An informative Bayesian structural equation model to assess source-specific health effects of air pollution

A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles or particulate matter (PM). Quantifying source-specific risk is a challenge because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions via a source apportionment or multivariate receptor analysis applied to a large number of observed elemental concentrations. Although source apportionment methods are well established for exposure assessment, little work has been done to evaluate the appropriateness of characterizing unobservable sources thus in health effects analyses. In this article, we propose a structural equation framework to assess source-specific health effects using speciated elemental data. This approach corresponds to fitting a receptor model and the health outcome model jointly, such that inferences on the health effects account for the fact that uncertainty is associated with the source contributions. Since the structural equation model (SEM) typically involves a large number of parameters, for small-sample settings, we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We compare via simulation the performance of our approach in estimating source-specific health effects to that of 2 existing approaches, a tracer approach and a 2-stage approach. Simulation results suggest that the proposed informative Bayesian SEM is effective in eliminating the bias incurred by the 2 existing approaches, even when the number of exposures is limited. We employ the proposed methods in the analysis of a concentrator study investigating the association between ST-segment, a cardiovascular outcome, and major sources of Boston PM and discuss the implications of our findings with respect to the design of future PM concentrator studies.     

The effects of species immigrations and changing conditions on isoetid communities

Historical data from the 1930s were compared with new data gathered during the 2000s to evaluate the effects of increased numbers of larger stature submersed species (both elodeids and characeans) on resident isoetid communities. The cover and species richness of submersed species were assessed in 23 seepage lakes in northwestern Wisconsin, USA, using randomly located 1 m × 1 m plots. Water clarity, conductivity and residential land use were determined on a whole-lake basis and the sediment type and water depth were recorded at each plot. The probability of elodeids or characeans occurring in isoetid plots increased with the number of elodeids and characeans gained by a lake since the 1930s, with additions ranging from two to 15 species per lake. However, not all species were equally likely to co-occur with isoetids. Six elodeid species (Najas flexilis, Najas gracillima, Potamogeton gramineus, Potamogeton pusillus, Potamogeton spirillus and Vallisneria americana) along with Chara spp. were the most frequent isoetid associates, while other species that were common in the lakes, such as Elodea canadensis and Potamogeton robbinsii, were less frequent in isoetid plots. The lake-wide proportion of isoetid plots colonized by elodeids or characeans ranged from 5% to 100%, with increasing conductivity and total elodeids (plus Chara spp. and Nitella spp.) the strongest predictors of this colonization. Approximately half (49.6%) of all isoetid plots sampled had elodeids or characeans present (39.2% elodeids, 7.4% elodeids and characeans, 3.0% characeans), and isoetid cover and species richness were lower when these larger stature species were present. The risks this colonization poses for the long-term viability of isoetid species appeared to depend on multiple factors, including whole-lake characteristics, opportunities for refuge, and connections among regional isoetid populations. There was evidence of a time lag between the introduction of elodeid or characean species to a lake and invasion of isoetid plots within the lake, a process that deserves further study.     

On latent-variable model misspecification in structural measurement error models for binary response

Summary .  We consider structural measurement error models for a binary response. We show that likelihood-based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent-variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean-squared error. Based on these and other findings, we create a new diagnostic method to detect latent-variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods.     

Statistical methods for analysis of radiation effects with tumor and dose location-specific information with application to the WECARE study of asynchronous contralateral breast cancer

Summary .  Methods for the analysis of individually matched case-control studies with location-specific radiation dose and tumor location information are described. These include likelihood methods for analyses that just use cases with precise location of tumor information and methods that also include cases with imprecise tumor location information. The theory establishes that each of these likelihood based methods estimates the same radiation rate ratio parameters, within the context of the appropriate model for location and subject level covariate effects. The underlying assumptions are characterized and the potential strengths and limitations of each method are described. The methods are illustrated and compared using the WECARE study of radiation and asynchronous contralateral breast cancer.     

短跑训练对运动员跟腱影响的超声测量研究

目的：研究短跑训练对运动员跟腱的影响。方法：选择从事运动训练4-6年的健康男、女短跑运动员为实验组（n=12）,同时选同龄健康非运动员男、女为对照组（n=12）,采用高频超声检测平静状态跟腱长度、横截面积,以及小腿三头肌等长收缩最大力量跖屈跟腱长度的变化。结果：跟腱长度：对照组男子168.5±9.2mm,女子162.4±9.8mm,实验组男子170.9±10.7mm,女子164.0±7.0mm。实验组和对照组组内、组间差别均无统计学意义。跟腱横截面积：对照组男子62.2±6.2mm2,女子47.1±4.5mm2,实验组男子65.6±2.9mm2,女子49.6±1.9mm2。同组内男子比女子跟腱横截面积大,差别有统计学意义（P＆lt;0.05）,但实验组和对照组组间差别无统计学意义。小腿三头肌等长收缩最大力跖屈时跟腱拉长值：对照组男子7.6±3.6mm,女子4.9±2.8mm,实验组男子11.1±2.9mm,女子7.9±3.1mm。男子比女子跟腱拉长值大、实验组比对照组拉长值大,差别均有统计学意义（P＆lt;0.05）。结论：短跑训练可增加跟腱可拉伸长度,跟腱的长度和横截面积未发生明显变化。高频超声可作为重要的测量手段用于...     

度量误差对模型参数估计值的影响及参数估计方法的比较研究

基于模型V=aDb,首先在Matlab下用模拟实验的方法,研究了度量误差对模型参数估计的影响,结果表明:当V的误差固定而D的误差不断增大时,用通常最小二乘法对模型进行参数估计,参数a的估计值不断增大,参数b的估计值不断减小,参数估计值随着 D的度量误差的增大越来越远离参数真实值;然后对消除度量误差影响的参数估计方法进行研究,分别用回归校准法、模拟外推法和度量误差模型方法对V和D都有度量误差的数据进行参数估计,结果表明:回归校准法、模拟外推法和度量误差模型方法都能得到参数的无偏估计,克服了用通常最小二乘法进行估计造成的参数估计的系统偏差,结果进一步表明度量误差模型方法优于回归校准法和模拟外推法．     

Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error

Summary : We propose a semiparametric Bayesian method for handling measurement error in nutritional epidemiological data. Our goal is to estimate nonparametrically the form of association between a disease and exposure variable while the true values of the exposure are never observed. Motivated by nutritional epidemiological data, we consider the setting where a surrogate covariate is recorded in the primary data, and a calibration data set contains information on the surrogate variable and repeated measurements of an unbiased instrumental variable of the true exposure. We develop a flexible Bayesian method where not only is the relationship between the disease and exposure variable treated semiparametrically, but also the relationship between the surrogate and the true exposure is modeled semiparametrically. The two nonparametric functions are modeled simultaneously via B‐splines. In addition, we model the distribution of the exposure variable as a Dirichlet process mixture of normal distributions, thus making its modeling essentially nonparametric and placing this work into the context of functional measurement error modeling. We apply our method to the NIH‐AARP Diet and Health Study and examine its performance in a simulation study.     

Uncertainty analysis of eddy flux measurements in typical ecosystems of ChinaFLUX

Fluxes of CO₂ (FCO₂) and energy (latent heat, LE; sensible heat, H) exchange between ecosystems and atmosphere, as measured by the eddy covariance technique, represent a fundamental data source for global-change research. However, little is known about the uncertainties of flux measurements at an ecosystem level in China. Here, we use data from six eddy covariance tower sites in ChinaFLUX, including two forested sites, three grassland sites, and one agricultural site, to conduct a cross-site analysis of random flux errors (RFEs) of FCO₂, LE, and H. By using the daily-differencing approach, paired observations are obtained to characterize the random error in these measurements. Our results show that: (1) The RFEs of FCO₂, LE, and H in different ecosystems of ChinaFLUX closely follow a double-exponential (Laplace) distribution, presumably due to a superposition of Gaussian distribution for high flux magnitude. (2) The RFEs of FCO₂, LE, and H are not homogeneous and appear to be a linear function of flux magnitude. (3) Except for H, the RFEs of FCO₂ and LE exhibit a distinct seasonal pattern. For FCO₂, the dependence of RFEs on wind speed varies somewhat according to vegetation type, whereas for LE and H, there is no such dependence. The effect of temperature on RFEs is not statistically significant (P < 0.05). Both the distribution and the relationship of RFEs with flux magnitude in ChinaFLUX are essentially in accord with those in AmeriFlux and CarboEurope.     

Measurement of bubble and pellet size distributions: past and current image analysis technology

Measurements of bubble and pellet size distributions are useful for biochemical process optimizations. The accuracy, representation, and simplicity of these measurements improve when the measurement is performed on-line and in situ rather than off-line using a sample. Historical and currently available measurement systems for photographic methods are summarized for bubble and pellet (morphology) measurement applications. Applications to cells, mycelia, and pellets measurements have driven key technological developments that have been applied for bubble measurements. Measurement trade-offs exist to maximize accuracy, extend range, and attain reasonable cycle times. Mathematical characterization of distributions using standard statistical techniques is straightforward, facilitating data presentation and analysis. For the specific application of bubble size distributions, selected bioreactor operating parameters and physicochemical conditions alter distributions. Empirical relationships have been established in some cases where sufficient data have been collected. In addition, parameters and conditions with substantial effects on bubble size distributions were identified and their relative effects quantified. This information was used to guide required accuracy and precision targets for bubble size distribution measurements from newly developed novel on-line and in situ bubble measurement devices.