Background: Laser scanning cytometry (LSC) is a new technology similar to flow cytometry but generates data from analysis of successive microscopic fields. Unlike its use in other applications, LSC‐generated data are not random when used for tissue sections, but are dependent on the microanatomy of the tissue and the distribution and expression of the protein under investigation. For valid LSC analysis, the data generated requires the evaluation of a sufficient tissue area to ensure an accurate representation of expression within the tissue of interest. Methods: In this report, we describe a simple and common sense method for determining the area of tissue required for sound LSC analysis by tracking the variation in the measure of target expression with increasing number of fields until it approaches zero. Results: This approach was used to evaluate the expression of immunohistochemical markers with differing tissue distributions in liver (PMP70, CYP1A2, and Ki67 positive macrophages) and a colorectal adenocarcinoma (activated caspase‐3 positive cells), which exhibited diffuse, regional (centrilobular), random, and irregular distribution patterns respectively. Conclusions: Analyses of these markers demonstrated that the amount of tissue area required to reach a steady measure of a parameter increased with increasing variability of the tissue distribution. © 2007 International Society for Analytical Cytology
<p>Fatigue life or crack growth predictions that use information from crack inspections have been applied to offshore structures and, in recent years, also to steel bridges. Apart from inspection data, a probabilistic crack growth model and knowledge of the distributions and correlations of the variables are required for such predictions. The performance and validity of such predictions has been demonstrated in laboratory environments but validations for actual, practical situations are currently lacking because of a lack of field data. In particular, realistic distributions and correlations for practice are difficult to obtain. This situation, however, has now changed. Extensive inspections of a specific bridge came recently available, showing multiple cracks in similar details. This provides unique data for validation purposes. This paper uses the inspection data to demonstrate the validity of probabilistic crack growth predictions for this real application.</p>
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