997 resultados para Gilmore, Melvin R. (Melvin Randolph), 1868-1940


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v.11:no.1(1912)

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Whole-body (WB) planar imaging has long been one of the staple methods of dosimetry, and its quantification has been formalized by the MIRD Committee in pamphlet no 16. One of the issues not specifically addressed in the formalism occurs when the count rates reaching the detector are sufficiently high to result in camera count saturation. Camera dead-time effects have been extensively studied, but all of the developed correction methods assume static acquisitions. However, during WB planar (sweep) imaging, a variable amount of imaged activity exists in the detector's field of view as a function of time and therefore the camera saturation is time dependent. A new time-dependent algorithm was developed to correct for dead-time effects during WB planar acquisitions that accounts for relative motion between detector heads and imaged object. Static camera dead-time parameters were acquired by imaging decaying activity in a phantom and obtaining a saturation curve. Using these parameters, an iterative algorithm akin to Newton's method was developed, which takes into account the variable count rate seen by the detector as a function of time. The algorithm was tested on simulated data as well as on a whole-body scan of high activity Samarium-153 in an ellipsoid phantom. A complete set of parameters from unsaturated phantom data necessary for count rate to activity conversion was also obtained, including build-up and attenuation coefficients, in order to convert corrected count rate values to activity. The algorithm proved successful in accounting for motion- and time-dependent saturation effects in both the simulated and measured data and converged to any desired degree of precision. The clearance half-life calculated from the ellipsoid phantom data was calculated to be 45.1 h after dead-time correction and 51.4 h with no correction; the physical decay half-life of Samarium-153 is 46.3 h. Accurate WB planar dosimetry of high activities relies on successfully compensating for camera saturation which takes into account the variable activity in the field of view, i.e. time-dependent dead-time effects. The algorithm presented here accomplishes this task.

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Based on the photographs (color slides) taken from the type specimens of Adetus species, the following synonyms are established: Adetus subcostatus Aurivillius, 1900 = A. auratoides Breuning, 1943 syn. nov.; A. leucostigma Bates, 1880 = A. venezuelensis Breuning, 1942 syn. nov.; A. pulchellus (Thomson, 1868) = A. laterialbus Breuning, 1942 syn. nov.; A. irregularis (Breuning,1939) = A. gracilis Breuning, 1940 syn. nov.; A. flavescens Melzer, 1934 = A. strandi Breuning, 1940 syn. nov. = A. mediofasciculatus Breuning, 1943 syn. nov.; A. modestus Melzer, 1934 = Atimuropsis densepunctata Breuning, 1939 syn. nov. New species described: Adetus multifasciatus sp. nov. (Brazil, São Paulo); A. lineatus sp. nov. (Brazil, Distrito Federal); A. linsleyi sp. nov. (Ecuador, Los ­os); A. pacaruaia sp. nov. (Peru, Huanuco), and A. pinima sp. nov. (Peru, Junin). Adetus marmoratus Breuning, 1942 is redescribed.

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Amplified Fragment Length Polymorphisms (AFLPs) are a cheap and efficient protocol for generating large sets of genetic markers. This technique has become increasingly used during the last decade in various fields of biology, including population genomics, phylogeography, and genome mapping. Here, we present RawGeno, an R library dedicated to the automated scoring of AFLPs (i.e., the coding of electropherogram signals into ready-to-use datasets). Our program includes a complete suite of tools for binning, editing, visualizing, and exporting results obtained from AFLP experiments. RawGeno can either be used with command lines and program analysis routines or through a user-friendly graphical user interface. We describe the whole RawGeno pipeline along with recommendations for (a) setting the analysis of electropherograms in combination with PeakScanner, a program freely distributed by Applied Biosystems; (b) performing quality checks; (c) defining bins and proceeding to scoring; (d) filtering nonoptimal bins; and (e) exporting results in different formats.