Assessment of source-specific health effects associated with an unknown number of major sources of multiple air pollutants: a unified Bayesian approach.

There has been increasing interest in assessing health effects associated with multiple air pollutants emitted by specific sources. A major difficulty with achieving this goal is that the pollution source profiles are unknown and source-specific exposures cannot be measured directly; rather, they need to be estimated by decomposing ambient measurements of multiple air pollutants. This estimation process, called multivariate receptor modeling, is challenging because of the unknown number of sources and unknown identifiability conditions (model uncertainty). The uncertainty in source-specific exposures (source contributions) as well as uncertainty in the number of major pollution sources and identifiability conditions have been largely ignored in previous studies. A multipollutant approach that can deal with model uncertainty in multivariate receptor models while simultaneously accounting for parameter uncertainty in estimated source-specific exposures in assessment of source-specific health effects is presented in this paper. The methods are applied to daily ambient air measurements of the chemical composition of fine particulate matter ([Formula: see text]), weather data, and counts of cardiovascular deaths from 1995 to 1997 for Phoenix, AZ, USA. Our approach for evaluating source-specific health effects yields not only estimates of source contributions along with their uncertainties and associated health effects estimates but also estimates of model uncertainty (posterior model probabilities) that have been ignored in previous studies. The results from our methods agreed in general with those from the previously conducted workshop/studies on the source apportionment of PM health effects in terms of number of major contributing sources, estimated source profiles, and contributions. However, some of the adverse source-specific health effects identified in the previous studies were not statistically significant in our analysis, which probably resulted because we incorporated parameter uncertainty in estimated source contributions that has been ignored in the previous studies into the estimation of health effects parameters.

Assessment of mobile source contributions in El Paso by PMF receptor modeling coupled with wind direction analysis.

It is well-known that El Paso is the only border area in Texas that has violated national air quality standards. Mobile source emissions (including vehicle exhaust) contribute significantly to air pollution, along with other sources including industrial, residential, and cross-border. This study aims at separating unobserved vehicle emissions from air-pollution mixtures indicated by ambient air quality data. The level of contributions from vehicle emissions to air pollution cannot be determined by simply comparing ambient air quality data with traffic levels because of the various other contributors to overall air pollution. To estimate contributions from vehicle emissions, researchers employed advanced multivariate receptor modeling called positive matrix factorization (PMF) to analyze hydrocarbon data consisting of hourly concentrations measured from the Chamizal air pollution monitoring station in El Paso. The analysis of hydrocarbon data collected at the Chamizal site in 2008 showed that approximately 25% of measured Total Non-Methane Hydrocarbons (TNMHC) was apportioned to motor vehicle exhaust. Using wind direction analysis, researchers also showed that the motor vehicle exhaust contributions to hydrocarbons were significantly higher when winds blow from the south (Mexico) than those when winds blow from other directions. The results from this research can be used to improve understanding source apportionment of pollutants measured in El Paso and can also potentially inform transportation planning strategies aimed at reducing emissions across the region.

Mineral preservatives in the wood of Stradivari and Guarneri.

Following the futile efforts of generations to reach the high standard of excellence achieved by the luthiers in Cremona, Italy, by variations of design and plate tuning, current interest is being focused on differences in material properties. The long-standing question whether the wood of Stradivari and Guarneri were treated with wood preservative materials could be answered only by the examination of wood specimens from the precious antique instruments. In a recent communication (Nature, 2006), we reported about the degradation of the wood polymers in instruments of Stradivari and Guarneri, which could be explained only by chemical manipulations, possibly by preservatives. The aim of the current work was to identify the minerals from the small samples of the maple wood which were available to us from the antique instruments. The ashes of wood from one violin and one cello by Stradivari, two violins by Guarneri, one viola by H. Jay, one violin by Gand-Bernardel were analyzed and compared with a variety of commercial tone woods. The methods of analysis were the following: back-scattered electron imaging, X-ray fluorescence maps for individual elements, wave-length dispersive spectroscopy, energy dispersive X-ray spectroscopy and quantitative microprobe analysis. All four Cremonese instruments showed the unmistakable signs of chemical treatments in the form of chemicals which are not present in natural woods, such as BaSO4, CaF2, borate, and ZrSiO4. In addition to these, there were also changes in the common wood minerals. Statistical evaluation of 12 minerals by discriminant analysis revealed: a. a difference among all four Cremona instruments, b. the difference of the Cremonese instruments from the French and English antiques, and c. only the Cremonese instruments differed from all commercial woods. These findings may provide the answer why all attempts to recreate the Stradivarius from natural wood have failed. There are many obvious implications with regard to how the green tone wood should be treated, which chould lead to changes in the practice of violin-making. This research should inspire others to analyze more antique violins for their chemical contents.

Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.

Verification of candidate biomarkers relies upon specific, quantitative assays optimized for selective detection of target proteins, and is increasingly viewed as a critical step in the discovery pipeline that bridges unbiased biomarker discovery to preclinical validation. Although individual laboratories have demonstrated that multiple reaction monitoring (MRM) coupled with isotope dilution mass spectrometry can quantify candidate protein biomarkers in plasma, reproducibility and transferability of these assays between laboratories have not been demonstrated. We describe a multilaboratory study to assess reproducibility, recovery, linear dynamic range and limits of detection and quantification of multiplexed, MRM-based assays, conducted by NCI-CPTAC. Using common materials and standardized protocols, we demonstrate that these assays can be highly reproducible within and across laboratories and instrument platforms, and are sensitive to low mug/ml protein concentrations in unfractionated plasma. We provide data and benchmarks against which individual laboratories can compare their performance and evaluate new technologies for biomarker verification in plasma.

A Univariate Extension of Jensen's Inequality.

The result in this paper explains some of the qualitative nature of Jensen's inequality. It is shown that the more disperse the distribution of a random variable is, the smaller is the expectation of any concave function of it. This result can be used to show the inadequacy of some current methods of reporting environmental data by using geometric means, and it extends the result of I. Billick, D. Shier, and C. H. Spiegelman, where symmetry of the error in environmental measurements is assumed.

An Iterative Calibration Curve Procedure.

Calibration curves are an important part of many measurement processes. The user of a fitted calibration curve must know its precision and accuracy. These are determined in a timely fashion using the data iteratively. This paper gives a method that divides the data into training and test groups. The test group is iteratively checked to see that a prechosen nominal confidence interval probability of coverage is met. If on the basis of this check the calibration experiment is completed, the nominal probability level is shown to still be valid.