845 resultados para Cardiorespiratory Fitness


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Mode of access: Internet.

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"Paper no. 32 of the Station for experimental evolution at Cold Spring Harbor, New York."

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"1 April 1973, AFM 50-34 ... 1 July 2003, AFPAM 23-2241."

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Stabilizing selection has been predicted to change genetic variances and covariances so that the orientation of the genetic variance-covariance matrix (G) becomes aligned with the orientation of the fitness surface, but it is less clear how directional selection may change G. Here we develop statistical approaches to the comparison of G with vectors of linear and nonlinear selection. We apply these approaches to a set of male sexually selected cuticular hydrocarbons (CHCs) of Drosophila serrata. Even though male CHCs displayed substantial additive genetic variance, more than 99% of the genetic variance was orientated 74.9degrees away from the vector of linear sexual selection, suggesting that open-ended female preferences may greatly reduce genetic variation in male display traits. Although the orientation of G and the fitness surface were found to differ significantly, the similarity present in eigenstructure was a consequence of traits under weak linear selection and strong nonlinear ( convex) selection. Associating the eigenstructure of G with vectors of linear and nonlinear selection may provide a way of determining what long-term changes in G may be generated by the processes of natural and sexual selection.

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Bioelectrical impedance measurements are widely used for the study of body composition. Commonly measurements are made at 50 kHz to estimate total body water or at low frequencies (< 10 kHz) to estimate extracellular fluid volume. These measurements can be obtained as single measurements at discrete frequencies, or as fitted data interpolated from plots of measurements made at multiple frequencies. This study compared single frequency and multiple frequency (MF) measurements taken in the intensive care environment. MF bioimpedance (4-1000 kHz) was measured on an adult with and without cardiorespiratory monitoring, and on babies in the neonatal intensive care unit. Measurements obtained at individual frequencies were plotted against frequency and examined for the presence of outlying points. Fitted data for measurements obtained at 5 kHz and 50 kHz with and without cardiorespiratory monitoring were compared. Significant artefacts were detected in measurements at approximately 50 kHz and at integral divisions of this frequency as a result of interference from cardiorespiratory monitors. Single frequency measurements taken at these frequencies may be subject to errors that would be difficult to detect without the aid of information obtained from MF measurements.

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Although safety is recognized as a critical issue in functional capacity evaluations (FCEs), it has rarely been investigated. This paper reports on the findings of a study which examined safety aspects of a new approach to FCE. Fourteen rehabilitation clients with chronic back pain participated in the study. Aspects examined included the pre-FCE screening procedures, the monitoring of performance and safety during the FCE, and the end of FCE measures and follow-up procedures. Support was found for the screening procedures of the approach, particularly blood pressure measurement, and for the combined approach to monitoring of the persons performance from biomechanical, physiological and psychophysical perspectives. Issues for FCE safety in general are identified and discussed, including the importance of screening procedures to determine readiness for FCEs and the issue of load handling in FCEs, especially in relation to clients with chronic back pain.

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Human perception is finely tuned to extract structure about the 4D world of time and space as well as properties such as color and texture. Developing intuitions about spatial structure beyond 4D requires exploiting other perceptual and cognitive abilities. One of the most natural ways to explore complex spaces is for a user to actively navigate through them, using local explorations and global summaries to develop intuitions about structure, and then testing the developing ideas by further exploration. This article provides a brief overview of a technique for visualizing surfaces defined over moderate-dimensional binary spaces, by recursively unfolding them onto a 2D hypergraph. We briefly summarize the uses of a freely available Web-based visualization tool, Hyperspace Graph Paper (HSGP), for exploring fitness landscapes and search algorithms in evolutionary computation. HSGP provides a way for a user to actively explore a landscape, from simple tasks such as mapping the neighborhood structure of different points, to seeing global properties such as the size and distribution of basins of attraction or how different search algorithms interact with landscape structure. It has been most useful for exploring recursive and repetitive landscapes, and its strength is that it allows intuitions to be developed through active navigation by the user, and exploits the visual system's ability to detect pattern and texture. The technique is most effective when applied to continuous functions over Boolean variables using 4 to 16 dimensions.

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Document classification is a supervised machine learning process, where predefined category labels are assigned to documents based on the hypothesis derived from training set of labelled documents. Documents cannot be directly interpreted by a computer system unless they have been modelled as a collection of computable features. Rogati and Yang [M. Rogati and Y. Yang, Resource selection for domain-specific cross-lingual IR, in SIGIR 2004: Proceedings of the 27th annual international conference on Research and Development in Information Retrieval, ACM Press, Sheffied: United Kingdom, pp. 154-161.] pointed out that the effectiveness of document classification system may vary in different domains. This implies that the quality of document model contributes to the effectiveness of document classification. Conventionally, model evaluation is accomplished by comparing the effectiveness scores of classifiers on model candidates. However, this kind of evaluation methods may encounter either under-fitting or over-fitting problems, because the effectiveness scores are restricted by the learning capacities of classifiers. We propose a model fitness evaluation method to determine whether a model is sufficient to distinguish positive and negative instances while still competent to provide satisfactory effectiveness with a small feature subset. Our experiments demonstrated how the fitness of models are assessed. The results of our work contribute to the researches of feature selection, dimensionality reduction and document classification.