•Species type, mycorrhizal association, and N-fixing ability all significantly affected soil C.•Forest floor C stock was higher under coniferous, ECM, or non-N-fixing trees.•Mineral soil C ...concentration and stock were higher under broadleaved, AM, or N-fixing trees.•Tree species effects on soil C were mediated by latitude, MAT, MAP, and forest stand age.
Selection of appropriate tree species is an important forest management decision that may affect sequestration of carbon (C) in soil. However, information about tree species effects on soil C stocks at the global scale remains unclear. Here, we quantitatively synthesized 850 observations from field studies that were conducted in a common garden or monoculture plantations to assess how tree species type (broadleaf vs. conifer), mycorrhizal association (arbuscular mycorrhizal (AM) vs. ectomycorrhizal (ECM)), and N-fixing ability (N-fixing vs. non-N-fixing), directly and indirectly, affect topsoil (with a median depth of 10 cm) C concentration and stock, and how such effects were influenced by environmental factors such as geographical location and climate. We found that (1) tree species type, mycorrhizal association, and N-fixing ability were all important factors affecting soil C, with lower forest floor C stocks under broadleaved (44%), AM (39%), or N-fixing (28%) trees respectively, but higher mineral soil C concentration (11%, 22%, and 156%) and stock (9%, 10%, and 6%) under broadleaved, AM, and N-fixing trees respectively; (2) tree species type, mycorrhizal association, and N-fixing ability affected forest floor C stock and mineral soil C concentration and stock directly or indirectly through impacting soil properties such as microbial biomass C and nitrogen; (3) tree species effects on mineral soil C concentration and stock were mediated by latitude, MAT, MAP, and forest stand age. These results reveal how tree species and their specific traits influence forest floor C stock and mineral soil C concentration and stock at a global scale. Insights into the underlying mechanisms of tree species effects found in our study would be useful to inform tree species selection in forest management or afforestation aiming to sequester more atmospheric C in soil for mitigation of climate change.
The standard two-stage approach for estimating non-linear dose–response curves based on aggregated data typically excludes those studies with less than three exposure groups. We develop the one-stage ...method as a linear mixed model and present the main aspects of the methodology, including model specification, estimation, testing, prediction, goodness-of-fit, model comparison, and quantification of between-studies heterogeneity. Using both fictitious and real data from a published meta-analysis, we illustrated the main features of the proposed methodology and compared it to a traditional two-stage analysis. In a one-stage approach, the pooled curve and estimates of the between-studies heterogeneity are based on the whole set of studies without any exclusion. Thus, even complex curves (splines, spike at zero exposure) defined by several parameters can be estimated. We showed how the one-stage method may facilitate several applications, in particular quantification of heterogeneity over the exposure range, prediction of marginal and conditional curves, and comparison of alternative models. The one-stage method for meta-analysis of non-linear curves is implemented in the dosresmeta R package. It is particularly suited for dose–response meta-analyses of aggregated where the complexity of the research question is better addressed by including all the studies.
Abstract
Child undernutrition is still a major public health concern in Bangladesh in spite of significant decline in the last few years. Climate change may impact the prevalence of undernutrition ...and its geographical variability through food security and recurring outbreaks of disease, as well as impede efforts to reduce the undernutrition burden. This study aims to evaluate rainfall and temperature associated with childhood malnutrition in Bangladesh. A spatial mixed effect logistic regression model was used to estimate the association between undernutrition (measured by the composite index of anthropometric failure) and residential area-level rainfall and temperatures, controlling for covariates and spatial effects of residential locations using national data from the 2017 to 2018 Bangladesh Demographic and Health Survey. Our findings indicate that an increase in rainfall was associated with increased odds of undernutrition adjusted odds ratio (AOR) 1.15, 95% confidence interval (CI) 1.07–1.24, whereas an increase in temperature was associated with decreased odds of undernutrition (AOR 0.86, 95% CI 0.80–0.93). We also found statistically significant structured and unstructured spatial variations in undernutrition, indicating locational effects. Investing in infrastructure and education could be potential strategies for mitigating the negative effects of climate change. High-risk regions in terms of climatic change and malnutrition could be given priority for intervention implementation.
•We present various genotype to phenotype models to predict complex phenotypes with G×E as function of genotypic and environmental inputs.•We show how genotype-to-phenotype models can be generalized ...to incorporate additional phenotypic information to improve yield predictions.•We discuss how to evaluate the utility of information from new phenotyping techniques in the context of predictive genotype-to-phenotype models.
New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.
•Treatment by visit interactions exist in HAM-D scores and depression status in first month.•Baseline anxiety and major depressive disorder (MDD) associated with HAM-D scores and depression ...status.•Cocaine use disorder associated with HAM-D score and depression status in non-MDD individuals.•Cannabis use disorder associated with HAM-D scores and depression status in MDD individuals.•Amphetamine use disorder associated with HAM-D score just in non-MDD individuals.•Anxiety was associated with HAM-D scores regardless of MDD.
This study aimed to evaluate the longitudinal treatment effect on depression measured by Hamilton Depression Rating Scale (HAM-D) score in a randomized clinical trial for the treatment of opioid use disorder (OUD).
We conducted a secondary data analysis of data from the National Institute on Drug Abuse's Clinical Trials Network Protocol-0051. Patients with OUD (N = 570) were randomized to receive buprenorphine/naloxone (BUP-NX, n = 287) or extended-release naltrexone injection (XR-NTX, n = 283). The HAM-D score was completed at baseline and follow-up visit up to 36 weeks. A linear mixed model analysis was performed for log transformed HAM-D score and a generalized linear mixed model analysis was conducted for depression status.
Compared with BUP-NX, subjects randomized to XR-NTX had higher HAM-D scores at weeks 1 and 3 (p<0.05). There were significant interactions between treatment and visit on HAM-D score and depression status during the first four weeks of treatments in individuals without lifetime major depressive disorder (MDD). Past year cocaine use was associated with HAM-D score and depression status just in individuals without MDD, whereas past year cannabis use was associated with HAM-D score and depression status just in individuals with MDD. Past year amphetamine use was associated with HAM-D score just in individuals without MDD, however, lifetime anxiety was associated with HAM-D scores regardless of MDD.
When prescribing XR-NTX, particularly in the first month of treatment, it is essential to monitor for depressive symptoms. Screening for depression and multiple substance abuse may help clinicians identify appropriate treatment.
Meta-Analysis of Proportions Schwarzer, Guido; Rücker, Gerta
Methods in molecular biology (Clifton, N.J.),
2022, Volume:
2345
Journal Article
The meta-analysis of single proportions has become a popular application over the last two decades. Especially, systematic reviews of prevalence studies are conducted in various fields of science, ...including medicine, ecology, psychology, or social sciences. In this chapter, we illustrate meta-analysis methods to pool single proportions and to compare proportions from two groups. We introduce classic approaches based on the inverse variance method as well as generalized linear mixed models taking the binary structure of the data into account. The most common transformations of proportions and their back-transformations are described both for individual studies and in the meta-analysis setting.
Abstract
Background
Intensive care unit (ICU) length of stay (LOS) and the risk adjusted equivalent (RALOS) have been used as quality metrics. The latter measures entail either ratio or difference ...formulations or ICU random effects (RE), which have not been previously compared.
Methods
From calendar year 2016 data of an adult ICU registry-database (Australia & New Zealand Intensive Care Society (ANZICS) CORE), LOS predictive models were established using linear (LMM) and generalised linear (GLMM) mixed models. Model fixed effects quality-metric formulations were estimated as RALOSR for LMM (geometric mean derived from log(ICU LOS)) and GLMM (day) and observed minus expected ICU LOS (OMELOS from GLMM). Metric confidence intervals (95%CI) were estimated by bootstrapping; random effects (RE) were predicted for LMM and GLMM. Forest-plot displays of ranked quality-metric point-estimates (95%CI) were generated for ICU hospital classifications (metropolitan, private, rural/regional, and tertiary). Robust rank confidence sets (point estimate and 95%CI), both marginal (pertaining to a singular ICU) and simultaneous (pertaining to all ICU differences), were established.
Results
The ICU cohort was of 94,361 patients from 125 ICUs (metropolitan 16.9%, private 32.8%, rural/regional 6.4%, tertiary 43.8%). Age (mean, SD) was 61.7 (17.5) years; 58.3% were male; APACHE III severity-of-illness score 54.6 (25.7); ICU annual patient volume 1192 (702) and ICU LOS 3.2 (4.9). There was no concordance of ICU ranked model predictions, GLMM versus LMM, nor for the quality metrics used, RALOSR, OMELOS and site-specific RE for each of the ICU hospital classifications. Furthermore, there was no concordance between ICU ranking confidence sets, marginal and simultaneous for models or quality metrics.
Conclusions
Inference regarding adjusted ICU LOS was dependent upon the statistical estimator and the quality index used to quantify any LOS differences across ICUs. That is, there was no “one best model”; thus, ICU “performance” is determined by model choice and any rankings thereupon should be circumspect.
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•Fire-excluded sandhills require density control of plant life forms for longleaf pine regeneration.•Midstory saplings do not affect longleaf pine regeneration.•Longleaf pine impedes ...its own regeneration when basal area exceeds threshold values.•Sand pine and evergreen hardwoods impede longleaf pine regeneration, but deciduous oaks facilitate it.
Longleaf pine researchers have quantified many aspects of regeneration, growth and yield, but have focused on productive sites such as pine flatwoods and upland pine. Sandhill longleaf pine woodlands represent >40 % of remnant stands of longleaf pine ecosystems; most have no history of agriculture due to poor site productivity, yet many have been degraded by resin collection, grazing and fire exclusion. Restoration of structure and function of sandhill longleaf pine depends on understanding early stand-development processes, which are governed by fire, propagule availability, and competition from a range of overstory tree species not limited to longleaf pine. We analyzed a 16-year monitoring dataset from Eglin Air Force Base in northwest Florida, USA, focusing on how four tree growth forms influenced longleaf pine juvenile stages of seedling and sapling numbers and ingrowth to the subadult size class. Analyses were done with generalized linear mixed models using Poisson, normal, or negative binomial error distributions according to the response variable.
Restoration activities were effective at reducing numbers of sapling evergreen hardwoods, and basal area of overstory sand pine and other non-longleaf conifers was reduced by 35 % over the 16-year study. We observed threshold values for adult longleaf pine effects on seedlings and ingrowth; seedling numbers were maximized at 7.8 m2/ha, and ingrowth was maximum at 5.1 m2/ha. Seedling density was strongly governed by mast year. Sand pine, an invasive native pine, was detrimental to longleaf pine at all juvenile life stages even at low basal area. Deciduous oaks, which have been observed to facilitate longleaf pine seedling establishment, instead facilitated ingrowth to the subadult size class. Evergreen hardwoods, which may dampen fire behavior, only decreased longleaf pine density at the sapling stage. Ongoing silvicultural restoration activities should continue to focus on frequent fire and sand pine removal; deciduous oaks should be retained for their facilitative value; and removal of evergreen hardwoods should not be a high priority as long as prescribed fire can propagate in these lower productivity sites.
•We derive a new robust KF algorithm, called dynamic noise-aware KF(DAKF).•The steady-state performance of the proposed DAKF is theoretically analyzed.•Simulation results show that DAKF performs well ...in suppressing non-Gaussian noise.
In this work, we focus on the online state estimation problem for linear systems in non-Gaussian measurement noise. Specifically, the measurement noise is modeled as a Gaussian mixture model (GMM). Then, Kalman innovation is used to approximate the current measurement noise, and dynamically perceive its responsiveness to each sub-model of the GMM. The responsiveness from different Gaussian scales is mapped to a new cost function, while the corresponding Kalman filter algorithm is derived. The theoretical steady-state error and computational complexity analysis of the algorithm are also given. The simulation and real experimental results agree with the theoretical predictions and demonstrate the superior performance of the proposed algorithm.
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