Evaluation and comparison of portable emissions measurement systems and federal reference methods for emissions from a back-up generator and a diesel truck operated on a chassis dynamometer.

There is considerable interest in portable emissions measurement systems (PEMS) for emission inventory and regulatory applications. For this study, four commercial PEMS were compared with a Federal Reference Method (FRM) for measuring emissions from a back-up generator (BUG) over steady-state loads and a diesel truck on transient and steady-state chassis dynamometer tests. The agreement between the PEMS and the FRM varied depending on the pollutant and the particular PEMS tested for both the BUG and chassis dynamometer testing. The best performing PEMS for both the BUG and chassis testing was within approximately 12% for NOx of the FRM. For the BUG testing, several PEMS showed agreement with the FRM within approximately 5% for CO2. For the chassis dynamometer testing, the best PEMS showed agreement typically within approximately 5% for CO2. PM measurements for the BUG testing were low compared to the FRM, with the best measurements approximately 20% lower. For the chassis testing, two PM PEMS showed a good correlation but a high bias, while the correlation was worse for the other two PEMS. For each emissions component, some PEMS under different test conditions showed considerably larger deviations than those for the best performing PEMS.

Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery.

BACKGROUND: We present an analysis of the utility of multispectral versus standard RGB imagery for routine H&E stained histopathology images, in particular for pixel-level classification of nuclei. Our multispectral imagery has 29 spectral bands, spaced 10 nm within the visual range of 420-700 nm. It has been hypothesized that the additional spectral bands contain further information useful for classification as compared to the 3 standard bands of RGB imagery. We present analyses of our data designed to test this hypothesis. RESULTS: For classification using all available image bands, we find the best performance (equal tradeoff between detection rate and false alarm rate) is obtained from either the multispectral or our "ccd" RGB imagery, with an overall increase in performance of 0.79% compared to the next best performing image type. For classification using single image bands, the single best multispectral band (in the red portion of the spectrum) gave a performance increase of 0.57%, compared to performance of the single best RGB band (red). Additionally, red bands had the highest coefficients/preference in our classifiers. Principal components analysis of the multispectral imagery indicates only two significant image bands, which is not surprising given the presence of two stains. CONCLUSION: Our results indicate that multispectral imagery for routine H&E stained histopathology provides minimal additional spectral information for a pixel-level nuclear classification task than would standard RGB imagery.

Detection of malignancy in cytology specimens using spectral-spatial analysis.

Despite low sensitivity (around 60%), cytomorphologic examination of urine specimens represents the standard procedure in the diagnosis and follow-up of bladder cancer. Although color is information-rich, morphologic diagnoses are rendered almost exclusively on the basis of spatial information. We hypothesized that quantitative assessment of color (more precisely, of spectral properties) using liquid crystal-based spectral fractionation, combined with genetic algorithm-based spatial analysis, can improve the accuracy of traditional cytologic examination. Images of various cytological specimens were collected every 10 nm from 400 to 700 nm to create an image stack. The resulting data sets were analyzed using the Los Alamos-developed GENetic Imagery Exploitation (GENIE) package, a hybrid genetic algorithm that segments (classifies) images using automatically 'learned' spatio-spectral features. In an evolutionary fashion, GENIE generates a series of algorithms or 'chromosomes', keeping the one with best fitness with respect to a user-defined training set. First, we tested the system to determine if it could recognize malignant cells using artificial cytology specimens constructed to completely avoid the requirement for human interpretation. GENIE was able to differentiate malignant from benign cells and to estimate their relative proportions in controlled mixtures. We then tested the system on routine cytology specimens. When targeted to detect malignant urothelial cells in cytology specimens, GENIE showed a combined sensitivity and specificity of 85 and 95%, in samples drawn from two separate institutions over a span of 4 years. When trained on cases initially diagnosed as 'atypical' but with unequivocal follow-up by biopsy, surgical specimen or cytology, GENIE showed efficiency superior to the cytopathologist with respect to predicting the follow-up result in a cohort of 85 cases. We believe that, in future, this type of methodology could be used as an ancillary test in cytopathology, in a manner analogous to immunostaining, in those situations when a definitive diagnosis cannot be rendered based solely on the morphology.