Search

Inter‐rater reliability (IRR) is one of the commonly used tools for assessing the quality of ratings from multiple raters. However, applicant selection procedures based on ratings from multiple raters usually result in a binary outcome; the applicant is either selected or not. This final outcome is not considered in IRR, which instead focuses on the ratings of the individual subjects or objects. We outline the connection between the ratings' measurement model (used for IRR) and a binary classification framework. We develop a simple way of approximating the probability of correctly selecting the best applicants which allows us to compute error probabilities of the selection procedure (i.e., false positive and false negative rate) or their lower bounds. We draw connections between the IRR and the binary classification metrics, showing that binary classification metrics depend solely on the IRR coefficient and proportion of selected applicants. We assess the performance of the approximation in a simulation study and apply it in an example comparing the reliability of multiple grant peer review selection procedures. We also discuss other possible uses of the explored connections in other contexts, such as educational testing, psychological assessment, and health‐related measurement, and implement the computations in the R package IRR2FPR. [ABSTRACT FROM AUTHOR]

The relationship between inward investments and local firms' productivity is contingent on several contextual conditions that collectively define the ability of firms and regions to recognise, assimilate and commercially apply external knowledge. Yet the empirical literature has been unable to account efficiently for such multidimensional sources of heterogeneous externalities. We introduce a novel two-stage empirical methodology that allows accounting for a wide range of moderating circumstances. Relying on a sample of 11,000 UK firms over the period 2012–2018, we show that while the nature of places affects the potential externalities from multinational enterprises (MNEs), what matters more are firm-level characteristics. This has important implications for regional policy, particularly in understanding the drivers of inequality, both within and across regions. [ABSTRACT FROM AUTHOR]

Face composites from eyewitnesses' memories are a valuable resource in tackling crime. Many studies have focused on identifying the best system to produce a nameable composite. In this article, it is described that how many of these studies do not provide reliable conclusions because they fail to treat the faces constructed as being a random factor and so make the stimulus-as-a-fixed-effect fallacy. Simulations are reported in which the statistical methodologies typically employed in these studies are performed on random data generated by a null effect. The first simulation shows that the typical analysis of variance (ANOVA) analysis in this field produces a significant effect (i.e., Type 1 error) 20% of the time. A further simulation shows that using generalized estimating equations (GEE) analysis (recently employed in this type of research) does not resolve the problem. Recommendations are made for the analysis of face-composite experiments to best evaluate and hence improve the quality of the face composites made by eyewitnesses. General Audience Summary: Eyewitness memories are never perfect, so reconstructing a person's face from memory is never entirely accurate. The goal of face-composite research is to identify the best techniques and technologies to help eyewitnesses produce an image that is most likely to be recognized by someone familiar with the target. This field advances by comparing different methods of face-composite construction to determine which performs best. The present article reviews research that compares these methods and reveals that the variability between the constructed images is often not considered. Data simulations show that typical methods of analysis can lead to incorrect conclusions. Alternative data analysis methods are proposed to accelerate the discovery of better ways to support witnesses in face-composite construction. [ABSTRACT FROM AUTHOR]

Agroforestry is gaining interest due to its potential in enhancing climate resilience and sustainability of farming systems. In this meta-analysis, the crop yield in agroforestry system compared to the control (sole crop) from thirty-six experimental field trials in Mediterranean countries was assessed. The response variable Wlog(RR) (i.e., the weighted natural logarithm of the response ratio) was analyzed by the 95% confidence intervals of mean and by fitting eight linear mixed models. Fixed effects, namely the tree cover (low, medium, high), the tree species (ash tree, chestnut, cork oak, holm oak, olive, poplar, walnut), and the crop species (alfalfa, barley, durum wheat, faba bean, forage, oat, pasture, pea, winter wheat) were significant (P = 0.030, P = 0.017, and P = 0.014, respectively), while the system type (alley cropping, silvo-arable, silvo-pastoral) was not. Among management practices (variety, pruning, fertilization, irrigation, crop age classes, imposed warming and drought, harvest time), only the fertilization significantly improved the response variable (P = 0.006), while the interaction of pruning × crop species was marginally significant (P = 0.065). Relatively large study heterogeneity was observed (Q = 72.6, I2 = 72%), which is quite common for agronomic meta-analysis. On the contrary, publication bias based on funnel plots and the Trim and Fill method suggested symmetrical distribution of studies. The sensitivity analysis for significant models identified room for improvements. Overall, we observed a negative effect of trees on crop yield that could be ascribed to the competition for light. Nonetheless, facilitation could be expected under extreme climate events, provided that agricultural practices will maximize synergies among tree cover, tree species, crop species, and management. Future works are encouraged to focus on the overall benefit agroforestry can provide at the field and landscape level, along with long-term monitoring to assess the whole lifespan of these systems and other companion planting options and designs in the Mediterranean region. [ABSTRACT FROM AUTHOR]

Background The relationship of serial NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) measurements with changes in cardiac features and outcomes in heart failure (HF) remains incompletely understood. We determined whether common clinical covariates impact these relationships. Methods and Results In 2 nationwide observational populations with HF, the relationship of serial NT‐proBNP measurements with serial echocardiographic parameters and outcomes was analyzed, further stratified by HF with reduced versus preserved left ventricular ejection fraction, inpatient versus outpatient enrollment, age, obesity, chronic kidney disease, atrial fibrillation, and attainment of ≥50% guideline‐recommended doses of renin‐angiotensin system inhibitors and β‐blockers. Among 1911 patients (mean±SD age, 65.1±13.4 years; 26.6% women; 62% inpatient and 38% outpatient), NT‐proBNP declined overall, with more rapid declines among inpatients, those with obesity, those with atrial fibrillation, and those attaining ≥50% guideline‐recommended doses. Each doubling of NT‐proBNP was associated with increases in left ventricular volume (by 6.1 mL), E/e′ (transmitral to mitral annular early diastolic velocity ratio) (by 1.4 points), left atrial volume (by 3.6 mL), and reduced left ventricular ejection fraction (by −2.1%). The effect sizes of these associations were lower among patients with HF with preserved ejection fraction, atrial fibrillation, or advanced age (Pinteraction

Accurate estimation of total aboveground biomass (TAGB) is an important challenge in evaluating and monitoring tree biomass. Thus, developing species-specific allometric models is essential. This study aimed to predict tree-level TAGB for Pinus brutia , Pinus pinea , Cupressus sempervirens , and the species-independent case using the most accurate allometric models, biomass conversion and expansion factor (BCEF), and mixed effect models in Golestan Province, Iran. The mean BCEFs for three species were 0.46, 0.47, and 0.86, respectively, and there was no significant difference (p >0.05) between TAGB predictions based on BCEF estimates for this study and observations of TAGB. The results revealed that compared with relative root mean square error (RMSE%) for the Intergovernmental Panel on Climate Change (IPCC) report–based BCEFs, the RMSE% for BCEFs estimated for this study were reduced by 46.91%. The results showed that a diameter at breast height (DBH), height (H), and wood density (ρ)-based model were the most accurate predictors for P. brutia (R 2 =0.98, RMSE%=14.11), whereas the DBH-based model and the DBH and H-based model were most accurate for P. pinea (R 2 =0.99, RMSE%=9.04) and C. sempervirens (R 2 =0.96, RMSE%=17.77), respectively. Compared to the allometric models, mixed-effect models using DBH, H, and ρ improved TAGB prediction for the species-independent case (3% increase in R 2 and 6.81% decrease in RMSE%), but not for models for P. brutia , P. pinea , and C. sempervirens. Study Implications: Accurate prediction of total tree-level aboveground biomass (TAGB) requires the most accurate allometric models plus accurate biomass conversion and expansion factors (BCEFs). The relevance of this study is that few allometric models have been developed to predict tree-level TAGB for Pinus brutia , Pinus pinea , and Cupressus sempervirens. We developed allometric models and estimated BCEFs for predicting TAGB for P. brutia , P. pinea , and C. sempervirens in Golestan Province, Iran. We provide accurate allometric models and BCEFs based on available field data. Also, we provide tools to help forest managers predict TAGB. [ABSTRACT FROM AUTHOR]

Inter-rater reliability (IRR), which is a prerequisite of high-quality ratings and assessments, may be affected by contextual variables, such as the rater's or ratee's gender, major, or experience. Identification of such heterogeneity sources in IRR is important for the implementation of policies with the potential to decrease measurement error and to increase IRR by focusing on the most relevant subgroups. In this study, we propose a flexible approach for assessing IRR in cases of heterogeneity due to covariates by directly modeling differences in variance components. We use Bayes factors (BFs) to select the best performing model, and we suggest using Bayesian model averaging as an alternative approach for obtaining IRR and variance component estimates, allowing us to account for model uncertainty. We use inclusion BFs considering the whole model space to provide evidence for or against differences in variance components due to covariates. The proposed method is compared with other Bayesian and frequentist approaches in a simulation study, and we demonstrate its superiority in some situations. Finally, we provide real data examples from grant proposal peer review, demonstrating the usefulness of this method and its flexibility in the generalization of more complex designs. [ABSTRACT FROM AUTHOR]

Alpine streams face rapid hydrological changes due to the effects of global warming, glacier melting, and increased water uses such as hydropower production. Defining environmental flows (e‐flow) is crucial to mitigate the ecological impacts of flow alterations. Among e‐flow assessment methods, hydraulic habitat models predict changes in habitat suitability for aquatic species under different flow scenarios. They couple hydraulic models of stream reaches with biological models relating the abundance of taxa to microhabitat hydraulics. However, there is currently no suitable biological models for alpine, often fishless streams. In this study, we develop biological models for dominant macroinvertebrate taxa in alpine streams and compare their responses to hydraulics with those published in lowland streams. Using data collected in 150 microhabitats along a gradient of shear stress within five alpine streams, we performed generalized linear mixed models relating macroinvertebrate abundance to microhabitat hydraulics (shear stress, flow velocity, Froude number and water depth). We developed biological models for 41 taxa, and observed significant microhabitat selection for shear stress (18 taxa), velocity (20), Froude number (21), and depth (11). Most of them presented consistent responses across studied alpine streams, with shear stress and velocity as the main drivers. For common taxa, shapes of macroinvertebrate responses to hydraulics were comparable with those observed in lowland streams. Nevertheless, taxa preferred slightly lower shear stress in alpine streams compared to lowland streams, probably due to high‐fine sediment and oxygen concentrations, especially for taxa feeding on autochthonous organic matter. Many (23%) abundant taxa are rheophilic in alpine streams, thereby threatened by flow reduction, including the glacial stream specialists Diamesinae and Rhithrogena delphinensis, which will be also affected by glacier retreat. Combined with hydraulic models, our biological models will facilitate more robust e‐flow assessments, thereby reducing the impacts of flow alterations on alpine aquatic ecosystems. [ABSTRACT FROM AUTHOR]

We extend Jasso and colleagues’ (Jasso, Shelly, Webster 2019) research into expressiveness and impartiality of justice evaluations, both methodologically and substantively. Substantively, using the concepts developed within the distributive justice framework, we bridge the justice evaluation function with theories of reward expectations and social identity to explain variation in expressiveness across situations. We hypothesize that such variation is systematically linked to the degree of (dis)similarity between the evaluator and the person being evaluated. Methodologically, we propose a novel analytical strategy to estimate expressiveness and to test hypotheses about its variability based on a series of mixed-effects models with random slopes and random intercepts. To verify our hypotheses, we use data from a recent vignette survey carried out in Poland on a nationwide sample. Our results are more consistent with reward expectation theory than with social identity theory.

Despite the critical role of seed dispersal for tree colonization during forest succession in wet tropical forests, successional trajectories of seed dispersal modes are poorly documented at the community level. Overall successional trends in wet tropical forests indicate that, over time, animal-dispersed and large-seeded species increase in relative abundance in woody vegetation, whereas wind-dispersed and small-seeded species decline. Increased abundance of animal-dispersed trees during succession may be attributed to higher rates of seed deposition by animals and higher survival rates of animal-dispersed species with larger seeds (diaspores) compared to wind-dispersed species. We compiled categorical information on seed size (maximum seed length) and dispersal mode (anemochory, autochory, or zoochory) for 240 canopy tree species in six naturally regenerating forests (11–45 years since the abandonment of pasture) and two old-growth forests in lowland wet forests in northeastern Costa Rica. We evaluated dispersal mode and seed size among tree species classified as second growth and old growth specialists, and generalists based on relative abundance data in these plots. Further, we compared long-term trajectories in the dispersal and seed-size profile for 240 species of canopy tree species for seedlings, saplings, and trees ≥5 cm dbh using annual vegetation survey data from 1997 to 2013. Seed size was significantly associated with dispersal mode, which was driven by anemochorous species with intermediate seeds 6 mm to 15 mm and autochorous seeds with large seeds >15 mm. Dispersal modes of canopy tree species exhibited clear directional trajectories, with decreasing relative abundance of anemochory and increasing relative abundance of zoochory. Zoochorous seedlings showed higher survival rates than anemochorous seedlings. Species with seeds ≤ 6 mm decreased in relative abundance through succession, while species with seeds >15 mm increased within seedling and sapling communities. Seedlings with seeds >15 mm showed higher survival rates than smaller seeds in second-growth forests. The study further demonstrates the importance of seed traits, such as dispersal mode and seed size, for community assembly during forest regrowth, and the importance of frugivorous animals in this process. The abundance of animal-dispersed tree species in different size classes can be a useful indicator of the recovery of biodiversity and species interactions during forest succession in wet tropical regions.

Postdivorce cohabitation has become increasingly common, but research on the influence of cohabitation on the marital climate of remarriages is limited. Research on first-order marriages suggests that the length (or duration) of the relationship may account for some of the influence of cohabitation. However, there remains a need to understand the influence of cohabitation on the unique experiences of remarriages. Using data from 1,889 newly remarried individuals, we fit mixed-effect models to test the effect of cohabitation and relationship duration on the marital climate indicators of marital quality, marital instability, and remarital problems. Relationship duration, but not cohabitation, was negatively associated with marital quality. Cohabitation, but not relationship duration, was positively associated with marital instability and negatively associated with remarital problems. Results indicate that cohabitation may both benefit and harm remarital climates. This may explain, in part, the inconsistent findings in the literature and highlights the importance of studying remarriages as a unique population. [ABSTRACT FROM AUTHOR]

The objectives of this study were to develop site index and variable-density yield models for Korean red pine (Pinus densiflora Siebold & Zucc.), Korean white pine (P. koraiensis S. & Z.), and Japanese larch (Larix kaempferi (Lamb.) Carrière) in Korea. The data were collected between 2012 and 2021 from repeatedly measured empirical plots in each target stand in the North Central region of Korea: Gangwon and North Gyeongsang provinces. To develop the site index for each species, a dominant height growth model by species was developed using the Chapman–Richards function. The site index was computed with a base age of 40 years and used as an independent variable to predict the stand volume. To develop the variable-density yield models, three stand density variables, the stand age, and the site index were applied. The stand density variables used were the stand basal area, the number of trees per hectare, and the relative density. All the models were successfully developed with significant parameters and reasonable fit statistics, and the residuals analyzed presented unbiased scatter plots. Yield models with the stand basal area, or the number of trees, can be used to predict the stand volume. The yield model with relative density was flexible to apply across the stand age because the input of the absolute stand density was not required. Model simulation and comparisons with other studies also supported the applicability of the models developed in this study. The models were found to be highly applicable for predicting and simulating these targeted stands, particularly in Korea. [ABSTRACT FROM AUTHOR]

Research in psychology is experiencing a rapid increase in the availability of intensive longitudinal data. To use such data for predicting feelings, beliefs, and behavior, recent methodological work suggested combinations of the longitudinal mixed-effect model with Lasso regression or with regression trees. The present article adds to this literature by suggesting an extension of these models that—in addition to a random effect for the mean level—also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After introducing the extended mixed-effect location scale (E-MELS), the extended mixed-effect location-scale Lasso model (Lasso E-MELS), and the extended mixed-effect location-scale tree model (E-MELS trees), we show how its parameters can be estimated using a marginal maximum likelihood approach. Using real and simulated example data, we illustrate how to use E-MELS, Lasso E-MELS, and E-MELS trees for building prediction models to forecast individuals' daily nervousness. The article is accompanied by an R package (called mels) and functions that support users in the application of the suggested models. [ABSTRACT FROM AUTHOR]

Abstract Background Attention deficit hyperactivity disorder (ADHD) is one of the most common childhood mental health disorders. Stimulant drugs as the most commonly used treatment and first-line therapy for ADHD have side effects. One of the newest approaches to select the best choices and optimize dosages of medications is personalized medicine. Methods This historical cohort study was carried out on the data taken from the period of 2008 to 2015. Eligible subjects were included in the study randomly. We used mixed-effects logistic regression models to personalize the dosage of Methylphenidate (MPH) in ADHD. The patients’ heterogeneity was considered using subject-specific random effects, which are treated as the realizations of a stochastic process. To recommend a personalized dosage for a new patient, a two-step procedure was proposed. In the first step, we obtained estimates for population parameters. In the second step, the dosage of the drug for a new patient was updated at each follow-up. Results Of the 221 children enrolled in the study, 169 (76.5%) were male and 52 (23.5%) were females. The overall mean age at the beginning of the study is 82.5 (± 26.5) months. In multivariable mixed logit model, three variables (severity of ADHD, time duration receiving MPH, and dosage of MPH) had a significant relationship with improvement. Based on this model the personalized dosage of MPH was obtained. Conclusions To determine the dosage of MPH for a new patient, the more the severity of baseline is, the more of an initial dose is required. To recommend the dose in the next times, first, the estimation of random coefficient should be updated. The optimum dose increased when the severity of ADHD increased. Also, the results show that the optimum dose of MPH as one proceeds through the period of treatment will decreased.

This study investigates gender gaps in digital skills among youth (15-24 years old) in 32 low- and middle-income economies using data from UNICEF-supported and internationally comparable Multiple Indicator Cluster Surveys (MICS). Utilizing a household fixed effects approach, we aim to isolate gender-based disparities from household-level variations. The intra-household analysis reveals significant inequalities, with biases against young women in possessing digital skills, including the very basic ones. Supplementary analysis using a mixed-effects model, which accounts separately for within- and between-household variation, highlights that wealthier households exhibit larger gender gaps in digital skills, disadvantaging young women primarily due to a floor effect in the poorest households. The paper concludes with policy implications aimed at reducing gender gaps in digital skills., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The issue of whether firm productivity is affected by agglomeration externalities is a longstanding area of research. However, the appropriate geographical level to better detect the effects of agglomeration economies and at which level these externalities work is still unclear. Using detailed firm‐level longitudinal data on 4927 manufacturing firms in the United Kingdom over the period 2008–2016, we investigate the relation between the microgeography of external agglomeration economies and firm productivity. We compare different geographical levels: city‐wide and narrowly defined neighborhoods around a firm. Results from a multilevel (mixed‐effect) model show that urbanization externalities play a role at a higher level of geographical aggregation, such as the city, whereas localization externalities operate at a finer level, within the city and in a closer neighborhood to the firm. Failing to control for more granular levels of geography results in confounding the two types of externalities. We also provide novel evidence that these externalities vary across firm (such as age, size, and productivity) and location (such as population density) characteristics. [ABSTRACT FROM AUTHOR]

Aim: The abundant centre hypothesis (ACH) predicts a negative relationship between species abundance and the distance to the geographical range centre. Since its formulation, empirical tests of the ACH have involved different settings (e.g. the distance to the ecological niche or to the geographical range centre), but studies found contrasting support for this hypothesis. Here, we evaluate whether these discrepancies might stem from differences regarding the context in which the ACH is tested (geographical or environmental), how distances are measured, how species envelopes are delineated, how the relationship is evaluated and which data are used. Location: The Americas. Time period: 1800–2017. Major taxa studied: Mammals, birds, fish, and tree seedlings. Methods: Using published abundance data for 801 species, together with species range maps, we tested the ACH using three distance metrics in both environmental and geographical spaces with range and niche envelopes delineated using two different algorithms, totalling 12 different settings. We then evaluated the distance–abundance relationship using correlation coefficients (traditional approach) and mixed‐effect models to reduce the effect of sampling noise on parameter estimates. Results: Similar to previous studies, correlation coefficients indicated an absence of effect of distance on abundance for all taxonomic groups and settings. In contrast, mixed‐effect models highlighted relationships of various strengths and shapes, with a tendency for more theoretically supported settings to provide stronger support for the ACH. The relationships were however not consistent across taxonomic groups and settings, and were sometimes even opposite to ACH expectations. Main conclusions: We found mixed and inconclusive results regarding the ACH. These results corroborate recent findings, and suggest either that our ability to predict abundances from the location of populations within geographical or environmental spaces is low, or that the data used here have a poor signal‐to‐noise‐ratio. The latter calls for further testing on other datasets using the same range of settings and methodological framework. [ABSTRACT FROM AUTHOR]

Recently, unit-Lindley distribution and its associated regression models have been developed as an alternative to Beta regression model for which continuous outcome in the unit interval (0 , 1). Proportion data usually occur in clinical trials, economics and social studies with hierarchical structures. In this study, unit-Lindley mixed-effect model is proposed and the appropriate likelihood analysis methods for parameter estimation are investigated. In the case of clustered or longitudinal proportion data in mixed-effect models, the full-likelihood function does not have a closed form. Parameter estimations of unit-Lindley mixed-effect model are obtained with Laplace and adaptive Gaussian quadrature approximation methods in this study. We analyzed a dataset on the proportion of households with insufficient water supply and sewage with some sociodemographic variables in the cities of Brazil by using unit-Lindley mixed-effect model including a random intercept as federative states of Brazil. Analysis results indicate that the proposed unit-Lindley mixed-effect model provides better fit than unit-Lindley regression model and beta mixed model. Also, in the simulation study the accuracy of the estimates of approximation methods are evaluated and compared via Monte Carlo simulation study in terms of bias and mean square error. [ABSTRACT FROM AUTHOR]

Forests provide important ecosystem services and renewable materials. Yet, under a future climate, optimal conditions will likely shift outside the current range for some tree species. This will challenge the persistence of populations to rely on inherent plasticity and genetic diversity to acclimate or adapt to future uncertain conditions. An opportunity to study such processes is offered by Scots pine (Pinus sylvestris L.), a forest tree with a large distribution range including populations locally adapted to a wide variety of environments, which hinders a range-wide assessment of the species to climate change. Here we evaluate tree height growth uncertainty of Scots pine marginal populations in Spain and the Nordic countries linked to their genetic adaptation promoted by different climatic drivers. Our aims are to: (i) review the main climatic drivers of Scots pine adaptation across its range; (ii) undertake provenance-based modeling and prediction of tree height under current and future climate scenarios including four representative concentration pathways (RCPs) and five general circulation models (GCMs) at two extremes of its climatic niche; (iii) estimate uncertainty in population tree height linked to the main drivers of local adaptation that may change among RCPs and GCMs in the Nordic countries and Spain. Our models revealed that tree height adaptation is mostly driven by drought in Spain and by photoperiod in the Nordic countries, whereas the literature review also highlighted temperature as a climatic driver for the Nordic region. Model predictions for the Nordic countries showed an overall increase in tree height but with high uncertainty in magnitude depending on the RCPs and GCMs whereas predictions for Spain showed tree height to be maintained in the north and reduced in the south, but with similar magnitudes among RCPs and GCMs. Both models predicted tree height outside the data range used to develop the models (extrapolation). Predictions using higher emission RCPs resulted in larger extrapolated areas, constituting a further source of uncertainty. An expanded network of Scots pine field trials throughout Europe, facilitated by data collection and international research collaboration, would limit the need for uncertain predictions based on extrapolation.

Aims: The link between spectral diversity and in‐situ plant biodiversity is one promising approach to using remote sensing for biodiversity assessment. Nevertheless, there is little evidence as to whether this link is maintained at fine scales, as well as to how it is influenced by vegetation's vertical complexity. Here we test, at the community level in grasslands, the link between diversity of the spectral signal (SDiv) and taxonomic diversity (TDiv), and the influence of vertical complexity. Methods: We used 196 1.5 m × 1.5 m experimental communities with different biodiversity levels. To measure vertical complexity, we quantified height diversity (HDiv) of the most abundant species in the community. TDiv was calculated using the Shannon index based on species cover. Canopy spectral information was gathered using an unmanned aerial vehicle (UAV) mounted with a multi‐spectral sensor providing spectral information via six 10‐nm bands covering the visible and near‐infrared region at a spatial resolution of 3 cm. We measured SDiv in a core area of 1 m ×1 m within the communities as mean Euclidean distance of all pixels in a feature space spanned between the two first components of a PCA calculated for the complete raster stack. We modelled SDiv through mixed‐effect linear models, using TDiv, HDiv, and their interaction as fixed‐effect predictors. Results: Contrary to our expectations, TDiv was negatively linked to SDiv. The diversity in plant height was positively related to SDiv. More importantly, diversity in plant height and TDiv had a significant negative interaction, meaning the more complex the vegetation was in terms of height, the more the SDiv–TDiv relationship became negative. Conclusions: Our results suggest that in order to exploit the SDiv–TDiv link for monitoring purposes, it needs to be contextualized. Moreover, the results highlight that communities' functional characteristics (i.e. plant height) mediate such a link, calling for new insights into the relation between SDiv and functional diversity. [ABSTRACT FROM AUTHOR]

Observations of the asteroids have been performed as long as it has been feasible by the available observational equipment. Recorded data, going back to 18 th century, allowed a classification of these celestial objects' hazardous status. Unfortunately, previous studies used methods that ignore subject dependency in Near-Earth Asteroids (NEA) data. This study aims to perform hazard classification of asteroids by proposing various statistical and machine learning methods on NEA data to overcome these shortcomings. We analyze data from 751 asteroids observed at irregular time intervals through the NASA. We compare algorithms suitable for longitudinal data structure, such as the Generalized Linear Mixed Models (GLMM), marginal model, GLMM-Tree, Historical Random Forest, GPBoost, and Spline. To the best of our knowledge and based on a comprehensive review of the existing literature, our study stands as the pioneering in the utilization of these advanced methodologies for the in-depth analysis of Near-Earth Asteroid (NEA) data. According to the findings, the accuracies of the models range from 0.89 to 0.99. The GPBoost model has the highest performance, while the marginal model has the poorest one. • Pioneering the application of advanced methodologies for near-Earth Asteroids. • Groundbreaking integration of data and subject dependencies into machine learning models. • High prediction power for the algorithms, range between 0.89 and 0.99. • Predicting future collision events and developing strategies for potentially hazardous asteroids. [ABSTRACT FROM AUTHOR]

The objectives of this study were to develop site index and variable-density yield models for Korean red pine (Pinus densiflora Siebold & Zucc.), Korean white pine (P. koraiensis S. & Z.), and Japanese larch (Larix kaempferi (Lamb.) Carrière) in Korea. The data were collected between 2012 and 2021 from repeatedly measured empirical plots in each target stand in the North Central region of Korea: Gangwon and North Gyeongsang provinces. To develop the site index for each species, a dominant height growth model by species was developed using the Chapman–Richards function. The site index was computed with a base age of 40 years and used as an independent variable to predict the stand volume. To develop the variable-density yield models, three stand density variables, the stand age, and the site index were applied. The stand density variables used were the stand basal area, the number of trees per hectare, and the relative density. All the models were successfully developed with significant parameters and reasonable fit statistics, and the residuals analyzed presented unbiased scatter plots. Yield models with the stand basal area, or the number of trees, can be used to predict the stand volume. The yield model with relative density was flexible to apply across the stand age because the input of the absolute stand density was not required. Model simulation and comparisons with other studies also supported the applicability of the models developed in this study. The models were found to be highly applicable for predicting and simulating these targeted stands, particularly in Korea.

Pulmonary tuberculosis (PTB) is characterized by lung granulomas, inflammation and tissue destruction. Here we used within-subject peripheral blood gene expression over time to correlate with the within-subject lung metabolic activity, as measured by positron emission tomography (PET) to identify biological processes and pathways underlying overall resolution of lung inflammation. We used next-generation RNA sequencing and [18F]FDG PET-CT data, collected at diagnosis, week 4, and week 24, from 75 successfully cured PTB patients, with the [18F]FDG activity as a surrogate for lung inflammation. Our linear mixed-effects models required that for each individual the slope of the line of [18F]FDG data in the outcome and the slope of the peripheral blood transcript expression data correlate, i.e., the slopes of the outcome and explanatory variables had to be similar. Of 10,295 genes that changed as a function of time, we identified 639 genes whose expression profiles correlated with decreasing [18F]FDG uptake levels in the lungs. Gene enrichment over-representation analysis revealed that numerous biological processes were significantly enriched in the 639 genes, including several well known in TB transcriptomics such as platelet degranulation and response to interferon gamma, thus validating our novel approach. Others not previously associated with TB pathobiology included smooth muscle contraction, a set of pathways related to mitochondrial function and cell death, as well as a set of pathways connecting transcription, translation and vesicle formation. We observed up-regulation in genes associated with B cells, and down-regulation in genes associated with platelet activation. We found 254 transcription factor binding sites to be enriched among the 639 gene promoters. In conclusion, we demonstrated that of the 10,295 gene expression changes in peripheral blood, only a subset of 639 genes correlated with inflammation in the lungs, and the enriched pathways provide a description of the biology of resolution of lung inflammation as detectable in peripheral blood. Surprisingly, resolution of PTB inflammation is positively correlated with smooth muscle contraction and, extending our previous observation on mitochondrial genes, shows the presence of mitochondrial stress. We focused on pathway analysis which can enable therapeutic target discovery and potential modulation of the host response to TB.

Pulmonary tuberculosis (PTB) is characterized by lung granulomas, inflammation and tissue destruction. Here we used within-subject peripheral blood gene expression over time to correlate with the within-subject lung metabolic activity, as measured by positron emission tomography (PET) to identify biological processes and pathways underlying overall resolution of lung inflammation. We used next-generation RNA sequencing and [18F]FDG PET-CT data, collected at diagnosis, week 4, and week 24, from 75 successfully cured PTB patients, with the [18F]FDG activity as a surrogate for lung inflammation. Our linear mixed-effects models required that for each individual the slope of the line of [18F]FDG data in the outcome and the slope of the peripheral blood transcript expression data correlate, i.e., the slopes of the outcome and explanatory variables had to be similar. Of 10,295 genes that changed as a function of time, we identified 639 genes whose expression profiles correlated with decreasing [18F]FDG uptake levels in the lungs. Gene enrichment over-representation analysis revealed that numerous biological processes were significantly enriched in the 639 genes, including several well known in TB transcriptomics such as platelet degranulation and response to interferon gamma, thus validating our novel approach. Others not previously associated with TB pathobiology included smooth muscle contraction, a set of pathways related to mitochondrial function and cell death, as well as a set of pathways connecting transcription, translation and vesicle formation. We observed up-regulation in genes associated with B cells, and down-regulation in genes associated with platelet activation. We found 254 transcription factor binding sites to be enriched among the 639 gene promoters. In conclusion, we demonstrated that of the 10,295 gene expression changes in peripheral blood, only a subset of 639 genes correlated with inflammation in the lungs, and the enriched pathways provide a description of the biology of resolution of lung inflammation as detectable in peripheral blood. Surprisingly, resolution of PTB inflammation is positively correlated with smooth muscle contraction and, extending our previous observation on mitochondrial genes, shows the presence of mitochondrial stress. We focused on pathway analysis which can enable therapeutic target discovery and potential modulation of the host response to TB. [ABSTRACT FROM AUTHOR]

Stem profile needs to be modeled with an accurate taper equation to produce reliable tree volume assessments. We propose a semiparametric method where few a priori functional form assumptions or parametric specification are required. We compared the diameter and volume predictions of a penalized spline regression (P-spline), P-spline extended with an additive dbh-class variable, and six alternative parametric taper equations including single, segmented, and variable-exponent equation forms. We used taper data from 147 loblolly pine (Pinus taeda L.) trees to fit the models and make comparisons. Here we show that the extended P-spline outperforms the parametric taper equations when used to predict outside bark diameter in the lower portion of the stem, up to 40% of the tree height where the more valuable wood products (62% of the total outside bark volume) are located. For volume, both P-spline models perform equal or better than the best parametric model, with taper calibration, which could result in possible savings on inventory costs by not requiring an additional measurement. Our findings suggest that assuming a priori fixed form in taper models imposes restrictions that fail to explain the tree form adequately compared with the proposed P-spline. [ABSTRACT FROM AUTHOR]

Background: The relationship of serial NT-proBNP (N-terminal pro-B-type natriuretic peptide) measurements with changes in cardiac features and outcomes in heart failure (HF) remains incompletely understood. We determined whether common clinical covariates impact these relationships., Methods and Results: In 2 nationwide observational populations with HF, the relationship of serial NT-proBNP measurements with serial echocardiographic parameters and outcomes was analyzed, further stratified by HF with reduced versus preserved left ventricular ejection fraction, inpatient versus outpatient enrollment, age, obesity, chronic kidney disease, atrial fibrillation, and attainment of ≥50% guideline-recommended doses of renin-angiotensin system inhibitors and β-blockers. Among 1911 patients (mean±SD age, 65.1±13.4 years; 26.6% women; 62% inpatient and 38% outpatient), NT-proBNP declined overall, with more rapid declines among inpatients, those with obesity, those with atrial fibrillation, and those attaining ≥50% guideline-recommended doses. Each doubling of NT-proBNP was associated with increases in left ventricular volume (by 6.1 mL), E/e' (transmitral to mitral annular early diastolic velocity ratio) (by 1.4 points), left atrial volume (by 3.6 mL), and reduced left ventricular ejection fraction (by -2.1%). The effect sizes of these associations were lower among patients with HF with preserved ejection fraction, atrial fibrillation, or advanced age ( P interaction <0.001). A landmark analysis identified that an SD increase in NT-proBNP over 6 months was associated with a 27% increase in the risk of the composite event of HF hospitalization or all-cause death between 6 months and 2 years (adjusted hazard ratio, 1.27 [95% CI, 1.15-1.40]; P <0.001)., Conclusions: The relationships between NT-proBNP and structural/functional remodeling differed by age, presence of atrial fibrillation, and HF phenotypes. The association of increased NT-proBNP with increased risk of adverse outcomes was consistent in all subgroups.

Microhabitat selection models are frequently used in rivers to evaluate anthropogenic effects on aquatic organisms. Fish models are generally developed from few rivers, with debatable statistical treatments for coping with overdispersed abundance distributions. Analyses of data from multiple rivers are needed to test their transferability and increase their relevance for stakeholders. Using 3,528 microhabitats sampled in nine French rivers during 129 surveys, we developed models for 35 specific size classes of 22 fish species. We used mixed‐effects generalized linear models (accounting for multiple surveys), involving B‐spline transformations (accounting for nonlinear responses) and assuming a negative binomial distribution (accounting for abundance overdispersion). We compared models of increasing complexity: no selection (M1), an "average" selection similar in all surveys (M2), two models with different selection across surveys (M3–M4). Of 132 univariate cases (specific size classes by habitat), 63% indicated selection for depth, 71% for velocity, 45% for substratum size and 13% for substratum heterogeneity. A total of 50 models were retained, involving 26/35 specific size classes. Model fits indicated low explained deviance (R2MF < 0.19) and higher rank correlations (ρ < 0.69) between observed and modelled values. However, Bayesian posterior predictive checks validated these results since excellent fits would generate R2MF lower than 0.59 and ρ lower than 0.78. We found high transferability among rivers and dates, because (a) M2 was the most appropriate in 26/50 cases; (b) the R2MF and ρ values by M2 was, respectively, 72% and 75% of that explained by the complex M4 and (c) independent river cross‐validations showed good transferability. Bivariate models for selected specific size classes improved univariate model fits (ρ from 0.30 to 0.38). Overall, using a nonlinear mixed‐effect approach, our results confirmed the relevance of "average" models based on several rivers for developing helpful e‐flow tools. Finally, our modelling approach opens opportunities for integrating additional effects as the spatial distribution of competitors. [ABSTRACT FROM AUTHOR]

Abstract Surface fires are often prescribed to favor oak (Quercus) regeneration in eastern forests, but there is potential for fire to damage residual overstory timber. This study evaluated the potential economic effects of prescribed fire on sawtimber volume and value across 139 stands, each with a known history of one to six prescribed fires, on the Hoosier, Mark Twain, Wayne, and Daniel Boone National Forests. Sawtimber volume and value losses were highly variable, ranging from 0 to 2,269 bd ft ac–1 and from US$0 to US$272.95 ac–1, respectively, for stands that had received at least one prescribed fire. Volume and value losses increased linearly by +0.9 percent and +1.5 percent per burn, respectively, that a stand received over the past 25 years. Stands with south-facing aspects had greater relative volume and value losses (+1.4 to +1.5 percent, respectively), but this influence was statistically less important than the number of burns in predictive models. The eastern national forests had much lower average relative volume and value losses, <3.0 percent, than the more western Mark Twain National Forest (10–15 percent loss). Species composition affected value loss associated with prescribed fire; red oaks experienced significantly higher value loss than all other species groups. [ABSTRACT FROM AUTHOR]

Background: Attention deficit hyperactivity disorder (ADHD) is one of the most common childhood mental health disorders. Stimulant drugs as the most commonly used treatment and first-line therapy for ADHD have side effects. One of the newest approaches to select the best choices and optimize dosages of medications is personalized medicine.Methods: This historical cohort study was carried out on the data taken from the period of 2008 to 2015. Eligible subjects were included in the study randomly. We used mixed-effects logistic regression models to personalize the dosage of Methylphenidate (MPH) in ADHD. The patients' heterogeneity was considered using subject-specific random effects, which are treated as the realizations of a stochastic process. To recommend a personalized dosage for a new patient, a two-step procedure was proposed. In the first step, we obtained estimates for population parameters. In the second step, the dosage of the drug for a new patient was updated at each follow-up.Results: Of the 221 children enrolled in the study, 169 (76.5%) were male and 52 (23.5%) were females. The overall mean age at the beginning of the study is 82.5 (± 26.5) months. In multivariable mixed logit model, three variables (severity of ADHD, time duration receiving MPH, and dosage of MPH) had a significant relationship with improvement. Based on this model the personalized dosage of MPH was obtained.Conclusions: To determine the dosage of MPH for a new patient, the more the severity of baseline is, the more of an initial dose is required. To recommend the dose in the next times, first, the estimation of random coefficient should be updated. The optimum dose increased when the severity of ADHD increased. Also, the results show that the optimum dose of MPH as one proceeds through the period of treatment will decreased. [ABSTRACT FROM AUTHOR]

Abstract Background A model of individual tree growth using simple predictors in a managed black beech (Fuscospora solandri (Hook.f.) Heenan & Smissen) forest could provide a useful tool for predicting future stand characteristics. Methods Data from permanent sample plots were used to develop a framework for modelling individual tree growth in Woodside forest, a managed black beech forest in north Canterbury (New Zealand). We tested three mixed-effect models to identify effects of sites, treatment (thinnings), individual tree size and competition on tree growth rates. Results A power function amended with variables specifying stand basal area and thinning treatment was best suited for black beech, explaining about 55% of the variation in growth rates. Treatment history (thinnings), as well as the individual tree size and the stand basal area, strongly affected tree diameter growth. Only 3% of the variation in diameter growth rates was explained by plot-specific effect which was less than observed in earlier studies. Conclusions All predictor variables (management history, individual tree diameter and stand basal area) are quite simple to measure in the field and could be easily used to predict diameter increments in managed or unmanaged forests. A limitation of our study was that available growth data in Woodside were from small plots, focused on a small number of trees and a narrow range of diameters. However, our results are a good starting point, providing a promising framework for further modelling of tree growth in Woodside forest from new permanent plot data.

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

Magnetic resonance imaging (MRI) of the brain provides important outcome measures in the longitudinal evaluation of disease activity and progression in MS subjects. Two common measures derived from brain MRI scans are the brain parenchymal fraction (BPF) and T2 hyperintense lesion volume (T2LV), and these measures are routinely assessed longitudinally in clinical trials and observational studies. When measuring each outcome longitudinally, observed changes may be potentially confounded by variability in MRI acquisition parameters between scans. In order to accurately model longitudinal change, the acquisition parameters should thus be considered in statistical models. In this paper, several models for including protocol as well as individual MRI acquisition parameters in linear mixed models were compared using a large dataset of 3453 longitudinal MRI scans from 1341 subjects enrolled in the CLIMB study, and model fit indices were compared across the models. The model that best explained the variance in BPF data was a random intercept and random slope with protocol specific residual variance along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. The model that best explained the variance in T2LV was a random intercept and random slope along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. In light of these findings, future studies pertaining to BPF and T2LV outcomes should carefully account for the protocol factors within longitudinal models to ensure that the disease trajectory of MS subjects can be assessed more accurately.

Infectious diseases are often affected by specific pairings of hosts and pathogens and therefore by both of their genomes. The integration of a pair of genomes into genome-wide association mapping can provide an exquisitely detailed view of the genetic landscape of complex traits. We present a statistical method, ATOMM (Analysis with a Two-Organism Mixed Model), that maps a trait of interest to a pair of genomes simultaneously; this method makes use of whole-genome sequence data for both host and pathogen organisms. ATOMM uses a two-way mixed-effect model to test for genetic associations and cross-species genetic interactions while accounting for sample structure including interactions between the genetic backgrounds of the two organisms. We demonstrate the applicability of ATOMM to a joint association study of quantitative disease resistance (QDR) in the Arabidopsis thaliana–Xanthomonas arboricola pathosystem. Our method uncovers a clear host–strain specificity in QDR and provides a powerful approach to identify genetic variants on both genomes that contribute to phenotypic variation. [ABSTRACT FROM AUTHOR]

Abstract: Aim: Habitat fragmentation and loss are two of the most important factors driving current biodiversity decline. Nonetheless, the relationship between biodiversity and landscape patterns appears more complex than generally expected, depending on the species and communities involved. We aim to enrich knowledge concerning the relationship between plant diversity and landscape patterns along linear landscapes, such as Mediterranean coastal dunes. A dedicated buffering method considering multiple nested extents was developed for sampling linear landscapes (e.g. coastal or fluvial), which traditionally present a challenge for standard round or square sampling buffering approaches. Location: Tyrrhenian coast of central Italy. Methods: Based on a database of plant community plots and functional traits from field measurements, for each plot we calculated taxonomic (TD) and functional (FD) diversity, which was further decomposed in functional evenness (FDeven) and mean trait dispersion (FDdisp). Relying on a land‐cover map, we computed a set of landscape metrics describing habitat loss, fragmentation and direct human disturbance at multiple extents around each plot. Diversity measures (TD, FD, FDeven and FDdisp) were then related to the landscape metrics at different scales via linear mixed‐effect models. Results: Overall, the relationship between plant species diversity and landscape patterns was weak. We observed different responses of TD, FD, FDeven and FDdisp, which only emerged at fine‐medium scales. TD decreased with habitat loss and disturbance, while FD only with disturbance. FDeven decreased in more fragmented areas, while FDdisp was not affected by the selected landscape parameters. Main conclusions: Like other transitional areas, coastal strand and dune ecosystems exhibit steep gradients in biotic and environmental factors, are dynamic in location, and could be among the earliest to be affected by environmental drivers. However, the response of Mediterranean coastal dune plant diversity to habitat loss and fragmentation is weak. For these reasons, we propose that these plant communities are adapted to the ever‐changing nature of the coastal environment and consequently to changes in landscape pattern. [ABSTRACT FROM AUTHOR]

In many real-life applications, the quality of products from a process is monitored by a functional relationship between a response variable and one or more explanatory variables. In these applications, methodologies of profile monitoring are used to check the stability of this relationship over time. In phase I of profile monitoring, historical data points that can be represented by curves (or profiles) are collected. In this article, 2 procedures are proposed for detecting outlying profiles in phase I data, by incorporating the local linear kernel smoothing within the framework of nonparametric mixed-effect models. We introduce a stepwise algorithm on the basis of the multiple testing viewpoint. Our simulation results for various linear and nonlinear profiles display the superior efficiency of our proposed monitoring procedures over some existing techniques in the literature. To illustrate the implementation of the proposed methods in phase I profile monitoring, we apply the methods on a vertical density profile dataset. [ABSTRACT FROM AUTHOR]

Allometric models of internodes are an important component of Functional-Structural Plant Models (FSPMs), which represent the shape of internodes in tree architecture and help our understanding of resource allocation in organisms. Constant allometry is always assumed in these models. In this paper, multilevel nonlinear mixed-effect models were used to characterize the variability of internode allometry, describing the relationship between the last internode length and biomass of Pinus tabulaeformis Carr. trees within the GreenLab framework. We demonstrated that there is significant variability in allometric relationships at the tree and different-order branch levels, and the variability decreases among levels from trees to first-order branches and, subsequently, to second-order branches. The variability was partially explained by the random effects of site characteristics, stand age, density, and topological position of the internode. Tree- and branch-level-specific allometric models are recommended because they produce unbiased and accurate internode length estimates. The model and method developed in this study are useful for understanding and describing the structure and functioning of trees.

Aim The general dynamic model ( GDM) proposed by Whittaker et al. () has become a widely accepted theoretical framework in island biogeography. In this study, we explore whether GDM predictions hold when overall plant diversity is deconstructed into major plant families. Location 101 islands from 14 oceanic archipelagos world-wide. Methods Occurrence data for all species of nine large, cosmopolitan flowering plant families were used to test predictions derived from the GDM. We analysed the effects of island area and age on species richness as well as number and percentage of single-island endemic species per family using mixed-effect models. Results Total species and endemic richness as well as the percentage of endemic species showed the predicted hump-shaped relationship with island age. The overall pattern was mainly driven by few species-rich plant families. Varying patterns were found for individual families, some of them opposing the general trend. In most cases, native and endemic species richness peaked much earlier in the island life cycle than suggested by the GDM. The contribution of area to the explained variation of all dependent variables was much higher than that of island age. Main conclusions The results suggest that biodiversity-age relationships reported for large taxonomic groups like plants are driven by only a few species-rich clades. The way these families influence the overall patterns of species richness and endemism is related to family-specific properties, such as evolutionary history or dispersal strategies. Deviances from the GDM predictions can hence largely be explained by family characteristics and considering taxon-specific traits may help to further improve the explanatory power of the GDM. Families not following the expectations, like Orchidaceae, may be particularly valuable candidates to unveil so far neglected drivers of island biogeographical patterns. [ABSTRACT FROM AUTHOR]

Streamflow characteristics are driven by specific flow-generation mechanisms, which are in turn determined by the biophysical properties of catchments. They provide important environmental services for society and ecosystems, regulating water supply and quality, flood mitigation, and the biological diversity of aquatic ecosystems. This study investigates how the drivers of streamflow characteristics vary at the level of regional management (regional [104 km2] and continental scales [107 km2]) in eastern Australia. Three hydrological signatures were used to represent streamflow characteristics: runoff coefficient, baseflow index, and zero flow ratio. Long-term streamflow data and 24 spatially distributed biophysical properties from 354 catchments in eastern Australia were analysed with random forest and generalized additive beta regression models to determine the dominant drivers of streamflow characteristics. We found that the main drivers of streamflow characteristics cannot be generalized from region to region and that specific biophysical variables govern their spatial variability. However, some important drivers such as the dryness index and the fraction of photosynthetically active radiation from vegetation explain the variability of streamflow characteristics at both regional and continental scales with differing importance. Our findings also suggest that soil properties have a significant effect on streamflow characteristics at regional scales. However, the relative importance of these soil properties varies among regions depending on the streamflow characteristics. This paper demonstrates that the drivers of streamflow characteristics are scale and region dependent, and biogeographically different regions have specific mechanisms governing streamflow. It opens an avenue to better connect the management perspectives of ecology and hydrology. [ABSTRACT FROM AUTHOR]

Tenofovir (TFV) and emtricitabine (FTC) are part of the recommended highly active antiretroviral therapy (ART). Both molecules show a large interindividual pharmacokinetic (PK) variability. Here, we modeled the concentrations of plasma TFV and FTC and their intracellular metabolites (TFV diphosphate [TFV-DP] and FTC triphosphate [FTC-TP]) collected after 4 and 24 weeks of treatment in 34 patients from the ANRS 134-COPHAR 3 trial. These patients received daily (QD) atazanavir (300 mg), ritonavir (100 mg), and a fixed-dose combination of coformulated TFV disoproxil fumarate (300 mg) and FTC (200 mg). Dosing history was collected using a medication event monitoring system. A three-compartment model with absorption delay ( T lag ) was selected to describe the PK of, respectively, TFV/TFV-DP and FTC/FTC-TP. TFV and FTC apparent clearances, 114 L/h (relative standard error [RSE] = 8%) and 18.1 L/h (RSE = 5%), respectively, were found to decrease with age. However, no significant association was found with the polymorphisms ABCC2 rs717620, ABCC4 rs1751034, and ABCB1 rs1045642. The model allows prediction of TFV-DP and FTC-TP concentrations at steady state with alternative regimens., Competing Interests: The authors declare no conflict of interest.