104 resultados para Estatística do erro


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OBJECTIVE: To adapted the critical velocity (CV), RAST test and lactate minimum (LM) to evaluation of female basketball players. METHODS: Twelve well-trained female basketball players (19 ± 1yrs) were submitted to four intensities running (10 - 14 km/h) at shuttle exercise until exhaustion, applied on alternate days. The linear model 'velocity vs. 1/tlim' was adopted to determine the aerobic (CV) and anaerobic (CCA) parameters. The lactate minimum test consisted of two phases: 1) hiperlactatemia induction using the RAST test and 2) incremental test composed by five shuttle run (20-m) at 7, 8, 9, 10, and 12 km/h. Blood samples were collected at the end of each stage. RESULTS: The velocity (vLM) and blood lactate concentration at LM were obtained by two polynomial adjustments: lactate vs. intensity (LM1) and lactate vs. time (LM2). ANOVA one-way, Student t-test and Pearson correlation were used for statistical analysis. The CV was obtained at 10.3 ± 0.2 km/h and the CCA estimated at 73.0 ± 3.4 m. The RAST was capable to induce the hiperlactatemia and to determine the Pmax (3.6 ± 0.2 W/kg), Pmed (2.8 ± 0.1 W/kg), Pmin (2.3 ± 0.1 W/kg) and FI (30 ± 3%). The vLM1 and vLM2 were obtained, respectively, at 9.47 ±0.13 km/h and 9.8 ± 0.13 km/h, and CV was higher than vLM1. CONCLUSION: The results suggest that the non-invasive model can be used to determine the aerobic and anaerobic parameters. Furthermore, the LM test adapted to basketball using RAST and progressive phase was effective to evaluate female athletes considering the specificity of modality, with high success rates observed in polynomial adjustment 'lactate vs. time' (LM2).

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Prey size is an important factor in food consumption. In studies of feeding ecology, prey items are usually measured individually using calipers or ocular micrometers. Among amphibians and reptiles, there are species that feed on large numbers of small prey items (e.g. ants, termites). This high intake makes it difficult to estimate prey size consumed by these animals. We addressed this problem by developing and evaluating a procedure for subsampling the stomach contents of such predators in order to estimate prey size. Specifically, we developed a protocol based on a bootstrap procedure to obtain a subsample with a precision error of at the most 5%, with a confidence level of at least 95%. This guideline should reduce the sampling effort and facilitate future studies on the feeding habits of amphibians and reptiles, and also provide a means of obtaining precise estimates of prey size.

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PURPOSE: To evaluate the prevalence of pterygium in a population-based sample at Botucatu City - São Paulo State, Brazil. METHODS: A population-based cross-sectional study with randomized clustered sampling of households was conducted in the urban area of the Botucatu City -São Paulo State, Brazil and 85.1% of the intended sample was evaluated. All participants were submitted to ophthalmologic examination and the data were statistically analyzed. RESULTS: The prevalence of pterygium lesion in Botucatu City was 8.12% (7.0% < CI < 9.2%), affecting mainly males (10.4% males X 6.5% females - 8.5% < CI < 12.3% for males and 5.1% < CI < 7.8% for females) with 49.6 ± 14.9 years old in average; 32.18% of the pterygium carriers aged between 40 and 50 years. CONCLUSIONS: The prevalence of pterygium at Botucatu is 8.12%, affecting most frequently 40-50 year-old males.

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PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física