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https://www.selleckchem.com/products/gdc-1971.html Recent advances in microscopy have made it possible to collect 3D topographic data, enabling more precise virtual comparisons based on the collected 3D data as a supplement to traditional comparison microscopy and 2D photography. Automatic comparison algorithms have been introduced for various scenarios, such as matching cartridge cases [1,2] or matching bullet striae [3-5]. One key aspect of validating these automatic comparison algorithms is to evaluate the performance of the algorithm on external tests, that is, using data which were not used to train the algorithm. Here, we present a discussion of the performance of the matching algorithm [6] in three studies conducted using different Ruger weapons. We consider the performance of three scoring measures random forest score, cross correlation, and consecutive matching striae (CMS) at the land-to-land level and, using Sequential Average Maxima scores, also at the bullet-to bullet level. Cross correlation and random forest scores both result in perfect discrimination of same-source and different-source bullets. At the land-to-land level, discrimination for both cross correlation and random forest scores (based on area under the curve, AUC) is excellent (≥0.90). One of the primary interests of forensic sciences is the study of traces, better conceived as silent witnesses to criminal activity whose existence is attributable to Locard's principle. Thus, textile fibers are commonly exploited as they are easily transferred during contact which can vary in intensity depending upon the type of activity that occurred. Regardless, current knowledge pertaining to fiber transfer mechanisms, particularly in regards to blended textiles, is limited. It is recognized that the intensity of the contact, the type of textile as well as the size and type of fibers composing it have a significant influence on the amount of fibers transferred. However, when the donor textile is blended (

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