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Microbial communities are vital to our lives, yet their ecological functioning and dynamics remain poorly understood. This understanding is crucial for assessing threats to these systems and leveraging their biotechnological applications. Given that temporal dynamics are linked to community functioning, this study investigated the drivers of community succession in the wine yeast community. We experimentally generated population dynamics data and used it to create an interpretable model with a gradient boosted regression tree approach. The model was trained on temporal data of viable species populations in various combinations, including pairs, triplets, and quadruplets, and was evaluated for predictive accuracy and input feature importance. Key findings revealed that the inoculation dosage of non- Saccharomyces species significantly influences their performance in mixed cultures, while Saccharomyces cerevisiae consistently dominates regardless of initial abundance. Additionally, we observed multispecies interactions where the dynamics of Wickerhamomyces anomalus were influenced by Torulaspora delbrueckii in pairwise cultures, but this interaction was altered by the inclusion of S. cerevisiae. This study provides insights into yeast community succession and offers valuable machine learning-based analysis techniques applicable to other microbial communities, opening new avenues for harnessing microbial communities. [ABSTRACT FROM AUTHOR]

Adaptive forest management gains importance as climate and disturbance regimes continue to change. Norway spruce monocultures are particularly vulnerable to those changes. Thus, there is a strong demand to convert pure spruce stands toward better adapted forest ecosystems. Silver fir has similar wood properties as Norway spruce, but is less vulnerable to droughts. Yet, little is known how to efficiently admix silver fir into Norway spruce monocultures. We analyzed seedling abundance in mature Norway spruce monocultures 1 year after soil scarification and silver fir seeding in a relatively dry region of southern Germany. Our objectives were to study the effect of soil scarification and seeding on regeneration density and to identify drivers of seedling abundance. We collected data on regeneration, forest structure, as well as soil and light conditions at 103 scarified and 25 untreated control plots. We used non-parametric independence tests to compare scarified and untreated plots. Subsequently, we used boosted regression trees to investigate the drivers of seedling abundance. Norway spruce strongly benefited from soil scarification (P  < .001), with seedling numbers even exceeding those of silver fir. Both species thrived in soils with high plant available water capacity. In contrast, seedling numbers were overall negatively affected by light. Moreover, we found a higher density of silver fir regeneration at greater distance from neighboring trees and in soils devoid of carbonate. Our study indicates that water, not light, is the limiting factor for both Norway spruce and silver fir seedlings in the study region. Seeding silver fir will likely be most effective underneath an intact canopy on deep, fine-textured soils without carbonate, while maximizing the distance to Norway spruce trees. In conclusion, silver fir seeding can be optimized to become an effective adaptive measure to diversify Norway spruce monocultures, and thus to create more resistant and resilient forest ecosystems. [ABSTRACT FROM AUTHOR]

Antimicrobial-resistance genes (ARGs) are spread among bacteria by horizontal gene transfer, however, the effect of environmental factors on the dynamics of the ARG in water environments has not been very well understood. In this systematic review, we employed the regression tree algorithm to identify the environmental factors that facilitate/inhibit the transfer of ARGs via conjugation in planktonic/biofilm-formed bacterial cells based on the results of past relevant research. Escherichia coli strains were the most studied genus for conjugation experiments as donor/recipient in the intra-genera category. Conversely, Pseudomonas spp. Acinetobacter spp. and Salmonella spp. were studied primarily as recipients across inter-genera bacteria. The conjugation efficiency (ce) was found to be highly dependent on the incubation period. Some antibiotics, such as nitrofurantoin (at ≥0.2 µg ml−1) and kanamycin (at ≥9.5 mg l−1) as well as metallic compounds like mercury (II) chloride (HgCl2, ≥3 µmol l−1), and vanadium (III) chloride (VCl3, ≥50 µmol l−1) had enhancing effect on conjugation. The highest ce value (−0.90 log10) was achieved at 15°C–19°C, with linoleic acid concentrations <8 mg l−1, a recognized conjugation inhibitor. Identifying critical environmental factors affecting ARG dissemination in aquatic environments will accelerate strategies to control their proliferation and combat antibiotic resistance. [ABSTRACT FROM AUTHOR]

Objectives: Determining which sites were important to differentiate polymyalgia rheumatica (PMR) from rheumatoid arthritis (RA) using 18F-fluorodeoxyglucose (FDG) positron emission tomography and computed tomography (PET-CT) is challenging. Methods: Patients with PMR or RA who were undergoing PET-CT were recruited at two mutual-aid hospitals in Japan between 2009 and 2018. Classification and regression tree (CART) analyses were performed to identify FDG uptake patterns that differentiated PMR from RA. Results: We enrolled 35 patients with PMR and 46 patients with RA. Univariate CART analysis showed that FDG uptake in the shoulder joints, spinous processes of the lumbar vertebrae, pubic symphysis, sternoclavicular joints, ischial tuberosities, greater trochanters, and hip joints differentiated PMR from RA. Multivariate CART analysis revealed that FDG uptake by at least one of the ischial tuberosities had the highest diagnostic value for distinguishing PMR from RA (sensitivity, 77.1%; specificity, 82.6%). We performed the same CART analysis to patients who had not undergone treatment (PMR, n = 28; RA, n = 9). Similar results were obtained, and sensitivity and specificity were increased (sensitivity, 89.3%; specificity, 88.8%). Conclusions: In PET-CT, FDG uptake by at least one of the ischial tuberosities best discriminates between PMR and RA. [ABSTRACT FROM AUTHOR]

Background and Aims Mediterranean ecosystems have a high vascular plant species richness (SR) relative to their surface area. This SR, representing the balance between speciation and extinction, has been attributed to multiple mechanisms that result in both high rates of speciation and/or low rates of extinction. An abiding question is, however, what is special about Mediterranean ecosystems that enables this high SR? Apart from the long-term climatic stability of the region, SR has also been related to resource availability, the many individuals hypothesis, resource spatial heterogeneity, temporal heterogeneity and biotic feedbacks. Methods Spatial patterns of species richness were related to climatic, edaphic and biotic variables and to spatial variability within the Greater Cape Floristic Region (GCFR) of South Africa. Boosted regression tree models were used to explore the strength of relationships between SR and environmental predictors related to each hypothesized mechanism. Key Results Water availability (i.e. precipitation) was a stronger predictor of SR than potential evapotranspiration or temperature. Scarcity of nutrients was also related to SR. There was no indication that SR was related to the density of individuals and only temporal heterogeneity induced by fire was related to SR. Spatial heterogeneities of climatic, edaphic and biotic variables were strongly associated with SR. Biotic interactions remain difficult to assess, although we have some evidence for a putative role in regulating SR. Conclusions While the lack of ecosystem-resetting disturbances (e.g. glaciation) is undoubtedly a key requirement for high species accumulation, predictably, no one explanation holds the key to understanding SR. In the GCFR high SR is the product of a combination of adequate water, nutrient scarcity, spatial and temporal heterogeneity, and possibly biotic feedbacks. [ABSTRACT FROM AUTHOR]

Many previous studies on the changes in zooplankton communities considered only subsets of the total community. In this study, we investigated the temporal dynamics of the mesozooplankton community structure, considering all taxa (holo- and meroplankton) sampled over the last five decades at Helgoland Roads. We identified two tipping years. The first one occurred in 1983, which is consistent with previous studies conducted on copepods. The second shift, even more pronounced, took place in the mid-2000s. During the first shift, most taxa significantly increased in abundance and kept high densities until the end of the 1990s. Then, we observed in the mid-2000s a sharp decrease in community diversity and abundances of almost all taxa. One of the aims of the study was to test the robustness depending on the choice of taxa. To test this, we selected different subsets of the total zooplankton community, both randomly as well as based on functionality. We observed very similar trends over time for all groups, showing the complete community experienced the same changes. However, the timing of the tipping years depended on the organisms considered. These results highlight that the observed changes in the planktonic community are surprisingly robust and visible in most planktonic organisms. [ABSTRACT FROM AUTHOR]

Forensic psychiatric hospitals regularly monitor the mental health and forensic risk factors of their patients. As part of this monitoring, staff score patients on various items. Common practice is to aggregate these scores across staff members. However, this is suboptimal because it assumes that assessors are interchangeable and that patients are independent. An improvement over averaging scores is the use of Cultural Consensus Theory (CCT), which imposes a hierarchical model across patients, staff members, and items. While accounting for differences between patients and staff members, CCT estimates a 'true' score for each patient on each item based on the consensus among staff members. Here, we apply a CCT model to data from a Dutch maximum-security forensic psychiatric centre and use the inferences to predict violent behaviour in patients. The CCT model outpredicts several alternatives, such as random forest and boosted regression trees, albeit by a small margin. We discuss practical limitations and directions for how future monitoring of patients could be adapted to maximize the added value of a CCT-based approach. [ABSTRACT FROM AUTHOR]

Marine fish experience shifts in their distribution due to changes in the physical and biological environments. These shifts pose challenges for fishery businesses and management in international fisheries organizations. In the western and central Pacific Ocean, changes in the spatial distribution of skipjack tuna (Katsuwonus pelamis) due to climate variability often influence fishing activities and economic benefits. This study provides an operational forecast to enhance the decision-making process for fishery managers and fishermen by informing them about fish distributions in the coming months. Monthly forecasts of skipjack tuna habitat are generated utilizing a species distribution model (boosted regression trees) in combination with real-time environmental forecasts. An alternative method (dynamic time warping) is proposed to improve monthly chlorophyll forecasts, which are crucial for enhancing skipjack tuna habitat forecasting. To assess the prediction skills of the model, a retrospective analysis was conducted, forecasting skipjack tuna distribution over 9-month periods and comparing the forecasts with observed occurrences. The results demonstrate that 67.9% of the observed skipjack tuna catches occurred within the forecasted habitats, indicating that the forecast model skillfully predicted skipjack tuna movements 9 months in advance. These monthly updated forecasts can serve as a potential tool for resource managers in developing effective management strategies and fishers in planning fishing operations sustainably and responsibly. [ABSTRACT FROM AUTHOR]

Missing data are a pervasive issue in observational studies using electronic health records or patient registries. It presents unique challenges for statistical inference, especially causal inference. Inappropriately handling missing data in causal inference could potentially bias causal estimation. Besides missing data problems, observational health data structures typically have mixed‐type variables ‐ continuous and categorical covariates ‐ whose joint distribution is often too complex to be modeled by simple parametric models. The existence of missing values in covariates and outcomes makes the causal inference even more challenging, while most standard causal inference approaches assume fully observed data or start their works after imputing missing values in a separate preprocessing stage. To address these problems, we introduce a Bayesian nonparametric causal model to estimate causal effects with missing data. The proposed approach can simultaneously impute missing values, account for multiple outcomes, and estimate causal effects under the potential outcomes framework. We provide three simulation studies to show the performance of our proposed method under complicated data settings whose features are similar to our case studies. For example, Simulation Study 3 assumes the case where missing values exist in both outcomes and covariates. Two case studies were conducted applying our method to evaluate the comparative effectiveness of treatments for chronic disease management in juvenile idiopathic arthritis and cystic fibrosis. [ABSTRACT FROM AUTHOR]

Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects of covariates are complex. Modern advances in computational hardware have led to an increasing interest in flexible Bayesian nonparametric methods for time‐to‐event data such as Bayesian additive regression trees (BART). We propose a novel approach that we call nonparametric failure time (NFT) BART in order to increase the flexibility beyond accelerated failure time (AFT) and proportional hazard models. NFT BART has three key features: (1) a BART prior for the mean function of the event time logarithm; (2) a heteroskedastic BART prior to deduce a covariate‐dependent variance function; and (3) a flexible nonparametric error distribution using Dirichlet process mixtures (DPM). Our proposed approach widens the scope of hazard shapes including nonproportional hazards, can be scaled up to large sample sizes, naturally provides estimates of uncertainty via the posterior and can be seamlessly employed for variable selection. We provide convenient, user‐friendly, computer software that is freely available as a reference implementation. Simulations demonstrate that NFT BART maintains excellent performance for survival prediction especially when AFT assumptions are violated by heteroskedasticity. We illustrate the proposed approach on a study examining predictors for mortality risk in patients undergoing hematopoietic stem cell transplant (HSCT) for blood‐borne cancer, where heteroskedasticity and nonproportional hazards are likely present. [ABSTRACT FROM AUTHOR]

Analysis of observational studies increasingly confronts the challenge of determining which of a possibly high‐dimensional set of available covariates are required to satisfy the assumption of ignorable treatment assignment for estimation of causal effects. We propose a Bayesian nonparametric approach that simultaneously (1) prioritizes inclusion of adjustment variables in accordance with existing principles of confounder selection; (2) estimates causal effects in a manner that permits complex relationships among confounders, exposures, and outcomes; and (3) provides causal estimates that account for uncertainty in the nature of confounding. The proposal relies on specification of multiple Bayesian additive regression trees models, linked together with a common prior distribution that accrues posterior selection probability to covariates on the basis of association with both the exposure and the outcome of interest. A set of extensive simulation studies demonstrates that the proposed method performs well relative to similarly‐motivated methodologies in a variety of scenarios. We deploy the method to investigate the causal effect of emissions from coal‐fired power plants on ambient air pollution concentrations, where the prospect of confounding due to local and regional meteorological factors introduces uncertainty around the confounding role of a high‐dimensional set of measured variables. Ultimately, we show that the proposed method produces more efficient and more consistent results across adjacent years than alternative methods, lending strength to the evidence of the causal relationship between SO2 emissions and ambient particulate pollution. [ABSTRACT FROM AUTHOR]

Background The Pittsburgh Performance Fatigability Index (PPFI) quantifies the percent decline in cadence using accelerometry during standardized walking tasks. Although PPFI has shown strong correlations with physical performance, the developmental sample was relatively homogenous and small, necessitating further validation. Methods Participants from the Study of Muscle, Mobility and Aging (N  = 805, age = 76.4 ± 5.0 years, 58% women, 85% White) wore an ActiGraph GT9X on the nondominant wrist during usual-paced 400 m walk. Tri-axial accelerations were analyzed to compute PPFI (higher score = greater fatigability). To evaluate construct and discriminant validity, Spearman correlations (r s) between PPFI and gait speed, Short Physical Performance Battery (SPPB), chair stand speed, leg peak power, VO2peak, perceived fatigability, and mood were examined. Sex-specific PPFI cut-points that optimally discriminated gait speed using classification and regression tree were then generated. Their discriminate power in relation to aforementioned physical performance were further evaluated. Results Median PPFI score was 1.4% (25th–75th percentile range: 0%–21.7%), higher among women than men (p  < .001). PPFI score was moderate-to-strongly correlated with gait speed (r s = −0.75), SPPB score (r s = −0.38), chair stand speed (r s = −0.36), leg peak power (r s = −0.34) and VO2peak (r s = −0.40), and less strongly with perceived fatigability (r s = 0.28–0.29), all p  < .001. PPFI score was not correlated with mood (| r s| < 0.08). Sex-specific PPFI cut-points (no performance fatigability: PPFI = 0%; mild performance fatigability: 0% < PPFI < 3.5% [women], 0% < PPFI < 5.4% [men]; moderate-to-severe performance fatigability: PPFI ≥ 3.5% [women], PPFI ≥ 5.4% [men]) discriminated physical performance (all p  < .001), adjusted for demographics and smoking status. Conclusion Our work underscores the utility of PPFI as a valid measure to quantify performance fatigability in future longitudinal epidemiologic studies and clinical/pharmaceutical trials. [ABSTRACT FROM AUTHOR]

The influence of abiotic and biotic factors on the temporal pattern of calling and breeding for many temperate anurans is well understood. However, few studies have documented patterns of reproduction in explosive-breeding tropical frogs or incorporated multiple environmental factors in their analyses, especially across multiple breeding seasons. We combine long-term natural history observations and automated data collection methods with boosted regression tree (BRT) analysis to determine the phenology and determinants of explosive breeding in the gliding treefrog, Agalychnis spurrelli. We monitored breeding for a total of 418 days across three breeding seasons and determined the relative importance of several environmental factors on the probability of calling and breeding activity. Our study population of A. spurrelli on Costa Rica's Osa Peninsula forms breeding aggregations up to 11 times per year during 1–2-day long explosive-breeding events, from late May to mid-September. Calling and breeding activity are strongly and positively related to accumulated rainfall during the previous 24 and 48–24 h before, particularly rainfall during the afternoon and evening. Day-of-year, days since breeding occurred, and lunar phase also influence reproductive activity. This study provides the first description and analysis of the phenology and factors that predict explosive breeding in A. spurrelli and illustrates the value of using automated data collection paired with BRTs for the analysis of complex ecological data. La influencia de factores abióticos y bióticos en el patrón temporal de cantos y reproducción de muchos anuros de zonas templadas es bien conocida. Sin embargo, pocos estudios han documentado patrones de reproducción en ranas tropicales, que se reproducen de manera explosiva, o han incorporado múltiples factores ambientales en sus análisis, especialmente a lo largo de múltiples temporadas de reproducción. Combinamos observaciones de historia natural a largo plazo y métodos automatizados de recolectar datos con análisis de árboles de regresión y técnicas de remuestreo ('boosted regression trees', BRT) para determinar la fenología y los determinantes de la reproducción explosiva en la rana planeadora, Agalychnis spurrelli. Monitoreamos la reproducción durante un total de 418 días a través de tres temporadas de reproducción y determinamos la importancia relativa de varios factores ambientales en la probabilidad de cantos y reproducción. Nuestra población de estudio de A. spurrelli en la Península de Osa en Costa Rica forma agregaciones reproductivas hasta 11 veces por año, los eventos de reproducción explosiva tienen 1 a 2 días de duración, desde finales de mayo hasta mediados de septiembre. Los cantos y la reproducción están fuerte y positivamente relacionados con la acumulación de lluvia en las 24 y 48 a 24 horas anteriores, particularmente durante la tarde y la noche. El día del año, los días desde la última reproducción, y la fase lunar también influyen la reproducción. Este estudio es la primera descripción y análisis de la fenología y los factores que predicen la reproducción explosiva en A. spurrelli e ilustra el valor de utilizar la recolección de datos automatizada junto con BRT para el análisis de datos ecológicos complejos. [ABSTRACT FROM AUTHOR]

Model selection aims to choose the most adequate model for the statistical analysis at hand. The model must be complex enough to capture the complexity of the data but should be simple enough not to overfit. In phylogenetics, the most common model selection scenario concerns selecting an adequate substitution and partition model for sequence evolution to infer a phylogenetic tree. Previously, several studies showed that substitution model under-parameterization can bias phylogenetic studies. Here, we explored the impact of substitution model over-parameterization in a Bayesian statistical framework. We performed simulations under the simplest substitution model, the Jukes–Cantor model, and compare posterior estimates of phylogenetic tree topologies and tree length under the true model to the most complex model, the |$\text{GTR}+\Gamma+\text{I}$| substitution model, including over-splitting the data into additional subsets (i.e. applying partitioned models). We explored 4 choices of prior distributions: the default substitution model priors of MrBayes, BEAST2, and RevBayes and a newly devised prior choice (Tame). Our results show that Bayesian inference of phylogeny is robust to substitution model over-parameterization and over-partitioning but only under our new prior settings. All 3 current default priors introduced biases for the estimated tree length. We conclude that substitution and partition model selection are superfluous steps in Bayesian phylogenetic inference pipelines if well-behaved prior distributions are applied and more effort should focus on more complex and biologically realistic substitution models. [ABSTRACT FROM AUTHOR]

We inspect how accurate machine learning (ML) is at forecasting realized variance of the Dow Jones Industrial Average index constituents. We compare several ML algorithms, including regularization, regression trees, and neural networks, to multiple heterogeneous autoregressive (HAR) models. ML is implemented with minimal hyperparameter tuning. In spite of this, ML is competitive and beats the HAR lineage, even when the only predictors are the daily, weekly, and monthly lags of realized variance. The forecast gains are more pronounced at longer horizons. We attribute this to higher persistence in the ML models, which helps to approximate the long memory of realized variance. ML also excels at locating incremental information about future volatility from additional predictors. Lastly, we propose an ML measure of variable importance based on accumulated local effects. This shows that while there is agreement about the most important predictors, there is disagreement on their ranking, helping to reconcile our results. [ABSTRACT FROM AUTHOR]

Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ1‐type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2‐norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database. [ABSTRACT FROM AUTHOR]

This paper introduces a flexible and adaptive nonparametric method for estimating the association between multiple covariates and power spectra of multiple time series. The proposed approach uses a Bayesian sum of trees model to capture complex dependencies and interactions between covariates and the power spectrum, which are often observed in studies of biomedical time series. Local power spectra corresponding to terminal nodes within trees are estimated nonparametrically using Bayesian penalized linear splines. The trees are considered to be random and fit using a Bayesian backfitting Markov chain Monte Carlo (MCMC) algorithm that sequentially considers tree modifications via reversible‐jump MCMC techniques. For high‐dimensional covariates, a sparsity‐inducing Dirichlet hyperprior on tree splitting proportions is considered, which provides sparse estimation of covariate effects and efficient variable selection. By averaging over the posterior distribution of trees, the proposed method can recover both smooth and abrupt changes in the power spectrum across multiple covariates. Empirical performance is evaluated via simulations to demonstrate the proposed method's ability to accurately recover complex relationships and interactions. The proposed methodology is used to study gait maturation in young children by evaluating age‐related changes in power spectra of stride interval time series in the presence of other covariates. [ABSTRACT FROM AUTHOR]

Fire debris analysis aims to detect and identify any ignitable liquid residues in burnt residues collected at a fire scene. Typically, the burnt residues are analysed using gas chromatography–mass spectrometry (GC–MS) and are manually interpreted. The interpretation process can be laborious due to the complexity and high dimensionality of the GC–MS data. Therefore, this study aims to compare the potential of classification and regression tree (CART) and naïve Bayes (NB) algorithms in analysing the pixel-level GC–MS data of fire debris. The data comprise 14 positive (i.e. fire debris with traces of gasoline) and 24 negative (i.e. fire debris without traces of gasoline) samples. The differences between the positive and negative samples were first inspected based on the mean chromatograms and scores plots of the principal component analysis technique. Then, CART and NB algorithms were independently applied to the GC–MS data. Stratified random resampling was applied to prepare three sets of 200 pairs of training and testing samples (i.e. split ratio of 7:3, 8:2, and 9:1) for estimating the prediction accuracies. Although both the positive and negative samples were hardly differentiated based on the mean chromatograms and scores plots of principal component analysis, the respective NB and CART predictive models produced satisfactory performances with the normalized GC–MS data, i.e. majority achieved prediction accuracy >70%. NB consistently outperformed CART based on the prediction accuracies of testing samples and the corresponding risk of overfitting except when evaluated using only 10% of samples. The accuracy of CART was found to be inversely proportional to the number of testing samples; meanwhile, NB demonstrated rather consistent performances across the three split ratios. In conclusion, NB seems to be much better than CART based on the robustness against the number of testing samples and the consistent lower risk of overfitting. [ABSTRACT FROM AUTHOR]

Objectives: To describe the long-term clinical course of each manifestation of Behçet’s disease (BD) and clarify factors involved in oral ulcer (OU) remission using clinical information of BD patients. Methods: We retrospectively studied 155 BD patients visiting our hospital (1989–2020). We defined remission criteria for each manifestation and examined long-term clinical changes. Classification and regression trees and multivariable analyses were performed to investigate OU prognostic factors; hazard ratios were used to assign scores to prognostic factors deemed significant [OU prognosis score (OuP score)]. Risk stratification was examined by dividing the OuP scores into four stages. Results: OUs appeared earliest, with the slowest decline in prevalence observed post-BD diagnosis. OU presence was the most common factor inhibiting complete remission. Young age at OU onset, never smoker, presence of genital ulcers, positive pathergy test, no usage of tumour necrosis factor inhibitors or of immunosuppressants, and long-term non-treatment or symptomatic treatment for OUs were poor OU prognostic factors. Based on multivariable analysis, the area under the curve of the OuP score to predict OU prognosis was 0.678. Conclusions: Remission criteria for each symptom clarified that OU had the greatest impact on complete BD remission. Faster OU remission was associated with earlier OU therapeutic intervention other than symptomatic treatment. [ABSTRACT FROM AUTHOR]

We consider inference from nonrandom samples in data-rich settings where high-dimensional auxiliary information is available both in the sample and the target population, with survey inference being a special case. We propose a regularized prediction approach that predicts the outcomes in the population using a large number of auxiliary variables such that the ignorability assumption is reasonable and the Bayesian framework is straightforward for quantification of uncertainty. Besides the auxiliary variables, we also extend the approach by estimating the propensity score for a unit to be included in the sample and also including it as a predictor in the machine learning models. We find in simulation studies that the regularized predictions using soft Bayesian additive regression trees yield valid inference for the population means and coverage rates close to the nominal levels. We demonstrate the application of the proposed methods using two different real data applications, one in a survey and one in an epidemiologic study. [ABSTRACT FROM AUTHOR]

Maternal exposure to environmental chemicals during pregnancy can alter birth and children's health outcomes. Research seeks to identify critical windows, time periods when exposures can change future health outcomes, and estimate the exposure–response relationship. Existing statistical approaches focus on estimation of the association between maternal exposure to a single environmental chemical observed at high temporal resolution (e.g., weekly throughout pregnancy) and children's health outcomes. Extending to multiple chemicals observed at high temporal resolution poses a dimensionality problem and statistical methods are lacking. We propose a regression tree–based model for mixtures of exposures observed at high temporal resolution. The proposed approach uses an additive ensemble of tree pairs that defines structured main effects and interactions between time‐resolved predictors and performs variable selection to select out of the model predictors not correlated with the outcome. In simulation, we show that the tree‐based approach performs better than existing methods for a single exposure and can accurately estimate critical windows in the exposure–response relation for mixtures. We apply our method to estimate the relationship between five exposures measured weekly throughout pregnancy and birth weight in a Denver, Colorado, birth cohort. We identified critical windows during which fine particulate matter, sulfur dioxide, and temperature are negatively associated with birth weight and an interaction between fine particulate matter and temperature. Software is made available in the R package dlmtree. [ABSTRACT FROM AUTHOR]

Missing data remains a very common problem in large datasets, including survey and census data containing many ordinal responses, such as political polls and opinion surveys. Multiple imputation (MI) is usually the go-to approach for analyzing such incomplete datasets, and there are indeed several implementations of MI, including methods using generalized linear models, tree-based models, and Bayesian non-parametric models. However, there is limited research on the statistical performance of these methods for multivariate ordinal data. In this article, we perform an empirical evaluation of several MI methods, including MI by chained equations (MICE) using multinomial logistic regression models, MICE using proportional odds logistic regression models, MICE using classification and regression trees, MICE using random forest, MI using Dirichlet process (DP) mixtures of products of multinomial distributions, and MI using DP mixtures of multivariate normal distributions. We evaluate the methods using simulation studies based on ordinal variables selected from the 2018 American Community Survey. Under our simulation settings, the results suggest that MI using proportional odds logistic regression models, classification and regression trees, and DP mixtures of multinomial distributions generally outperform the other methods. In certain settings, MI using multinomial logistic regression models is able to achieve comparable performance, depending on the missing data mechanism and amount of missing data. [ABSTRACT FROM AUTHOR]

Significant progress in previous decades has led to several methodologies developed to facilitate the design of optimal antimicrobial dosing. In this review, we highlight common pharmacokinetic/pharmacodynamic (PKPD) modeling techniques and their roles in guiding rational dosing regimen design. In the early drug development phases, dose fractionation studies identify the PKPD index most closely associated with bacterial killing. Once discerned, this index is linked to clinical efficacy end points, and classification and regression tree analysis can be used to define the PKPD target goal. Monte Carlo simulations integrate PKPD and microbiological data to identify dosing strategies with a high probability of achieving the established PKPD target. Results then determine dosing regimens to investigate and/or validate the findings of randomized controlled trials. Further improvements in PKPD modeling could lead to an era of precision dosing and personalized therapeutics. [ABSTRACT FROM AUTHOR]

The concept of nestedness originated from the field of biogeography decades ago and has been widely used in metacommunities and biological interaction networks, but there is still a lack of research within local communities. Moreover, studies on nestedness usually rarely incorporate the functional traits of the species and the environmental characteristics of the sites. In this study, we constructed a species presence–absence matrix of a 50-ha forest plot, used the simulated annealing algorithm to reveal the maximum nested structure and further tested the significance of nestedness patterns by constructing null ensembles. The nested ranks were used to represent the orders of species and quadrats in the maximum nestedness matrix. The regression tree analysis was used to reveal the relationships of nested ranks with environmental factors and functional traits. We found that the co-occurrence pattern of local plant communities was significantly nested. The regression tree results showed that the nested ranks of quadrats were determined by soil available phosphorus, soil water content, soil organic carbon and soil pH. Intraspecific variation of functional traits, including leaf C, leaf pH, leaf dry matter content and maximum photosynthetic rate rather than means of functional traits, provided a better explanation for the formation of species' nested ranks. Understanding the causes of species and quadrats nested ranks provides novel lens and useful insights into ecological processes underlying nestedness, and further improves our knowledge of how local plant communities are assembled. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the parameterized complexity of model checking for Dependence and Independence logic, which are well studied logics in the area of Team Semantics. We start with a list of nine immediate parameterizations for this problem, namely the number of disjunctions (i.e. splits)/(free) variables/universal quantifiers, formula-size, the tree-width of the Gaifman graph of the input structure, the size of the universe/team and the arity of dependence atoms. We present a comprehensive picture of the parameterized complexity of model checking and obtain a division of the problem into tractable and various intractable degrees. Furthermore, we also consider the complexity of the most important variants (data and expression complexity) of the model checking problem by fixing parts of the input. [ABSTRACT FROM AUTHOR]

Accurate and effective prediction of mutation-induced protein energy change remains a great challenge and of great interest in computational biology. However, high resource consumption and insufficient structural information of proteins severely limit the experimental techniques and structure-based prediction methods. Here, we design a structure-independent protocol to accurately and effectively predict the mutation-induced protein folding free energy change with only sequence, physicochemical and evolutionary features. The proposed clustered tree regression protocol is capable of effectively exploiting the inherent data patterns by integrating unsupervised feature clustering by K-means and supervised tree regression using XGBoost, and thus enabling fast and accurate protein predictions with different mutations, with an average Pearson correlation coefficient of 0.83 and an average root-mean-square error of 0.94kcal/mol. The proposed sequence-based method not only eliminates the dependence on protein structures, but also has potential applications in protein predictions with rare structural information. [ABSTRACT FROM AUTHOR]

Ciliates represent a crucial link between phytoplankton and bacteria and mesozooplankton in pelagic food webs, but little is known about the processes influencing the dynamics of individual species. Using long-term, high-frequency observations, we compared the diversity and the temporal variability in biomass and species composition of the ciliate community in large, deep, mesotrophic Lake Constance to that of the phytoplankton and rotifer communities in the same lake. Furthermore, we used boosted regression trees to evaluate possible environmental predictors (temperature, three prey groups, four predator/competitor groups) influencing ciliate net growth. The biomass of all ciliate species showed a common, recurrent seasonal pattern, often with peaks in spring and summer. The ciliate community was more diverse than the rotifer community, exhibited highly synchronous dynamics and its species were regularly encountered during the season. The top-down control by copepods likely contributes to the ciliates' synchronized decline prior to the clear-water phase when food concentration is still high. The high temporal autocorrelation of the ciliate biomasses together with the inter-annual recurrent seasonal patterns and the low explanatory power of the environmental predictors suggest that the dynamics of individual ciliate species are strictly controlled, yet it remains difficult to determine the responsible factors. [ABSTRACT FROM AUTHOR]

In causal inference problems, one is often tasked with estimating causal effects which are analytically intractable functionals of the data‐generating mechanism. Relevant settings include estimating intention‐to‐treat effects in longitudinal problems with missing data or computing direct and indirect effects in mediation analysis. One approach to computing these effects is to use the g‐formula implemented via Monte Carlo integration; when simulation‐based methods such as the nonparametric bootstrap or Markov chain Monte Carlo are used for inference, Monte Carlo integration must be nested within an already computationally intensive algorithm. We develop a widely‐applicable approach to accelerating this Monte Carlo integration step which greatly reduces the computational burden of existing g‐computation algorithms. We refer to our method as accelerated g‐computation (AGC). The algorithms we present are similar in spirit to multiple imputation, but require removing within‐imputation variance from the standard error rather than adding it. We illustrate the use of AGC on a mediation analysis problem using a beta regression model and in a longitudinal clinical trial subject to nonignorable missingness using a Bayesian additive regression trees model. [ABSTRACT FROM AUTHOR]

Popular parametric and semiparametric hazards regression models for clustered survival data are inappropriate and inadequate when the unknown effects of different covariates and clustering are complex. This calls for a flexible modeling framework to yield efficient survival prediction. Moreover, for some survival studies involving time to occurrence of some asymptomatic events, survival times are typically interval censored between consecutive clinical inspections. In this article, we propose a robust semiparametric model for clustered interval‐censored survival data under a paradigm of Bayesian ensemble learning, called soft Bayesian additive regression trees or SBART (Linero and Yang, 2018), which combines multiple sparse (soft) decision trees to attain excellent predictive accuracy. We develop a novel semiparametric hazards regression model by modeling the hazard function as a product of a parametric baseline hazard function and a nonparametric component that uses SBART to incorporate clustering, unknown functional forms of the main effects, and interaction effects of various covariates. In addition to being applicable for left‐censored, right‐censored, and interval‐censored survival data, our methodology is implemented using a data augmentation scheme which allows for existing Bayesian backfitting algorithms to be used. We illustrate the practical implementation and advantages of our method via simulation studies and an analysis of a prostate cancer surgery study where dependence on the experience and skill level of the physicians leads to clustering of survival times. We conclude by discussing our method's applicability in studies involving high‐dimensional data with complex underlying associations. [ABSTRACT FROM AUTHOR]

In studies of maternal exposure to air pollution, a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposures to PM$_{2.5}$ and birth weight in a Colorado, USA birth cohort. [ABSTRACT FROM AUTHOR]

Large observational data are increasingly available in disciplines such as health, economic and social sciences, where researchers are interested in causal questions rather than prediction. In this paper, we examine the problem of estimating heterogeneous treatment effects using non‐parametric regression‐based methods, starting from an empirical study aimed at investigating the effect of participation in school meal programs on health indicators. First, we introduce the setup and the issues related to conducting causal inference with observational or non‐fully randomized data, and how these issues can be tackled with the help of statistical learning tools. Then, we review and develop a unifying taxonomy of the existing state‐of‐the‐art frameworks that allow for individual treatment effects estimation via non‐parametric regression models. After presenting a brief overview on the problem of model selection, we illustrate the performance of some of the methods on three different simulated studies. We conclude by demonstrating the use of some of the methods on an empirical analysis of the school meal program data. [ABSTRACT FROM AUTHOR]

We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals with heterogeneous treatment effects in observational data. The CIT algorithms are extensions of the classification and regression tree algorithm that use splitting criteria based on subgroup‐specific treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g‐formula, and doubly robust estimators of subgroup‐specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients. [ABSTRACT FROM AUTHOR]

Data synthesis is an effective statistical approach for reducing data disclosure risk. Generating fully synthetic data might minimize such risk, but its modeling and application can be difficult for data from large, complex surveys. This article extended the two-stage imputation to simultaneously impute item missing values and generate fully synthetic data. A new combining rule for making inferences using data generated in this manner was developed. Two semiparametric missing data imputation models were adapted to generate fully synthetic data for skewed continuous variable and sparse binary variable, respectively. The proposed approach was evaluated using simulated data and real longitudinal data from the Health and Retirement Study. The proposed approach was also compared with two existing synthesis approaches: (1) parametric regressions models as implemented in IVEware ; and (2) nonparametric Classification and Regression Trees as implemented in synthpop package for R using real data. The results show that high data utility is maintained for a wide variety of descriptive and model-based statistics using the proposed strategy. The proposed strategy also performs better than existing methods for sophisticated analyses such as factor analysis. [ABSTRACT FROM AUTHOR]

A compositional tree refers to a tree structure on a set of random variables where each random variable is a node and composition occurs at each non‐leaf node of the tree. As a generalization of compositional data, compositional trees handle more complex relationships among random variables and appear in many disciplines, such as brain imaging, genomics and finance. We consider the problem of sparse regression on data that are associated with a compositional tree and propose a transformation‐free tree‐based regularized regression method for component selection. The regularization penalty is designed based on the tree structure and encourages a sparse tree representation. We prove that our proposed estimator for regression coefficients is both consistent and model selection consistent. In the simulation study, our method shows higher accuracy than competing methods under different scenarios. By analysing a brain imaging data set from studies of Alzheimer's disease, our method identifies meaningful associations between memory decline and volume of brain regions that are consistent with current understanding. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is the problem of determining the likelihood of a landslide occurrence in a particular area with respect to the geographical and morphological properties of the area. This paper presents a hybrid method, namely SkySlide , that incorporates clustering, skyline operator, classification and majority voting principle for region-scale landslide susceptibility assessment. Clustering and skyline operator are utilized to model landslides while classification and majority voting principle are utilized to assess landslide susceptibility. The contribution of the study is 2-fold. First, the proposed method requires properties of landslide-occurring data only to model landslides. Second, the proposed method is evaluated on imbalanced data and experimental results include performance metrics of imbalanced data. Experiments conducted on two real-life datasets show that clustering greatly improves performance of SkySlide. Experiments further demonstrate that SkySlide achieves higher class balance accuracy, Matthews correlation coefficient, geometric mean and bookmaker informedness scores compared with the most commonly used methods for landslide susceptibility assessment such as support vector machines, logistic regression and decision trees. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method. [ABSTRACT FROM AUTHOR]

Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level. [ABSTRACT FROM AUTHOR]

Background There is a dearth of research to understand which children, among those who are exposed at home to tuberculosis (TB), are at the highest risk of TB disease, to tailor care. We sought to identify predictors of TB progression in children. Methods We conducted a prospective cohort study of children living with adults with pulmonary TB in Lima, Peru (2009–2012). We applied classification and regression tree analysis to examine potential predictors of incident TB disease during 12 months in 3 age groups (0–4, 5–9, and 10–14 years). We calculated the relative risk (RR) for top predictors in each age group. Results Among 4545 children 0–14 years old, 156 (3.4%) were diagnosed with TB within 1 year of household exposure to TB (3.4%, 2.3%, and 4.7% in children 0–4, 5–9, and 10–14 years old, respectively). The most important predictor of TB was having a positive tuberculin skin test (TST) result, with RRs of 6.6 (95% CI, 4.0–10.7), 6.6 (95% CI, 3.2–13.6), and 5.2 (95% CI, 3.0–9.0) in the age groups 0–4, 5–9, and 10–14 years, respectively. In young children with a positive TST, not using isoniazid preventive treatment further increased risk of disease (RR, 12.2 [95% CI, 3.8–39.2]). Conclusions We present a tool that identifies child household contacts at high risk of TB disease progression based on data collected during contact tracing. In addition to the use of TB preventive therapy for all children exposed at home to TB, those children at highest risk of progressing to TB disease may benefit from more frequent follow-up. [ABSTRACT FROM AUTHOR]

Background: Cut-points to define slow walking speed have largely been derived from expert opinion.Methods: Study participants (13 589 men and 5043 women aged ≥65years) had walking speed (m/s) measured over 4-6 m (mean ± SD: 1.20 ± 0.27 m/s in men and 0.94 ± 0.24 m/s in women.) Mobility limitation was defined as any self-reported difficulty with walking approximately 1/4 mile (prevalence: 12.6% men, 26.4% women). Sex-stratified classification and regression tree (CART) models with 10-fold cross-validation identified walking speed cut-points that optimally discriminated those who reported mobility limitation from those who did not.Results: Among 5043 women, CART analysis identified 2 cut-points, classifying 4144 (82.2%) with walking speed ≥0.75 m/s, which we labeled as "fast"; 478 (9.5%) as "intermediate" (walking speed ≥0.62 m/s but <0.75 m/s); and 421 (8.3%) as "slow" (walking speed <0.62 m/s). Among 13 589 men, CART analysis identified 3 cut-points, classifying 10 001 (73.6%) with walking speed ≥1.00 m/s ("very fast"); 2901 (21.3%) as "fast" (walking speed ≥0.74 m/s but <1.00 m/s); 497 (3.7%) as "intermediate" (walking speed ≥0.57 m/s but <0.74 m/s); and 190 (1.4%) as "slow" (walking speed <0.57 m/s). Prevalence of self-reported mobility limitation was lowest in the "fast" or "very fast" (11% for men and 19% for women) and highest in the "slow" (60.5% in men and 71.0% in women). Rounding the 2 slower cut-points to 0.60 m/s and 0.75 m/s reclassified very few participants.Conclusions: Cut-points in walking speed of approximately 0.60 m/s and 0.75 m/s discriminate those with self-reported mobility limitation from those without. [ABSTRACT FROM AUTHOR]

During the last two decades, a number of countries or cities established heat‐health warning systems in order to alert public health authorities when some heat indicator exceeds a predetermined threshold. Different methods were considered to establish thresholds all over the world, each with its own strengths and weaknesses. The common ground is that current methods are based on exposure‐response function estimates that can fail in many situations. The present paper aims at proposing several data‐driven methods to establish thresholds using historical data of health issues and environmental indicators. The proposed methods are model‐based regression trees (MOB), multivariate adaptive regression splines (MARS), the patient rule‐induction method (PRIM) and adaptive index models (AIM). These methods focus on finding relevant splits in the association between indicators and the health outcome but do it in different fashions. A simulation study and a real‐world case study hereby compare the discussed methods. Results show that proposed methods are better at predicting adverse days than current thresholds and benchmark methods. The results nonetheless suggest that PRIM is overall the more reliable method with low variability of results according to the scenario or case. [ABSTRACT FROM AUTHOR]

Background Clinical studies have demonstrated inferior cure rates when metronidazole (MTZ) is used to treat Clostridioides difficile infection (CDI). We hypothesized that a newly identified, heme-inducible form of reduced MTZ susceptibility in C. difficile leads to higher odds of initial clinical failure in patients with CDI treated with MTZ. Methods This multicenter cohort study included adults diagnosed with CDI between 2017 and 2018. C. difficile isolated from stool samples underwent agar dilution MTZ susceptibility testing with incorporation of fresh heme. Blinded investigators reviewed medical records for initial clinical failure and other relevant clinical variables. Classification and regression tree (CART) analysis was used to identify the MTZ minimum inhibitory concentration (MIC) breakpoint that was predictive of initial clinical failure. Results were confirmed using univariate and multivariable logistic regression analyses to account for potential confounders. Results Of the 356 patients included, 72% received MTZ-based therapy and 27% experienced initial clinical failure. CART analysis identified an MTZ MIC ≥1 µg/mL above which patients had a higher rate of initial clinical failure. MTZ MICs ranged from 0.25 to 8 µg/mL (MIC50/90 = 0.25/2 µg/mL), and approximately 18% of isolates had MTZ MICs ≥1 µg/mL. In multivariable analysis, an MTZ MIC ≥1 µg/mL was an independent predictor of initial clinical failure in patients receiving an MTZ-based treatment regimen (odds ratio, 2.27 [95% confidence interval, 1.18–4.34]). Conclusions Using a reproducible method to determine C. difficile MICs to MTZ, a breakpoint of ≥1 µg/mL identified patients at higher risk of initial clinical failure. [ABSTRACT FROM AUTHOR]

We investigate whether generating synthetic data can be a viable strategy for providing access to detailed geocoding information for external researchers, without compromising the confidentiality of the units included in the database. Our work was motivated by a recent project at the Institute for Employment Research in Germany that linked exact geocodes to the Integrated Employment Biographies, a large administrative database containing several million records. We evaluate the performance of three synthesizers regarding the trade-off between preserving analytical validity and limiting disclosure risks: one synthesizer employs Dirichlet Process mixtures of products of multinomials, while the other two use different versions of Classification and Regression Trees (CART). In terms of preserving analytical validity, our proposed synthesis strategy for geocodes based on categorical CART models outperforms the other two. If the risks of the synthetic data generated by the categorical CART synthesizer are deemed too high, we demonstrate that synthesizing additional variables is the preferred strategy to address the risk-utility trade-off in practice, compared to limiting the size of the regression trees or relying on the strategy of providing geographical information only on an aggregated level. We also propose strategies for making the synthesizers scalable for large files, present analytical validity measures and disclosure risk measures for the generated data, and provide general recommendations for statistical agencies considering the synthetic data approach for disseminating detailed geographical information. [ABSTRACT FROM AUTHOR]

The scientific value of the next generation of large continuum surveys would be greatly increased if the redshifts of the newly detected sources could be rapidly and reliably estimated. Given the observational expense of obtaining spectroscopic redshifts for the large number of new detections expected, there has been substantial recent work on using machine learning techniques to obtain photometric redshifts. Here, we compare the accuracy of the predicted photometric redshifts obtained from deep learning (DL) with the k- nearest neighbour (kNN) and the decision tree regression (DTR) algorithms. We find using a combination of near-infrared, visible, and ultraviolet magnitudes, trained upon a sample of Sloan Digital Sky Survey quasi-stellar objects, that the kNN and DL algorithms produce the best self-validation result with a standard deviation of σΔ z = 0.24 (σΔ z (norm) = 0.11). Testing on various subsamples, we find that the DL algorithm generally has lower values of σΔ z , in addition to exhibiting a better performance in other measures. Our DL method, which uses an easy to implement off-the-shelf algorithm with neither filtering nor removal of outliers, performs similarly to other, more complex, algorithms, resulting in an accuracy of Δ z < 0.1 up to z ∼ 2.5. Applying the DL algorithm trained on our 70 000 strong sample to other independent (radio-selected) data sets, we find σΔ z ≤ 0.36 (σΔ z (norm) ≤ 0.17) over a wide range of radio flux densities. This indicates much potential in using this method to determine photometric redshifts of quasars detected with the Square Kilometre Array. [ABSTRACT FROM AUTHOR]

Write HT ht for the training data estimator. To calculate the first term in (1), they regress the full estimator on HT ht . Rather than permuting HT ht , they replace the estimated coefficients for HT ht in the full model with values of zero. This brings us to the authors second estimator HT ht (Algorithm 2), which we have discussed in more detail. [Extracted from the article]

Causal inference with observational longitudinal data and time‐varying exposures is often complicated by time‐dependent confounding and attrition. The G‐computation formula is one approach for estimating a causal effect in this setting. The parametric modelling approach typically used in practice relies on strong modelling assumptions for valid inference and moreover depends on an assumption of missing at random, which is not appropriate when the missingness is missing not at random (MNAR) or due to death. In this work we develop a flexible Bayesian semi‐parametric G‐computation approach for assessing the causal effect on the subpopulation that would survive irrespective of exposure, in a setting with MNAR dropout. The approach is to specify models for the observed data using Bayesian additive regression trees, and then, use assumptions with embedded sensitivity parameters to identify and estimate the causal effect. The proposed approach is motivated by a longitudinal cohort study on cognition, health and ageing and we apply our approach to study the effect of becoming a widow on memory. We also compare our approach to several standard methods. [ABSTRACT FROM AUTHOR]

The spread and impact of invasive species in exotic ranges can be mitigated by increased understanding of pest invasion dynamics. Here, we used geospatial analyses and habitat suitability modeling to characterize the invasion of an important vineyard pest, vine mealybug (Planococcus ficus Signoret, Hemiptera: Pseudococcidae), using nearly 15,000 trapping records from throughout Napa County, California, between 2012 and 2017. Spatial autocorrelation among P. ficus detections was strongest at distances of ~250 m and detectable at regional scales (up to 40 km), estimates of the rate and directionality of spread were highly idiosyncratic, and P. ficus detection hotspots were spatiotemporally dynamic. Generalized linear model, boosted regression tree, and random forest modeling methods performed well in predicting habitat suitability for P. ficus. The most important predictors of P. ficus occurrence were a positive effect of precipitation in the driest month, and negative effects of elevation and distance to nearest winery. Our results indicate that 250-m quarantine and treatment zones around P. ficus detections are likely sufficient to encompass most local establishment and spread, and that implementing localized regulatory procedures may limit inadvertent P. ficus spread via anthropogenic pathways. Finally, surveys of P. ficus presence at >300 vineyard sites validated that habitat suitability estimates were significantly and positively associated with P. ficus frequency of occurrence. Our findings indicate that habitat suitability predictions may offer a robust tool for identifying areas in the study region at risk to future P. ficus invasion and prioritizing locations for early detection and preventative management efforts. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to establish age- and sex-specific pediatric reference intervals of serum potassium (K), sodium (Na), chlorine (Cl), calcium (Ca), magnesium (Mg), and phosphorus (P) using a direct sampling technique.Methods: In accordance with the a priori approach, healthy individuals (n = 6,466, aged 1 month to <18 years) were enrolled from five regions in Jilin Province, China, and all analytes were performed in the center laboratory. Reference intervals were divided according to the regression tree and Harris and Boyd's method, and then they were calculated by the nonparametric rank method. The dynamic changes of reference intervals were evaluated by the lambda-mu-sigma (LMS) method.Results: Reference intervals of serum Na and Ca were divided into three age-specific partitions. The concentrations of K, Cl, and Mg remained stable with age. However, only dramatic sex-specific changes of P were shown in those 11 to less than 13 years old and 13 to less than 15 years old, with an earlier peak time in females than in males. The correlation between Na and Cl was the strongest among all serum electrolytes (r = 0.31).Conclusions: Serum electrolyte reference intervals for children and adolescents were established by regression tree, z test, and the LMS method, which provide a more accurate interpretation for diagnosis and prognosis evaluation of clinical pediatric diseases. [ABSTRACT FROM AUTHOR]

Summary: Although circular data occur in a wide range of scientific fields, the methodology for distributional modelling and probabilistic forecasting of circular response variables is quite limited. Most of the existing methods are built on generalized linear and additive models, which are often challenging to optimize and interpret. Specifically, capturing abrupt changes or interactions is not straightforward but often relevant, e.g. for modelling wind directions subject to different wind regimes. Additionally, automatic covariate selection is desirable when many predictor variables are available, as is often the case in weather forecasting. To address these challenges we suggest a general distributional approach using regression trees and random forests to obtain probabilistic forecasts for circular responses. Using trees simplifies model estimation as covariates are used only for partitioning the data and subsequently just a simple von Mises distribution is fitted in the resulting subgroups. Circular regression trees are straightforward to interpret, can capture non‐linear effects and interactions, and automatically select covariates affecting location and/or scale in the von Mises distribution. Circular random forests regularize and smooth the effects from an ensemble of trees. The new methods are applied to probabilistic wind direction forecasting at two Austrian airports, considering other common approaches as a benchmark. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide Open in new tab Download slide OBJECTIVES Many adults with repaired tetralogy of Fallot will require a pulmonary valve replacement (PVR), but there is no consensus on the best timing. In this study, we aim to evaluate the impact of age at PVR on outcomes. METHODS This is a national multicentre retrospective study including all patients >15 years of age with repaired tetralogy of Fallot who underwent their first PVR between 2000 and 2013. The optimal age cut-off was identified using Cox regression and classification and regression tree analysis. RESULTS A total of 707 patients were included, median age 26 (15–72) years. The mortality rate at 10 years after PVR was 4.2%, and the second PVR rate of 6.8%. Age at PVR of 35 years was identified as the optimal cut-off in relation to late mortality. Patients above 35 years of age had a 5.6 fold risk of death at 10 years compared with those with PVR under 35 years (10.4% vs 1.3%, P  < 0.001), more concomitant tricuspid valve repair/replacement (15.1% vs 5.7%, P  < 0.001) and surgical arrhythmia treatment (18.4% vs 5.9%, P  < 0.001). In those under 50 years, there was an 8.7 fold risk of late death compared with the general population, higher for those with PVR after 35 than those with PVR below 35 years (hazard ratio 9.9 vs 7.4). CONCLUSIONS Patients above 35 years of age with repaired tetralogy of Fallot have significantly worse mortality after PVR, compared with younger patients and a higher burden of mortality relative to the general population. This suggests that there are still cases where the timing of initial PVR is not optimal, warranting a re-evaluation of criteria for intervention. [ABSTRACT FROM AUTHOR]

Background: It remains uncertain whether the association between physical activity (PA) and falls is U-shaped, and few studies have explored the potential mediation of PA accumulation pattern.Methods: We measured PA in 671 community-dwelling older adults (82.7 ± 3.8 years) using wrist-worn accelerometer for 7 days. PA was further classified to bouted PA (≥10 minutes bout length) and sporadic PA (<10 minutes bout length) for subanalysis. Fall incidence in the following 12-month was recorded through tri-monthly telephone interviews. Classification and Regression Tree analysis was used to identify two optimal cutoff values of each PA measurement to predict falls. Participants were then divided into "inactive," "moderately active," and "highly active" groups accordingly. Negative binomial regression models were used to estimate the association between the PA measures and fall incidence.Results: Six hundred and thirty-nine participants completed 12-month follow-up. Ninety-three (14.6%) experienced a total of 118 falls. Inactive and highly active older adults had higher falls per person month relative to the moderately active group (inactive: incidence rate ratios [IRR] = 2.372, 95% confidence interval [CI] = 1.317-4.271; highly active: IRR = 2.731, 95% CI = 1.196-6.232). Subanalyses found similar significant finding with bouted PA (p < .001) but not sporadic PA (p ≥ .221). The association between bouted PA and falls remained significant even after adjusting fall incidence for bouted activity time (inactive: IRR = 3.636, 95% CI = 2.238-5.907; highly active: IRR = 1.823, 95% CI = 1.072-3.1). Further adjustments for fall-related risk factors did not meaningfully change the results.Conclusion: A U-shaped relationship was identified between bouted but not sporadic PA and fall incidence. There is an approximately twofold increase in fall rate in highly active older adults even after adjusting for activity time. [ABSTRACT FROM AUTHOR]