The Experts below are selected from a list of 96843 Experts worldwide ranked by ideXlab platform
Marc G. Aucoin - One of the best experts on this subject based on the ideXlab platform.
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A correction method for Systematic Error in (1)H-NMR time-course data validated through stochastic cell culture simulation.
BMC Systems Biology, 2015Co-Authors: Stanislav Sokolenko, Marc G. AucoinAbstract:Background The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct Systematic Error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed.
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A correction method for Systematic Error in ^1H-NMR time-course data validated through stochastic cell culture simulation
BMC Systems Biology, 2015Co-Authors: Stanislav Sokolenko, Marc G. AucoinAbstract:Background The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct Systematic Error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed. Results Realistic metabolic data was successfully simulated using a 4-step process. Starting with a set of metabolite concentration time-courses from a metabolomic experiment, each time-course was classified as either increasing, decreasing, concave, or approximately constant. Trend shapes were simulated from generic functions corresponding to each classification. The resulting shapes were then scaled to simulated compound concentrations. Finally, the scaled trends were perturbed using a combination of random and Systematic Errors. To detect Systematic Errors, a nonparametric fit was applied to each trend and percent deviations calculated at every timepoint. Systematic Errors could be identified at time-points where the median percent deviation exceeded a threshold value, determined by the choice of smoothing model and the number of observed trends. Regardless of model, increasing the number of observations over a time-course resulted in more accurate Error estimates, although the improvement was not particularly large between 10 and 20 samples per trend. The presented algorithm was able to identify Systematic Errors as small as 2.5 % under a wide range of conditions. Conclusion Both the simulation framework and Error correction method represent examples of time-course analysis that can be applied to further developments in ^1H-NMR methodology and the more general application of quantitative metabolomics.
J Schou - One of the best experts on this subject based on the ideXlab platform.
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the shrinking sun a Systematic Error in local correlation tracking of solar granulation
Astronomy and Astrophysics, 2016Co-Authors: Bjorn Loptien, A C Birch, Thomas L Duvall, L Gizon, J SchouAbstract:Context. Local correlation tracking of granulation (LCT) is an important method for measuring horizontal flows in the photosphere. This method exhibits a Systematic Error that looks like a flow converging toward disk center, which is also known as the shrinking-Sun effect. Aims. We aim to study the nature of the shrinking-Sun effect for continuum intensity data and to derive a simple model that can explain its origin. Methods. We derived LCT flow maps by running the LCT code Fourier Local Correlation Tracking (FLCT) on tracked and remapped continuum intensity maps provided by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). We also computed flow maps from synthetic continuum images generated from STAGGER code simulations of solar surface convection. We investigated the origin of the shrinking-Sun effect by generating an average granule from synthetic data from the simulations. Results. The LCT flow maps derived from the HMI data and the simulations exhibit a shrinking-Sun effect of comparable magnitude. The origin of this effect is related to the apparent asymmetry of granulation originating from radiative transfer effects when observing with a viewing angle inclined from vertical. This causes, in combination with the expansion of the granules, an apparent motion toward disk center.
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the shrinking sun a Systematic Error in local correlation tracking of solar granulation
arXiv: Solar and Stellar Astrophysics, 2016Co-Authors: Bjorn Loptien, A C Birch, Thomas L Duvall, L Gizon, J SchouAbstract:Context. Local correlation tracking of granulation (LCT) is an important method for measuring horizontal flows in the photosphere. This method exhibits a Systematic Error that looks like a flow converging towards disk center, also known as the shrinking-Sun effect. Aims. We aim at studying the nature of the shrinking-Sun effect for continuum intensity data and at deriving a simple model that can explain its origin. Methods. We derived LCT flow maps by running the local correlation tracking code FLCT on tracked and remapped continuum intensity maps provided by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory. We also computed flow maps from synthetic continuum images generated from STAGGER code simulations of solar surface convection. We investigated the origin of the shrinking-Sun effect by generating an average granule from synthetic data from the simulations. Results. The LCT flow maps derived from HMI and from the simulations exhibit a shrinking-Sun effect of comparable magnitude. The origin of this effect is related to the apparent asymmetry of granulation originating from radiative transfer effects when observing with a viewing angle inclined from vertical. This causes, in combination with the expansion of the granules, an apparent motion towards disk center.
Stanislav Sokolenko - One of the best experts on this subject based on the ideXlab platform.
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A correction method for Systematic Error in (1)H-NMR time-course data validated through stochastic cell culture simulation.
BMC Systems Biology, 2015Co-Authors: Stanislav Sokolenko, Marc G. AucoinAbstract:Background The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct Systematic Error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed.
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A correction method for Systematic Error in ^1H-NMR time-course data validated through stochastic cell culture simulation
BMC Systems Biology, 2015Co-Authors: Stanislav Sokolenko, Marc G. AucoinAbstract:Background The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct Systematic Error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed. Results Realistic metabolic data was successfully simulated using a 4-step process. Starting with a set of metabolite concentration time-courses from a metabolomic experiment, each time-course was classified as either increasing, decreasing, concave, or approximately constant. Trend shapes were simulated from generic functions corresponding to each classification. The resulting shapes were then scaled to simulated compound concentrations. Finally, the scaled trends were perturbed using a combination of random and Systematic Errors. To detect Systematic Errors, a nonparametric fit was applied to each trend and percent deviations calculated at every timepoint. Systematic Errors could be identified at time-points where the median percent deviation exceeded a threshold value, determined by the choice of smoothing model and the number of observed trends. Regardless of model, increasing the number of observations over a time-course resulted in more accurate Error estimates, although the improvement was not particularly large between 10 and 20 samples per trend. The presented algorithm was able to identify Systematic Errors as small as 2.5 % under a wide range of conditions. Conclusion Both the simulation framework and Error correction method represent examples of time-course analysis that can be applied to further developments in ^1H-NMR methodology and the more general application of quantitative metabolomics.
Jingyan Song - One of the best experts on this subject based on the ideXlab platform.
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a novel Systematic Error compensation algorithm based on least squares support vector regression for star sensor image centroid estimation
Sensors, 2011Co-Authors: Jun Yang, Bin Liang, Tao Zhang, Jingyan SongAbstract:The star centroid estimation is the most important operation, which directly affects the precision of attitude determination for star sensors. This paper presents a theoretical study of the Systematic Error introduced by the star centroid estimation algorithm. The Systematic Error is analyzed through a frequency domain approach and numerical simulations. It is shown that the Systematic Error consists of the approximation Error and truncation Error which resulted from the discretization approximation and sampling window limitations, respectively. A criterion for choosing the size of the sampling window to reduce the truncation Error is given in this paper. The Systematic Error can be evaluated as a function of the actual star centroid positions under different Gaussian widths of star intensity distribution. In order to eliminate the Systematic Error, a novel compensation algorithm based on the least squares support vector regression (LSSVR) with Radial Basis Function (RBF) kernel is proposed. Simulation results show that when the compensation algorithm is applied to the 5-pixel star sampling window, the accuracy of star centroid estimation is improved from 0.06 to 6 × 10−5 pixels.
Bjorn Loptien - One of the best experts on this subject based on the ideXlab platform.
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the shrinking sun a Systematic Error in local correlation tracking of solar granulation
Astronomy and Astrophysics, 2016Co-Authors: Bjorn Loptien, A C Birch, Thomas L Duvall, L Gizon, J SchouAbstract:Context. Local correlation tracking of granulation (LCT) is an important method for measuring horizontal flows in the photosphere. This method exhibits a Systematic Error that looks like a flow converging toward disk center, which is also known as the shrinking-Sun effect. Aims. We aim to study the nature of the shrinking-Sun effect for continuum intensity data and to derive a simple model that can explain its origin. Methods. We derived LCT flow maps by running the LCT code Fourier Local Correlation Tracking (FLCT) on tracked and remapped continuum intensity maps provided by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). We also computed flow maps from synthetic continuum images generated from STAGGER code simulations of solar surface convection. We investigated the origin of the shrinking-Sun effect by generating an average granule from synthetic data from the simulations. Results. The LCT flow maps derived from the HMI data and the simulations exhibit a shrinking-Sun effect of comparable magnitude. The origin of this effect is related to the apparent asymmetry of granulation originating from radiative transfer effects when observing with a viewing angle inclined from vertical. This causes, in combination with the expansion of the granules, an apparent motion toward disk center.
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the shrinking sun a Systematic Error in local correlation tracking of solar granulation
arXiv: Solar and Stellar Astrophysics, 2016Co-Authors: Bjorn Loptien, A C Birch, Thomas L Duvall, L Gizon, J SchouAbstract:Context. Local correlation tracking of granulation (LCT) is an important method for measuring horizontal flows in the photosphere. This method exhibits a Systematic Error that looks like a flow converging towards disk center, also known as the shrinking-Sun effect. Aims. We aim at studying the nature of the shrinking-Sun effect for continuum intensity data and at deriving a simple model that can explain its origin. Methods. We derived LCT flow maps by running the local correlation tracking code FLCT on tracked and remapped continuum intensity maps provided by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory. We also computed flow maps from synthetic continuum images generated from STAGGER code simulations of solar surface convection. We investigated the origin of the shrinking-Sun effect by generating an average granule from synthetic data from the simulations. Results. The LCT flow maps derived from HMI and from the simulations exhibit a shrinking-Sun effect of comparable magnitude. The origin of this effect is related to the apparent asymmetry of granulation originating from radiative transfer effects when observing with a viewing angle inclined from vertical. This causes, in combination with the expansion of the granules, an apparent motion towards disk center.