Mixed-effects Approach Research Articles

Strong-motion duration predictive models from subduction interface earthquakes recorded in the hill zone of the Valley of Mexico

Many studies have found that ground-motion duration has a considerable influence on damage to conventional civil structures. Historically, Mexico City has been severely affected by long-duration distant subduction earthquakes because of the heterogeneous local site conditions underlying the subsoil of the city, which significantly amplify the soil response. Using a reliable database of earthquakes that occurred in the subduction zone of the Pacific Coast of Mexico, recorded in the hill zone of the Valley of Mexico from 1985 to 2020, predictive models are proposed in this study from existing definitions of strong-motion duration. These models were developed using a mixed-effects approach, which is a statistical method that can consider the correlation between data recorded from the same event. Such models have valuable applications in seismic and structural engineering because they can accurately define the dimension and randomness of strong-motion duration.

An Integrative Approach for Improved Assessment of Cardiovascular Safety Data.

Cardiovascular adverse effects in drug development are a major source of compound attrition. Characterization of blood pressure (BP), heart rate (HR), stroke volume (SV), and QT-interval prolongation are therefore necessary in early discovery. It is, however, common practice to analyze these effects independently of each other. High-resolution time courses are collected via telemetric techniques, but only low-resolution data are analyzed and reported. This ignores codependencies among responses (HR, BP, SV, and QT-interval) and separation of system (turnover properties) and drug-specific properties (potencies, efficacies). An analysis of drug exposure-time and high-resolution response-time data of HR and mean arterial blood pressure was performed after acute oral dosing of ivabradine, sildenafil, dofetilide, and pimobendan in Han-Wistar rats. All data were modeled jointly, including different compounds and exposure and response time courses, using a nonlinear mixed-effects approach. Estimated fractional turnover rates [h-1, relative standard error (%RSE) within parentheses] were 9.45 (15), 30.7 (7.8), 3.8 (13), and 0.115 (1.7) for QT, HR, total peripheral resistance, and SV, respectively. Potencies (nM, %RSE within parentheses) were IC 50 = 475 (11), IC 50 = 4.01 (5.4), EC 50 = 50.6 (93), and IC 50 = 47.8 (16), and efficacies (%RSE within parentheses) were I max = 0.944 (1.7), Imax = 1.00 (1.3), E max = 0.195 (9.9), and Imax = 0.745 (4.6) for ivabradine, sildenafil, dofetilide, and pimobendan. Hill parameters were estimated with good precision and below unity, indicating a shallow concentration-response relationship. An equilibrium concentration-biomarker response relationship was predicted and displayed graphically. This analysis demonstrates the utility of a model-based approach integrating data from different studies and compounds for refined preclinical safety margin assessment. SIGNIFICANCE STATEMENT: A model-based approach was proposed utilizing biomarker data on heart rate, blood pressure, and QT-interval. A pharmacodynamic model was developed to improve assessment of high-resolution telemetric cardiovascular safety data driven by different drugs (ivabradine, sildenafil, dofetilide, and pimobondan), wherein system- (turnover rates) and drug-specific parameters (e.g., potencies and efficacies) were sought. The model-predicted equilibrium concentration-biomarker response relationships and was used for safety assessment (predictions of 20% effective concentration, for example) of heart rate, blood pressure, and QT-interval.

1322. Quantifying the Effects of Frequently Prescribed Antimicrobials with Perceived Potential for QT Interval Prolongation during the COVID-19 Era

BackgroundCountless diseases and medications have been implicated in the past as causing prolongation of the QT interval. Their unique role through the means of quantifying the definite magnitude of relative risk they contribute during hospitalization still requires further investigation. The aim of this study was to describe the impact of commonly used anti-infectives on the QT interval in hospitalized patients during the COVID-19 era.MethodsDemographic information, medical history, laboratory data, medication administration history and ECG recording data was collected from the electronic records of adult patients admitted to two urban hospitals. A mixed effects approach with four sub-models for the QT interval comprised of: heart rate, circadian rhythm, gender, and the drug (regressed as the cumulative mg dose administered over time) and disease effects was used. Fixed and random effects with between occasion variability were estimated for the parameters with a Bayesian approach using the STAN software.ResultsData from 2180 patients were used with baseline characteristics shown in Table 1. Observed vs. predicted plots based on the training (Figure 1.A) and validation data set (Figure 1.B) showed excellent fit. The parameters for QTc0, α, gender, and circadian rhythm were identified within the range previously described (Table 2.). Similarly, the model correctly identified the impact of acute or chronic diseases on the QT interval. Model coefficient estimates [mean (95% CI) of 0.010 (0.006, 0.15) and 0.0045 (0.0013, 0.01100) msec/mg cumulative dose, respectively] suggest that patients treated with conventional regimens of fluconazole and levofloxacin are most likely to present with a QT interval increase > 5 msec, the cutoff threshold of regulatory concern.Figure 1. A-B Table 1. Table 2. ConclusionThe model developed accurately identified the impact baseline risk factors and concomitant medications have on the QT interval. When adjusted for these confounding variables, estimates of QT interval prolongation show that treatment with fluconazole and levofloxacin pose a considerable risk; while treatment with azithromycin or hydroxychloroquine is of moderate risk for QT interval prolongation.Disclosures All Authors: No reported disclosures

Predicting Alzheimer’s conversion in mild cognitive impairment patients using longitudinal neuroimaging and clinical markers

Patients with mild cognitive impairment (MCI) have a high risk for conversion to Alzheimer's disease (AD). Early diagnose of AD in MCI subjects could help to slow or halt the disease progression. Selecting a set of relevant markers from multimodal data to predict conversion from MCI to probable AD has become a challenging task. The aim of this paper is to quantify the impact of longitudinal predictive models with single- or multisource data for predicting MCI-to-AD conversion and identifying a very small subset of features that are highly predictive of conversion. We developed predictive models of MCI-to-AD progression that combine magnetic resonance imaging (MRI)-based markers (cortical thickness and volume of subcortical structures) with neuropsychological tests. These models were built with longitudinal data and validated using baseline values. By using a linear mixed effects approach, we modeled the longitudinal trajectories of the markers. A set of longitudinal features potentially discriminating between MCI subjects who convert to dementia and those who remain stable over a period of 3 years was obtained. Classifier were trained using the marginal longitudinal trajectory residues from the selected features. Our best models predicted conversion with 77% accuracy at baseline (AUC = 0.855, 84% sensitivity, 70% specificity). As more visits were available, longitudinal predictive models improved their predictions with 84% accuracy (AUC = 0.912, 83% sensitivity, 84% specificity). The proposed approach was developed, trained and evaluated using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with a total of 2491 visits from 610 subjects.

Population pharmacokinetics of ATR inhibitor berzosertib in phase I studies for different cancer types

PurposeBerzosertib (formerly M6620) is the first-in-class inhibitor of ataxia–telangiectasia and Rad3-related protein, a key component of the DNA damage response, and being developed in combination with chemotherapy for the treatment of patients with advanced cancers. The objectives of this analysis were to characterize the pharmacokinetics (PK) of berzosertib across multiple studies and parts, estimate inter-individual variability, and identify covariates that could explain such variability.MethodsA population PK analysis was performed using the combined dataset from two phase I clinical studies (NCT02157792, EudraCT 2013-005100-34) in patients with advanced cancers receiving an intravenous infusion of berzosertib alone or in combination with chemotherapy. The analysis included data from 240 patients across 11 dose levels (18–480 mg/m2). Plasma concentration data were modeled with a non-linear mixed-effect approach and clinical covariates were evaluated.ResultsPK data were best described by a two-compartment linear model. For a typical patient, the estimated clearance (CL) and intercompartmental CL were 65 L/h and 295 L/h, respectively, with central and peripheral volumes estimated to be 118 L and 1030 L, respectively. Several intrinsic factors were found to influence berzosertib PK, but none were considered clinically meaningful due to a very limited effect. Model simulations indicated that concentrations of berzosertib exceeded p-Chk1 (proximal pharmacodynamic biomarker) IC50 at recommended phase II doses in combination with carboplatin, cisplatin, and gemcitabine.ConclusionsThere was no evidence of a clinically significant PK interaction between berzosertib and evaluated chemo-combinations. The covariate analysis did not highlight any need for dosing adjustments in the population studied to date.Clinical Trial informationNCT02157792, EudraCT 2013-005100-34

Pharmacokinetic variability of phenobarbital: a systematic review of population pharmacokinetic analysis.

Population pharmacokinetics with Bayesian forecasting provides for an effective approach when individualized drug dosing, while phenobarbital is a narrow therapeutic index drug that requires therapeutic drug monitoring. To date, several population pharmacokinetic models have been developed for phenobarbital, these showing a number of significant predictors of phenobarbital clearance and volume of distribution. We have therefore conducted a systematic review to summarize how these predictors affect phenobarbital pharmacokinetics as well as their relationships with pharmacokinetic parameters. A systematic search for studies of phenobarbital population pharmacokinetics that were carried out in humans and that employed a nonlinear mixed-effect approaches was made using the PubMed, Scopus, CINAHL Complete, and ScienceDirect databases. The search covered the period from these databases' inception to March 2020. Eighteen studies were included in this review, all of which used a one-compartment structure. The estimated phenobarbital clearance and volume of distribution ranged from 0.0034 to 0.0104L/h/kg and 0.37 to 1.21L/kg, respectively, with body weight, age, and concomitant antiepileptic drugs being the three most frequently identified predictors of clearance. Most models were validated through the use of an advanced internal approach. Phenobarbital clearance may be predicted from previously developed population pharmacokinetic models and their significant covariate-parameter relationships along with Bayesian forecasting. However, when applying these models in a target population, an external evaluation of these models using the target population is warranted, and it is recommended that future research be conducted to investigate the link between population pharmacokinetics and pharmacodynamics.

Pharmacokinetic and pharmacodynamic analysis of neutropenia following nab-paclitaxel administration in Japanese patients with metastatic solid cancer.

To develop a pharmacokinetic (PK) and pharmacodynamic (PD) model for neutropenia following nab-paclitaxel administration and identify factors associated with drug disposition and changes in neutrophil counts in patients with solid cancer. PK/PD analysis by nonlinear mixed effects approach was performed using the data from 27 patients who participated in phase I studies of nab-paclitaxel monotherapy conducted in Japan. The patients were treated with either weekly (80, 100, or 125mg/m2) or every 3weeks (200, 260, or 300mg/m2). The observed paclitaxel concentrations in whole blood and neutrophil counts in the first cycle were used for PK/PD analysis. Covariate analysis was performed to identify factors affecting PK and the decrease in neutrophil counts. The developed 3-compartment, non-linear PK model described relationships of body surface area with total body clearance and volume of distribution for the peripheral compartment. Covariate factors affecting neutrophil counts were age and serum albumin level. Simulation based on the developed PK/PD model showed a substantial impact of age and serum albumin level on the time course of neutrophil counts after nab-paclitaxel administration. Advanced age was related to greater decrease in neutrophil counts, and serum albumin level, inversely related to change in neutrophil counts. We have developed a novel PK/PD model for nab-paclitaxel in which age and serum albumin level were considered clinically important covariate factors. This model needs to be further validated using a larger patient population.

Fixed dose rivaroxaban can be used in extremes of bodyweight: A population pharmacokinetic analysis

BackgroundEmerging safety and efficacy data for rivaroxaban suggest traditional therapy and rivaroxaban are comparable in the morbidly obese. However, real‐world data that indicate pharmacokinetic (PK) parameters are comparable at the extremes of body size are lacking. The International Society of Thrombosis and Haemostasis Scientific and Standardisation Committee (ISTH SSC) suggests avoiding the use of direct oral anticoagulants (DOACs) in patients weighing >120 kg or with a body mass index >40 kg/m2 and gives no recommendation on the use of DOACs in those <50 kg. ObjectivesTo generate a population PK model to understand the influence of bodyweight on rivaroxaban exposure from clinical practice data. MethodRivaroxaban plasma concentrations and patient characteristics were collated between 2013 and 2018 at King's College Hospital anticoagulation clinic. A population PK model was developed using a nonlinear mixed effects approach and then used to simulate rivaroxaban concentrations at the extremes of bodyweight. ResultsA robust population PK model derived from 913 patients weighing between 39 kg and 172 kg was developed. The model included data from n = 86 >120 kg, n = 74 BMI >40 kg/m2, and n = 30 <50 kg. A one‐compartment model with between‐subject variability on clearance and a proportional error model best described the data. Creatinine clearance calculated by Cockcroft‐Gault, with lean bodyweight as the weight descriptor in this equation, was the most significant covariate influencing rivaroxaban exposure. ConclusionsOur work demonstrates rivaroxaban can be used at extremes of bodyweight provided renal function is satisfactory. We recommend that the ISTH SSC revises the current guidance with respect to rivaroxaban at extremes of body size.

Comparative Population Pharmacokinetics of Darunavir in SARS-CoV-2 Patients vs. HIV Patients: The Role of Interleukin-6.

BackgroundDarunavir is an anti-HIV protease inhibitor repurposed for SARS-CoV-2 treatment.ObjectiveThe aim of this study was to assess the population pharmacokinetics of darunavir in SARS-CoV-2 patients compared with HIV patients.MethodsTwo separate models were created by means of a nonlinear mixed-effect approach. The influence of clinical covariates on each basic model was tested and the association of significant covariates with darunavir parameters was assessed at multivariate regression and classification and regression tree (CART) analyses. Monte Carlo simulation assessed the influence of covariates on the darunavir concentration versus time profile.ResultsA one-compartment model well-described darunavir concentrations in both groups. In SARS-CoV-2 patients (n = 30), interleukin (IL)-6 and body surface area were covariates associated with darunavir oral clearance (CL/F) and volume of distribution (Vd), respectively; no covariates were identified in HIV patients (n = 25). Darunavir CL/F was significantly lower in SARS-CoV-2 patients compared with HIV patients (4.1 vs. 10.3 L/h; p < 0.001). CART analysis found that an IL-6 level of 18 pg/mL may split the SARS-CoV-2 population in patients with low versus high darunavir CL/F (mean ± standard deviation 3.47 ± 1.90 vs. 8.03 ± 3.24 L/h; proportion of reduction in error = 0.46). Median (interquartile range) darunavir CL/F was significantly lower in SARS-CoV-2 patients with IL-6 levels ≥ 18 pg/mL than in SARS-CoV-2 patients with IL-6 levels < 18 pg/mL or HIV patients (2.78 [2.16–4.47] vs. 7.24 [5.88–10.38] vs. 9.75 [8.45–13.79] L/h, respectively; p < 0.0001). Increasing IL-6 levels affected darunavir concentration versus time simulated profiles. We hypothesized that increases in IL-6 levels associated with severe SARS-CoV-2 disease may downregulate the cytochrome P450 (CYP) 3A4-mediated metabolism of darunavir.ConclusionsThis is a proof-of-concept of SARS-CoV-2 disease–drug interactions, and may support the need for optimal dose selection of sensitive CYP3A4 substrates in severe SARS-CoV-2 patients.Electronic supplementary materialThe online version of this article (10.1007/s40262-020-00933-8) contains supplementary material, which is available to authorized users.

Caregiver depression and child behaviour problems: A longitudinal mixed effects approach

AbstractCaregiver mental health has a powerful influence on child well‐being. Both maternal depression and paternal depression predict behaviour problems in young children. However, little is known about the nature of this link over time. The present study leveraged a large sample of at‐risk families to examine the impact of maternal and paternal depression on trajectories of child behaviour from age 3 to 15. Data came from the At‐Home substudy of the Fragile Families and Child Well‐Being Study, which observed a cohort of children born 1998–2000 in 20 large American cities (N = 1,090). Multiple imputation by chained equations addressed missing data to maximize available information without biasing models. Linear mixed effects models tested the association between caregiver depression and child behaviour over 12 years; interaction models tested whether this association changed as children transitioned into adolescence. Results showed caregiver depression is related with increases in both child internalizing and externalizing problems, and these relationships strengthened over time. Findings inform risk trajectories of distinct behavioural problems, shedding light on the development of child psychopathology and highlighting opportunities for intervention.

Incorporating stand density effects in modeling the taper of red pine plantations

Taper models are used to estimate the diameter at any height along the bole of a tree. Individual tree volume can then be calculated based on these diameters and corresponding heights. As tree diameters are affected by stand density, inside- and outside-bark taper models that incorporate stand density information were developed for trees in red pine (Pinus resinosa Aiton) plantations. Data used in this study came from stem analysis on 150 red pine trees sampled from 30 even-aged, monospecific plantations across Ontario, Canada. A nonlinear mixed-effects approach was applied in fitting these taper models. Several forms of stand density were evaluated for both inside- and outside-bark diameters. A combination of stand densities expressed as [Formula: see text] (BA, basal area; TPH, trees per hectare) explained the most variation in stem taper of trees grown in red pine plantations in Ontario. This variable was highly significant in the regression and improved the predictive accuracies of both inside- and outside-bark taper models. The taper models presented here are dimensionally compatible. Therefore, these models are applicable for data using any system of units without adjusting parameter values.

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach.

Here, we developed an individual-tree model of 5-year basal area increments based on a dataset including 21898 Picea asperata trees from 779 sample plots located in Xinjiang Province, northwest China. To prevent high correlations among observations from the same sampling unit, we developed the model using a linear mixed-effects approach with random plot effect to account for stochastic variability. Various tree- and stand-level variables, such as indices for tree size, competition, and site condition, were included as fixed effects to explain the residual variability. In addition, heteroscedasticity and autocorrelation were described by introducing variance functions and autocorrelation structures. The optimal linear mixed-effects model was determined by several fit statistics: Akaike's information criterion, Bayesian information criterion, logarithm likelihood, and a likelihood ratio test. The results indicated that significant variables of individual-tree basal area increment were the inverse transformation of diameter at breast height, the basal area of trees larger than the subject tree, the number of trees per hectare, and elevation. Furthermore, errors in variance structure were most successfully modeled by the exponential function, and the autocorrelation was significantly corrected by first-order autoregressive structure (AR(1)). The performance of the linear mixed-effects model was significantly improved relative to the model using ordinary least squares regression.

Assessing the performance of different devices in railway wheelset inspection

• Inspection data to compare measurement devices in railway wheelset maintenance. • Mixed-effects approach superior for assessing agreement between inspection devices. • Quantification of different sources of variability through Linear Mixed Model (LMM). • Impact of different parameters in the wheelset maintenance data acquisition. • No clear differences due to measurement errors observed in the flange thickness. Data acquisition plays a significant role in determining and optimizing maintenance actions for railway wheelsets, as small inaccuracies translate into decreasing useful life, unnecessary downtime and costs. In this study, a comparison between data acquired from three different inspection devices: i) manual (gauge device), ii) laser and iii) under-floor wheel lathe; is presented for three wheelset parameters: flange thickness, flange height, and flange slope. Using condition data to assess the agreement of measurements by contrasting several devices in a real-world case study is challenging, as many of the commonly adopted assumptions in controlled experiments are not met. Therefore, a linear mixed model (LMM) approach is proposed to enable a comparison of performance under those limitations. Findings support the use of LMM and showing its ability to capture and account for the differences among the various groups, highlighting statistical significant performances of the inspection devices.

Population pharmacokinetic/pharmacodynamic modelling of nifekalant in healthy Chinese volunteers

Nifekalant is a class III antiarrhythmic drug, and its major adverse effect is prolongation of the QT interval. This study analysed data generated from a pharmacokinetic (PK) study to develop a population PK/pharmacodynamics (PD) model for describing the relationship between plasma concentrations and prolongation of the QT interval over time following intravenous administration of nifekalant. This open-labelled, phase I clinical study comprised two dose level groups of eight healthy Chinese volunteers. Concentrations of nifekalant in plasma samples collected at set time-points were determined using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. A PK/PD model was constructed using a non-linear mixed-effects approach (Phoenix NLME 8.1). Furthermore, demographic covariates of the model were investigated and a concentration factor (ConcƟ) was introduced as the only covariate which improved the performance of the model. The final population PK model exhibited one-order elimination with two-compartment distribution and adequately described nifekalant plasma concentrations over time. The QT interval prolongation was best described by an indirect effect model with an inhibition build-up effect, representing the relationship between plasma concentrations and effect. The final population PK/PD model may facilitate more accurate predictions of the drug profile in clinical settings in the future.

Dynamic growth models for Caragana korshinskii shrub biomass in China

The Caragana korshinskii shrub is a widely distributed plant found in arid regions and plays an important role in ecological environment protection. Accurate estimations of shrub biomass are particularly important for natural resource management decision making. 114 individual C. korshinskii shrubs from three regions were collected in northwest China in this study. With regions as fixed (dummy variables) and random effects, the nonlinear least square (NLS) regression approach, nonlinear fixed effects (NLFE) approach and nonlinear mixed effects (NLME) approach were developed to predict dynamic growth of total, aboveground, stem, foliage, and root biomass values of C. korshinskii shrub based on logistic function. Results revealed that both NLFE and NLME models performed better than NLS, which indicated that regions were important factors influenced shrub biomass dynamic growth. Additionally, NLME models had a smaller Bayesian information criterion (BIC) than NLFE models. For NLME models, the random effects of regions mainly influenced the growth rate and asymptotic value of the dynamic growth curve, and there was no significant influence on the values associated with the curve shape. Moreover, the modified NLME models with heteroscedasticity exhibited extremely significant differences (p < 0.0001) when compared to NLME models by the likelihood ratio (LR) test. The NLME models were proved to be an efficient approach for considering the random effects on shrub biomass dynamic growth and accounted for the heteroscedasticity of shrub biomass data.

Demographic and Socioeconomic Predictors of Behavioral Trajectories from Age 3 to 15: A Longitudinal Mixed Effects Approach

Socioeconomically disadvantaged children face elevated risk for behavior problems. Greater understanding of the relationships between a range of socioeconomic factors and behavioral trajectories across childhood is needed to improve prevention efforts. The present study leveraged a large longitudinal survey to examine how gender, race, household socioeconomic status, and home environment influence behavior from early childhood to adolescence. Multivariate imputation by chained equations maximized available data across 12 years. Linear mixed effects models compared behavioral trajectories by gender, as well as the longitudinal effects of socioeconomic status on internalizing and externalizing behavior problems accounting for within- and between-child differences from age 3 to 15. Results indicated declines in both internalizing and externalizing scores over time. On average, boys displayed higher initial scores, but more rapid declines; by age 15, boys scored substantially lower than girls on internalizing problems, while externalizing scores were similar for both genders. Household income (βInternalizing = −0.02; βExternalizing = −0.01) and higher quality interior home environment (βInternalizing = −0.43; βExternalizing = −0.60) both protected against behavioral problems while perceived material hardship and exterior home environment had no effects. Gender moderated the effects of race and the interior environment on externalizing problems. Findings suggest unique developmental trajectories by gender whereby girls display more persistent internalizing problems and African American boys face highest risk for externalizing problems. Children in socioeconomically disadvantaged families face elevated risk for behavior problems, but the home environment remains an opportunity for prevention.

Macroinvertebrate metrics responses to morphological alteration in Italian rivers

The responses of river macroinvertebrates to hydromorphological alteration are often considered weak or unclear. It is therefore crucial to verify if and how existing invertebrate-based approaches can reveal the effects of hydromorphological modification. We analyzed the responses of benthic metrics to morphological impairment, with emphasis on the STAR_ICM index, legally required for macroinvertebrate-based ecological status assessment in Italy. A Principal Component Analysis (PCA) was run to condense information on morphological impairment. The major gradient (Component 1) expressed a combination of bank and channel modifications opposed to tree-related features indicating the presence of comparatively unmodified habitats. Jointly, habitat descriptors including Habitat Modification Score (HMS) derived from the application of a habitat survey method were calculated. Spearman rank correlations between biological metrics and morphological impairment indicators (PCA scores and HMS) were significant. A linear mixed-effects regression approach was applied to relate HMS and STAR_ICMi across a wide geographical context. HMS explained > 60% of STAR_ICMi variability, in the absence of apparent water pollution. Results demonstrated that morphological information resumed with habitat survey methods is meaningful for the biological community and that HMS can support the interpretation of ecological status across rivers’ types and in different environmental settings.

Modeling Height–Diameter Relationship for Poplar Plantations Using Combined-Optimization Multiple Hidden Layer Back Propagation Neural Network

Relationship of total height and diameter at breast height (hereafter diameter) of the trees is generally nonlinear, and therefore has complex characteristics, which can be accurately described by the height-diameter model developed using the back propagation (BP) neural network approach. The multiple hidden layered-BP neural network has several hidden layers and neurons, and is therefore considered more appropriate modeling approach compared to the single hidden layered-BP neural network approach. However, the former approach is not widely applied for tree height prediction due to absence of the effective optimization method, but it can be done using the BP neural network modeling approach. The poplar (Populus spp. L.) plantation data acquired from Guangdong province of China were used for evaluating the BP neural network modeling approach and compared its results with those obtained from the traditional regression modeling and mixed-effects modeling approaches. We determined the best BP neural network structure with two hidden layers and five neurons in each layer, and logistic sigmoid transfer functions. Relative to the Mitscherlich height-diameter model that had the highest fitting precision among the six traditional height-diameter models evaluated, coefficient of determination (R2) of the neural network height-diameter model increased by 10.3%, root mean squares error (RMSE) and mean absolute error (MAE) decreased by 12% and 13.5%, respectively. The BP neural network height-diameter model also appeared more accurate than the mixed-effects height-diameter model. Our study proposes the method of determining the optimal numbers of hidden layers, neurons of each layer, and transfer functions in the BP neural network structure. This method can be useful for other modeling studies of similar or different types, such as tree crown modeling, height, and diameter increments modeling, and so on.

Disease pharmacokinetic-pharmacodynamic modelling in acute intermittent porphyria to support the development of mRNA-based therapies.

Acute intermittent porphyria (AIP) results from haplo-insufficiency of the porphobilinogen deaminase (PBGD) gene encoding the third enzyme in the haem biosynthesis pathway. As liver is the main organ of pathology for AIP, emerging therapies that restore enzyme hepatic levels are appealing. The objective of this work was to develop a mechanistic-based computational framework to describe the effects of novel PBGD mRNA therapy on the accumulation of neurotoxic haem precursors in small and large animal models. Liver PBGD activity data and/or 24-hr urinary haem precursors were obtained from genetic AIP mice and wild-type mice, rats, rabbits, and macaques. To mimic acute attacks, porphyrogenic drugs were administered over one or multiple challenges, and animals were used as controls or treated with different PBGD mRNA products. Available experimental data were sequentially used to build and validate a semi-mechanistic mathematical model using non-linear mixed-effects approach. The developed framework accounts for the different biological processes involved (i.e., mRNA sequence, release from lipid nanoparticle and degradation, mRNA translation, increased PBGD activity in liver, and haem precursor metabolism) in a simplified mechanistic fashion. The model, validated using external data, shows robustness in the extrapolation of PBGD activity data in rat, rabbit, and non-human primate species. This quantitative framework provides a valuable tool to compare PBGD mRNA drug products during early preclinical stages, optimize the amount of experimental data required, and project results to humans, thus supporting drug development and clinical dose and dosing regimen selection.

Implications of land use transitions and climate change on local flooding in urban areas: An assessment of 42 Indian cities

Urban development induced land transitions affect urban hydrology, resulting in increased flooding risks. Climate change-related precipitation changes are an added complexity to the flood risks of cities. This study examines the role of land use changes in determining the occurrence of urban flooding events across 42 Indian cities under current and future climate change scenarios. Landsat images for 1990, 2000, 2010, and 2017 have been processed using a hybrid classification technique to determine the land use shares for all cities. A typical event-count study using newspaper archives has been conducted to create a flooding event database. A multilevel model employing logistic mixed-effects approach was used. Future projections of the occurrence of flooding events for nine models under three climate change-related Representative Concentration Pathways (RCPs)—2.6, 4.5, and 8.5—and three urban development scenarios have been carried out. The results suggest that cities should preserve the land uses that act as a sponge—the green, open and blue spaces. As these spaces decrease, the projected flooding events increase. Under the RCP 2.6 scenario, the number of flooding events is significantly lower (95 % confidence) than under RCPs 4.5 and 8.5. The expected flooding occurrences between RCP4.5 and RCP8.5 are not significantly different (95 % confidence) for many scenarios, suggesting that Indian cities should aim for a world temperature increase of below 2 °C, or devastating consequences are imminent. This study highlights the need for Indian cities to undertake integrated spatial planning measures for a resilient, sustainable urban future.