Population and temperature effects on Lucilia sericata (Diptera: Calliphoridae) body size and minimum development time.

Understanding how ecological conditions influence physiological responses is fundamental to forensic entomology. When determining the minimum postmortem interval with blow fly evidence in forensic investigations, using a reliable and accurate model of development is integral. Many published studies vary in results, source populations, and experimental designs. Accordingly, disentangling genetic causes of developmental variation from environmental causes is difficult. This study determined the minimum time of development and pupal sizes of three populations of Lucilia sericata Meigen (Diptera: Calliphoridae; from California, Michigan, and West Virginia) at two temperatures (20 degrees C and 33.5 degrees C). Development times differed significantly between strain and temperature. In addition, California pupae were the largest and fastest developing at 20 degrees C, but at 33.5 degrees C, though they still maintained their rank in size among the three populations, they were the slowest to develop. These results indicate a need to account for genetic differences in development, and genetic variation in environmental responses, when estimating a postmortem interval with entomological data.

Sensitivity of trends in geometric mean blood levels to random measurement errors.

A statistical model is investigated that expresses observations, such as blood lead levels, as an additive function of true levels and random measurement errors. Both empirical results (obtained from a series of computer simulation experiments) and theoretical results indicate how certain summary statistics for the observations vary in response to random measurement errors. Such results are applied to a very large data base of pediatric blood lead levels collected in New York City during 1970-1976, and they indicate that the observed trends in geometric mean blood lead levels are not significantly altered by the possible presence of measurement errors.

Design Aspects of Scheffe's Calibration Theory Using Linear Splines.

The measurement process uncertainty is propagated through the use of a calibration curve. The magnitude and direction of this uncertainty depends on the choice of the controllable variable in producing the calibration curve; in other words, the design of the calibration experiment. In this paper this design is discussed in the context of Scheffé's approach to the uncertainties of a calibration curve and in particular for the case in which the calibration curve is a linear spline. A class of appropriate designs is given, which depend on the location of the knots and the slopes of the segments. One of these designs is quickly calculable and can be found without a computer. Based on these results, a design approach is suggested for the case in which the knots are not known exactly.

Reported emissions of organic gases are not consistent with observations.

Regulatory agencies and photochemical models of ozone rely on self-reported industrial emission rates of organic gases. Incorrect self-reported emissions can severely impact on air quality models and regulatory decisions. We compared self-reported emissions of organic gases in Houston, Texas, to measurements at a receptor site near the Houston ship channel, a major petrochemical complex. We analyzed hourly observations of total nonmethane organic carbon and 54 hydrocarbon compounds from C-2 to C-9 for the period June through November, 1993. We were able to demonstrate severe inconsistencies between reported emissions and major sources as derived from the data using a multivariate receptor model. The composition and the location of the sources as deduced from the data are not consistent with the reported industrial emissions. On the other hand, our observationally based methods did correctly identify the location and composition of a relatively small nearby chemical plant. This paper provides strong empirical evidence that regulatory agencies and photochemical models are making predictions based on inaccurate industrial emissions.

A transparent tool for seemingly difficult calibrations: the parallel calibration method.

A new easy-to-understand calibration method for the analysis of spectral data is developed. The "parallel calibration" method is logically simple and intuitive yet often provides an improvement over more complex standard calibration methods. A description of the algorithm with a technical justification for the parallel algorithm is presented, underscoring the simplicity of the approach. In addition, performance as compared to that of the standard methods of classical least-squares (CLS) and partial least-squares (PLS) regression is studied. Calibrations are carried out on a computer-generated simulation data set as well as two scientific data sets. The results show that the parallel method gives results comparable to or better than those of CLS and PLS methods in terms of mean squared error.

Theoretical Justification of Wavelength Selection in PLS Calibration: Development of a New Algorithm.

The mathematical basis of improved calibration through selection of informative variables for partial least-squares calibration has been identified. A theoretical investigation of calibration slopes indicates that including uninformative wavelengths negatively affect calibrations by producing both large relative bias toward zero and small additive bias away from the origin. These theoretical results are found regardless of the noise distribution in the data. Studies are performed to confirm this result using a previously used selection method compared to a new method, which is designed to perform more appropriately when dealing with data having large outlying points by including estimates of spectral residuals. Three different data sets are tested with varying noise distributions. In the first data set, Gaussian and log-normal noise was added to simulated data which included a single peak. Second, near-infrared spectra of glucose in cell culture media taken with an FT-IR spectrometer were analyzed. Finally, dispersive Raman Stokes spectra of glucose dissolved in water were assessed. In every case considered here, improved prediction is produced through selection, but data with different noise characteristics showed varying degrees of improvement depending on the selection method used. The practical results showed that, indeed, including residuals into ranking criteria improves selection for data with noise distributions resulting in large outliers. It was concluded that careful design of a selection algorithm should include consideration of spectral noise distributions in the input data to increase the likelihood of successful and appropriate selection.