994 resultados para Measuring parameters


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Department of Physics, Cochin University of Science and Technology

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1. Many farmland bird species have undergone significant declines. It is important to predict the effect of agricultural change on these birds and their response to conservation measures. This requirement could be met by mechanistic models that predict population size from the optimal foraging behaviour and fates of individuals within populations. A key component of these models is the functional response, the relationship between food and competitor density and feeding rate. 2. This paper describes a method for measuring functional responses of farmland birds, and applies this method to a declining farmland bird, the corn bunting Miliaria calandra L. We derive five alternative models to predict the functional responses of farmland birds and parameterize these for corn bunting. We also assess the minimum sample sizes required to predict accurately the functional response. 3. We show that the functional response of corn bunting can be predicted accurately from a few behavioural parameters (searching rate, handling time, vigilance time) that are straightforward to measure in the field. These parameters can be measured more quickly than the alternative of measuring the functional response directly. 4. While corn bunting violated some of the assumptions of Holling's disk equation (model 1 in our study), it still provided the most accurate fit to the observed feeding rates while remaining the most statistically simple model tested. Our other models may be more applicable to other species, or corn bunting feeding in other locations. 5. Although further tests are required, our study shows how functional responses can be predicted, simplifying the development of mechanistic models of farmland bird populations.

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Many well-established statistical methods in genetics were developed in a climate of severe constraints on computational power. Recent advances in simulation methodology now bring modern, flexible statistical methods within the reach of scientists having access to a desktop workstation. We illustrate the potential advantages now available by considering the problem of assessing departures from Hardy-Weinberg (HW) equilibrium. Several hypothesis tests of HW have been established, as well as a variety of point estimation methods for the parameter which measures departures from HW under the inbreeding model. We propose a computational, Bayesian method for assessing departures from HW, which has a number of important advantages over existing approaches. The method incorporates the effects-of uncertainty about the nuisance parameters--the allele frequencies--as well as the boundary constraints on f (which are functions of the nuisance parameters). Results are naturally presented visually, exploiting the graphics capabilities of modern computer environments to allow straightforward interpretation. Perhaps most importantly, the method is founded on a flexible, likelihood-based modelling framework, which can incorporate the inbreeding model if appropriate, but also allows the assumptions of the model to he investigated and, if necessary, relaxed. Under appropriate conditions, information can be shared across loci and, possibly, across populations, leading to more precise estimation. The advantages of the method are illustrated by application both to simulated data and to data analysed by alternative methods in the recent literature.

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We describe a Bayesian approach to analyzing multilocus genotype or haplotype data to assess departures from gametic (linkage) equilibrium. Our approach employs a Markov chain Monte Carlo (MCMC) algorithm to approximate the posterior probability distributions of disequilibrium parameters. The distributions are computed exactly in some simple settings. Among other advantages, posterior distributions can be presented visually, which allows the uncertainties in parameter estimates to be readily assessed. In addition, background knowledge can be incorporated, where available, to improve the precision of inferences. The method is illustrated by application to previously published datasets; implications for multilocus forensic match probabilities and for simple association-based gene mapping are also discussed.

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A quasi-optical interferometric technique capable of measuring antenna phase patterns without the need for a heterodyne receiver is presented. It is particularly suited to the characterization of terahertz antennas feeding power detectors or mixers employing quasi-optical local oscillator injection. Examples of recorded antenna phase patterns at frequencies of 1.4 and 2.5 THz using homodyne detectors are presented. To our knowledge, these are the highest frequency antenna phase patterns ever recovered. Knowledge of both the amplitude and phase patterns in the far field enable a Gauss-Hermite or Gauss-Laguerre beam-mode analysis to be carried out for the antenna, of importance in performance optimization calculations, such as antenna gain and beam efficiency parameters at the design and prototype stage of antenna development. A full description of the beam would also be required if the antenna is to be used to feed a quasi-optical system in the near-field to far-field transition region. This situation could often arise when the device is fitted directly at the back of telescopes in flying observatories. A further benefit of the proposed technique is simplicity for characterizing systems in situ, an advantage of considerable importance as in many situations, the components may not be removable for further characterization once assembled. The proposed methodology is generic and should be useful across the wider sensing community, e.g., in single detector acoustic imaging or in adaptive imaging array applications. Furthermore, it is applicable across other frequencies of the EM spectrum, provided adequate spatial and temporal phase stability of the source can be maintained throughout the measurement process. Phase information retrieval is also of importance to emergent research areas, such as band-gap structure characterization, meta-materials research, electromagnetic cloaking, slow light, super-lens design as well as near-field and virtual imaging applications.

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RATIONALE: An altered gastric emptying (GE) rate has been implicated in the aetiology of obesity. The (13)C-octanoic acid breath test (OBT) is frequently used to measure GE, and the cumulative percentage of (13)C recovered (cPDR) is a common outcome measure. However, true cPDR in breath is dependent on accurate measurement of carbon dioxide production rate (VCO(2)). The current study aimed to quantify differences in the (13)C OBT results obtained using directly measured VCO(2) (VCO(2DM)) compared with (i) predicted from resting VCO(2) (VCO(2PR)) and (ii) predicted from body surface area VCO(2) (VCO(2BSA)). METHODS: The GE rate of a high-fat test meal was assessed in 27 lean subjects using the OBT. Breath samples were gathered during the fasted state and at regular intervals throughout the 6-h postprandial period for determination of (13)C-isotopic enrichment by continuous-flow isotope-ratio mass spectrometry. The VCO(2) was measured directly from exhaled air samples and the PDR calculated by three methods. The bias and the limits of agreement were calculated using Bland-Altman plots. RESULTS: Compared with the VCO(2DM), the cPDR was underestimated by VCO(2PR) (4.8%; p = 0.0001) and VCO(2BSA) (2.7%; p = 0.02). The GE T(half) was underestimated by VCO(2PR) (13 min; p = 0.0001) and VCO(2BSA) (10 min; p = 0.01), compared with VCO(2DM). CONCLUSIONS: The findings highlight the importance of directly measuring VCO(2)production rates throughout the (13)C OBT and could partly explain the conflicting evidence regarding the effect of obesity on GE rates.

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Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.

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Background: Recently, there has been an increasing in the impact of oral health on atherosclerosis and subsequent cardiovascular disease. The aim of this study is to investigate the association between chronic periodontitis and cardiovascular risk markers. Methods: Forty patients with periodontitis and 40 healthy gender-, body mass index-, and age-matched individuals were compared by measuring total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, levels of cytokines, antibodies against oxidized low-density lipoprotein, thiobarbituric acid reactive substances, total and differential white blood cell counts, and the non-linear index of refraction. Results: The levels of triglycerides and high-density lipoprotein in periodontitis patients were significantly higher and lower, respectively (P=0.002 and P=0.0126), compared to controls. Total cholesterol, low-density lipoprotein, and lipid peroxide levels were the same in both groups (P = 0.2943, P = 0.1284, and P = 0.067, respectively). Interleukin (IL)-6 and -8, antibodies against oxidized low-density lipoprotein, and leukocyte and neutrophil counts were significantly higher in periodontitis patients (P<0.05). The value of the non-linear index of refraction of low-density lipoprotein solutions was higher in the controls (P = 0.015) compared to individuals with periodontitis. Conclusion: Our results confirmed and further strengthened the suggested association between coronary artery disease and periodontitis. J Periodontol 2009;80:378-388.

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This study ranks the contribution of various fibre, yarn and fabric attributes to the pilling of wool knitwear. On the basis of an artificial neural network modelling, a combination of sensitivity analysis, forwards/backwards search and genetic algorithms was used to identify the importance of various fibre/yarn/fabric input parameters. The three different techniques show broad similarities in their assessment of which input parameters are important or are not important in affecting fabric pilling. The ranking shows that fabric cover factor has the most effect on pilling, followed by yarn count and thin places, fibre length, yarn twist, etc. It is further illustrated that the directional trend of the predicted pilling outputs for a selection of inputs was in line with the expected behaviour. To verify the findings of input feature selection, input factors deemed to have a small effect on the predicted pilling output, such as fibre length and diameter variations and curvature, were removed and the subsequent performance statistically compared to the original multi-layer perceptron. Differences between the outputs predicted by the original and pruned models are found not to be statistically significant at the 5% significance level. Results from this study may help manufacturers and knitwear designers in choosing the most appropriate materials and structures to reduce the pilling propensity of wool knitwear.

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Improved preservation of order flow history from the automation of derivative trading platforms suggests that traders are potentially learning from the recent history of both order and trade parameters. Consequently, a model to measure price discovery should encapsulate the dynamic interaction between the price-size coordinates of orders and trades. The Hasbrouck (1991) model is extended to measure the summary informativeness of order size and trade size. The two models are used to test for price discovery improvements in the FTSE 100 index futures market from order flow consolidation post deletion of its E-mini counterpart. The informativeness of trades has declined sharply, while the informativeness of orders has risen significantly in the post deletion sample.

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Objective
Ethnicity influences health in many ways. For example, type 2 
diabetes (T2DM) is disproportionately prevalent among certain ethnic groups. Assessing ethnicity is difficult, and numerous proxy measures are used to capture its various components. Australian guidelines specify a set of variables for measuring ethnicity, and how such parameters should be categorised. Using T2DM data collections as an illustrative example, this study sought to examine how ethnicity is measured in Australian health databases and, by comparing current practice with Australia’s existing benchmark recommendations, to identify potential areas for improvement of the health data landscape.


Design
We identified databases containing information from which ethnic group-specific estimates of T2DM burden may be gleaned. For each database, details regarding ethnicity variables were extracted, and compared with the Australian guidelines. 

Results

Data collection instruments for 32 relevant databases were reviewed. Birthplace was recorded in 27 databases (84%), but mode of birthplace assessment varied. Indigenous status was commonly recorded (78%, n=25), but only nine databases recorded other aspects of self-perceived race/ethnicity. Of 28 survey/audit databases, 14 accommodated linguistic preferences other than English, and 11 either excluded non-English speakers or those for whom a translator was not available, or only offered questionnaires in English.

Conclusions

Considerable variation exists in the measurement of ethnicity in Australian health data- sets. While various markers of ethnicity provide complementary information about the ethnic profile within a data-set, nonuniform measurement renders comparison between data-sets difficult. A standardised approach is necessary, and identifying the ethnicity variables that are particularly relevant to the health sector is warranted. Including self identified ethnicity in Australia’s set of recommended indicators and as a core component of the national census should be considered. Globalisation and increasing migration mean that these findings have implications internationally, including for multi-ethnic countries throughout North America and Europe.

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The development of new quantitative magnetic resonance imaging (MRI) technologies open new opportunities for measurements of mass transport in porous media. The current work examines a simple miscible displacement process of H2O and D2O in porous media samples. Laboratory measurements of dispersion in porous media traditionally monitor the effluent intensity of an injected tracer. We employ MRI to obtain quantitative water saturation profiles, and to measure dispersion in rock core plugs. The saturation profiles are modeled with PHREEQC, a fluid transport modeling program. We demonstrate how independent magnetic resonance measurements can be employed to estimate three important input parameters for PHREEQC, mobile porosity, immobile porosity, and dispersivity. Bulk Carr Purcell Meiboom Gill (CPMG) T2 distribution measurements were undertaken to estimate mobile and immobile porosity. Bulk alternating-pulsed-gradient-stimulated-echo (APGSTE) measurements were undertaken to measure dispersivity. The imaging method employed, T2 mapping Spin Echo Single Point Imaging (SE-SPI), also provides information about the pore size distributions in the rock cores, and how the fluid occupancy of the pores changes during the displacement process.

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 Background: Efficient and reliable surveillance and notification systems are vital for monitoring public health and disease outbreaks. However, most surveillance and notification systems are affected by a degree of underestimation (UE) and therefore uncertainty surrounds the 'true' incidence of disease affecting morbidity and mortality rates. Surveillance systems fail to capture cases at two distinct levels of the surveillance pyramid: from the community since not all cases seek healthcare (under-ascertainment), and at the healthcare-level, representing a failure to adequately report symptomatic cases that have sought medical advice (underreporting). There are several methods to estimate the extent of under-ascertainment and underreporting. Methods. Within the context of the ECDC-funded Burden of Communicable Diseases in Europe (BCoDE)-project, an extensive literature review was conducted to identify studies that estimate ascertainment or reporting rates for salmonellosis and campylobacteriosis in European Union Member States (MS) plus European Free Trade Area (EFTA) countries Iceland, Norway and Switzerland and four other OECD countries (USA, Canada, Australia and Japan). Multiplication factors (MFs), a measure of the magnitude of underestimation, were taken directly from the literature or derived (where the proportion of underestimated, under-ascertained, or underreported cases was known) and compared for the two pathogens. Results: MFs varied between and within diseases and countries, representing a need to carefully select the most appropriate MFs and methods for calculating them. The most appropriate MFs are often disease-, country-, age-, and sex-specific. Conclusions: When routine data are used to make decisions on resource allocation or to estimate epidemiological parameters in populations, it becomes important to understand when, where and to what extent these data represent the true picture of disease, and in some instances (such as priority setting) it is necessary to adjust for underestimation. MFs can be used to adjust notification and surveillance data to provide more realistic estimates of incidence. © 2014 Gibbons et al.; licensee BioMed Central Ltd.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)