965 resultados para Erythrocytes count


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We’re having a really great run on our nylons. In the 1960's and 1970's Lightning Fastener's market share was being challenged by cheaper, but less dependable imports.

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L’administration de fer dextran à 10 mg/kg intramusculaire (IM) est un traitement empirique couramment recommandé en médecine aviaire lors d’hémorragie ou d’anémie. L’objectif principal de cette étude était d’évaluer les effets de ce traitement sur l’anémie chez les oiseaux de proie. Deux types d’individus ont été utilisés : des crécerelles d’Amérique (Falco sparverius) où une anémie par perte de sang externe aiguë a été créée (deux phlébotomies de 20-40 % du volume sanguin total à un intervalle de 6 h) et des oiseaux de proie sauvages de différentes espèces souffrant d’anémies diverses. L’ensemble des oiseaux a été subdivisé aléatoirement en groupe traitement (fer dextran 10 mg/kg IM) et contrôle (NaCl 0,9% IM). Un suivi dans le temps a été réalisé afin d’étudier leur récupération de l’anémie, la présence d’effets secondaires au traitement et l’impact d’une administration de fer sur ces réserves. Aucune différence significative n’a été observée entre les deux groupes en ce qui concerne les signes cliniques, l’hématocrite, le pourcentage des polychromatophiles/réticulocytes, la densité cellulaire et le fer de la moelle osseuse, la créatine kinase et le fer plasmatique. La majorité des crécerelles ont présenté une myosite au site d’injection du fer. Nos résultats suggèrent qu’une administration de 10 mg/kg de fer dextran IM n’a pas d’effet sur l’érythropoïèse des rapaces souffrant d’anémie par perte de sang externe aiguë, qu’elle provoque une légère inflammation au site d’injection et qu’elle n’influence pas les réserves de fer. Le comptage des réticulocytes en anneau et des polychromatophiles semble être deux méthodes équivalentes.

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Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

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Knowing the number of words in a Word 2010 document or in a text selection within a file can be very useful. See how to use Word’s word count features. For best viewing Download the video.

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Knowing the number of words in a Word 2011 document or in a text selection within a file can be very useful. See how to use Word’s word count features. For best viewing Download the video.

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Spot puede contar del 1 al 10. Y para probar esta habilidad hace un viaje a la granja con su papá. A los niños les encantará aprender a contar con Spot y sus amigos del corral, y compartir la diversión de encontrar las respuestas escondidas bajo las solapas.

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Tom no puede dormir aunque está rodeado de sus juguetes de peluche, por lo que su padre le sugiere que intente contar ovejas. Comienza a contar , pero la séptima oveja, delgada y ágil, desaparece por el armario del dormitorio antes de que el niño pestañee. El niño intenta contar otras criaturas y es sorprendido por lobos, pitones, cabras monteses, piratas, pingüinos, vampiros, fantasmas, y tigres hasta que, afortunadamente, cierra la puerta apaga la luz, y puede dormir. El cuento es acumulativo en verso. En doble página aparecen las criaturas en una variedad de tamaños, ángulos y formas, dando la sensación de movimiento. La página de la derecha es un recuadro de texto y conjuntos de siluetas. Para leer en voz alta, y para contar hasta cien. El texto es ideal para llamar la atención sobre la gran variedad de opciones de palabras y ortografía.

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While over-dispersion in capture–recapture studies is well known to lead to poor estimation of population size, current diagnostic tools to detect the presence of heterogeneity have not been specifically developed for capture–recapture studies. To address this, a simple and efficient method of testing for over-dispersion in zero-truncated count data is developed and evaluated. The proposed method generalizes an over-dispersion test previously suggested for un-truncated count data and may also be used for testing residual over-dispersion in zero-inflation data. Simulations suggest that the asymptotic distribution of the test statistic is standard normal and that this approximation is also reasonable for small sample sizes. The method is also shown to be more efficient than an existing test for over-dispersion adapted for the capture–recapture setting. Studies with zero-truncated and zero-inflated count data are used to illustrate the test procedures.

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None of the current surveillance streams monitoring the presence of scrapie in Great Britain provide a comprehensive and unbiased estimate of the prevalence of the disease at the holding level. Previous work to estimate the under-ascertainment adjusted prevalence of scrapie in Great Britain applied multiple-list capture–recapture methods. The enforcement of new control measures on scrapie-affected holdings in 2004 has stopped the overlapping between surveillance sources and, hence, the application of multiple-list capture–recapture models. Alternative methods, still under the capture–recapture methodology, relying on repeated entries in one single list have been suggested in these situations. In this article, we apply one-list capture–recapture approaches to data held on the Scrapie Notifications Database to estimate the undetected population of scrapie-affected holdings with clinical disease in Great Britain for the years 2002, 2003, and 2004. For doing so, we develop a new diagnostic tool for indication of heterogeneity as well as a new understanding of the Zelterman and Chao’s lower bound estimators to account for potential unobserved heterogeneity. We demonstrate that the Zelterman estimator can be viewed as a maximum likelihood estimator for a special, locally truncated Poisson likelihood equivalent to a binomial likelihood. This understanding allows the extension of the Zelterman approach by means of logistic regression to include observed heterogeneity in the form of covariates—in case studied here, the holding size and country of origin. Our results confirm the presence of substantial unobserved heterogeneity supporting the application of our two estimators. The total scrapie-affected holding population in Great Britain is around 300 holdings per year. None of the covariates appear to inform the model significantly.

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The contribution investigates the problem of estimating the size of a population, also known as the missing cases problem. Suppose a registration system is targeting to identify all cases having a certain characteristic such as a specific disease (cancer, heart disease, ...), disease related condition (HIV, heroin use, ...) or a specific behavior (driving a car without license). Every case in such a registration system has a certain notification history in that it might have been identified several times (at least once) which can be understood as a particular capture-recapture situation. Typically, cases are left out which have never been listed at any occasion, and it is this frequency one wants to estimate. In this paper modelling is concentrating on the counting distribution, e.g. the distribution of the variable that counts how often a given case has been identified by the registration system. Besides very simple models like the binomial or Poisson distribution, finite (nonparametric) mixtures of these are considered providing rather flexible modelling tools. Estimation is done using maximum likelihood by means of the EM algorithm. A case study on heroin users in Bangkok in the year 2001 is completing the contribution.

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Background: The present paper investigates the question of a suitable basic model for the number of scrapie cases in a holding and applications of this knowledge to the estimation of scrapie-ffected holding population sizes and adequacy of control measures within holding. Is the number of scrapie cases proportional to the size of the holding in which case it should be incorporated into the parameter of the error distribution for the scrapie counts? Or, is there a different - potentially more complex - relationship between case count and holding size in which case the information about the size of the holding should be better incorporated as a covariate in the modeling? Methods: We show that this question can be appropriately addressed via a simple zero-truncated Poisson model in which the hypothesis of proportionality enters as a special offset-model. Model comparisons can be achieved by means of likelihood ratio testing. The procedure is illustrated by means of surveillance data on classical scrapie in Great Britain. Furthermore, the model with the best fit is used to estimate the size of the scrapie-affected holding population in Great Britain by means of two capture-recapture estimators: the Poisson estimator and the generalized Zelterman estimator. Results: No evidence could be found for the hypothesis of proportionality. In fact, there is some evidence that this relationship follows a curved line which increases for small holdings up to a maximum after which it declines again. Furthermore, it is pointed out how crucial the correct model choice is when applied to capture-recapture estimation on the basis of zero-truncated Poisson models as well as on the basis of the generalized Zelterman estimator. Estimators based on the proportionality model return very different and unreasonable estimates for the population sizes. Conclusion: Our results stress the importance of an adequate modelling approach to the association between holding size and the number of cases of classical scrapie within holding. Reporting artefacts and speculative biological effects are hypothesized as the underlying causes of the observed curved relationship. The lack of adjustment for these artefacts might well render ineffective the current strategies for the control of the disease.

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Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.

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None of the current surveillance streams monitoring the presence of scrapie in Great Britain provide a comprehensive and unbiased estimate of the prevalence of the disease at the holding level. Previous work to estimate the under-ascertainment adjusted prevalence of scrapie in Great Britain applied multiple-list capture-recapture methods. The enforcement of new control measures on scrapie-affected holdings in 2004 has stopped the overlapping between surveillance sources and, hence, the application of multiple-list capture-recapture models. Alternative methods, still under the capture-recapture methodology, relying on repeated entries in one single list have been suggested in these situations. In this article, we apply one-list capture-recapture approaches to data held on the Scrapie Notifications Database to estimate the undetected population of scrapie-affected holdings with clinical disease in Great Britain for the years 2002, 2003, and 2004. For doing so, we develop a new diagnostic tool for indication of heterogeneity as well as a new understanding of the Zelterman and Chao's lower bound estimators to account for potential unobserved heterogeneity. We demonstrate that the Zelterman estimator can be viewed as a maximum likelihood estimator for a special, locally truncated Poisson likelihood equivalent to a binomial likelihood. This understanding allows the extension of the Zelterman approach by means of logistic regression to include observed heterogeneity in the form of covariates-in case studied here, the holding size and country of origin. Our results confirm the presence of substantial unobserved heterogeneity supporting the application of our two estimators. The total scrapie-affected holding population in Great Britain is around 300 holdings per year. None of the covariates appear to inform the model significantly.