RESUMO
Unusual deformation structures, similar to those observed in rocks from known and suspected meteorite impact craters, are observed in inclusions of basement rock in the Onaping formation at Sudbury, Ontario. These features, which include planar sets in quartz parallel to the (0001) and (10i3) planes, suggest that the Onaping formation consists of shocked and melted material deposited immediately after a meteorite impact which formed the Sudbury basin.
RESUMO
Fullerenes (C60 and C70) have been identified by laser desorption, laser desorption post-ionization, and high-resolution electron-impact mass spectrometry in shock-produced breccias (Onaping Formation) of the Sudbury impact structure in Ontario, Canada. The C60 isotope is present at a level of a few parts per million. The fullerenes were likely synthesized within the impact plume from the carbon contained in the bolide. The oxidation of the fullerenes during the 1.85 billion years of exposure was apparently prevented by the presence of sulfur in the form of sulfide-silicate complexes associated with the fullerenes.
Assuntos
Carbono/análise , Fulerenos , Sedimentos Geológicos/análise , Meteoroides , Sedimentos Geológicos/química , Ontário , Silicatos/análise , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz , Sulfetos/análiseRESUMO
Plagioclase feldspar, clinopyroxene, and ilmenite in a polished thin section of a type A crystalline rock were analyzed. The clinopyroxene grains are compositionally variable, and both high Ca and low Ca phases are present. The plagioclase is compositionally homogeneous. The ilmenite is chemically homogeneous except for occasional, small areas of high local chromium concentration. Accessory minerals are: apatite (containing Cl, F, Y, and Ce), troilite, and metallic iron. Glassy spherules from the lunar soil are for the most part similar in composition to the crystalline rocks; however, some appear to have been monomineralic. The crystalline rock has apparently formed by relatively rapid cooling of a silicate melt under conditions of low oxygen partial pressure. Many components of the soil appear to have formed by meteoritic impact.
RESUMO
OBJECTIVE: To examine the utility of artificial neural networks (ANNs) for differentiating patients with Alzheimer disease from healthy control subjects and for staging the degree of dementia. DESIGN: Comparison of the classification abilities of ANNs with the statistical technique of linear discriminant analysis (LDA) using the results of 11 neuropsychological tests as predictors. PARTICIPANTS: Ninety-two patients with a diagnosis of probable Alzheimer disease (referred from a geriatric clinic) and 43 elderly control subjects (independently solicited). The patients met National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association criteria for probable dementia, with clinical ratings of dementia severity derived from the Cambridge Examination for Mental Disorders of the Elderly (CAMDEX). MAIN OUTCOME MEASURES: Classifications between and within groups were determined by using LDA and ANNs, and more detailed comparisons of the 2 methods were performed by using chi2 analyses and unweighted and weighted kappa statistics. RESULTS: Linear discriminant analysis correctly identified 71.9% of cases. Artificial neural networks, trained to classify the subjects using the same data, correctly classified 91.1% of the cases. Subsidiary analyses showed that although both techniques effectively discriminated between the control subjects and patients with dementia, the ANNs were more powerful in discriminating severity levels within the dementia population. The analyses for goodness of fit revealed that the ANN classification produced a better fit to the actual data. A comparison of the weighted proportion of agreement between the criterion and predictor variables also showed that the ANNs clearly outperformed LDA in classification accuracy for the full data set and patients-only data set. CONCLUSION: The results demonstrate the utility of ANNs for group classification of patients with Alzheimer disease and elderly controls and for staging dementia severity using neuropsychological data.