320 resultados para Interval estimation


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Purpose:Race appears to be associated with myopiogenesis, with East Asians showing high myopia prevalence. Considering structural variations in the eye, it is possible that retinal shapes are different between races. The purpose of this study was to quantify and compare retinal shapes between racial groups using peripheral refraction (PR) and peripheral eye lengths (PEL). Methods:A Shin-Nippon SRW5000 autorefractor and a Haag-Streit Lenstar LS900 biometer measured PR and PEL, respectively, along horizontal (H) and vertical (V) fields out to ±35° in 5° steps in 29 Caucasian (CA), 16 South Asian (SA) and 23 East Asian (EA) young adults (spherical equivalent range +0.75D to –5.00D in all groups). Retinal vertex curvature Rv and asphericity Q were determined from two methods: a) PR (Dunne): The Gullstrand-Emsley eye was modified according to participant’s intraocular lengths and anterior cornea curvature. Ray-tracing was performed at each angle through the stop, altering cornea asphericity until peripheral astigmatism matched experimental measurements. Retinal curvature and hence retinal co-ordinate intersection with the chief ray were altered until sagittal refraction matched its measurement. b) PEL: Ray-tracing was performed at each angle through the anterior corneal centre of curvature of the Gullstrand-Emsley eye. Ignoring lens refraction, retinal co-ordinates relative to the fovea were determined from PEL and trigonometry. From sets of retinal co-ordinates, conic retinal shapes were fitted in terms of Rv and Q. Repeated-measures ANOVA were conducted on Rv and Q, and post hoc t-tests with Bonferroni correction were used to compare races. Results:In all racial groups both methods showed greater Rv for the horizontal than for the vertical meridian and greater Rv for myopes than emmetropes. Rv was greater in EA than in CA (P=0.02), with Rv for SA being intermediate and not significantly different from CA and EA. The PEL method provided larger Rv than the PR method: PEL: EA vs CA 87±13 vs 83±11 m-1 (H), 79±13 vs 72±14 m-1 (V); PR: EA vs CA 79±10 vs 67±10 m-1 (H), 71±17 vs 66±12 m-1 (V). Q did not vary significantly with race. Conclusions:Estimates of Rv, but not of Q, varied significantly with race. The greater Rv found in EA than in CA and the comparatively high prevalence rate of myopia in many Asian countries may be related.

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This study investigated the effects of high-intensity interval training (HIIT) vs. work-matched moderate-intensity continuous exercise (MOD) on metabolism and counterregulatory stress hormones. In a randomized and counterbalanced order, 10 well-trained male cyclists and triathletes completed a HIIT session [81.6 ± 3.7% maximum oxygen consumption (V̇o2 max); 72.0 ± 3.2% peak power output; 792 ± 95 kJ] and a MOD session (66.7 ± 3.5% V̇o2 max; 48.5 ± 3.1% peak power output; 797 ± 95 kJ). Blood samples were collected before, immediately after, and 1 and 2 h postexercise. Carbohydrate oxidation was higher (P = 0.037; 20%), whereas fat oxidation was lower (P = 0.037; −47%) during HIIT vs. MOD. Immediately after exercise, plasma glucose (P = 0.024; 20%) and lactate (P < 0.01; 5.4×) were higher in HIIT vs. MOD, whereas total serum free fatty acid concentration was not significantly different (P = 0.33). Targeted gas chromatography-mass spectromtery metabolomics analysis identified and quantified 49 metabolites in plasma, among which 11 changed after both HIIT and MOD, 13 changed only after HIIT, and 5 changed only after MOD. Notable changes included substantial increases in tricarboxylic acid intermediates and monounsaturated fatty acids after HIIT and marked decreases in amino acids during recovery from both trials. Plasma adrenocorticotrophic hormone (P = 0.019), cortisol (P < 0.01), and growth hormone (P < 0.01) were all higher immediately after HIIT. Plasma norepinephrine (P = 0.11) and interleukin-6 (P = 0.20) immediately after exercise were not significantly different between trials. Plasma insulin decreased during recovery from both HIIT and MOD (P < 0.01). These data indicate distinct differences in specific metabolites and counterregulatory hormones following HIIT vs. MOD and highlight the value of targeted metabolomic analysis to provide more detailed insights into the metabolic demands of exercise.

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Background Burden of disease estimates for South Africa have highlighted the particularly high rates of injuries related to interpersonal violence compared with other regions of the world, but these figures tell only part of the story. In addition to direct physical injury, violence survivors are at an increased risk of a wide range of psychological and behavioral problems. This study aimed to comprehensively quantify the excess disease burden attributable to exposure to interpersonal violence as a risk factor for disease and injury in South Africa. Methods The World Health Organization framework of interpersonal violence was adapted. Physical injury mortality and disability were categorically attributed to interpersonal violence. In addition, exposure to child sexual abuse and intimate partner violence, subcategories of interpersonal violence, were treated as risk factors for disease and injury using counterfactual estimation and comparative risk assessment methods. Adjustments were made to account for the combined exposure state of having experienced both child sexual abuse and intimate partner violence. Results Of the 17 risk factors included in the South African Comparative Risk Assessment study, interpersonal violence was the second leading cause of healthy years of life lost, after unsafe sex, accounting for 1.7 million disability-adjusted life years (DALYs) or 10.5% of all DALYs (95% uncertainty interval: 8.5%-12.5%) in 2000. In women, intimate partner violence accounted for 50% and child sexual abuse for 32% of the total attributable DALYs. Conclusions The implications of our findings are that estimates that include only the direct injury burden seriously underrepresent the full health impact of interpersonal violence. Violence is an important direct and indirect cause of health loss and should be recognized as a priority health problem as well as a human rights and social issue. This study highlights the difficulties in measuring the disease burden from interpersonal violence as a risk factor and the need to improve the epidemiological data on the prevalence and risks for the different forms of interpersonal violence to complete the picture. Given the extent of the burden, it is essential that innovative research be supported to identify social policy and other interventions that address both the individual and societal aspects of violence.

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With the increasing need to adapt to new environments, data-driven approaches have been developed to estimate terrain traversability by learning the rover’s response on the terrain based on experience. Multiple learning inputs are often used to adequately describe the various aspects of terrain traversability. In a complex learning framework, it can be difficult to identify the relevance of each learning input to the resulting estimate. This paper addresses the suitability of each learning input by systematically analyzing the impact of each input on the estimate. Sensitivity Analysis (SA) methods provide a means to measure the contribution of each learning input to the estimate variability. Using a variance-based SA method, we characterize how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We propose an approach built on Analysis of Variance (ANOVA) decomposition to examine the prediction made in a near-to-far learning framework based on multi-task GP regression. We demonstrate the approach by analyzing the impact of driving speed and terrain geometry on the prediction of the rover’s attitude and chassis configuration in a Marsanalogue terrain using our prototype rover Mawson.

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There is considerable scientific interest in personal exposure to ultrafine particles. Owing to their small size, these particles are able to penetrate deep into the lungs, where they may cause adverse respiratory, pulmonary and cardiovascular health effects. This article presents Bayesian hierarchical models for estimating and comparing inhaled particle surface area in the lung.

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Height is a critical variable for helicopter hover control. In this paper we discuss, and present experimental results for, two different height sensing techniques: ultrasonic and stereo imaging, which have complementary characteristics. Feature-based stereo is used which provides a basis for visual odometry and attitude estimation in the future.

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Height is a critical variable for helicopter hover control. In this paper we discuss, and present experimental results for, two different height sensing techniques: ultrasonic and stereo imaging, which have complementary characteristics. Feature-based stereo is used which provides a basis for visual odometry and attitude estimation in the future.

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It is traditional to initialise Kalman filters and extended Kalman filters with estimates of the states calculated directly from the observed (raw) noisy inputs, but unfortunately their performance is extremely sensitive to state initialisation accuracy: good initial state estimates ensure fast convergence whereas poor estimates may give rise to slow convergence or even filter divergence. Divergence is generally due to excessive observation noise and leads to error magnitudes that quickly become unbounded (R.J. Fitzgerald, 1971). When a filter diverges, it must be re initialised but because the observations are extremely poor, re initialised states will have poor estimates. The paper proposes that if neurofuzzy estimators produce more accurate state estimates than those calculated from the observed noisy inputs (using the known state model), then neurofuzzy estimates can be used to initialise the states of Kalman and extended Kalman filters. Filters whose states have been initialised with neurofuzzy estimates should give improved performance by way of faster convergence when the filter is initialised, and when a filter is re started after divergence