The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
Renxiao Liu - One of the best experts on this subject based on the ideXlab platform.
-
Estimation of non-Constant Variance in isothermal titration calorimetry using an ITC measurement model.
PloS one, 2020Co-Authors: Lan Chen, Renxiao LiuAbstract:Isothermal titration calorimetry (ITC) is the gold standard for accurate measurement of thermodynamic parameters in solution reactions. In the data processing of ITC, the non-Constant Variance of the heat requires special consideration. The Variance function approach has been successfully applied in previous studies, but is found to fail under certain conditions in this work. Here, an explicit ITC measurement model consisting of main thermal effects and error components has been proposed to quantitatively evaluate and predict the non-Constant Variance of the heat data under various conditions. Monte Carlo simulation shows that the ITC measurement model provides higher accuracy and flexibility than Variance function in high c-value reactions or with additional error components, for example, originated from the fluctuation of the concentrations or other properties of the solutions. The experimental design of basic error evaluation is optimized accordingly and verified by both Monte Carlo simulation and experiments. An easy-to-run Python source code is provided to illustrate the establishment of the ITC measurement model and the estimation of heat Variances. The accurate and reliable non-Constant Variance of heat is helpful to the application of weighted least squares regression, the proper evaluation or selection of the reaction model.
-
A practical method to evaluate major statistical errors in isothermal titration calorimetry
Thermochimica Acta, 2020Co-Authors: Lan Chen, Renxiao LiuAbstract:Abstract Continuous efforts have been devoted to improve the accuracy and reliability of isothermal titration calorimetry (ITC) in terms of instrumental design, modeling and data processing. Among various factors, background noise, proportional error and injection volume error lead to heteroscedasticity (non-Constant Variance) of the titration heat. The Variance function method provides an efficient way to estimate the non-Constant Variance and weight of the titration heat. Following this approach, in this work, the heat residual recorded by calorimeter model of Nano ITC Standard Volume was examined in detail. Based on the CaCl2-EDTA saturated titration and water blank titration, 9 heat Variances, each from 60 injections, have been analyzed, which provide reliable information about the Variance function. The major statistical errors for general titrations were estimated and the non-Constant Variance distribution of typical titration (BaCl2 to 18-Crown-6) was successfully predicted.
Lan Chen - One of the best experts on this subject based on the ideXlab platform.
-
Estimation of non-Constant Variance in isothermal titration calorimetry using an ITC measurement model.
PloS one, 2020Co-Authors: Lan Chen, Renxiao LiuAbstract:Isothermal titration calorimetry (ITC) is the gold standard for accurate measurement of thermodynamic parameters in solution reactions. In the data processing of ITC, the non-Constant Variance of the heat requires special consideration. The Variance function approach has been successfully applied in previous studies, but is found to fail under certain conditions in this work. Here, an explicit ITC measurement model consisting of main thermal effects and error components has been proposed to quantitatively evaluate and predict the non-Constant Variance of the heat data under various conditions. Monte Carlo simulation shows that the ITC measurement model provides higher accuracy and flexibility than Variance function in high c-value reactions or with additional error components, for example, originated from the fluctuation of the concentrations or other properties of the solutions. The experimental design of basic error evaluation is optimized accordingly and verified by both Monte Carlo simulation and experiments. An easy-to-run Python source code is provided to illustrate the establishment of the ITC measurement model and the estimation of heat Variances. The accurate and reliable non-Constant Variance of heat is helpful to the application of weighted least squares regression, the proper evaluation or selection of the reaction model.
-
A practical method to evaluate major statistical errors in isothermal titration calorimetry
Thermochimica Acta, 2020Co-Authors: Lan Chen, Renxiao LiuAbstract:Abstract Continuous efforts have been devoted to improve the accuracy and reliability of isothermal titration calorimetry (ITC) in terms of instrumental design, modeling and data processing. Among various factors, background noise, proportional error and injection volume error lead to heteroscedasticity (non-Constant Variance) of the titration heat. The Variance function method provides an efficient way to estimate the non-Constant Variance and weight of the titration heat. Following this approach, in this work, the heat residual recorded by calorimeter model of Nano ITC Standard Volume was examined in detail. Based on the CaCl2-EDTA saturated titration and water blank titration, 9 heat Variances, each from 60 injections, have been analyzed, which provide reliable information about the Variance function. The major statistical errors for general titrations were estimated and the non-Constant Variance distribution of typical titration (BaCl2 to 18-Crown-6) was successfully predicted.
Ghanim Ullah - One of the best experts on this subject based on the ideXlab platform.
-
thermal activation by power limited coloured noise
New Journal of Physics, 2005Co-Authors: Peter Jung, Alexander Neiman, Muhammad K N Afghan, Suhita Nadkarni, Ghanim UllahAbstract:We consider thermal activation in a bistable potential in the presence of correlated (Ornstein–Uhlenbeck) noise. Escape rates are discussed as a function of the correlation time of the noise at a Constant Variance of the noise. In contrast to a large body of previous work, where the Variance of the noise decreases with increasing correlation time of the noise, we find a bell-shaped curve for the escape rate with a vanishing rate at zero and infinite correlation times. We further calculate threshold crossing rates driven by energy-constrained coloured noise.
Jailson S. Alcaniz - One of the best experts on this subject based on the ideXlab platform.
-
How does an incomplete sky coverage affect the Hubble Constant Variance
The European Physical Journal C, 2019Co-Authors: Carlos A. P. Bengaly, Uendert Andrade, Jailson S. AlcanizAbstract:We address the $\simeq 4.4\sigma$ tension between local and the CMB measurements of the Hubble Constant using simulated Type Ia Supernova (SN) data-sets. We probe its directional dependence by means of a hemispherical comparison through the entire celestial sphere as an estimator of the $H_0$ cosmic Variance. We perform Monte Carlo simulations assuming isotropic and non-uniform distributions of data points, the latter coinciding with the real data. This allows us to incorporate observational features, such as the sample incompleteness, in our estimation. We obtain that this tension can be alleviated to $3.4\sigma$ for isotropic realizations, and $2.7\sigma$ for non-uniform ones. We also find that the $H_0$ Variance is largely reduced if the data-sets are augmented to 4 and 10 times the current size. Future surveys will be able to tell whether the Hubble Constant tension happens due to unaccounted cosmic Variance, or whether it is an actual indication of physics beyond the standard cosmological model.
Michael Unser - One of the best experts on this subject based on the ideXlab platform.
-
short basis functions for Constant Variance interpolation
Proceedings of the SPIE International Symposium on Medical Imaging: Image Processing (MI'08), 2008Co-Authors: Philippe Thevenaz, Thierry Blu, Michael UnserAbstract:An interpolation model is a necessary ingredient of intensity-based registration methods. The properties of such a model depend entirely on its basis function, which has been traditionally characterized by features such as its order of approximation and its support. However, as has been recently shown, these features are blind to the amount of registration bias created by the interpolation process alone; an additional requirement that has been named Constant-Variance interpolation is needed to remove this bias. In this paper, we present a theoretical investigation of the role of the interpolation basis in a registration context. Contrarily to published analyses, ours is deterministic; it nevertheless leads to the same conclusion, which is that Constant-Variance interpolation is beneficial to image registration. In addition, we propose a novel family of interpolation bases that can have any desired order of approximation while maintaining the Constant-Variance property. Our family includes every Constant-Variance basis we know of. It is described by an explicit formula that contains two free functional terms: an arbitrary 1-periodic binary function that takes values from {-1, 1}, and another arbitrary function that must satisfy the partition of unity. These degrees of freedom can be harnessed to build many family members for a given order of approximation and a fixed support. We provide the example of a symmetric basis with two orders of approximation that is supported over [-3 ⁄ 2, 3 ⁄ 2]; this support is one unit shorter than a basis of identical order that had been previously published.
-
Medical Imaging: Image Processing - Short Basis Functions for Constant-Variance Interpolation
Medical Imaging 2008: Image Processing, 2008Co-Authors: Philippe Thevenaz, Thierry Blu, Michael UnserAbstract:An interpolation model is a necessary ingredient of intensity-based registration methods. The properties of such a model depend entirely on its basis function, which has been traditionally characterized by features such as its order of approximation and its support. However, as has been recently shown, these features are blind to the amount of registration bias created by the interpolation process alone; an additional requirement that has been named Constant-Variance interpolation is needed to remove this bias. In this paper, we present a theoretical investigation of the role of the interpolation basis in a registration context. Contrarily to published analyses, ours is deterministic; it nevertheless leads to the same conclusion, which is that Constant-Variance interpolation is beneficial to image registration. In addition, we propose a novel family of interpolation bases that can have any desired order of approximation while maintaining the Constant-Variance property. Our family includes every Constant-Variance basis we know of. It is described by an explicit formula that contains two free functional terms: an arbitrary 1-periodic binary function that takes values from {-1, 1}, and another arbitrary function that must satisfy the partition of unity. These degrees of freedom can be harnessed to build many family members for a given order of approximation and a fixed support. We provide the example of a symmetric basis with two orders of approximation that is supported over [-3 ⁄ 2, 3 ⁄ 2]; this support is one unit shorter than a basis of identical order that had been previously published.
-
Short basis functions for Constant-Variance interpolation - art. no. 69142L
2008Co-Authors: Philippe Thevenaz, Thierry Blu, Michael UnserAbstract:An interpolation model is a necessary ingredient of intensity-based registration methods. The properties of such a model depend entirely on its basis function, which has been traditionally characterized by features such as its order of approximation and its support. However, as has been recently shown, these features are blind to the amount of registration bias created by the interpolation process alone; an additional requirement that has been named Constant-Variance interpolation is needed to remove this bias.