16 research outputs found

    Validation of an arterial tortuosity measure with application to hypertension collection of clinical hypertensive patients

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    <p>Abstract</p> <p>Background</p> <p>Hypertension may increase tortuosity or twistedness of arteries. We applied a centerline extraction algorithm and tortuosity metric to magnetic resonance angiography (MRA) brain images to quantitatively measure the tortuosity of arterial vessel centerlines. The most commonly used arterial tortuosity measure is the distance factor metric (DFM). This study tested a DFM based measurement’s ability to detect increases in arterial tortuosity of hypertensives using existing images. Existing images presented challenges such as different resolutions which may affect the tortuosity measurement, different depths of the area imaged, and different artifacts of imaging that require filtering.</p> <p>Methods</p> <p>The stability and accuracy of alternative centerline algorithms was validated in numerically generated models and test brain MRA data. Existing images were gathered from previous studies and clinical medical systems by manually reading electronic medical records to identify hypertensives and negatives. Images of different resolutions were interpolated to similar resolutions. Arterial tortuosity in MRA images was measured from a DFM curve and tested on numerically generated models as well as MRA images from two hypertensive and three negative control populations. Comparisons were made between different resolutions, different filters, hypertensives versus negatives, and different negative controls.</p> <p>Results</p> <p>In tests using numerical models of a simple helix, the measured tortuosity increased as expected with more tightly coiled helices. Interpolation reduced resolution-dependent differences in measured tortuosity. The Korean hypertensive population had significantly higher arterial tortuosity than its corresponding negative control population across multiple arteries. In addition one negative control population of different ethnicity had significantly less arterial tortuosity than the other two.</p> <p>Conclusions</p> <p>Tortuosity can be compared between images of different resolutions by interpolating from lower to higher resolutions. Use of a universal negative control was not possible in this study. The method described here detected elevated arterial tortuosity in a hypertensive population compared to the negative control population and can be used to study this relation in other populations.</p

    Multi‐proxy analyses of Late Cretaceous coprolites from Germany

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    A total of 462 coprolites from three localities exposing Upper Cretaceous deposits in the MĂŒnster Basin, northwestern Germany, have been subjected to an array of analytical techniques, with the aim of elucidating ancient trophic structures and predator–prey interactions. The phosphatic composition, frequent bone inclusions, size and morphology collectively suggest that most, if not all, coprolites were produced by carnivorous (predatory or scavenging) vertebrates. The bone inclusions further indicate that the coprolite producers preyed principally upon fish. Putative host animals include bony fish, sharks and marine reptiles – all of which have been previously recorded from the MĂŒnster Basin. The presence of borings and other traces on several coprolites implies handling by coprophagous organisms. Remains of epibionts are also common, most of which have been identified as the encrusting bivalve Atreta. Palynological analyses of both the coprolites and host rocks reveal a sparse assemblage dominated by typical Late Cretaceous dinoflagellates, and with sub‐ordinate fern spores, conifer pollen grains and angiosperm pollen grains. The dinoflagellate key taxon Exochosphaeridium cenomaniense corroborates a Cenomanian age for the Plenus Marl, from which most studied coprolites derive. The findings of this study highlight the potential of a multiproxy approach when it comes to unravelling the origin, composition and importance of coprolites in palaeoecosystem analyses.MEE and JL acknowledge the Swedish Research Council for funding. AL acknowledges the Royal Physiographic Society of Lund for funding. MQ is funded by the Department of Organismal Biology (Uppsala University). BWR acknowledges the Department of Forensic Medicine, Copenhagen University. VV acknowledges funding from the Lund University Carbon Cycle Centre (LUCCI)</p
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