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The use of amino acid racemization (AAR) for estimating ages of Quaternary fossils usually requires a combination of kinetic and effective temperature modeling or independent age calibration of analyzed samples. Because of limited availability of calibration samples, age estimates are often based on model extrapolations from single calibration points over wide ranges of D/L values. Here we present paired AAR and 87Sr/ 86Sr results for Pleistocene mollusks from the North Carolina Coastal Plain, USA. 87Sr/ 86Sr age estimates, derived from the lookup table of McArthur et al. [McArthur, J.M., Howarth, R.J., Bailey, T.R., 2001. Strontium isotopic stratigraphy: LOWESS version 3: best fit to the marine Sr-isotopic curve for 0-509 Ma and accompanying Look-up table for deriving numerical age. Journal of Geology 109, 155-169], provide independent age calibration over the full range of amino acid D/L values, thereby allowing comparisons of alternative kinetic models for seven amino acids. The often-used parabolic kinetic model is found to be insufficient to explain the pattern of racemization, although the kinetic pathways for valine racemization and isoleucine epimerization can be closely approximated with this function. Logarithmic and power law regressions more accurately represent the racemization pathways for all amino acids. The reliability of a non-linear model for leucine racemization, developed and refined over the past 20 years, is confirmed by the 87Sr/ 86Sr age results. This age model indicates that the subsurface record (up to 80m thick) of the North Carolina Coastal Plain spans the entire Quaternary, back to ???2.5Ma. The calibrated kinetics derived from this age model yield an estimate of the effective temperature for the study region of 11??2??C., from which we estimate full glacial (Last Glacial Maximum - LGM) temperatures for the region on the order of 7-10??C cooler than present. These temperatures compare favorably with independent paleoclimate information
To make Quantitative Radiology (QR) a reality in radiological practice, computerized body-wide Automatic Anatomy Recognition (AAR) becomes essential. With the goal of building a general AAR system that is not tied to any specific organ system, body region, or image modality, this paper presents an AAR methodology for localizing and delineating all major organs in different body regions based on fuzzy modeling ideas and a tight integration of fuzzy models with an Iterative Relative Fuzzy Connectedness (IRFC) delineation algorithm. The methodology consists of five main steps: (a) gathering image data for both building models and testing the AAR algorithms from patient image sets existing in our health system; (b) formulating precise definitions of each body region and organ and delineating them following these definitions; (c) building hierarchical fuzzy anatomy models of organs for each body region; (d) recognizing and locating organs in given images by employing the hierarchical models; and (e) delineating the organs following the hierarchy. In Step (c), we explicitly encode object size and positional relationships into the hierarchy and subsequently exploit this information in object recognition in Step (d) and delineation in Step (e). Modality-independent and dependent aspects are carefully separated in model encoding. At the model building stage, a learning process is carried out for rehearsing an optimal threshold-based object recognition method. The recognition process in Step (d) starts from large, well-defined objects and proceeds down the hierarchy in a global to local manner. A fuzzy model-based version of the IRFC algorithm is created by naturally integrating the fuzzy model constraints into the delineation algorithm. The AAR system is tested on three body regions - thorax (on CT), abdomen (on CT and MRI), and neck (on MRI and CT) - involving a total of over 35 organs and 130 data sets (the total used for model building and testing). The training and 2ff7e9595c
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