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Fu-hu Liu - One of the best experts on this subject based on the ideXlab platform.
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Statistical Behavior of Lepton Pair Spectrum in the Drell-Yan Process and Signal from Quark-Gluon Plasma in High-Energy Collisions
'Hindawi Limited', 2021Co-Authors: Xu-hong Zhang, Fu-hu LiuAbstract:We analyze the transverse momentum (pT) spectra of lepton pairs (ℓℓ¯) generated in the Drell-Yan process, as detected in proton-nucleus (pion-nucleus) and proton-(anti)proton collisions by ten collaborations over a center-of-mass energy sNN or s if in a simplified form) range from ~20 GeV to above 10 TeV. Three types of probability density functions (the convolution of two Lévy-Tsallis functions, the two-component Erlang Distribution, and the convolution of two Hagedorn functions) are utilized to fit and analyze the pT spectra. The fit results are approximately in agreement with the collected experimental data. Consecutively, we obtained the variation law of related parameters as a function of s and invariant mass Q. In the fit procedure, a given Lévy-Tsallis (or Hagedorn) function can be regarded as the probability density function of transverse momenta contributed by a single quark (q) or anti-quark (q¯). The Drell-Yan process is then described by the statistical method
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Statistical Behavior of Lepton Pair Spectrum in Drell-Yan Process and Signal from Quark-Gluon Plasma in High Energy Collisions
'Hindawi Limited', 2021Co-Authors: Zhang Xu-hong, Fu-hu LiuAbstract:We analyze the transverse momentum ($p_{T}$) spectra of lepton pairs ($\ell\bar \ell$) generated in the Drell-Yan process, as detected in proton-nucleus (pion-nucleus) and proton-(anti)proton collisions by ten collaborations over a center-of-mass energy ($\sqrt{s_{NN}}$ or $\sqrt{s}$ if in a simplified form) range from $\sim20$ GeV to above 10 TeV. Three types of probability density functions (the convolution of two L\'{e}vy-Tsallis functions, the two-component Erlang Distribution, and the convolution of two Hagedorn functions) are utilized to fit and analyze the $p_{T}$ spectra. The fit results are approximately in agreement with the collected experimental data. Consecutively, we obtained the variation law of related parameters as a function of $\sqrt{s}$ and invariant mass ($Q$). In the fit procedure, a given L\'{e}vy-Tsallis (or Hagedorn) function can be regarded as the probability density function of transverse momenta contributed by a single quark ($q$) or anti-quark ($\bar q$). The Drell-Yan process is then described by the statistical method.Comment: 19 pages, 7 figures. Advances in High Energy Physics, accepte
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Initial- and Final-State Temperatures of Emission Source from Differential Cross-Section in Squared Momentum Transfer in High-Energy Collisions
'Hindawi Limited', 2021Co-Authors: Qi Wang, Fu-hu Liu, Khusniddin K. OlimovAbstract:The differential cross-section in squared momentum transfer of ρ, ρ0, ω, ϕ, f0980, f11285, f01370, f11420, f01500, and J/ψ produced in high-energy virtual photon-proton (γ∗p), photon-proton (γp), and proton-proton (pp) collisions measured by the H1, ZEUS, and WA102 Collaborations is analyzed by the Monte Carlo calculations. In the calculations, the Erlang Distribution, Tsallis Distribution, and Hagedorn function are separately used to describe the transverse momentum spectra of the emitted particles. Our results show that the initial- and final-state temperatures increase from lower squared photon virtuality to a higher one and decrease with the increase of center-of-mass energy
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Initial and final-state temperatures of emission source from differential cross-section in squared momentum transfer in high energy collisions
'Hindawi Limited', 2021Co-Authors: Qi Wang, Fu-hu Liu, Olimov, Khusniddin K.Abstract:The differential cross-section in squared momentum transfer of $\rho$, $\rho^0$, $\omega$, $\phi$, $f_{0}(980)$, $f_{1}(1285)$, $f_{0}(1370)$, $f_{1}(1420)$, $f_{0}(1500)$, and $J/\psi$ produced in high energy virtual photon-proton ($\gamma$$^{*} p$), photon-proton ($\gamma p$), and proton-proton ($pp$) collisions measured by the H1, ZEUS, and WA102 Collaborations are analyzed by the Monte Carlo calculations. In the calculations, the Erlang Distribution, Tsallis Distribution, and Hagedorn function are separately used to describe the transverse momentum spectra of the emitted particles. Our results show that the initial and final-state temperatures increase from lower squared photon virtuality to higher one, and decrease with increasing of center-of-mass energy.Comment: 18 pages, 6 figures. Advances in High Energy Physics, Accepte
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Initial-state temperature of light meson emission source from squared momentum transfer spectra in high-energy collisions
2021Co-Authors: Qi Wang, Fu-hu Liu, Olimov, Khusniddin K.Abstract:The squared momentum transfer spectra of light mesons, $\pi^0$, $\pi^+$, $\eta$, and $\rho^0$, produced in high-energy virtual photon-proton ($\gamma^{*} p$) $\rightarrow {\rm meson + nucleon}$ process in electron-proton ($ep$) collisions measured by the CLAS Collaboration are analyzed by the Monte Carlo calculations, where the transfer undergoes from the incident $\gamma^*$ to emitted meson or equivalently from the target proton to emitted nucleon. In the calculations, the Erlang Distribution from a multi-source thermal model is used to describe the transverse momentum spectra of emitted particles. Our results show that the average transverse momentum ($\langle p_T\rangle$) and the initial-state temperature ($T_i$) increase from lower squared photon virtuality ($Q^2$) and Bjorken variable ($x_B$) to higher one. This renders that the excitation degree of emission source, which is described by $\langle p_T\rangle$ and $T_i$, increases with increasing of $Q^2$ and $x_B$.Comment: 17 pages, 6 figures. Frontiers in Physics, accepte
Franck Saintmarcoux - One of the best experts on this subject based on the ideXlab platform.
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pharmacokinetic tools for the dose adjustment of ciclosporin in haematopoietic stem cell transplant patients
British Journal of Clinical Pharmacology, 2014Co-Authors: Jean-baptiste Woillard, Vincent Lebreton, Michael Neely, Pascal Turlure, Stéphane Girault, Jean Debord, Pierre Marquet, Franck SaintmarcouxAbstract:Aims Ciclosporin A (CsA) is used in the prophylaxis and treatment of acute and chronic graft vs. host disease after haematopoietic stem cell (HSCT) transplantation. Our objective was to build and compare three independent Bayesian estimators of CsA area under the curve (AUC) using a limited sampling strategy (LSS), to assist in dose adjustment. Methods The Bayesian estimators were developed using in parallel: two independent parametric modelling approaches (nonmem® and iterative two stage (ITS) Bayesian modelling) and the non-parametric adaptive grid method (Pmetrics®). Seventy-two full pharmacokinetic profiles (at pre-dose and 0.33, 0.66, 1, 2, 3, 4, 6, 8 and 12h after dosing) collected from 40 HSCT patients given CsA were used to build the pharmacokinetic models, while 15 other profiles (n = 7) were kept for validation. For each Bayesian estimator, AUCs estimated using the full profiles were compared with AUCs estimated using three samples. Results The pharmacokinetic profiles were well fitted using a two compartment model with first order elimination, combined with a gamma function for the absorption phase with ITS and Pmetrics or an Erlang Distribution with nonmem. The derived Bayesian estimators based on a C0-C1 h-C4 h sampling schedule (best LSS) accurately estimated CsA AUC(0,12 h) in the validation group (n = 15; nonmem: bias (mean ± SD)/RMSE 2.05% ± 13.31%/13.02%; ITS: 4.61% ± 10.56%/11.20%; Pmetrics: 0.30% ± 10.12%/10.47%). The dose chosen confronting the three results led to a pertinent dose proposal. Conclusions The developed Bayesian estimators were all able to predict ciclosporin AUC(0,12 h) in HSCT patients using only three blood with minimal bias and may be combined to increase the reliability of CsA dose adjustment in routine.
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population pharmacokinetics and bayesian estimation of tacrolimus exposure in renal transplant recipients on a new once daily formulation
Clinical Pharmacokinectics, 2010Co-Authors: Khaled Benkali, Jean-baptiste Woillard, Franck Saintmarcoux, Lionel Rostaing, Aurelie Premaud, Saik Urien, Nassim Kamar, Pierre MarquetAbstract:Advagraf® is a new extended-release once-daily formulation of tacrolimus, a potent immunosuppressant widely used in renal transplantation. The aims of this study were (i) to develop a population pharmacokinetic model for once-daily tacrolimus in adult renal transplant patients; and (ii) to develop a Bayesian estimator able to reliably estimate individual pharmacokinetic parameters and exposure indices. Full pharmacokinetic profiles obtained from 41 adult renal transplant patients who had been switched from ciclosporin to a single daily dose of the new once-daily tacrolimus formulation for more than 6 months were analysed. Tacrolimus concentrations were measured using validated turbulent flow chromatography-tandem mass spectrometry methods. Population parameters were computed using nonlinear mixed-effect modelling software (NONMEM® Version VI). The patients were randomly divided into (i) a model-building test group (n = 29); and (ii) a validation group (n= 12). Population pharmacokinetic analysis was performed to estimate the effects on tacrolimus pharmacokinetics of demographic characteristics (sex, bodyweight, age), drug interaction with prednisolone, laboratory test results (the haematocrit, haemaglobin level and serum creatinine level) and cytochrome P450 (CYP) 3A5 (CYP3A5) genetic polymorphism. The population pharmacokinetic model was further refined by taking into account all of the data from the 41 patients, and the final model was validated using a bootstrap and a visual predictive check. For Bayesian estimation, the best limited-sampling strategy was determined on the basis of the D-optimality criterion and validation performed in the validation group. The trapezoidal area under the whole-blood concentration time curve from 0 to 24 hours (AUC24) of tacrolimus varied by up to 50% for the same trough concentration value. The pharmacokinetics of once-daily tacrolimus were well described by a two-compartment model combined with an Erlang Distribution to describe the absorption phase. The CYP3A5 genotype was the only covariate retained in the final model. The apparent clearance of tacrolimus was 2-fold higher in expressers (with the CYP3A5*1/*1 and CYP3A5*1/*3 genotypes) than in non-expressers (with the CYP3A5*3/*3 genotype). This factor explained around 25% of the interindividual variability in the apparent clearance. A posteriori Bayesian estimation allowed accurate prediction of the AUC24 of once-daily tacrolimus, using just three sampling times (0, 1 and 3 hours post-dose) with a nonsignificant mean bias of 0.7% (range 16–20%) and good precision (root mean square error 9%). Population pharmacokinetic analysis of once-daily tacrolimus in renal transplant recipients resulted in identification of the CYP3A5*1/*3 genotype as a significant covariate on the apparent clearance of tacrolimus, and the design of an accurate maximum a posteriori Bayesian estimator based on three blood concentration measurements and this covariate. Such a tool could be helpful for comparing different exposure indices or different target levels. It could contribute to improvement of the efficacy and tolerability of once-daily tacrolimus in some patients.
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patient characteristics influencing ciclosporin pharmacokinetics and accurate bayesian estimation of ciclosporin exposure in heart lung and kidney transplant patients
Clinical Pharmacokinectics, 2006Co-Authors: Franck Saintmarcoux, Pierre Marquet, Evelyne Jacqzaigrain, Nicole Bernard, Philippe Thiry, Yann Le Meur, Annick RousseauAbstract:Population pharmacokinetic studies of ciclosporin microemulsion are needed to identify the individual factors influencing ciclosporin pharmacokinetic variability in transplant patients and to design efficient tools for the accurate estimation of ciclosporin overall exposure (area under the plasma concentration-time curve from 0 to 12 hours [AUC12]). In the present retrospective study, a large database of heart, lung (with or without cystic fibrosis) and kidney (both adult and paediatric) transplant patients receiving ciclosporin microemulsion was analysed with the aims of (i) building a population pharmacokinetic model and finding the main covariates linked with ciclosporin microemulsion pharmacokinetic parameters; and (ii) developing a maximum a posteriori probability Bayesian estimator (MAP-BE) to estimate ciclosporin microemulsion pharmacokinetic parameters using a limited-sampling strategy. 3072 concentration data from 147 patients (i.e. 309 full pharmacokinetic profiles) were analysed using the nonlinear mixed-effects model program NONMEM. The influence of numerous covariates was tested, and the final model was validated by data splitting. For Bayesian estimation, the best limited-sampling strategy was determined based on the D-optimality criterion, and validation performed in an independent group of 60 patients. The pharmacokinetics of ciclosporin microemulsion were accurately described by a two-compartment model with Erlang Distribution for the absorption process. The type of graft and post-transplantation period were identified as significant sources of variability of the absorption parameter. Both apparent volume of the central compartment after oral administration (V1/F) and apparent oral clearance (CL/F) increased with bodyweight. The best limited-sampling strategy for Bayesian estimation was 0 hour, 1 hour and 3 hour post-dose, providing accurate estimation of ciclosporin microemulsion AUC12 in all patients of the test group, with a mean bias of 2.0 ± 10.5% (range: −19.1% to −21.4% and 95% CI −0.6, +4.7). Population pharmacokinetic analysis of ciclosporin microemulsion in allograft transplants resulted in the design of a new pharmacokinetic model for ciclosporin microemulsion, identification of significant covariates and the design of an accurate MAP-BE based on three blood concentrations and these covariates.
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population pharmacokinetic modeling of oral cyclosporin using nonmem comparison of absorption pharmacokinetic models and design of a bayesian estimator
Therapeutic Drug Monitoring, 2004Co-Authors: Alexandra Rousseau, Franck Saintmarcoux, Frederic Leger, Le Y Meur, Gilles Paintaud, M Buchler, P MarquetAbstract:There have been very few population pharmacokinetic (PopPK) studies and Bayesian forecasting methods dealing with cyclosporin (CsA) so far, probably because of the difficulty of modeling the particular absorption profiles of CsA. The present study was conducted in stable renal transplant patients treated with Neoral and employed the NONMEM program. Its goals were (1) to develop a population pharmacokinetic model for CsA based on an Erlang frequency Distribution (which describes asymmetric S-shaped absorption profiles) combined with a 2-compartment model; (2) to compare this model with models combining a time-lag parameter and either a zero-order or first-order rate constant and with a model based on a Weibull Distribution; and (3) to develop a PK Bayesian estimator for full AUC estimation based on that "Erlang model." The PopPK model was developed in an index set of 70 patients, and then individual PK parameters and AUC were estimated in 10 other patients using Bayesian estimation. The "Erlang" model best described the data, with mean absorption time (MAT), apparent clearance (CL/F), and apparent volume of the central compartment (Vc/F) of 0.78 hours, 26.3 L/h, and 76 L, respectively (interindividual variability CV = 33, 30, and 48%). Bayesian estimation allowed accurate prediction of systemic exposure using only 3 samples collected at 0, 1, and 3 hours. Regression analysis found no significant difference between the predicted and observed concentrations (10 per patient), and AUC(0-12) were estimated with a nonsignificant bias (0.6 to 8.7%) and good precision (RMSE = 5.3%). In conclusion, the Erlang Distribution best described CsA absorption profiles, and a Bayesian estimator developed using this model and a mixed-effect PK modeling program provided accurate estimates of CsA systemic exposure using only 3 blood samples.
Chuan Chen - One of the best experts on this subject based on the ideXlab platform.
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An Embedded Markov Chain Modeling Method for Movement-Based Location Update Scheme
2020Co-Authors: Peipei Liu, Yu Liu, Chuan ChenAbstract:Abstract-In this paper, an embedded Markov chain model is proposed to analyze the signaling cost of the Movement-Based Location Update (MBLU) scheme under which a Location Update (LU) occurs whenever the number of cells crossed reaches a threshold, called movement threshold. Compared with existing literature, this paper has the following advantages. 1) This paper proposes an embedded Markov chain model in which the cell residence time follows Hyper-Erlang Distribution. 2) This paper considers the Location Area (LA) architecture. 3) This paper emphasize the dependency between the cell and LA residence times using a fluid flow model. Close-form expressions for the signaling cost produced by LU and paging operations are derived, and their accuracy is validated by simulation. Based on the derived analytical expressions, we conduct numerical studies to investigate the impact of diverse parameters on the signaling cost
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an embedded markov chain modeling method for movement based location update scheme
Journal of Communications, 2015Co-Authors: Liangquan Ge, Chuan ChenAbstract:In this paper, an embedded Markov chain model is proposed to analyze the signaling cost of the Movement-Based Location Update (MBLU) scheme under which a Location Update (LU) occurs whenever the number of cells crossed reaches a threshold, called movement threshold. Compared with existing literature, this paper has the following advantages. 1) This paper proposes an embedded Markov chain model in which the cell residence time follows Hyper-Erlang Distribution. 2) This paper considers the Location Area (LA) architecture. 3) This paper emphasize the dependency between the cell and LA residence times using a fluid flow model. Close-form expressions for the signaling cost produced by LU and paging operations are derived, and their accuracy is validated by simulation. Based on the derived analytical expressions, we conduct numerical studies to investigate the impact of diverse parameters on the signaling cost. Index Terms—Embedded markov chain, hyper-Erlang Distribution, Location Management (LM), Movement-Based Location Update (MBLU).
P Marquet - One of the best experts on this subject based on the ideXlab platform.
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population pharmacokinetic modeling of oral cyclosporin using nonmem comparison of absorption pharmacokinetic models and design of a bayesian estimator
Therapeutic Drug Monitoring, 2004Co-Authors: Alexandra Rousseau, Franck Saintmarcoux, Frederic Leger, Le Y Meur, Gilles Paintaud, M Buchler, P MarquetAbstract:There have been very few population pharmacokinetic (PopPK) studies and Bayesian forecasting methods dealing with cyclosporin (CsA) so far, probably because of the difficulty of modeling the particular absorption profiles of CsA. The present study was conducted in stable renal transplant patients treated with Neoral and employed the NONMEM program. Its goals were (1) to develop a population pharmacokinetic model for CsA based on an Erlang frequency Distribution (which describes asymmetric S-shaped absorption profiles) combined with a 2-compartment model; (2) to compare this model with models combining a time-lag parameter and either a zero-order or first-order rate constant and with a model based on a Weibull Distribution; and (3) to develop a PK Bayesian estimator for full AUC estimation based on that "Erlang model." The PopPK model was developed in an index set of 70 patients, and then individual PK parameters and AUC were estimated in 10 other patients using Bayesian estimation. The "Erlang" model best described the data, with mean absorption time (MAT), apparent clearance (CL/F), and apparent volume of the central compartment (Vc/F) of 0.78 hours, 26.3 L/h, and 76 L, respectively (interindividual variability CV = 33, 30, and 48%). Bayesian estimation allowed accurate prediction of systemic exposure using only 3 samples collected at 0, 1, and 3 hours. Regression analysis found no significant difference between the predicted and observed concentrations (10 per patient), and AUC(0-12) were estimated with a nonsignificant bias (0.6 to 8.7%) and good precision (RMSE = 5.3%). In conclusion, the Erlang Distribution best described CsA absorption profiles, and a Bayesian estimator developed using this model and a mixed-effect PK modeling program provided accurate estimates of CsA systemic exposure using only 3 blood samples.
Alexandra Rousseau - One of the best experts on this subject based on the ideXlab platform.
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population pharmacokinetic modeling of oral cyclosporin using nonmem comparison of absorption pharmacokinetic models and design of a bayesian estimator
Therapeutic Drug Monitoring, 2004Co-Authors: Alexandra Rousseau, Franck Saintmarcoux, Frederic Leger, Le Y Meur, Gilles Paintaud, M Buchler, P MarquetAbstract:There have been very few population pharmacokinetic (PopPK) studies and Bayesian forecasting methods dealing with cyclosporin (CsA) so far, probably because of the difficulty of modeling the particular absorption profiles of CsA. The present study was conducted in stable renal transplant patients treated with Neoral and employed the NONMEM program. Its goals were (1) to develop a population pharmacokinetic model for CsA based on an Erlang frequency Distribution (which describes asymmetric S-shaped absorption profiles) combined with a 2-compartment model; (2) to compare this model with models combining a time-lag parameter and either a zero-order or first-order rate constant and with a model based on a Weibull Distribution; and (3) to develop a PK Bayesian estimator for full AUC estimation based on that "Erlang model." The PopPK model was developed in an index set of 70 patients, and then individual PK parameters and AUC were estimated in 10 other patients using Bayesian estimation. The "Erlang" model best described the data, with mean absorption time (MAT), apparent clearance (CL/F), and apparent volume of the central compartment (Vc/F) of 0.78 hours, 26.3 L/h, and 76 L, respectively (interindividual variability CV = 33, 30, and 48%). Bayesian estimation allowed accurate prediction of systemic exposure using only 3 samples collected at 0, 1, and 3 hours. Regression analysis found no significant difference between the predicted and observed concentrations (10 per patient), and AUC(0-12) were estimated with a nonsignificant bias (0.6 to 8.7%) and good precision (RMSE = 5.3%). In conclusion, the Erlang Distribution best described CsA absorption profiles, and a Bayesian estimator developed using this model and a mixed-effect PK modeling program provided accurate estimates of CsA systemic exposure using only 3 blood samples.