Search

Voriconazole is a broad-spectrum triazole antifungal agent. A number of studies have revealed that the impact of C-reactive protein (CRP) on voriconazole pharmacokinetics was associated with the CYP2C19 phenotype. However, the combined effects of CYP2C19 genetic polymorphisms and inflammation on voriconazole pharmacokinetics have not been considered in previous population pharmacokinetic (PPK) studies, especially in the Chinese population. This study aimed to analyze the impact of inflammation on the pharmacokinetics of voriconazole in patients with different CYP2C19 genotypes and optimize the dosage of administration. Data were obtained retrospectively from adult patients aged ≥16 years who received voriconazole for invasive fungal infections from October 2020 to June 2023. Plasma voriconazole levels were measured via high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). CYP2C19 genotyping was performed using the fluorescence in situ hybridization method. A PPK model was developed using the nonlinear mixed-effect model (NONMEM). The final model was validated using bootstrap, visual predictive check (VPC), and normalized prediction distribution error (NPDE). The Monte Carlo simulation was applied to evaluate and optimize the dosing regimens. A total of 232 voriconazole steady-state trough concentrations from 167 patients were included. A one-compartment model with first order and elimination adequately described the data. The typical clearance (CL) and the volume of distribution (V) of voriconazole were 3.83 L/h and 134 L, respectively. The bioavailability was 96.5%. Covariate analysis indicated that the CL of voriconazole was substantially influenced by age, albumin, gender, CRP, and CYP2C19 genetic variations. The V of voriconazole was significantly associated with body weight. An increase in the CRP concentration significantly decreased voriconazole CL in patients with the CYP2C19 normal metabolizer (NM) and intermediate metabolizer (IM), but it had no significant effect on patients with the CYP2C19 poor metabolizer (PM). The Monte Carlo simulation based on CRP levels indicated that patients with high CRP concentrations required a decreased dose to attain the therapeutic trough concentration and avoid adverse drug reactions in NM and IM patients. These results indicate that CRP affects the pharmacokinetics of voriconazole and is associated with the CYP2C19 phenotype. Clinicians dosing voriconazole should consider the patient’s CRP level, especially in CYP2C19 NMs and IMs. [ABSTRACT FROM AUTHOR]

Study objectives: TLD-1 is a novel pegylated liposomal doxorubicin (PLD) formulation aiming to optimise the PLD efficacy-toxicity ratio. We aimed to characterise TLD-1's population pharmacokinetics using non-compartmental analysis and nonlinear mixed-effects modelling. Methods: The PK of TLD-1 was analysed by performing a non-compartmental analysis of longitudinal doxorubicin plasma concentration measurements obtained from a clinical trial in 30 patients with advanced solid tumours across a 4.5-fold dose range. Furthermore, a joint parent-metabolite PK model of doxorubicinentrapped, doxorubicinfree, and metabolite doxorubicinol was developed. Interindividual and interoccasion variability around the typical PK parameters and potential covariates to explain parts of this variability were explored. Results: Medians ± standard deviations of dose-normalised doxorubicinentrapped+free Cmax and AUC0−∞ were 0.342 ± 0.134 mg/L and 40.1 ± 18.9 mg·h/L, respectively. The median half-life (95 h) was 23.5 h longer than the half-life of currently marketed PLD. The novel joint parent-metabolite model comprised a one-compartment model with linear release (doxorubicinentrapped), a two-compartment model with linear elimination (doxorubicinfree), and a one-compartment model with linear elimination for doxorubicinol. Body surface area on the volumes of distribution for free doxorubicin was the only significant covariate. Conclusion: The population PK of TLD-1, including its release and main metabolite, were successfully characterised using non-compartmental and compartmental analyses. Based on its long half-life, TLD-1 presents a promising candidate for further clinical development. The PK characteristics form the basis to investigate TLD-1 exposure-response (i.e., clinical efficacy) and exposure-toxicity relationships in the future. Once such relationships have been established, the developed population PK model can be further used in model-informed precision dosing strategies. Clinical trial registration: ClinicalTrials.gov–NCT03387917–January 2, 2018 [ABSTRACT FROM AUTHOR]

Many clinical trials generate both longitudinal biomarker and time‐to‐event data. We might be interested in their relationship, as in the case of tumor size and overall survival in oncology drug development. Many well‐established methods exist for analyzing such data either sequentially (two‐stage models) or simultaneously (joint models). Two‐stage modeling (2stgM) has been challenged (i) for not acknowledging that biomarkers are endogenous covariable to the survival submodel and (ii) for not propagating the uncertainty of the longitudinal biomarker submodel to the survival submodel. On the other hand, joint modeling (JM), which properly circumvents both problems, has been criticized for being time‐consuming, and difficult to use in practice. In this paper, we explore a third approach, referred to as a novel two‐stage modeling (N2stgM). This strategy reduces the model complexity without compromising the parameter estimate accuracy. The three approaches (2stgM, JM, and N2stgM) are formulated, and a Bayesian framework is considered for their implementation. Both real and simulated data were used to analyze the performance of such approaches. In all scenarios, our proposal estimated the parameters approximately as JM but without being computationally expensive, while 2stgM produced biased results. [ABSTRACT FROM AUTHOR]

The stand basal area, closely related to age, site quality, and stand density, is an important factor for predicting forest growth and yield. The accurate estimation of site quality is especially a key component in the stand basal area model. We utilized sample plots with Picea asperata Mast. as the dominant species in the multi-period National Forest Inventory (NFI) dataset to establish a site index (SI) model including climate effects through the difference form of theoretical growth equations and mixed-effects models. We combined the SI calculated from the SI model, stand age, and stand density index to construct a basal area growth model for Picea asperata Mast. stands. The results show that the Korf model is the best SI base model for Picea asperata Mast. The mean temperatures in summer and winter precipitation were used as the fixed parameters to construct a nonlinear model. Ultimately, elevation, origin, and region, as random effects, were incorporated into the mixed-effects model. The coefficients (R2) of determination of the base model, the nonlinear model including climate, and the nonlinear mixed-effects model are 0.869, 0.899, and 0.921, with root-mean-square errors (RMSEs) of 1.320, 1.315, and 1.301, respectively. Among the basal area models, the Richards model has higher precision. And the basal area model including an SI incorporating climatic factors had a higher determination coefficient (R2) of 0.918 than that of the model including an SI without considering climatic effects. The mixed-effects model incorporating climatic and topographic factors shows a better fitting performance of SI, resulting in a higher precision of the basal area model. This indicates that in the development of forest growth models, both biophysical and climatic factors should be comprehensively considered. [ABSTRACT FROM AUTHOR]

Daptomycin is gaining prominence for the treatment of methicillin-resistant Staphylococcus aureus infections. However, the dosage selection for daptomycin in critically ill patients remains uncertain, especially in Chinese patients. This study aimed to establish the population pharmacokinetics of daptomycin in critically ill patients, optimize clinical administration plans, and recommend appropriate dosage for critically ill patients in China. The study included 64 critically ill patients. Blood samples were collected at the designated times. The blood daptomycin concentration was determined using validated liquid chromatography-tandem mass spectrometry. A nonlinear mixedeffects model was applied for the population pharmacokinetic analysis and Monte Carlo simulations of daptomycin. The results showed a twocompartment population pharmacokinetic model of daptomycin in critically ill adult Han Chinese patients. Monte Carlo simulations revealed that a daily dose of 400 mg of daptomycin was insufficient for the majority of critically ill adult patients to achieve the anti-infective target. For critically ill adult patients with normal renal function (creatinine clearance rate >90 mL/min), the probability of achieving the target only reached 90% when the daily dose was increased to 700 mg. For patients undergoing continuous renal replacement therapy (CRRT), 24 h administration of 500 mg met the pharmacodynamic goals and did not exceed the safety threshold in most patients. Therefore, considering its efficacy and safety, intravenous daptomycin doses are best scaled according to creatinine clearance, and an increased dose is recommended for critically ill patients with hyperrenalism. For patients receiving CRRT, medication is recommended at 24 h intervals. [ABSTRACT FROM AUTHOR]

Gehang Ju,1– 3 Xin Liu,1– 3 Wenyu Yang,4 Nuo Xu,4 Lulu Chen,3,5 Chenchen Zhang,6 Qingfeng He,4 Xiao Zhu,4,* Dongsheng Ouyang1– 3,5,* 1Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 2Institute of Clinical Pharmacology, Central South University, Changsha, People’s Republic of China; 3Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China; 4Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China; 5Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China; 6School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Dongsheng Ouyang; Xiao Zhu, Email 801940@csu.edu.cn; xiaozhu@fudan.edu.cnIntroduction: Isoniazid (INH) is a crucial first-line anti tuberculosis (TB) drug used in adults and children. However, various factors can alter its pharmacokinetics (PK). This article aims to establish a population pharmacokinetic (popPK) models repository of INH to facilitate clinical use.Methods: A literature search was conducted until August 23, 2022, using PubMed, Embase, and Web of Science databases. We excluded published popPK studies that did not provide full model parameters or used a non-parametric method. Monte Carlo simulation works was based on RxODE. The popPK models repository was established using R. Non-compartment analysis was based on IQnca.Results: Fourteen studies included in the repository, with eleven studies conducted in adults, three studies in children, one in pregnant women. Two-compartment with allometric scaling models were commonly used as structural models. NAT2 acetylator phenotype significantly affecting the apparent clearance (CL). Moreover, postmenstrual age (PMA) influenced the CL in pediatric patients. Monte Carlo simulation results showed that the geometric mean ratio (95% Confidence Interval, CI) of PK parameters in most studies were within the acceptable range (50.00– 200.00%), pregnant patients showed a lower exposure. After a standard treatment strategy, there was a notable exposure reduction in the patients with the NAT2 RA or nonSA (IA/RA) phenotype, resulting in a 59.5% decrease in AUC0-24 and 83.2% decrease in Cmax (Infants), and a 49.3% reduction in AUC0-24 and 73.5% reduction in Cmax (Adults).Discussion: Body weight and NAT2 acetylator phenotype are the most significant factors affecting the exposure of INH. PMA is a crucial factor in the pediatric population. Clinicians should consider these factors when implementing model-informed precision dosing of INH. The popPK model repository for INH will aid in optimizing treatment and enhancing patient outcomes.Keywords: Isoniazid, model-informed precision dosing, population pharmacokinetics, nonlinear mixed-effects model

Tenofovir alafenamide fumarate (TAF) is a first‐line drug for treating hepatitis B virus infection. This study aimed to establish the prodrug–metabolite population pharmacokinetic (PK) model for TAF and its metabolite tenofovir (TFV) in healthy Chinese volunteers and evaluate the factors affecting the PK. Using 1043 TAF and 1198 TFV plasma sample concentrations collected from 67 healthy volunteers, a population PK model was developed using the nonlinear mixed‐effects model. The 1‐compartment model containing 4 transit compartments and the 2‐compartment model accurately described the PK of TAF and TFV, respectively. Covariates such as meal state and sex were found to be statistically significant and potentially clinically relevant. Both internal and external validations demonstrated good stability and predictive performance of the connected model. This study elucidated the PK process by which TAF was absorbed, converted, and finally metabolized and eliminated as TFV, and explored the sources of interindividual variability between TAF and TFV. [ABSTRACT FROM AUTHOR]

In advanced cancer patients, tumor burden is calculated using the sum of the longest diameters (SLD) of the target lesions, a measure that lumps all lesions together and ignores intra‐patient heterogeneity. Here, we used a rich dataset of 342 metastatic bladder cancer patients treated with a novel immunotherapy agent to develop a Bayesian multilevel joint model that can quantify heterogeneity in lesion dynamics and measure their impact on survival. Using a nonlinear model of tumor growth inhibition, we estimated that dynamics differed greatly among lesions, and inter‐lesion variability accounted for 21% and 28% of the total variance in tumor shrinkage and treatment effect duration, respectively. Next, we investigated the impact of individual lesion dynamics on survival. Lesions located in the liver and in the bladder had twice as much impact on the instantaneous risk of death compared to those located in the lung or the lymph nodes. Finally, we evaluated the utility of individual lesion follow‐up for dynamic predictions. Consistent with results at the population level, the individual lesion model outperformed a model relying only on SLD, especially at early landmark times and in patients with liver or bladder target lesions. Our results show that an individual lesion model can characterize the heterogeneity in tumor dynamics and its impact on survival in advanced cancer patients. [ABSTRACT FROM AUTHOR]

The stand basal area, closely related to age, site quality, and stand density, is an important factor for predicting forest growth and yield. The accurate estimation of site quality is especially a key component in the stand basal area model. We utilized sample plots with Picea asperata Mast. as the dominant species in the multi-period National Forest Inventory (NFI) dataset to establish a site index (SI) model including climate effects through the difference form of theoretical growth equations and mixed-effects models. We combined the SI calculated from the SI model, stand age, and stand density index to construct a basal area growth model for Picea asperata Mast. stands. The results show that the Korf model is the best SI base model for Picea asperata Mast. The mean temperatures in summer and winter precipitation were used as the fixed parameters to construct a nonlinear model. Ultimately, elevation, origin, and region, as random effects, were incorporated into the mixed-effects model. The coefficients (R2) of determination of the base model, the nonlinear model including climate, and the nonlinear mixed-effects model are 0.869, 0.899, and 0.921, with root-mean-square errors (RMSEs) of 1.320, 1.315, and 1.301, respectively. Among the basal area models, the Richards model has higher precision. And the basal area model including an SI incorporating climatic factors had a higher determination coefficient (R2) of 0.918 than that of the model including an SI without considering climatic effects. The mixed-effects model incorporating climatic and topographic factors shows a better fitting performance of SI, resulting in a higher precision of the basal area model. This indicates that in the development of forest growth models, both biophysical and climatic factors should be comprehensively considered.

Although posaconazole tablets show relatively low variability in pharmacokinetics (PK), the proportion of patients achieving the PK/PD target at the approved uniform dose for both prophylaxis and therapy is not satisfactory. The aim of this study was to develop a posaconazole population PK model in lung-transplant recipients and to propose a covariate-based dosing optimization for both prophylaxis and therapy. In this prospective study, 80 posaconazole concentrations obtained from 32 lung-transplant patients during therapeutic drug monitoring were analyzed using nonlinear mixed-effects modelling, and a Monte Carlo simulation was used to describe the theoretical distribution of posaconazole PK profiles at various dosing regimens. A one-compartment model with both linear absorption and elimination best fit the concentration–time data. The population apparent volume of distribution was 386.4 L, while an apparent clearance of 8.8 L/h decreased by 0.009 L/h with each year of the patient's age. Based on the covariate model, a dosing regimen of 200 mg/day for prophylaxis in patients ˃60 years, 300 mg/day for prophylaxis in patients ˂60 years and for therapy in patients ˃60 years, and 400 mg/day for therapy in patients ˂60 years has been proposed. At this dosing regimen, the PK/PD target for prophylaxis and therapy is reached in 95% and 90% of population, respectively, representing significantly improved outcomes in comparison with the uniform dose. [ABSTRACT FROM AUTHOR]

Compatible taper and volume equations for pure and natural Scots pine stands in the northeastern part of Turkey (Ardahan Province) were developed using a nonlinear mixed-effects modeling approach. Experimental data were obtained from 137 felled sample trees in different diameter and height classes. The most successful model (Jiang et al. 2005) explained 98.3% of the variance in stem diameter estimation and the RMSE, ME, MAE, AIC and BIC value obtained using this model were 1.955 cm, 0.043 cm, 1.300 cm, 9783.8 and 9812.6, respectively. Considering the criterion values of AIC, BIC and -2LnL, the model with random-effects in two parameters (b1 and b3) was the most successful for Scots pine. While the mixed model including random parameters did not completely solved the problem of the autocorrelation of errors in this study, the use of the autoregressive error structure AR(1) eliminated the autocorrelation in the residuals. In addition, the best estimation results among 20 different calibration options were obtained using the option of measuring two tree diameters at d1.30 and d5.30 with validation data. The most successful model explained 99.18% of the total variance in stem volume estimation in Scots pine.

Competition among free branches in the tree canopy is an important factor influencing branch length growth. Therefore, there is a need to quantify this competition and to understand the impact of the regression technique on the predictive accuracy of the growth of free branch length (GFBL) model in a Larix principis-rupprechtii plantation. This study focused on an L. principis-rupprechtii plantation in Saihanba Mechanized Forest Farm. Five competition indices based on 2176-branch data points from 76 trees were used to quantify the branch competition, and three regression techniques (nonlinear least squares (NLS), nonlinear mixed-effects model (NLME), and nonlinear quantile regression (NQR)) were used to construct the GFBL model including the branch competition index. The results showed that the Chapman–Richards growth function, including the diameter at breast height (DBH) and depth of branch into crown (DINC), was the optimal equation for describing the GFBL in the studied L. principis-rupprechtii plantation. The branch competition index (CI) was found to be optimal for quantifying the branch competition when used with the maximum value parameter ( a 0 ) of the Chapman–Richards growth function. The three parameter estimation methods were compared, and the NLME, which included the CI, was found to have the highest predictive accuracy. The results of this study can act as a reference for improving the management, assessing the management effectiveness, and enhancing the quality of L. principis-rupprechtii plantations. [ABSTRACT FROM AUTHOR]

Although posaconazole tablets show relatively low variability in pharmacokinetics (PK), the proportion of patients achieving the PK/PD target at the approved uniform dose for both prophylaxis and therapy is not satisfactory. The aim of this study was to develop a posaconazole population PK model in lung-transplant recipients and to propose a covariate-based dosing optimization for both prophylaxis and therapy. In this prospective study, 80 posaconazole concentrations obtained from 32 lung-transplant patients during therapeutic drug monitoring were analyzed using nonlinear mixed-effects modelling, and a Monte Carlo simulation was used to describe the theoretical distribution of posaconazole PK profiles at various dosing regimens. A one-compartment model with both linear absorption and elimination best fit the concentration–time data. The population apparent volume of distribution was 386.4 L, while an apparent clearance of 8.8 L/h decreased by 0.009 L/h with each year of the patient’s age. Based on the covariate model, a dosing regimen of 200 mg/day for prophylaxis in patients ˃60 years, 300 mg/day for prophylaxis in patients ˂60 years and for therapy in patients ˃60 years, and 400 mg/day for therapy in patients ˂60 years has been proposed. At this dosing regimen, the PK/PD target for prophylaxis and therapy is reached in 95% and 90% of population, respectively, representing significantly improved outcomes in comparison with the uniform dose.

An accurate estimate of the site index is essential for informing decision-making in forestry. In this study, we developed site index (SI) models using stem analysis data to estimate the site index and the dominant height growth for Larix gmelinii var. principis-rupprechtii in northern China. The data included 5122 height–age pairs from 75 dominant trees in 29 temporary sample plots (TSPs). Nine commonly used growth functions were parameterized using the modeling method, which accounts for heterogeneous variance and autocorrelation in the time-series data and introduces sample plot-level random effects in the model. The results show that the Duplat and Tran-Ha I model with random effects described the largest proportion of the dominant height variation. This model accurately evaluated the site quality and predicted the dominant tree height growth in natural Larix forests in the Guandi Mountain region. As an important supplement in improving methods for site quality evaluation, the model may serve as a fundamental tool in the scientific management of larch forests. The research results can inform an accurate evaluation of the site quality and predict the growth of the dominant height in a larch forest in the Guandi Mountain forest area as well as provide a theoretical basis for forest site quality evaluation at similar sites. [ABSTRACT FROM AUTHOR]

Aims: Linezolid is often used for the infections caused by drug‐resistant Gram‐positive bacteria. Recent studies suggest that large between‐subject variability (BSV) and within‐subject variability could alter drug pharmacokinetics (PK) during linezolid therapy due to pathophysiological changes. This review synthesized information on linezolid population PK studies and summarized the significant covariates that influence linezolid PK. Methods: A literature search was performed using PubMed, Web of Science and Embase from their inception to 30 September 2021. Published studies were included if they contained data analysing linezolid PK parameters in humans using a population approach with a nonlinear mixed‐effects model. Results: Twenty‐five studies conducted in adults and five in paediatrics were included. One‐ and two‐compartment models were the commonly used structural models for linezolid. Body size (weight, lean body weight and body surface area), creatinine clearance (CLcr) and age significantly influenced linezolid PK. The median clearance (CL) values (ranges) in infants (0.128 L/h/kg [0.121–0.135]] and children (0.107 L/h/kg [0.088–0.151]] were higher than in adults (0.098 L/h/kg [0.044–0.237]]. For patients with severe renal impairment (CLcr ≤ 30 mL/min), the CL was 37.2% (15.2–55.3%) lower than in patients with normal renal function. Conclusion: The optimal linezolid dosage should be adjusted based on the patient's body size, renal function and age. More studies are needed to explore the exact mechanism of linezolid elimination and evaluate the PK characteristics in paediatric patients. [ABSTRACT FROM AUTHOR]

Voriconazole is a broad-spectrum triazole antifungal agent. A number of studies have revealed that the impact of C-reactive protein (CRP) on voriconazole pharmacokinetics was associated with the CYP2C19 phenotype. However, the combined effects of CYP2C19 genetic polymorphisms and inflammation on voriconazole pharmacokinetics have not been considered in previous population pharmacokinetic (PPK) studies, especially in the Chinese population. This study aimed to analyze the impact of inflammation on the pharmacokinetics of voriconazole in patients with different CYP2C19 genotypes and optimize the dosage of administration. Data were obtained retrospectively from adult patients aged ≥16 years who received voriconazole for invasive fungal infections from October 2020 to June 2023. Plasma voriconazole levels were measured via high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). CYP2C19 genotyping was performed using the fluorescence in situ hybridization method. A PPK model was developed using the nonlinear mixed-effect model (NONMEM). The final model was validated using bootstrap, visual predictive check (VPC), and normalized prediction distribution error (NPDE). The Monte Carlo simulation was applied to evaluate and optimize the dosing regimens. A total of 232 voriconazole steady-state trough concentrations from 167 patients were included. A one-compartment model with first order and elimination adequately described the data. The typical clearance (CL) and the volume of distribution (V) of voriconazole were 3.83 L/h and 134 L, respectively. The bioavailability was 96.5%. Covariate analysis indicated that the CL of voriconazole was substantially influenced by age, albumin, gender, CRP, and CYP2C19 genetic variations. The V of voriconazole was significantly associated with body weight. An increase in the CRP concentration significantly decreased voriconazole CL in patients with the CYP2C19 normal metabolizer (NM) and intermediate metabolizer (IM), but it had no significant effect on patients with the CYP2C19 poor metabolizer (PM). The Monte Carlo simulation based on CRP levels indicated that patients with high CRP concentrations required a decreased dose to attain the therapeutic trough concentration and avoid adverse drug reactions in NM and IM patients. These results indicate that CRP affects the pharmacokinetics of voriconazole and is associated with the CYP2C19 phenotype. Clinicians dosing voriconazole should consider the patient's CRP level, especially in CYP2C19 NMs and IMs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ling, Yang, Dong, Jiang, Zou and Hu.)

Competition among free branches in the tree canopy is an important factor influencing branch length growth. Therefore, there is a need to quantify this competition and to understand the impact of the regression technique on the predictive accuracy of the growth of free branch length (GFBL) model in a Larix principis-rupprechtii plantation. This study focused on an L. principis-rupprechtii plantation in Saihanba Mechanized Forest Farm. Five competition indices based on 2176-branch data points from 76 trees were used to quantify the branch competition, and three regression techniques (nonlinear least squares (NLS), nonlinear mixed-effects model (NLME), and nonlinear quantile regression (NQR)) were used to construct the GFBL model including the branch competition index. The results showed that the Chapman–Richards growth function, including the diameter at breast height (DBH) and depth of branch into crown (DINC), was the optimal equation for describing the GFBL in the studied L. principis-rupprechtii plantation. The branch competition index (CI) was found to be optimal for quantifying the branch competition when used with the maximum value parameter (a0) of the Chapman–Richards growth function. The three parameter estimation methods were compared, and the NLME, which included the CI, was found to have the highest predictive accuracy. The results of this study can act as a reference for improving the management, assessing the management effectiveness, and enhancing the quality of L. principis-rupprechtii plantations.

• Iontophoresis provided successful transdermal delivery of drugs required to induce β-adrenergic sweat rate responses, including non-ionized or poorly ionized isoproterenol and aminophylline. • Modelling test responses by a nonlinear mixed-effects approach allowed evaluating metrics of CFTR-dependent effects while integrating inter- and intra-subject variability. • Secretory capacity and kinetic rate of CFTR-dependent sweating were blunted in CF. In carriers, kinetic rate was apparently lowered and secretory capacity was preserved. • The work promotes advances in the precision of biomarkers, needed to accelerate drug development in CF. Two CFTR-dependent β-adrenergic sweat rate tests applying intradermal drug injections were reported to better define diagnosis and efficacy of CFTR-directed therapies. The aim of this work was to develop and test a needle-free image-based test and to provide an accurate analysis of the responses. The modified method was conducted by applying two successive iontophoresis sessions using the Macroduct device. Efficiency of drug delivery was tested by evaporimetry. Cholinergically stimulated sweating was evoked by pilocarpine iontophoresis. β-adrenergically stimulated sweating was obtained by iontophoresis of isoproterenol and aminophylline in the presence of atropine and ascorbic acid. A nonlinear mixed-effects (NLME) approach was applied to model volumes of sweat and subject-specific effects displaying inter- and intra-subject variability. Iontophoresis provided successful transdermal delivery of all drugs, including almost neutral isoproterenol and aminophylline. Pilocarpine was used at a concentration ∼130-times lower than that used in the classical Gibson and Cooke sweat test. Addition of ascorbic acid lowered the pH of the solution, made it stable, prevented isoproterenol degradation and promoted drug iontophoresis. Maximal secretory capacity and kinetic rate of β-adrenergic responses were blunted in CF. A cutoff of 5.2 minutes for ET50, the time to reach the half maximal secretion, discriminated CF from controls with a 100% sensitivity and specificity. Heterozygous showed an apparently reduced kinetic rate and a preserved secretory capacity. We tested a safe, well-tolerated needle-free image-based sweat test potentially applicable in children. Modelling responses by NLME allowed evaluating metrics of CFTR-dependent effects reflecting secretory capacity and kinetic rate. [ABSTRACT FROM AUTHOR]

Tree height–diameter relationship is very important in forest investigation, describing forest structure and estimating carbon storage. Climate change may modify the relationship. However, our understanding of the effects of climate change on the height–diameter allometric relationship is still limited at large scales. In this study, we explored how climate change effects on the relationship varied with tree species and size for larch plantations in northern and northeastern China. Based on the repeated measurement data of 535 plots from the 6th to 8th national forest inventory of China, climate-sensitive tree height–diameter models of larch plantations in north and northeast China were developed using two-level nonlinear mixed effect (NLME) method. The final model was used to analyze the height–diameter relationship of different larch species under RCP2.6, RCP 4.5, and RCP8.5 climate change scenarios from 2010 to 2100. The adjusted coefficient of determination R a d j 2 , mean absolute error (MAE) and root mean squared error (RMSE) of the NLME models for calibration data were 0.92, 0.76 m and 1.06 m, respectively. The inclusion of climate variables mean annual temperature (MAT) and Hargreaves climatic moisture deficit (CMD) with random effects was able to increase R a d j 2 by 19.5% and reduce the AIC (Akaike's information criterion), MAE and RMSE by 22.2%, 44.5% and 41.8%, respectively. The climate sensitivity of larch species was ranked as L. gmelinii > the unidentified species group > L. principis > L. kaempferi > L. olgensis under RCP4.5, but L. gmelinii > L. principis > the unidentified species group > L. olgensis > L. kaempferi under RCP2.6 and RCP8.5. Large trees were more sensitive to climate change than small trees. [ABSTRACT FROM AUTHOR]

Daptomycin is gaining prominence for the treatment of methicillin-resistant Staphylococcus aureus infections. However, the dosage selection for daptomycin in critically ill patients remains uncertain, especially in Chinese patients. This study aimed to establish the population pharmacokinetics of daptomycin in critically ill patients, optimize clinical administration plans, and recommend appropriate dosage for critically ill patients in China. The study included 64 critically ill patients. Blood samples were collected at the designated times. The blood daptomycin concentration was determined using validated liquid chromatography-tandem mass spectrometry. A nonlinear mixed-effects model was applied for the population pharmacokinetic analysis and Monte Carlo simulations of daptomycin. The results showed a two-compartment population pharmacokinetic model of daptomycin in critically ill adult Han Chinese patients. Monte Carlo simulations revealed that a daily dose of 400 mg of daptomycin was insufficient for the majority of critically ill adult patients to achieve the anti-infective target. For critically ill adult patients with normal renal function (creatinine clearance rate >90 mL/min), the probability of achieving the target only reached 90% when the daily dose was increased to 700 mg. For patients undergoing continuous renal replacement therapy (CRRT), 24 h administration of 500 mg met the pharmacodynamic goals and did not exceed the safety threshold in most patients. Therefore, considering its efficacy and safety, intravenous daptomycin doses are best scaled according to creatinine clearance, and an increased dose is recommended for critically ill patients with hyperrenalism. For patients receiving CRRT, medication is recommended at 24 h intervals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wu, Zheng, Zhang, Wang, Lv, Xi and Wu.)

First generation cephalosporins such cephalothin of cefazolin are indicated for antimicrobial prophylaxis for clean and clean contaminated surgical procedures because its antimicrobial spectrum, relative low toxicity and cost. Anesthesia and surgery could alter the pharmacokinetic behavior of different drugs administered perioperative by many mechanisms that affect distribution, metabolism or excretion processes. Intravenous administration of the antimicrobial within 30 and 60 min before incision is recommended in order to reach therapeutic serum and tissue concentrations and redosing is recommended if the duration of the procedure exceeds two half-life of the antimicrobial. To the author's knowledge there are no pharmacokinetic studies of cephalothin in dogs under anesthesia/surgery conditions. The aim of this study was (1) to evaluate the pharmacokinetics of cephalothin in anesthetized dogs undergoing ovariohysterectomy by a nonlinear mixed-effects model and to determine the effect of anesthesia/surgery and other individual covariates on its pharmacokinetic behavior; (2) to determine the MIC and conduct a pharmacodynamic modeling of time kill curves assay of cephalothin against isolates of Staphylococcus spp. isolated from the skin of dogs; (3) to conduct a PK/PD analysis by integration of the obtained nonlinear mixed-effects models in order to evaluate the antimicrobial effect of changing concentrations on simulated bacterial count; and (4) to determine the PK/PD endpoints and PK/PDco values in order to predict the optimal dose regimen of cephalothin for antimicrobial prophylaxis in dogs. Anesthesia/surgery significantly reduced cephalothin clearance by 18.78%. Based on the results of this study, a cephalothin dose regimen of 25 mg/kg q6h by intravenous administration showed to be effective against Staphylococcus spp. isolates with MIC values ≤2 μg/mL and could be recommended for antimicrobial prophylaxis for clean surgery in healthy dogs., Competing Interests: Declaration of competing interest Authors declare no conflicts of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

An accurate estimate of the site index is essential for informing decision-making in forestry. In this study, we developed site index (SI) models using stem analysis data to estimate the site index and the dominant height growth for Larix gmelinii var. principis-rupprechtii in northern China. The data included 5122 height–age pairs from 75 dominant trees in 29 temporary sample plots (TSPs). Nine commonly used growth functions were parameterized using the modeling method, which accounts for heterogeneous variance and autocorrelation in the time-series data and introduces sample plot-level random effects in the model. The results show that the Duplat and Tran-Ha I model with random effects described the largest proportion of the dominant height variation. This model accurately evaluated the site quality and predicted the dominant tree height growth in natural Larix forests in the Guandi Mountain region. As an important supplement in improving methods for site quality evaluation, the model may serve as a fundamental tool in the scientific management of larch forests. The research results can inform an accurate evaluation of the site quality and predict the growth of the dominant height in a larch forest in the Guandi Mountain forest area as well as provide a theoretical basis for forest site quality evaluation at similar sites.

Analyzing the progression of Alzheimer's disease (AD) is challenging due to lacking sensitivity in currently available measures. AD stages are typically defined based on cognitive cut-offs, but this results in heterogeneous patient groups. More accurate modeling of the continuous progression of the disease would enable more accurate patient prognosis. To address these issues, we propose a new multivariate continuous-time disease progression (MCDP) model. The model is formulated as a nonlinear mixed-effects model that aligns patients based on their predicted disease progression along a continuous latent disease timeline. The model is evaluated using long-term follow-up data from 2152 participants in the Alzheimer's Disease Neuroimaging Initiative. The MCDP model was used to simultaneously model three cognitive scales; the Alzheimer's Disease Assessment Scale-cognitive subscale, the Mini-Mental State Examination, and the Clinical Dementia Rating scale-sum of boxes. Compared with univariate modeling and previously proposed multivariate disease progression models, the MCDP model showed superior ability to predict future patient trajectories. Finally, based on the multivariate disease timeline estimated using the MCDP model, the sensitivity of the individual items of the cognitive scales along the different stages of disease was analyzed. The analysis showed that delayed memory recall items had the highest sensitivity in the early stages of disease, whereas language and attention items were sensitive later in disease. [ABSTRACT FROM AUTHOR]

Tree height–diameter relationship is very important in forest investigation, describing forest structure and estimating carbon storage. Climate change may modify the relationship. However, our understanding of the effects of climate change on the height–diameter allometric relationship is still limited at large scales. In this study, we explored how climate change effects on the relationship varied with tree species and size for larch plantations in northern and northeastern China. Based on the repeated measurement data of 535 plots from the 6th to 8th national forest inventory of China, climate-sensitive tree height–diameter models of larch plantations in north and northeast China were developed using two-level nonlinear mixed effect (NLME) method. The final model was used to analyze the height–diameter relationship of different larch species under RCP2.6, RCP 4.5, and RCP8.5 climate change scenarios from 2010 to 2100. The adjusted coefficient of determination Radj2, mean absolute error (MAE) and root mean squared error (RMSE) of the NLME models for calibration data were 0.92, 0.76 m and 1.06 m, respectively. The inclusion of climate variables mean annual temperature (MAT) and Hargreaves climatic moisture deficit (CMD) with random effects was able to increase Radj2 by 19.5% and reduce the AIC (Akaike’s information criterion), MAE and RMSE by 22.2%, 44.5% and 41.8%, respectively. The climate sensitivity of larch species was ranked as L. gmelinii > the unidentified species group > L. principis > L. kaempferi > L. olgensis under RCP4.5, but L. gmelinii > L. principis > the unidentified species group > L. olgensis > L. kaempferi under RCP2.6 and RCP8.5. Large trees were more sensitive to climate change than small trees.

Nonlinear mixed-effects modeling is one of the most popular tools for analyzing repeated measurement data, particularly for applications in the biomedical fields. Multiple integration and nonlinear optimization are the two major challenges for likelihood-based methods in nonlinear mixed-effects modeling. To solve these problems, approaches based on empirical Bayesian estimates have been proposed by breaking the problem into a nonlinear mixed-effects model with no covariates and a linear regression model without random effect. This approach is time-efficient as it involves no covariates in the nonlinear optimization. However, covariate effects based on empirical Bayesian estimates are underestimated and the bias depends on the extent of shrinkage. Marginal correction method has been proposed to correct the bias caused by shrinkage to some extent. However, the marginal approach appears to be suboptimal when testing covariate effects on multiple model parameters, a situation that is often encountered in real-world data analysis. In addition, the marginal approach cannot correct the inaccuracy in the associated p -values. In this paper, we proposed a simultaneous correction method (nSCEBE), which can handle the situation where covariate analysis is performed on multiple model parameters. Simulation studies and real data analysis showed that nSCEBE is accurate and efficient for both effect-size estimation and p -value calculation compared with the existing methods. Importantly, nSCEBE can be >2000 times faster than the standard mixed-effects models, potentially allowing utilization for high-dimension covariate analysis for longitudinal or repeated measured outcomes. [ABSTRACT FROM AUTHOR]

ABSTRACT Lactation records from cows of the southwestern Paraná state, Brazil, form the dataset of this study. We applied the information-theoretic approach to evaluate the ability of the nonlinear Wood, Brody, Dijkstra, and Gamma functions to fit to these data by employing a two-step technique based on nonlinear mixed-effects models and generalized linear mixed-effects models. Wood's equation was fitted with the combination of a first-order autoregressive correlation structure and a variance function to account for heteroscedasticity. This version was the best choice to mimic lactation records. Some geometric attributes of Wood's model were deduced, mainly the ascending specific rate from parturition to peak milk yield and the descending specific rate as a measure of the lactation persistence of the milk yield at peak production. Breed and parity order of the cows were assumed as fixed effects to obtain a reliable model fitting process. Regardless of breed, first-order parity cows had greater persistency than their older counterparts, and the greater the ascending rate of milk yield from the parturition to the peak, the sharper the decrease in milk yield post-peak; therefore, the rates (absolute values) of ascending and descending phases correlated positively. Nonetheless, the actual estimated values of the descending phase rates are negative. Wood's equation was flexible enough to mimic either concave- and convex-shaped lactation profiles. The correlations between both peak milk yield and random estimates for β with total milk yield per lactation were positive. However, peak milk yield might not be the only variable used for ranking cows; the total milk yield integrates all information of the lactation profile through the estimated parameters of Wood's equation.

Summary: Nonlinear mixed‐effects (NLME) modeling is one of the most powerful tools for analyzing longitudinal data especially under the sparse sampling design. The determinant of the Fisher information matrix is a commonly used global metric of the information that can be provided by the data under a given model. However, in clinical studies, it is also important to measure how much information the data provide for a certain parameter of interest under the assumed model, for example, the clearance in population pharmacokinetic models. This paper proposes a new, easy‐to‐interpret information metric, the "relative information" (RI), which is designed for specific parameters of a model and takes a value between 0% and 100%. We establish the relationship between interindividual variability for a specific parameter and the variance of the associated parameter estimator, demonstrating that, under a "perfect" experiment (eg, infinite samples or/and minimum experimental error), the RI and the variance of the model parameter estimator converge, respectively, to 100% and the ratio of the interindividual variability for that parameter and the number of subjects. Extensive simulation experiments and analyses of three real datasets show that our proposed RI metric can accurately characterize the information for parameters of interest for NLME models. The new information metric can be readily used to facilitate study designs and model diagnosis. [ABSTRACT FROM AUTHOR]

Tree age (AGE) and stocking degree (P) strongly influence tree shape, but their effects have been neglected in most tree profile equations. In addition, data used to build traditional tree profile equations usually do not meet the statistical requirements of independence and identical distribution of observations. Therefore, our main objectives were to present a method to improve taper equations with measurements easily collected in tree inventories (age, stocking degree) and also improve the statistical accuracy of those equations by selecting parameters with a more rigorous way than what is traditionally being done. We evaluated the effects of incorporating age and stocking degree as regressors in tree profile equations selected among 30 candidate foundation equations and parameterized with data from 1858 Larix gmelinii (Rupr.) trees growing in the northern China. We used nonlinear mixed-effects models to minimize statistical problems present when building traditional tree profile equations: lack of independence and identical distribution of observations, random effects related to individual trees. Equations incorporating age and stocking degree significantly improved their accuracy. When the equation parameters were estimated with mixed-effects models containing exponential variance functions and accounting for non-independence of observations from the same tree, diameters at any height along the tree bole were more accurately estimated. We demonstrate a new methodology to build more accurate tree profile equations that could support better economic valorization of timber and improve calculations of carbon flows in forests, not only for natural L. gmelinii forest but for other species growing in dense natural stands around the globe. [ABSTRACT FROM AUTHOR]

Using biomarkers to model disease course effectively and make early prediction is a challenging but critical path to improving diagnostic accuracy and designing preventive trials for neurological disorders. Leveraging the domain knowledge that certain neuroimaging biomarkers may reflect the disease pathology, we propose a model inspired by the neural mass model from cognitive neuroscience to jointly model nonlinear dynamic trajectories of the biomarkers. Under a nonlinear mixed‐effects model framework, we introduce subject‐ and biomarker‐specific random inflection points to characterize the critical time of underlying disease progression as reflected in the biomarkers. A latent liability score is shared across biomarkers to pool information. Our model allows assessing how the underlying disease progression will affect the trajectories of the biomarkers, and, thus, is potentially useful for individual disease management or preventive therapeutics. We propose an EM algorithm for maximum likelihood estimation, where in the E step, a normal approximation is used to facilitate numerical integration. We perform extensive simulation studies and apply the method to analyze data from a large multisite natural history study of Huntington's Disease (HD). The results show that some neuroimaging biomarker inflection points are early signs of the HD onset. Finally, we develop an online tool to provide the individual prediction of the biomarker trajectories given the medical history and baseline measurements. [ABSTRACT FROM AUTHOR]

Nonlinear mixed-effects modeling is a popular approach to describe the temporal trajectory of repeated measurements of clinical endpoints collected over time in clinical trials, to distinguish the within-subject and the between-subject variabilities, and to investigate clinically important risk factors (covariates) that may partly explain the between-subject variability. Due to the complex computing algorithms involved in nonlinear mixed-effects modeling, estimation of covariate effects is often time-consuming and error-prone owing to local convergence. We develop a fast and accurate estimation method based on empirical Bayes estimates from the base mixed-effects model without covariates, and simple regressions outside of the nonlinear mixed-effect modeling framework. Application of the method is illustrated using a pharmacokinetic dataset from an anticoagulation drug for the prevention of major cardiovascular events in patients with acute coronary syndrome. Both the application and extensive simulations demonstrated that the performance of this high-throughput method is comparable to the commonly used maximum likelihood estimation in nonlinear mixed-effects modeling. [ABSTRACT FROM AUTHOR]

• 174 values of radioiodine uptake are summarized to 10 parameters in a few seconds. • A population model is fitted to the radioiodine uptake values with high accuracy. • The fitted model predicts ("guess") unknown observations with high accuracy too. • Using the modeling, two uptake values per patient have same information as three. • This method could be extended to other areas where repeated measurements are made. The application of an individualised dosimetric procedure for radioiodine therapy requires the intensive use of resources in nuclear medicine facilities. In practice, the amount of data taken per patient is too limited to obtain an accurate estimate of the absorbed dose in the thyroid. The individualised absorbed dose estimates can be enhanced using statistical tools for population-based approaches. The aim of this work was to build a population biokinetic model of thyroid uptake and elimination of radioiodine using a nonlinear mixed-effects approach in patients with Graves' disease. Input data for the model development were taken from a dosimetric method based on 123I imaging data. 123I decay-corrected uptake values were estimated at 4, 24, and 96 h post-administration and for 58 patients. The root mean squared error (RMSE) for predicted 123I uptake values by the fitted model was 4%. The root mean squared error of prediction (RMSEP) for out-of-sample 123I uptake values, computed by a leave-one-out cross-validation, was 12%. We calculated 131I activity to administer from out-of-sample predicted 123I uptake values and compared the result with that calculated from observed 123I uptake values. RMSEP values for therapeutic activity revealed that there were measuring points with higher weight than others in the model. The mixed-effects approach can be used to enhance the accuracy of dosimetric calculations in therapies using 131I. Assessing the accuracy of the predictive model enables choosing among different time-sampling schedules of the radioiodine thyroid uptake curve. This methodology can also be applied in other areas of radiation dosimetry. [ABSTRACT FROM AUTHOR]

Key message: Models were developed to predict spatial distribution of specific leaf area (SLA) and horizontal distribution of leaf area for western larch. Cardinal branch direction significantly influenced both SLA and horizontal leaf area distributions.Abstract: Leaf area, specific leaf area (SLA), and their spatial distribution in the crown are important indicators of biological response to changes in growing conditions including light and water availability. Western larch (Larix occidentalis Nutt.) is a deciduous coniferous pioneer species in the U.S. Inland Northwest known for its rapid growth, high-quality wood, and ecological importance. Analysis with nonlinear models revealed that SLA and horizontal leaf area distributions differ between cardinal quadrants of juvenile western larch crowns. SLA was significantly higher in the more illuminated, southern side of the crown, and leaf area peaked closer to the stem in the southwest quadrant. Similar to other conifers, horizontal distributions of foliage in western larch also shifted further outward towards the branch tips with increasing depth in the crown. Models developed for horizontal distribution of leaf area and spatial distribution of specific leaf area for western larch may be extended by future researchers to predict its response to environmental variables or management practices. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

Truncated regression models arise in many applications where it is not possible to observe values of the response variable that are above or below certain thresholds. We propose a Bayesian truncated beta nonlinear mixed-effects model by considering the truncated variable to follow a truncated beta distribution. The mean parameter of the distribution is modeled by a nonlinear function of unknown fixed parameters and covariates and by random effects. The proposed model is suitable for response variables, y, bounded to an interval without the need to consider a transformed variable to apply the well-known beta regression model and its extensions, which are primarily appropriate for responses in the interval. Bayesian estimates and credible intervals are computed based on draws from the posterior distribution of parameters generated using an MCMC procedure. Posterior predictive checks, Bayesian standardized residuals and a Bayesian influence measures are considered for model diagnostics. Model selection is performed using the sum of log-CPO metric and a Bayesian model selection criterion based on Bayesian mixture modeling. Simulated datasets are used for prior sensitivity analysis and to evaluate finite sample properties of Bayesian estimates. The model is applied to a real dataset on soil–water retention. [ABSTRACT FROM AUTHOR]

Variable-top merchantable volume and weight equations are needed to estimate the volume and weight of various portions of loblolly pine tree boles with changing utilization standards and potential biomass energy and carbon credit markets. A new approach was proposed to develop merchantable volume (or weight) equations, based on the similarity and four properties of cumulative relative profiles. The newly derived cumulative equations were fitted with a nonlinear mixed-effects modeling approach and then compared with previously published equations. The new equations provided more accurate predictions of loblolly pine stem outside-bark volume, green weight with bark, and dry weight with or without bark to any desired height in the southern United States. [ABSTRACT FROM AUTHOR]

This paper introduces a class of mixed-effects models for joint modeling of spatially correlated intensity variation and warping variation in two-dimensional (2D) images. Spatially correlated intensity variation and warp variation are modeled as random effects, resulting in a nonlinear mixed-effects model that enables simultaneous estimation of template and model parameters by optimization of the likelihood function. We propose an algorithm for fitting the model which alternates estimation of variance parameters and image registration. This approach avoids the potential estimation bias in the template estimate that arises when treating registration as a preprocessing step. We apply the model to datasets of facial images and 2D brain magnetic resonance images to illustrate the simultaneous estimation and prediction of intensity and warp effects. [ABSTRACT FROM AUTHOR]

Key message: A climate-sensitive aboveground biomass model developed by combining a nonlinear mixed-effects model and dummy variable approach led to higher prediction accuracy of biomass than those without climatic variables for three larch species. Abstract: As native species and being widely distributed in northeastern and northern China, larch forests play a pivotal role in maintaining forest ecosystem functions and mitigation of carbon concentration at the atmosphere. However, the spatial sensitivity of growing and aboveground biomass (AGB) of larch species to climate change is not known. In this study, a climate-sensitive AGB model was developed by combining nonlinear mixed-effects modeling and a dummy variable approach to account for the spatial sensitivity of AGB of three larch species including Dahurian larch ( Larix gmelinii (Rupr.) Kuzen.), Korean larch ( Larix olgensis Henry.) and Prince Rupprecht larch ( Larix principis rupprechtii Mayr.) to climate change in northeastern and northern China. We examined the AGB values of 256 larch trees growing in five secondary climate zones: mid-temperature humid climatic zone, mid-temperature sub-humid climatic zone, mid-temperature semi-arid climatic zone, cool temperature humid climatic zone and warm temperature sub-humid climatic zone in northeastern and northern China. The results showed that the long-term averages of growing season temperature and total growing season precipitation, and the mean temperature and precipitation of wettest quarter, had significant ( α = 0.05) effects on AGB. The prediction accuracy of the developed model was much higher than that of the model at the population average level and its base model. Excessive rain and high mean temperature during the growing season increased the AGB values of larch trees, while abundant precipitation and high mean temperature in the wettest quarter led to the decrease of AGB. The AGB of the larch species from the mid-temperature semi-arid climatic zone had the greatest values, which indicated that climate conditions were more favorable in this zone than in the other four secondary climate zones. It is expected that the findings from this study can be combined with the knowledge of adaptive management to reduce the risks and uncertainties associated with forest management decisions. [ABSTRACT FROM AUTHOR]

An approach based on the nearest neighbors techniques is presented for producing thematic maps of forest cover (forest/non-forest) and total stand volume for the Terai region in southern Nepal. To create the forest cover map, we used a combination of Landsat TM satellite data and visual interpretation data, i.e., a sample grid of visual interpretation plots for which we obtained the land use classification according to the FAO standard. These visual interpretation plots together with the field plots for volume mapping originate from an operative forest inventory project, i.e., the Forest Resource Assessment of Nepal (FRA Nepal) project. The field plots were also used in checking the classification accuracy. MODIS satellite data were used as a reference in a local correction approach conducted for the relative calibration of Landsat TM images. This study applied a non-parametric k-nearest neighbor technique (k-NN) to the forest cover and volume mapping. A tree height prediction approach based on a nonlinear, mixed-effects (NLME) modeling procedure is presented in the Appendix. The MODIS image data performed well as reference data for the calibration approach applied to make the Landsat image mosaic. The agreement between the forest cover map and the field observed values of forest cover was substantial in Western Terai (KHAT 0.745) and strong in Eastern Terai (KHAT 0.825). The forest cover and volume maps that were estimated using the k-NN method and the inventory data from the FRA Nepal project are already appropriate and valuable data for research purposes and for the planning of forthcoming forest inventories. Adaptation of the methods and techniques was carried out using Open Source software tools.

Joint models for a wide class of response variables and longitudinal measurements consist on a mixed-effects model to fit longitudinal trajectories whose random effects enter as covariates in a generalized linear model for the primary response. They provide a useful way to assess association between these two kinds of data, which in clinical studies are often collected jointly on a series of individuals and may help understanding, for instance, the mechanisms of recovery of a certain disease or the efficacy of a given therapy. When a nonlinear mixed-effects model is used to fit the longitudinal trajectories, the existing estimation strategies based on likelihood approximations have been shown to exhibit some computational efficiency problems (De la Cruz et al., 2011). In this article we consider a Bayesian estimation procedure for the joint model with a nonlinear mixed-effects model for the longitudinal data and a generalized linear model for the primary response. The proposed prior structure allows for the implementation of an MCMC sampler. Moreover, we consider that the errors in the longitudinal model may be correlated. We apply our method to the analysis of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. We also conduct a simulation study to assess the importance of modelling correlated errors and quantify the consequences of model misspecification. [ABSTRACT FROM AUTHOR]

On the basis of a multilevel nonlinear mixed‑effects model approach, branch diameter and length growth models were developed for aPinus koraiensisplantation in north‑east China. The models developed were able to better capture the residual variation successfully by partitioning the residual variance into plot‑, tree‑ and branch‑ level variations via random parameter modeling at the three levels. In addition to random effects, various time series correlation structures were evaluated to account for residual autocorrelation, and the AR(1) and ARMA(1,1) structures were selected for the branch diameter and length growth models, respectively. Model validation results using an independent data set confirmed that multilevel mixed models with an appropriate correlation structure produced more accurate and precise branch‑specified diameter and length predictions. Overall, the models were suitable in describing the trends and inherent variability of crown profile and good enough to be included in growth simulation systems forPinus koraiensisplantations. [ABSTRACT FROM AUTHOR]

HIV dynamic models, a set of ordinary differential equations (ODEs), have provided new understanding of the pathogenesis of HIV infection and the treatment effects of antiviral therapies. However, to estimate parameters for ODEs is very challenging due to the complexity of this nonlinear system. In this article, we propose a comprehensive procedure to deal with this issue. In the proposed procedure, a series of cutting-edge statistical methods and techniques are employed, including nonparametric mixed-effects smoothing-based methods for ODE models and stochastic approximation expectation–maximization (EM) approach for mixed-effects ODE models. A simulation study is performed to validate the proposed approach. An application example from a real HIV clinical trial study is used to illustrate the usefulness of the proposed method. [ABSTRACT FROM PUBLISHER]