A Bayesian model for estimating the malaria transition probabilities considering individuals lost to follow-up

It is known that patients may cease participating in a longitudinal study and become lost to follow-up. The objective of this article is to present a Bayesian model to estimate the malaria transition probabilities considering individuals lost to follow-up. We consider a homogeneous population, and it is assumed that the considered period of time is small enough to avoid two or more transitions from one state of health to another. The proposed model is based on a Gibbs sampling algorithm that uses information of lost to follow-up at the end of the longitudinal study. To simulate the unknown number of individuals with positive and negative states of malaria at the end of the study and lost to follow-up, two latent variables were introduced in the model. We used a real data set and a simulated data to illustrate the application of the methodology. The proposed model showed a good fit to these data sets, and the algorithm did not show problems of convergence or lack of identifiability. We conclude that the proposed model is a good alternative to estimate probabilities of transitions from one state of health to the other in studies with low adherence to follow-up.     

基于产业集群的企业重点投资地区决策的仿真

用多Agent仿真技术,研究了基于产业集群的台资IT企业在大陆重点投资地区的决策问题。提出了针对这一问题的、将集群竞争力评价的GEM模型、多Agent仿真技术及演化博弈相结合的思路与方法。仿真模型对集群的研究方法以及多Agent的应用领域有拓展意义,而仿真发现与结论可为政府制定有关政策提供参考。     

Effects of the Mechanism of Receptor-Mediated Gene Expression on the Shape of the Dose-Response Curve

A mathematical model of receptor-mediated gene expression that includes receptor binding of natural and xenobiotic ligands, protein synthesis and degradation, and metabolism of the xenobiotic ligand was created to identify the determinants of the shape of the dose-response profile. Values of the model's parameters were varied to reflect alternative mechanisms of expression of the protein. These assumptions had dramatic effects on the computed response to a bolus dose of the xenobiotic ligand. If all processes in the model exhibit hyperbolic kinetics, the dose-response curves can appear sigmoidal but actually be linear with a positive slope at low doses. The slope of the curve only approached zero at low dose, indicative of a threshold for response, if binding of the xenobiotic ligand to the receptor exhibited positive cooperativity (ligand binding at one site increases the affinity for ligand at another binding site on the receptor). Positive cooperativity in the rate-limiting step of protein synthesis produced dose-response curves which were "U-shaped" at low doses, also indicative of a threshold. Positive cooperativity in the metabolism of the xenobiotic ligand produced dose-response curves that increased more rapidly than linearly with increasing dose. The model illustrates the fact that response cannot be predicted from qualitative mechanistic arguments alone; any assessment of risk to health from xenobiotic chemicals must be based on a detailed quantitative examination of the kinetic behavior of each chemical species individually.     

Uncertainties in Pharmacokinetic Modeling for Perchloroethylene: II. Comparison of Model Predictions with Data for a Variety of Different Parameters

In this paper we compare expectations derived from 10 different human physiologically based pharmacokinetic models for perchloroethylene with data on absorption via inhalation, and concentrations in alveolar air and venous blood. Our most interesting finding is that essentially all of the models show a time pattern of departures of predictions of air and blood levels relative to experimental data that might be corrected by more sophisticated model structures incorporating either (a) heterogeneity of the fat compartment (with respect to either perfusion or partition coefficients or both) or (b) intertissue diffusion of perchloroethylene between the fat and muscle/VRG groups. Similar types of corrections have recently been proposed to reduce analogous anomalies in the fits of pharmacokinetic models to the data for several volatile anesthetics.^(17-20) A second finding is that models incorporating resting values for alveolar ventilation in the region of 5.4 L/min seemed to be most compatible with the most reliable set of perchloroethylene uptake data.     

Floods and Climate Change: Interactions and Impacts

Whether the floods experienced during the last decade in Germany and in other European countries are triggered or worsened by human activities has been the subject of a great deal of debate. Possible anthropogenic activities leading to increased flood risk include river regulation measures, intensified land use and forestry, and emissions of greenhouse gases causing a change in the global climate. This article discusses the latter by reviewing the existing knowledge on the subject. First, the relevance, capabilities, and limitations of climate models for the simulation and analysis of flood risk under aspects of the anthropogenic climate change are described. Special consideration is given here to differences between the "typical" spatial scale of climate models and hydrological flood models. Second, observations of trends in climate variables relevant for river flooding issues are summarized. Special emphasis is put on the Rhine and other German catchment areas. Third, the possibilities of modeling the different parts of the "cascade of flood risk" are summarized, introducing the special features of meteorological, hydrological, and river hydraulic models.     

利用智能主体分析"时尚"的涌现

时尚作为一种社会现象,是由个别人物的倡导和示范,先在少数人中流行,然后迅速蔓延到社会.多数人出于新奇与从众心理而仿效.对于它的研究有助于人们把握这种社会现象的产生与蔓延过程,深入认识和控制它的发展.由于它的复杂多样性,当前还缺乏对它的定量分析与研究.论文利用基于智能主体的仿真方法,分析了时尚的涌现过程.文中建立了两种模型,用于确定公众能否接受某种时尚的临界值.一种模型基于公众能获取全部信息,另一种则基于个人只能获取邻近信息.     

Use of Markov Chain Monte Carlo Analysis with a Physiologically-Based Pharmacokinetic Model of Methylmercury to Estimate Exposures in U.S. Women of Childbearing Age

A Bayesian approach, implemented using Markov Chain Monte Carlo (MCMC) analysis, was applied with a physiologically‐based pharmacokinetic (PBPK) model of methylmercury (MeHg) to evaluate the variability of MeHg exposure in women of childbearing age in the U.S. population. The analysis made use of the newly available National Health and Nutrition Survey (NHANES) blood and hair mercury concentration data for women of age 16–49 years (sample size, 1,582). Bayesian analysis was performed to estimate the population variability in MeHg exposure (daily ingestion rate) implied by the variation in blood and hair concentrations of mercury in the NHANES database. The measured variability in the NHANES blood and hair data represents the result of a process that includes interindividual variation in exposure to MeHg and interindividual variation in the pharmacokinetics (distribution, clearance) of MeHg. The PBPK model includes a number of pharmacokinetic parameters (e.g., tissue volumes, partition coefficients, rate constants for metabolism and elimination) that can vary from individual to individual within the subpopulation of interest. Using MCMC analysis, it was possible to combine prior distributions of the PBPK model parameters with the NHANES blood and hair data, as well as with kinetic data from controlled human exposures to MeHg, to derive posterior distributions that refine the estimates of both the population exposure distribution and the pharmacokinetic parameters. In general, based on the populations surveyed by NHANES, the results of the MCMC analysis indicate that a small fraction, less than 1%, of the U.S. population of women of childbearing age may have mercury exposures greater than the EPA RfD for MeHg of 0.1 μg/kgg/day, and that there are few, if any, exposures greater than the ATSDR MRL of 0.3 μgg/kgg/day. The analysis also indicates that typical exposures may be greater than previously estimated from food consumption surveys, but that the variability in exposure within the population of U.S. women of childbearing age may be less than previously assumed.     

Development of a Screening Approach to Interpret Human Biomonitoring Data on Volatile Organic Compounds: Reverse Dosimetry on Biomonitoring Data for Trichloroethylene

A screening approach is developed for volatile organic compounds (VOCs) to estimate exposures that correspond to levels measured in fluids and/or tissues in human biomonitoring studies. The approach makes use of a generic physiologically-based pharmacokinetic (PBPK) model coupled with exposure pattern characterization, Monte Carlo analysis, and quantitative structure property relationships (QSPRs). QSPRs are used for VOCs with minimal data to develop chemical-specific parameters needed for the PBPK model. The PBPK model is capable of simulating VOC kinetics following multiple routes of exposure, such as oral exposure via water ingestion and inhalation exposure during shower events. Using published human biomonitoring data of trichloroethylene (TCE), the generic model is evaluated to determine how well it estimates TCE concentrations in blood based on the known drinking water concentrations. In addition, Monte Carlo analysis is conducted to characterize the impact of the following factors: (1) uncertainties in the QSPR-estimated chemical-specific parameters; (2) variability in physiological parameters; and (3) variability in exposure patterns. The results indicate that uncertainty in chemical-specific parameters makes only a minor contribution to the overall variability and uncertainty in the predicted TCE concentrations in blood. The model is used in a reverse dosimetry approach to derive estimates of TCE concentrations in drinking water based on given measurements of TCE in blood, for comparison to the U.S. EPA's Maximum Contaminant Level in drinking water. This example demonstrates how a reverse dosimetry approach can be used to facilitate interpretation of human biomonitoring data in a health risk context by deriving external exposures that are consistent with a biomonitoring data set, thereby permitting comparison with health-based exposure guidelines.     

信贷环境与类型差异情景下的PPP项目政府经济承诺决策

为破解政府在PPP项目中存在的"承诺不足"与"承诺过度"决策难题，基于项目类型和信贷环境差异构建四种决策情景，融合优化决策与实物期权理论构建政府经济承诺决策模型，通过Kuhn-Tucker条件分析模型解的条件与边界并设计计算机求解算法，以W市城东污水处理项目为例进行数值模拟探讨政府经济承诺决策规律。结果表明：情景差异将会影响政府经济承诺决策；政府经济承诺可以提升项目价值，但较高的经济承诺未必导致较高的承诺成本；政府应对收益波动型项目给予更低的经济承诺，应在信贷收紧时给予更高的经济承诺，反之亦然；政府可以通过转移收益分配权和增加投资额来降低经济承诺，且不同情景效果具有差异。本文系统刻画了信贷环境与类型差异对经济承诺决策的影响，对于明晰经济承诺的决策成本及其后果、控制PPP项目金融风险、优化我国政府经济承诺决策等具有积极价值。