基于贝叶斯的苯并(α)芘PBPK模型优化与健康风险评估应用

应用基于生理的药代动力学(PBPK)模型预测苯并(α)芘(BaP)暴露的人体内部剂量,基于贝叶斯的马尔科夫链蒙特卡洛模拟(MCMC)方法对模型参数进行校准和优化,最后运用已优化的模型对BaP内暴露基准值进行推导.研究发现,基于贝叶斯的MCMC方法对模型后验参数校准后,模型精度明显提高,两个数据集验证结果显示残差平方和分别降低了72%和94%.PBPK模型以BaP和子代谢物3-羟基苯并(α)芘(3-OHBaP)的体内动力学过程为结构基础,模拟BaP体内浓度分布大小为脂肪>肾脏>皮肤>缓慢灌注组织>快速灌注组织>静脉血>肝脏;3-OHBaP体内浓度分布大小为肾脏>快速灌注组织>脂肪>肺>静脉血>缓慢灌注组织>肝脏>皮肤.敏感性分析显示,快速灌注组织-血分配系数对模型输出影响最大,灵敏度系数超过了200%;排泄系数影响最小,只有肾小球过滤率K_BR的灵敏度系数超过了1%.以美国国家环境保护局推荐的参考浓度2.0×10^-6mg/m³为外暴露安全基准值,基于PBPK模型推导了职业暴露的BaP生物监测当量(BE),结果显示BE值为0.405pmol/mol肌酐(尿液3-OHBaP平均浓度),为基于人体内暴露剂量水平进行定量健康风险评估奠定了基础.     

车装焊复合加工装备的服役可靠性评估

目的评估某型航天用车装焊复合加工装备在服役过程中的服役可靠性。方法首先分析装备常见的故障模式,统计装备在使用过程中的故障数据,由于装备故障数据样本量较少,所以提出基于贝叶斯理论的小样本车装焊复合加工装备可靠性评估方法,其中通过马尔科夫链蒙特卡罗(MCMC)法抽样解决贝叶斯理论中后验积分复杂的问题。结果确定了车装焊复合加工装备的寿命威布尔分布模型,并运用贝叶斯方法,计算出该车装焊复合加工装备的平均无故障工作时间(MTBF)。结论评估结果略低于设计要求,原因是目前该装备处于服役初期,服役初期故障数据较多。待进入稳定服役期时,故障率会有一定程度的降低后趋于稳定,MTBF会有一定幅度的增加,所以该车装焊复合加工装备MTBF基本满足设计要求。     

Bayesian areal wombling via adjacency modeling

Recently, public health professionals and other geostatistical researchers have shown increasing interest in boundary analysis, the detection or testing of zones or boundaries that reveal sharp changes in the values of spatially oriented variables. For areal data (i.e., data which consist only of sums or averages over geopolitical regions), Lu and Carlin (Geogr Anal 37: 265–285, 2005) suggested a fully model-based framework for areal wombling using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo (MCMC) methods, and showed the approach to have advantages over existing non-stochastic alternatives. In this paper, we develop Bayesian areal boundary analysis methods that estimate the spatial neighborhood structure using the value of the process in each region and other variables that indicate how similar two regions are. Boundaries may then be determined by the posterior distribution of either this estimated neighborhood structure or the regional mean response differences themselves. Our methods do require several assumptions (including an appropriate prior distribution, a normal spatial random effect distribution, and a Bernoulli distribution for a set of spatial weights), but also deliver more in terms of full posterior inference for the boundary segments (e.g., direct probability statements regarding the probability that a particular border segment is part of the boundary). We illustrate three different remedies for the computing difficulties encountered in implementing our method. We use simulation to compare among existing purely algorithmic approaches, the Lu and Carlin (2005) method, and our new adjacency modeling methods. We also illustrate more practical modeling issues (e.g., covariate selection) in the context of a breast cancer late detection data set collected at the county level in the state of Minnesota.     

Spatiotemporal variations of nitrate sources and dynamics in a typical agricultural riverine system under monsoon climate

Nitrogen pollution is a serious environmental issue in the Danjiangkou Reservoir region (DRR), the water source of the South-to-North Water Diversion Project of China. In this research, seasonal surveys and a bi-weekly time series survey were conducted in the Qihe River Basin, one of the most densely populated agricultural basins in the DRR. Hydrochemical compositions (NO₃^? and Cl^?), dual isotopes (δD-H₂O, δ¹⁸O-H₂O, δ¹⁵N-NO₃^?, and δ¹⁸O-NO₃^?), and a Markov Chain Monte Carlo isotope mixing model were jointly applied to unravel the sources, migrations, and transformations of the nitrate (NO₃^?) in the basin. It was revealed that the mixing between different sources was the main process controlling the isotopic compositions of the riverine NO₃^? in the upper-middle reaches. In contrast, denitrification occurred in the lower reaches. For the first time, the sources of NO₃^? were quantified at a basin scale in the DRR. Overall, the river transported 484.2 tons/year of NO₃-N to the reservoir, of which 32.6%, 36.4%, 28.0%, and 3.0% was from soil organic nitrogen, chemical fertilizer, residential sewage and atmospheric precipitation, respectively. The NO₃-N fluxes of the different sources were regulated by the monsoon climate and anthropogenic activities. For example, high precipitation and intense fertilization resulted in severe nonpoint source pollution. Denitrification thrived in soils and reservoirs in wet seasons. Temperature could regulate the migration, nitrification and denitrification processes. Based on the results, we suggest that the management strategies dealing with nitrogen pollution issue in the DRR should follow the specific spatiotemporal characteristics of NO₃^? sources, migration and transformation mechanisms.     

Multivariate Bayesian analysis of atmosphere–ocean general circulation models

Numerical experiments based on atmosphere–ocean general circulation models (AOGCMs) are one of the primary tools in deriving projections for future climate change. Although each AOGCM has the same underlying partial differential equations modeling large scale effects, they have different small scale parameterizations and different discretizations to solve the equations, resulting in different climate projections. This motivates climate projections synthesized from results of several AOGCMs’ output. We combine present day observations, present day and future climate projections in a single highdimensional hierarchical Bayes model. The challenging aspect is the modeling of the spatial processes on the sphere, the number of parameters and the amount of data involved. We pursue a Bayesian hierarchical model that separates the spatial response into a large scale climate change signal and an isotropic process representing small scale variability among AOGCMs. Samples from the posterior distributions are obtained with computer-intensive MCMC simulations. The novelty of our approach is that we use gridded, high resolution data covering the entire sphere within a spatial hierarchical framework. The primary data source is provided by the Coupled Model Intercomparison Project (CMIP) and consists of 9 AOGCMs on a 2.8 by 2.8 degree grid under several different emission scenarios. In this article we consider mean seasonal surface temperature and precipitation as climate variables. Extensions to our model are also discussed.     

Detecting pattern in biological stressor response relationships using model based cluster analysis

Environmental monitoring of aquatic systems is needed to estimate the quality of the systems, to evaluate standards and to study stressor–response relationships. Monitoring programs often focus on the collection of biological, chemical and physical measures of the system. An important concern is the effect of chemical and physical stressors on the biological community. Evaluation of relationships may be difficult as the extent of the relationship is not known. From a management perspective, interest is on what factors affect the biological community and where these factors have an influence. The focus of this paper is on the use of regression based cluster analysis as a tool for finding relationships between a single biological response and a suite of environmental stressors. The approach to cluster analysis uses a penalized regression classification likelihood and Markov Chain Model Composition Monte Carlo. This approach allows for simultaneous development of regression models and clustering of the regression models. The method is applied to the analysis of a data set describing stressors/response relationship in Ohio.     

Spatial statistics for modeling of abundance and distribution of wildlife species in the Masai Mara ecosystem, Kenya

This study illustrates the use of modern statistical procedures for better wildlife management by addressing three key issues: determination of abundance, modeling of animal distributions and variability of diversity in space and time. Prior information in Markov Chain Monte Carlo (MCMC) methods is used to improve estimates of abundance. Measures of autocorrelation are included when modeling distributions of animal counts, and a diversity index to indicate species abundance and richness for large herbivores is developed. Data from the Masai Mara ecosystem in Kenya are used to develop and demonstrate these procedures. The new abundance estimates are up to 35% more accurate than those obtained by existing methods. Significant temporal changes in spatial patterns are found from a space-time analysis of elephant counts over a 20-year period, with strong interactions over 5 km and 6 months space and time separations, respectively. The new diversity index is sensitive to both high abundance and species richness and is also able to capture year to year variation. It indicates an overall marginal decrease in diversity for large herbivores in the Mara ecosystem. The space-time analyses and diversity index can easily be computed thereby providing tools for rapid decision making.     

我国能源消费需求的时变弹性分析

为了科学分析相关经济因素变化对我国能源需求的影响效应,本文在提取能源消费需求主要影响因素的基础上,建立了基于Bayesian理论的时变系数回归模型,利用MCMC方法获得了各时变回归系数的估计.同时,利用HP滤波方法,得到了能源消费需求相对于各相关因素弹性系数的趋势及波动情况,并就能源需求对各相关经济因素弹性系数的趋势变动原因进行了分析,结果表明:①在不同的时间段内,能源价格、产业结构、经济增长、全国总人口以及能源消耗结构对能源消费需求的影响强度具有时变效应;②电力价格变动对降低能源消费的作用最为显著,但边际效应在逐年下降;煤炭价格的提高对能源消费的增加具有很大推进作用,但"煤电联动"、能源价格市场化的建设可能进一步抑制能源需求;③城镇化建设进程可进一步优化人口结构、产业结构以及能源消费结构,这将在很大程度上限制能源需求的加速增长.     

能源价格变动对中国节能降耗的影响效应

在分析我国能源比价扭曲对能耗强度影响效应的基础上,建立了Bayesian时变动态回归模型,测算了能源价格变动对能耗的影响效果及变动趋势。主要结论为:①能源比价关系的调整比单种能源价格的国际接轨更为重要。相比国际市场的能源比价结构,中国能源商品价格结构的扭曲度提高了中国的能耗强度,促进了第二产业的增长。②能源价格的变动对单位GDP能耗的影响效应具有显著的时变特征。在电力、煤炭、石油三种能源中,电力价格的变动对能耗强度的影响最大。电力价格变化对单位GDP能耗的影响边际效应在逐年下降,但节能的效果仍很显著。煤电价格联动制约及"从量计征"的资源税偏低使得煤炭价格的上涨反而加大了能耗强度,且1996年以来煤炭价格的提高对单位GDP能耗上升的影响效应还在不断加强,原油价格的提高对单位GDP能耗的影响在不同的时间段有不同的作用,但相对煤炭及电力价格来说,石油价格的影响效应最小。2002年以前石油价格的上升对能耗的上升起助推作用,2003年后石油价格的上升对能耗的上升起降低作用且强度逐年加大。     

A spatial zero-inflated poisson regression model for oak regeneration

Ecological counts data are often characterized by an excess of zeros and spatial dependence. Excess zeros can occur in regions outside the range of the distribution of a given species. A zero-inflated Poisson regression model is developed, under which the species range is determined by a spatial probit model, including physical variables as covariates. Within that range, species counts are independently drawn from a Poisson distribution whose mean depends on biotic variables. Bayesian inference for this model is illustrated using data on oak seedling counts. Received: May 2004 / Revised: December 2004