省110份茶叶样品的总砷和无机砷含量进行测试,并就产地、包装、流通环节等进行调查分析.结果 经对采集的110个茶叶样品进行总砷检测,最高0.152 mg/kg,均值0.0325 mg/kg,仅1件检出无机砷,检测值为0.0591 mg/kg,均符合国家...     

Statistical analysis of sensory profiling data: data reduction and generalised Procrustes analysis

The aim of the study is to show the sensory differences between 16 varieties of potatoes assessed twice by 14 judges with 30 attributes. Instead of simply performing a statistical method taking into account the variability among judges and yielding a judge consensus space, a selection step is carried out. It aims at reducing the data to keep the information relevant to the objective of the analysis. Thus the sensory attributes are selected on their discriminative ability and on the extent to which the judges agreed on their meaning. The judges are chosen according to their performance.     

Merging models and biomonitoring data to characterize sources and pathways of human exposure to organophosphorus pesticides in the Salinas Valley of California

We characterize cumulative intakes of organophosphorus (OP) pesticides in an agricultural region of California by drawing on human biomonitoring data, California pesticide use reporting (PUR) data, and limited environmental samples together with outputs from the CalTOX multimedia, multipathway, source-to-dose model. The study population is the CHAMACOS cohort of almost 600 pregnant Latina women in the Salinas Valley region. We use model estimates of OP intake and urinary dialkylphosphate (DAP) metabolite excretion to develop premises about relative contributions from different exposure sources and pathways. We evaluate these premises by comparing the magnitude and variation of DAPs in the CHAMACOS cohort with those of the whole U.S. population using data from the National Health and Nutrition Examination Survey (NHANES). This comparison supports the premise that diet is the common and dominant exposure pathway in both populations. Biomarker comparisons and model results support the observation that, relative to NHANES, the CHAMACOS population has a statistically significant (p < 0.001) added intake of OP pesticides with low inter-individual variability. We attribute the magnitude and small variance of this intake to residential nondietary exposures from local agricultural OP uses. These results show that mass-balance models can estimate exposures for OP pesticides within the range measured by biological monitoring.     

Tobacco use and acculturation among Californians of Korean descent: a behavioral epidemiological analysis.

This study presents population estimates of cigarette use among adults of Korean descent residing in California. Data were drawn from telephone interviews with adults (N=2,830) developed from a random sampling of listed persons in California with Korean surnames. A total of 86% of attempted interviews were completed, and 85% of the interviews were conducted in Korean. Less acculturated men and more acculturated women reported higher present and predicted future rates of smoking after multivariate statistical controls were applied. Sharply divergent rates of cigarette use were found between the genders. Although men did not smoke their first cigarette or smoke cigarettes regularly earlier than women, both groups began smoking later than adults born in the United States. To be effective, tobacco intervention efforts must be tailored specifically to cultures of each minority. Results suggest that acculturation processes influence tobacco use differentially by gender, and future research is needed to identify the implications of processes in tobacco initiation and cessation.     

Generalised PLS_Cluster: an extension of PLS_Cluster for interpretable hierarchical clustering of multivariate data

In the agro-food industry, determining the quality of products is of utmost importance. Among the numerous analytical techniques available, rapid and non-destructive instrumental methods, such as Near Infrared spectroscopy, are often preferred. However in order to extract information from the acquired spectral data, chemometrical methods (i.e., mathematical or statistical methods) are nearly always required. PLS_Cluster was proposed by Barros et al. [A.S. Barros and D.N. Rutledge 70:99 2004] as an unsupervised clustering method, which, being based on PLS regression, furnishes interpretable B-coefficients for each node in the dendrogram. In the present paper a generalisation of the original PLS_Cluster method is proposed. The original version of PLS_Cluster was dichotomic, the samples being assigned to just two groups at each node. As well, the membership function used for the assignment of the samples was not always optimal. This new algorithm yields more satisfactory results, and has the additional advantage over other clustering methods of being predictive. A further extension of Generalised PLS_Cluster is also proposed, i.e. Interval PLS_Cluster, in which the PLS_Cluster algorithm is applied on a window moving across the spectra. This may result in different clusterings of the samples for different spectral regions and enables the determination of those variables responsible for the observed clusterings. Application of Generalised- and Interval- PLS_Cluster to several data sets gave satisfactory results.     

Modeling the probability of arsenic in groundwater in New England as a tool for exposure assessment

We developed a process-based model to predict the probability of arsenic exceeding 5 microg/L in drinking water wells in New England bedrock aquifers. The model is being used for exposure assessment in an epidemiologic study of bladder cancer. One important study hypothesis that may explain increased bladder cancer risk is elevated concentrations of inorganic arsenic in drinking water. In eastern New England, 20-30% of private wells exceed the arsenic drinking water standard of 10 micrograms per liter. Our predictive model significantly improves the understanding of factors associated with arsenic contamination in New England. Specific rock types, high arsenic concentrations in stream sediments, geochemical factors related to areas of Pleistocene marine inundation and proximity to intrusive granitic plutons, and hydrologic and landscape variables relating to groundwater residence time increase the probability of arsenic occurrence in groundwater. Previous studies suggest that arsenic in bedrock groundwater may be partly from past arsenical pesticide use. Variables representing historic agricultural inputs do not improve the model, indicating that this source does not significantly contribute to current arsenic concentrations. Due to the complexity of the fractured bedrock aquifers in the region, well depth and related variables also are not significant predictors.     

Migration of lead and arsenic from food contact paper into a food simulant and assessment of their consumer exposure safety

ABSTRACT

Paper is one of the most commonly used food packaging materials. During the production of packaging paper, it is possible for trace amounts of heavy metals to be incorporated as contaminants. These could migrate into food when packaging paper (food contact paper) is used for cooking, storing and eating. The aim of this study was to determine the migration of lead (Pb) and arsenic (As) from food contact paper into a food simulant and then to assess human safety through the estimated daily intake (EDI) with consumption factor. Migration tests were conducted for 310 samples using 4% acetic acid as a food simulant at 25°C for 10 min and at 95°C for 30 min. Concentrations of Pb and As in a food simulant were quantified by inductively coupled plasma mass spectrometry. LODs for Pb and As were 0.002 and 0.005 µg L^?1, respectively. The migration of Pb from food contact paper ranged from not detected (ND) to 17.5 μg L^?1 at 25°C for 10 min and from 0.10 to 25.6 μg L^?1 at 95°C for 30 min while As ranged from ND to 0.44 μg L^?1 at 25°C for 10 min and from ND to 0.87 μg L^?1 at 95°C for 30 min. The migration of Pb and As determined in this study confirm that the human exposure was within safe levels based on the EDI of food contact paper compared with the provisional tolerable weekly intake for Pb of 25 μg kg^?1 bw and for As of 15 μg kg^?1 bw.     

Electrical conductivity of milk: measurement, modifiers, and meta analysis of mastitis detection performance.

The physics, physiology, and pathology of electrical conductivity of milk are described. Based on a meta analysis, the use of electrical conductivity as a mastitis detection tool is discussed. Most reports were based on subclinical mastitis data. The gold standards of the different reports are discussed. With an overall sensitivity of 66% and an overall specificity of 94%, the predictive value of a positive electrical conductivity test remains low in a low prevalence population. The use of on-line systems for clinical mastitis detection is discussed. On-line systems that combine multiple data and perform multifactorial analyses will be of interest to the dairy industry.     

Bayesian analysis of lactation curves of Holstein-Friesian cattle using a nonlinear model

A Bayesian procedure was developed for fitting Wood's incomplete Gamma function to test-day milk records of Spanish Holstein Friesian cattle. Each parameter of Wood's function was considered as a dependent variable in a submodel that accounted for systematic effects and genetic relationships among animals. Marginal posterior distributions of model parameters were obtained using Gibbs sampling. Variables of economic interest, such as 305-d yield, persistency, peak yield, and days in milk at peak day were predicted as functions of Wood's function curve parameters. Heritability estimates were 0.26, 0.32, and 0.19 for parameters of Wood's function and 0.26, 0.14, 0.26, and 0.05 for 305-d yield, persistency, peak yield, and days in milk at peak yield. These estimates indicate that it is possible to modify the shape of the lactation curve through genetic selection. Genetic correlations between parameters of Wood's curve and the aforementioned functions of these parameters suggest that selection for 305-d milk yield would result in higher and later peak yield, but only a slight improvement in persistency is expected.     

Technical note: PaGELL v.1.5: A flexible parametric program for the Bayesian analysis of longevity data within the context of animal breeding

This technical note presents the program PaGELL v.1.5 (Parametric Genetic Evaluation of Lifespan in Livestock), a flexible software program to analyze (right-censored) longevity data in livestock populations, with a special emphasis on the genetic evaluation of the breeding stock. This software relies on a parametric generalization of the proportional hazard model; more specifically, the baseline hazard function follows a Weibull process and flexibility is gained by including an additional time-dependent effect with the number of change points defined by the user. The program can accommodate 3 different sources of variation (i.e., systematic, permanent environmental, and additive genetic effects) and both fixed and time-dependent patterns (only for systematic and permanent environmental effects). Analyses are performed within a Bayesian context by sampling from the joint posterior distribution of the model, and model fit can be easily determined by the calculation of the deviance information criterion. Although this software has already been used on field data sets, its performance has been double-checked on simulated data set, and results are presented in this technical note. PaGELL v.1.5 was written in Fortran 95 language and, after compiling with the GNU Fortran Compiler v.4.7 and later, it has been tested in Windows, Linux, and MacOS operating systems (both 32- and 64-bit platforms). This program is available at http://www.casellas.info/files/pageII.zip.