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Background: Prior studies on longevity often examine predictors in isolation and rely solely on baseline information, limiting our understanding of the most important predictors and their dynamic nature. In this study, we used an innovative regression tree model to explore the common characteristics of those who lived longer than their age and sex peers in 35-years follow-up. We identified different pathways leading to a long life, and examined to how changes in characteristics over 10 years (from 1979 to 1989) affect the findings on longevity predictors., Methods: Data was obtained from the "Tampere Longitudinal Study on Ageing" (TamELSA) in Finland. Survey data was collected in 1979 from 1056 participants aged 60-89 years (49.8% men). In 1989, a second survey was conducted among 432 survivors from the 1979 cohort (40.2% men). Dates of death were provided by the Finnish Population Register until 2015. We employed an individual measure of longevity known as the realized probability of dying (RPD), which was calculated based on each participant's age and sex, utilizing population life tables. RPD is based on a comparison of the survival time of each individual of a specific age and sex with the survival time of his/her peers in the total population. A regression tree analysis was used to examine individual-based longevity with RPD as an outcome., Results: This relative measure of longevity (RPD) provided a complex regression tree where the most important characteristics were self-rated health, years of education, history of smoking, and functional ability. We identified several pathways leading to a long life such as individuals with (1) good self-rated health (SRH), short smoking history, and higher education, (2) good SRH, short smoking history, lower education, and excellent mobility, and (3) poor SRH but able to perform less demanding functions, aged 75 or older, willing to do things, and sleeping difficulties. Changes in the characteristics over time did not change the main results., Conclusion: The simultaneous examination of a broad range of potential predictors revealed that longevity can be achieved under very different conditions and is achieved by heterogeneous groups of people., (© 2024. The Author(s).)

Using data from the nationally representative National Immunization Survey (NIS), we applied conditional linear regression tree methodology to examine relationships between demographic and geographic factors and propensity of receiving various doses of COVID-19 vaccine over time; these analyses identified temporal changes in these relationships that heretofore had not been identified using conventional logistical regression methodologies. Three regression tree models were built using an R package, Recursive Partitioning for Modeling Survey (rpms), to examine propensities over time of receiving a (1) first dose of a two-dose COVID-19 mRNA primary vaccination series or single dose of the Janssen vaccine (vaccine initiation), (2) primary series completion, and (3) monovalent booster dose, using a conditional linear effect model. Persons ≥50 years were more likely to complete a primary series and receive a first booster dose; persons reporting having received non-COVID-19 vaccines recently were more likely to initiate vaccination, complete the primary series, and get a first booster dose; persons reporting having work or school requirements were more likely to complete the primary series. Persons not reporting having received non-COVID-19 vaccines in 2 years but reporting having work or school vaccination requirements were more likely to initiate vaccination than those without work/school requirements. Among persons not reporting having received non-COVID-19 vaccines in 2 years and not reporting having work or school vaccination requirements, those aged ≥50 years were more likely to initiate vaccination than were younger adults. Propensity of receiving various doses was correlated with age, having recently received non-COVID 19 vaccines, and having vaccination requirements at work or school. Regression tree methodology enabled modeling of different COVID-19 vaccination dose propensities as a linear effect of time, revealed changes in relationships over time between demographic factors and propensity of receipt of different doses, and identified populations that may benefit from vaccination outreach efforts., Competing Interests: Declaration of competing interest 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., (Published by Elsevier Ltd.)

Background: Intracortical inhibitory/faciliatory measures are affected after stroke; however, the evidence is conflicting., Objective: This meta-analysis aimed to investigate the changes in motor threshold (MT), motor evoked potential (MEP), short-interval intracortical inhibition (SICI), and intracortical facilitation (ICF), and identify sources of study variability using a machine learning approach., Methods: We identified studies that objectively evaluated corticospinal excitability and intracortical inhibition/facilitation after stroke using transcranial magnetic stimulation. Pooled within- (ie, affected hemisphere [ AH ] vs unaffected hemisphere [ UH ]) and between-subjects (ie, AH and UH vs Control ) standardized mean differences were computed. Decision trees determined which factors accurately predicted studies that showed alterations in corticospinal excitability and intracortical inhibition/facilitation., Results: A total of 35 studies (625 stroke patients and 328 healthy controls) were included. MT was significantly increased and MEP was significantly decreased (ie, reduced excitability) in the AH when compared with the UH and Control ( P  < .01). SICI was increased (ie, reduced inhibition) for the AH when compared with the UH , and for the AH and UH when compared with Control ( P  < .001). ICF was significantly increased (ie, increased facilitation) in the AH when compared with UH ( P  = .016) and decreased in UH when compared with Control ( P  < 0.001). Decision trees indicated that demographic and methodological factors accurately predicted (73%-86%) studies that showed alterations in corticospinal and intracortical excitability measures., Conclusions: The findings indicate that stroke alters corticospinal and intracortical excitability measures. Alterations in SICI and ICF may reflect disinhibition of the motor cortex after stroke, which is contrary to the notion that stroke increases inhibition of the affected side., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The stretch-shortening cycle (SSC) has been classified into fast (<250 ms) and slow (>250 ms) groups based on ground contact time (GCT) threshold values. However, there are gaps in the literature on how the 250 ms threshold value was found and which variables affect it. The purpose of this study is to validate the 250 ms threshold by investigating the factors affecting this threshold. For this purpose, force-time variables during a drop jump (DJ) with a force plate and achilles tendon (AT) muscle-tendon unit mechanical properties using shear-wave elastography in 46 recreationally active men were analysed. A regression tree analysis was conducted using R studio to classify GCT with correlated variables ( p  < 0.05). The new GCT threshold values (GCT < 188 ms, 188 ≤ GCT < 222 ms and GCT ≥ 222 ms) were found according to the lowest root mean square error of approximation value (0.1985) at reactive strength index. Comparisons of GCT groups showed significant differences in force, time, power variables and AT length ( p  < 0.05). AT length is the main variable differentiating GCT groups: Short AT results in a short GCT and long AT results in a long GCT. This study reveals that SSC can be classified into three groups using new GCT threshold values, offering a new perspective for SSC assessment.

Background: Reducing health disparities is a public health issue. Identification of low-health-interest populations is important, but a definition of people with low health interest has not yet been established. We aimed to quantitatively define low-health-interest populations., Methods: A nationwide cross-sectional internet survey was conducted in 2022. We compiled regression tree (RT) analyses with/without adjustment for age, sex, and socioeconomic status with the 12-item Interest in Health Scale (IHS, score range 12-48) as an explanatory variable and the 10 composite health behaviors as a dependent variable. We defined the first IHS branching condition from the root node as a lower-health-interest group and the terminal node with the lowest health behaviors as the lowest-health-interest group., Results: The mean IHS value of 22,263 analyzed participants was 32.1 ± 5.6; it was higher in females and in those who were aged over 45 years, had a high education, a high income, or a spouse. The first branching condition was IHS 31.5, and the terminal node branched at 24.5, before/after adjustment for covariates., Conclusions: We determined the cutoff values of the IHS as <32 for a lower-health-interest group and <25 for the lowest-health-interest group. Using these cutoffs might enable us to reveal the characteristics of low-health-interest populations.

Background: Empirical studies have demonstrated associations between ten original adverse childhood experiences (ACEs) and multiple health outcomes. Identifying expanded ACEs can capture the burden of other childhood adversities that may have important health implications., Objective: We sought to identify childhood adversities that warrant consideration as expanded ACEs. We hypothesized that experiencing expanded and original ACEs would be associated with poorer adult health outcomes compared to experiencing original ACEs alone., Participants: The 11,545 respondents of the National Longitudinal Surveys (NLS) and Child and Young Adult Survey were 48.9 % female, 22.7 % Black, 15.8 % Hispanic, 36.1 % White, 1.7 % Asian/Native Hawaiian/Pacific Islander/Native American/Native Alaskan, and 7.5 % Other., Methods: This study used regression trees and generalized linear models to identify if/which expanded ACEs interacted with original ACEs in association with six health outcomes., Results: Four expanded ACEs-basic needs instability, lack of parental love and affection, community stressors, and mother's experience with physical abuse during childhood -significantly interacted with general health, depressive symptom severity, anxiety symptom severity, and violent crime victimization in adulthood (all p-values <0.005). Basic needs instability and/or lack of parental love and affection emerged as correlates across multiple outcomes. Experiencing lack of parental love and affection and original ACEs was associated with greater anxiety symptoms (p = 0.022)., Conclusions: This is the first study to use supervised machine learning to investigate interaction effects among original ACEs and expanded ACEs. Two expanded ACEs emerged as predictors for three adult health outcomes and warrant further consideration in ACEs assessments., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Anthropogenic influences significantly modify the hydrochemical properties and material flow in riverine ecosystems across Asia, potentially accounting for 40-50% of global emissions. Despite the pervasive impact on Asian rivers, there is a paucity of studies investigating their correlation with carbon dioxide (CO 2 ) emissions. In this study, we computed the partial pressure of CO 2 (pCO 2 ) using the carbonate equilibria-based model (pCO 2 SYS) and examined its correlation with hydrochemical parameters from historical records at 91 stations spanning 2013-2021 in the Ganga River. The investigation unveiled substantial spatial heterogeneity in the pCO 2 across the Ganga River. The pCO 2 concentration varied from 1321.76 μatm, 1130.98 μatm, and 1174.33 μatm in the upper, middle, and lower stretch, respectively, with a mean of 1185.29 μatm. Interestingly, the upper stretch exhibited elevated mean pCO 2 and FCO 2 levels (fugacity of CO 2 : 3.63 gm 2 d -1 ) compared to the middle and lower stretch, underscoring the intricate interplay between hydrochemistry and CO 2 dynamics. In the context of pCO 2 fluctuations, nitrate concentrations in the upper segment and levels of biological oxygen demand (BOD) and dissolved oxygen (DO) in the middle and lower segments are emerging as crucial explanatory factors. Furthermore, regression tree (RT) and importance analyses pinpointed biochemical oxygen demand (BOD) as the paramount factor influencing pCO 2 variations across the Ganga River (n = 91). A robust negative correlation between BOD and FCO 2 was also observed. The distinct longitudinal patterns of both parameters may induce a negative correlation between BOD and pCO 2 . Therefore, comprehensive studies are necessitated to decipher the underlying mechanisms governing this relationship. The present insights are instrumental in comprehending the potential of CO 2 emissions in the Ganga River and facilitating riverine restoration and management. Our findings underscore the significance of incorporating South Asian rivers in the evaluation of the global carbon budget., Competing Interests: Declaration of competing interest 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., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of Applied Microbiology International.)

This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models-an artificial neural network (ANN), a multiple linear regression, a support vector machine, and a regression tree-are employed and compared for performance, using evaluation metrics such as mean absolute deviation, root mean square error, coefficient of correlation, and mean absolute percentage error. After preprocessing 1030 samples, the dataset is split into two subsets: 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process.

Despite a decrease in the prevalence of low birth weight (LBW) over time, its ongoing significance as a public health concern in Bangladesh remains evident. Low birth weight is believed to be a contributing factor to infant mortality, prolonged health complications, and vulnerability to non-communicable diseases. This study utilizes nationally representative data from the Multiple Indicator Cluster Surveys (MICS) conducted in 2012-2013 and 2019 to explore factors associated with birth weight. Modeling birth weight data considers interactions among factors, clustering in data, and spatial correlation. District-level maps are generated to identify high-risk areas for LBW. The average birth weight has shown a modest increase, rising from 2.93 kg in 2012-2013 to 2.96 kg in 2019. The study employs a regression tree, a popular machine learning algorithm, to discern essential interactions among potential determinants of birth weight. Findings from various models, including fixed effect, mixed effect, and spatial dependence models, highlight the significance of factors such as maternal age, household head's education, antenatal care, and few data-driven interactions influencing birth weight. District-specific maps reveal lower average birth weights in the southwestern region and selected northern districts, persisting across the two survey periods. Accounting for hierarchical structure and spatial autocorrelation improves model performance, particularly when fitting the most recent round of survey data. The study aims to inform policy formulation and targeted interventions at the district level by utilizing a machine learning technique and regression models to identify vulnerable groups of children requiring heightened attention., 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 Author(s).)

Purpose: Identify patient subgroups with different functional outcomes after SCI and study the association between functional status and initial ISNCSCI components., Methods: Using CART, we performed an observational cohort study on data from 675 patients enrolled in the Rick-Hansen Registry(RHSCIR) between 2014 and 2019. The outcome was the Spinal Cord Independence Measure (SCIM) and predictors included AIS, NLI, UEMS, LEMS, pinprick(PPSS), and light touch(LTSS) scores. A temporal validation was performed on data from 62 patients treated between 2020 and 2021 in one of the RHSCIR participating centers., Results: The final CART resulted in four subgroups with increasing totSCIM according to PPSS, LEMS, and UEMS: 1)PPSS < 27(totSCIM = 28.4 ± 16.3); 2)PPSS ≥ 27, LEMS < 1.5, UEMS < 45(totSCIM = 39.5 ± 19.0); 3)PPSS ≥ 27, LEMS < 1.5, UEMS ≥ 45(totSCIM = 57.4 ± 13.8); 4)PPSS ≥ 27, LEMS ≥ 1.5(totSCIM = 66.3 ± 21.7). The validation model performed similarly to the original model. The adjusted R-squared and F-test were respectively 0.556 and 62.2(P-value <0.001) in the development cohort and, 0.520 and 31.9(P-value <0.001) in the validation cohort., Conclusion: Acknowledging the presence of four characteristic subgroups of patients with distinct phenotypes of functional recovery based on PPSS, LEMS, and UEMS could be used by clinicians early after tSCI to plan rehabilitation and establish realistic goals. An improved sensory function could be key for potentiating motor gains, as a PPSS ≥ 27 was a predictor of a good function.

This paper introduces the Bradley-Terry regression trunk model, a novel probabilistic approach for the analysis of preference data expressed through paired comparison rankings. In some cases, it may be reasonable to assume that the preferences expressed by individuals depend on their characteristics. Within the framework of tree-based partitioning, we specify a tree-based model estimating the joint effects of subject-specific covariates over and above their main effects. We, therefore, combine a tree-based model and the log-linear Bradley-Terry model using the outcome of the comparisons as response variable. The proposed model provides a solution to discover interaction effects when no a-priori hypotheses are available. It produces a small tree, called trunk, that represents a fair compromise between a simple interpretation of the interaction effects and an easy to read partition of judges based on their characteristics and the preferences they have expressed. We present an application on a real dataset following two different approaches, and a simulation study to test the model's performance. Simulations showed that the quality of the model performance increases when the number of rankings and objects increases. In addition, the performance is considerably amplified when the judges' characteristics have a high impact on their choices., (© 2022. The Author(s).)

Bisphenol A (BPA) is an endocrine disruptor used in food contact materials, by the application of polycarbonate plastics and epoxy resins. The main objective of this study is to compare the estimate of daily BPA exposure at 13 years of age and in the adult Portuguese population, using different methodological approaches, and assess the associations between this exposure and sociodemographic characteristics., Methodology: Cross-sectional data of 13-years follow-up from a population-based birth cohort Generation XXI (GXXI) (n = 2804) and from the National Food, Nutrition and Physical Activity Survey (IAN-AF 2015-2016) (n = 3845, ≥18 years old) was used. Dietary information was collected through three food diaries for adolescents and two non-consecutive 24-hour-recalls for adults. To estimate the daily exposure to BPA, three methodological approaches were used. "Food groups attribution" merged the food consumption data with the concentration of BPA in food groups. "Regression tree model" and "random forest" combined food consumption information with urinary BPA, measured in a subsample of 24-hour urine (in adolescents n = 216, and in adults n = 82), both used to predict BPA exposure in the remaining sample. The fit-index of the methodologies was assessed through the root mean square error (RMSE), mean absolute error (MAE) and Spearman correlation coefficient (ρ). Associations between BPA exposure and sociodemographic variables were tested by linear regression models, adjusted for sex, age groups (in adults) and educational level. Tolerable Daily Intake (TDI) of 0.2 ng/kg body weight (bw), recently proposed by the European Food Safety Authority (EFSA), was used for the risk characterization of BPA exposure., Results: The "random forest" was found as the best methodology to estimate the daily BPA exposure (adolescents: RMSE = 0.989, MAE = 0.727, ρ = 0.168; adults: RMSE = 0.193, MAE = 0.147, ρ = 0.250). The median dietary BPA exposure, calculated by "food groups attribution", was 79.1 and 46.1 ng/kg bw/day for adolescents and adults, respectively, while "random forest" estimated a BPA exposure of 26.7 and 38.0 ng/kg bw/day. 99.9% of the Portuguese population presented a daily exposure above TDI. Male adolescents, females and higher educated adults, were those more exposed to BPA., Conclusions: The estimated daily BPA exposure strongly depends on the methodological approach. Food groups attribution may overestimate the exposure while the random forest appears to be a better methodological approach to estimate BPA exposure. Nevertheless, for all methods, the Portuguese population presented an unsafe BPA exposure by largely exceeding the safe levels proposed by EFSA., Competing Interests: Declaration of Competing Interest 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., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

A quantitative understanding of the relationship between comprehensive health levels, such as healthy life expectancy and their related factors, through a highly explanatory model is important in both health research and health policy making. In this study, we developed a regression model that combines multiple linear regression and a random forest model, exploring the relationship between men's healthy life expectancy in Japan and regional variables from open sources at the city level as an illustrative case. Optimization of node-splitting in each decision tree was based on the total mean-squared error of multiple regression models in binary-split child nodes. Variations of standardized partial regression coefficients for each city were obtained as the ensemble of multiple trees and visualized on scatter plots. By considering them, interaction terms with piecewise linear functions were exploratorily introduced into a final multiple regression model. The plots showed that the relationship between the healthy life expectancy and the explanatory variables could differ depending on the cities' characteristics. The procedure implemented here was suggested as a useful exploratory method for flexibly implementing interactions in multiple regression models while maintaining interpretability.

Introduction: The mechanisms linking motor function to Alzheimer's disease (AD) progression have not been well studied, despite evidence of AD pathology within motor brain regions. Thus, there is a need for new motor measure that is sensitive and specific to AD., Methods: In a sample of 121 older adults (54 cognitive unimpaired [CU], 35 amnestic Mild Cognitive Impairment [aMCI], and 32 probable mild AD), intrasubject standard deviation (ISD) across six trials of a novel upper-extremity motor task was predicted with volumetric regional gray matter and neuropsychological scores using classification and regression tree (CART) analyses., Results: Both gray matter and neuropsychological CART models indicated that motor task ISD (our measure of motor learning) was related to cortical regions and cognitive test scores associated with memory, executive function, and visuospatial skills. CART models also accurately distinguished motor task ISD of MCI and probable mild AD from CU., Discussion: Variability in motor task performance across practice trials may be valuable for understanding preclinical and early-stage AD., Competing Interests: Declaration of competing interest Sydney Schaefer and Kevin Duff are members of Neurosessments LLC. All other authors declare no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Reliable prediction of clinical progression over time can improve the outcomes of depression. Little work has been done integrating various risk factors for depression, to determine the combinations of factors with the greatest utility for identifying which individuals are at the greatest risk., Materials and Methods: This study demonstrates that data-driven Machine Learning (ML) methods such as Random Effects/Expectation Maximization (RE-EM) trees and Mixed Effects Random Forest (MERF) can be applied to reliably identify variables that have the greatest utility for classifying subgroups at greatest risk for depression. 185 young adults completed measures of depression risk, including rumination, worry, negative cognitive styles, cognitive and coping flexibilities and negative life events, along with symptoms of depression. We trained RE-EM trees and MERF algorithms and compared them to traditional Linear Mixed Models (LMMs) predicting depressive symptoms prospectively and concurrently with cross-validation., Results: Our results indicated that the RE-EM tree and MERF methods model complex interactions, identify subgroups of individuals and predict depression severity comparable to LMM. Further, machine learning models determined that brooding, negative life events, negative cognitive styles, and perceived control were the most relevant predictors of future depression levels., Conclusion: Random effects machine learning models have the potential for high clinical utility and can be leveraged for interventions to reduce vulnerability to depression.

Vitamin D deficiency and insufficiency is a global health issue: an association has been demonstrated between vitamin D deficiency and a myriad of acute and chronic illnesses. Data regarding vitamin D status among children hospitalized with non-critical illnesses are scanty. We aimed to: (1) identify profiles of children hospitalized due to non-critical illnesses, using vitamin D levels as the driving outcome; (2) assess the association between patient profiles and length of stay. The study included 854 patients (1−17 years old) who underwent blood tests for detecting vitamin D levels. A regression tree was used to stratify patients. The relationship between vitamin D levels and length of stay was explored using Poisson regression. The regression tree identified three subgroups. Group A (16%): African, North African, Hispanic, and Indian patients. Group B (62%): Caucasian and Asian patients hospitalized for respiratory, metabolic, ill-defined, infective, and genitourinary diseases. Group C (22%): Caucasian and Asian patients hospitalized for digestive, nervous, and musculoskeletal diseases, blood and skin diseases, and injuries. Mean serum vitamin D level (ng/mL) was 13.7 (SD = 9.4) in Group A, 20.5 (10.0) in Group B, and 26.2 (12.6) in Group C. Group B was associated with the highest BMI z-score (p < 0.001) and the highest frequency of preterm births (p = 0.041). Mean length of stay was longer in Group A than in the other groups (p < 0.001) and decreased significantly by 9.8% (p = 0.024) in Group A and by 5% (p = 0.029) in Group B per 10 ng/mL increase in vitamin D level. We identified three subgroups of hospitalized children, defined according to ethnicity and discharge diagnosis, and characterized by increasing vitamin D levels. Vitamin D levels were associated with length of hospitalization.

COVID-19 has swept the world, and the unprecedented decline in transit ridership has been noticed. However, little attention has been paid to the resilience of the transportation system, particularly in medium-sized cities. Drawing upon a light rail ridership dataset in Salt Lake County from 2017 to 2021, we develop a novel method to measure the vulnerability and resilience of transit ridership using a Bayesian structure time series model. The results show that government policies have a more significant impact than the number of COVID-19 cases on transit ridership. Regarding the built environment, a highly compact urban design might reduce the building coverage ratio and makes transit stations more vulnerable and less resilient. Furthermore, the high rate of minorities is the primary reason for the drops in transit ridership. The findings are valuable for understanding the vulnerability and resilience of transit ridership to pandemics for better coping strategies in the future., 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., (© 2022 Elsevier Ltd. All rights reserved.)

Introduction: Accurate subtyping of NSCLC into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is the cornerstone of NSCLC diagnosis. Cytology samples reveal higher rates of classification failures, that is, subtyping as non-small cell carcinoma-not otherwise specified (NSCC-NOS), as compared with histology specimens. This study aims to identify specific algorithms on the basis of known cytomorphologic features that aid accurate and successful subtyping of NSCLC on cytology., Methods: A total of 13 expert cytopathologists participated anonymously in an online survey to subtype 119 NSCLC cytology cases (gold standard diagnoses being LUAD in 80 and LUSC in 39) enriched for nonkeratinizing LUSC. They selected from 23 predefined cytomorphologic features that they used in subtyping. Data were analyzed using machine learning algorithms on the basis of random forest method and regression trees., Results: From 1474 responses recorded, concordant cytology typing was achieved in 53.7% (792 of 1474) responses. NSCC-NOS rates on cytology were similar among gold standard LUAD (36%) and LUSC (38%) cases. Misclassification rates were higher in gold standard LUSC (17.6%) than gold standard LUAD (5.5%; p < 0.0001). Keratinization, when present, recognized LUSC with high accuracy. In its absence, the machine learning algorithms developed on the basis of experts' choices were unable to reduce cytology NSCC-NOS rates without increasing misclassification rates., Conclusions: Suboptimal recognition of LUSC in the absence of keratinization remains the major hurdle in improving cytology subtyping accuracy with such cases either failing classification (NSCC-NOS) or misclassifying as LUAD. NSCC-NOS seems to be an inevitable morphologic diagnosis emphasizing that ancillary immunochemistry is necessary to achieve accurate subtyping on cytology., (Copyright © 2022 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Quantifying forest systems is of importance for ecological services and economic benefits in ecosystem models. This study aims to map the percent tree cover (PTC) of various forest stands in the Buyuk Menderes Basin, located in the western part of Turkey with different characteristics in the Mediterranean and Terrestrial transition regions Sentinel-2 data with 10-m spatial resolution. In recent years, some researches have been carried out in different fields to show the capabilities and potential of Sentinel-2 satellite sensors. However, the limited number of PTC researches conducted with Sentinel-2 images reveals the importance of this study. This study aimed to demonstrate reliable PTC data in landscape planning or ecosystem modeling by introducing an advanced approach with high spatial, spectral, and temporal resolution and more cost-effective. In this study, a regression tree algorithm, one of the popular machine learning techniques for ecological modeling, was used to estimate the tree cover's dependent variable based on high-resolution monthly metrics' spectral signatures. Six frames of TripleSat images were used as training data in the regression tree. Monthly Sentinel-2 bands and produced metrics including NDVI, LAI, fCOVER, MSAVI2, and MCARI were almost the first time used as predictor variables. Stepwise linear regression (SLR) was applied to select these predictor bands in the regression tree and a correlation coefficient of 0.83 was obtained. Result PTC maps were produced and the results were evaluated based on coniferous and broadleaf. The results were tested using high spatial resolution TripleSat images and higher model accuracy was determined in both forest types. The high correlation is due to the Sentinel 2 satellite's band characteristics and the metrics are directly related to the tree cover. As a result, the high-accuracy availability of the Sentinel2 satellite is seen to map the PTC on a regional scale, including complex forest types between the Mediterranean and terrestrial transition climates., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: To operationalize an intersectionality framework using a novel statistical approach and with these efforts, improve the estimation of disparities in access (i.e., wait time to treatment entry) to opioid use disorder (OUD) treatment beyond race., Data Source: Sample of 941,286 treatment episodes collected in 2015-2017 in the United States from the Treatment Episodes Data Survey (TEDS-A) and a subset from California (n = 188,637) and Maryland (n = 184,276), states with the largest sample of episodes., Study Design: This retrospective subgroup analysis used a two-step approach called virtual twins. In Step 1, we trained a classification model that gives the probability of waiting (1 day or more). In Step 2, we identified subgroups with a higher probability of differences due to race. We tested three classification models for Step 1 and identified the model with the best estimation., Data Collection: Client data were collected by states during personal interviews at admission and discharge., Principal Findings: Random forest was the most accurate model for the first step of subgroup analysis. We found large variation across states in racial disparities. Stratified analysis of two states with the largest samples showed critical factors that augmented disparities beyond race. In California, factors such as service setting, referral source, and homelessness defined the subgroup most vulnerable to racial disparities. In Maryland, service setting, prior episodes, receipt of medication-assisted opioid treatment, and primary drug use frequency augmented disparities beyond race. The identified subgroups had significantly larger racial disparities., Conclusions: The methodology used in this study enabled a nuanced understanding of the complexities in disparities research. We found state and service factors that intersected with race and augmented disparities in wait time. Findings can help decision makers target modifiable factors that make subgroups vulnerable to waiting longer to enter treatment., (© 2021 The Authors. Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.)

Background: Every year, volunteers of the Belgian Red Cross provide onsite medical care at more than 8000 mass gathering events and other manifestations. Today standardized planning tools for optimal preventive medical resource use during these events are lacking. This study aimed to develop and validate a prediction model of patient presentation rate (PPR) and transfer to hospital rate (TTHR) at mass gatherings in Belgium., Methods: More than 200,000 medical interventions from 2006 to 2018 were pooled in a database. We used a subset of 28 different mass gatherings (194 unique events) to develop a nonlinear prediction model. Using regression trees, we identified potential predictors for PPR and TTHR at these mass gatherings. The additional effect of ambient temperature was studied by linear regression analysis. Finally, we validated the prediction models using two other subsets of the database., Results: The regression tree for PPR consisted of 7 splits, with mass gathering category as the most important predictor variable. Other predictor variables were attendance, number of days, and age class. Ambient temperature was positively associated with PPR at outdoor events in summer. Calibration of the model revealed an R 2 of 0.68 (95% confidence interval 0.60-0.75). For TTHR, the most determining predictor variables were mass gathering category and predicted PPR (R 2  = 0.48). External validation indicated limited predictive value for other events (R 2  = 0.02 for PPR; R 2  = 0.03 for TTHR)., Conclusions: Our nonlinear model performed well in predicting PPR at the events used to build the model on, but had poor predictive value for other mass gatherings. The mass gathering categories "outdoor music" and "sports event" warrant further splitting in subcategories, and variables such as attendance, temperature and resource deployment need to be better recorded in the future to optimize prediction of medical usage rates, and hence, of resources needed for onsite emergency medical care., (© 2022. The Author(s).)

Meteorological parameters modulate transmission of the SARS-Cov-2 virus, the causative agent related to coronavirus disease-2019 (COVID-19) development. However, findings across the globe have been inconsistent attributed to several confounding factors. The aim of the present study was to investigate the relationship between reported meteorological parameters from July 1 to October 31, 2020, and the number of confirmed COVID-19 cases in 4 Brazilian cities: São Paulo, the largest city with the highest number of cases in Brazil, and the cities with greater number of cases in the state of Parana during the study period (Curitiba, Londrina and Maringa). The assessment of meteorological factors with confirmed COVID-19 cases included atmospheric pressure, temperature, relative humidity, wind speed, solar irradiation, sunlight, dew point temperature, and total precipitation. The 7- and 15-day moving averages of confirmed COVID-19 cases were obtained for each city. Pearson's correlation coefficients showed significant correlations between COVID-19 cases and all meteorological parameters, except for total precipitation, with the strongest correlation with maximum wind speed (0.717, <0.001) in São Paulo. Regression tree analysis demonstrated that the largest number of confirmed COVID-19 cases was associated with wind speed (between ≥0.3381 and <1.173 m/s), atmospheric pressure (<930.5mb), and solar radiation (<17.98e +3 ). Lower number of cases was observed for wind speed <0.3381 m/s and temperature <23.86°C. Our results encourage the use of meteorological information as a critical component in future risk assessment models.

Exposure to online drinking on social media is associated with real-life alcohol consumption. Building on the Theory of planned behavior, the current study substantially adds to this line of research by identifying the predictors of sharing drunk references on social media. Based on a cross-sectional survey among 1639 adolescents with a mean age of 15 (59% female), this study compares and discusses multiple regression tree algorithms predicting the sharing of drunk references. More specifically, this paper compares the accuracy of classification and regression tree, bagging, random forest and extreme gradient boosting algorithms. The analysis indicates that four concepts are central to predicting adolescents' sharing of drunk references: (1) exposure to them on social media; (2) the perceived injunctive norms of the mother towards alcohol consumption; (3) the perceived descriptive norms of best friends towards alcohol consumption; and (4) willingness to drink alcohol. The most accurate results were obtained using extreme gradient boosting. This study provides theoretical, practical, and methodological conclusions. It shows that maternal norms toward alcohol consumption are a central predictor for sharing drunk references. Therefore, future media literacy interventions should take an ecological perspective. In addition, this analysis indicates that regression trees are an advantageous method in youth research, combining accurate predictions with straightforward interpretations.

Background: BRAF and MEK inhibitors combination, including dabrafenib (D) and trametinib (T) have transformed the treatment of BRAF V600-mutant advanced melanoma patients, including patients with brain metastasis (BM). In a large phase IIIb, single-arm, open-label, multicenter French study, we assessed safety, response to treatment, progression-free survival (PFS) and factors associated with progression, and stratified the population into risk groups., Methods: Patients with unresectable, advanced, BRAF V600-mutant melanoma were included, including those with the presence of BM, Eastern Cooperative Oncology Group Performance Status (ECOG PS) ≤2, elevated lactate dehydrogenase (LDH) or previous melanoma treatments. Responses were determined locally, without central review. PFS was estimated using the Kaplan-Meier analysis and modelled with multivariate Cox model. Risk subgroups were identified using a regression tree analysis., Results: Between March 2015 and November 2016, 856 patients received at least one D + T dose. Overall, 92% had stage IV melanoma, 38% ECOG PS ≥1, 32% BM and 37.5% elevated LDH. Median PFS was 8.02 months (95% confidence interval [CI] 7.33-8.77). Significant factors associated with lower PFS were ECOG PS ≥1, elevated LDH, ≥3 metastatic sites and presence of BM. Patients with <3 metastatic sites, ECOG = 0 and no BM had the highest probability of PFS at 6 months (83%, 95% CI 76-87) and 12 months (56%, 95% CI 47-64), respectively., Conclusions: This is the largest prospective study in advanced BRAF V600-mutant melanoma patients treated with D + T, conducted in conditions close to 'real-world practice'. We confirm previous findings that LDH, ECOG PS and ≥3 metastatic sites are associated with shorter PFS, but the real-world setting introduces BM as a major prognostic factor., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitors play a key role in treating Alzheimer's disease. This study proposes an approach that integrates a modified binary particle swarm optimization (PSO) with a machine learning algorithm for building QSAR models to predict the activity of inhibitors for AChE and BuChE enzymes. More precisely, it uses a transfer function to convert the continuous search space of PSO to binary. Furthermore, it utilizes the concepts of catfish effect and chaotic map to improve exploration ability in searching for an optimum subset of descriptors for QSAR model constructions. Then, through a statistical method, it employs a machine learning algorithm to evaluate the fitness value of each candidate subset of features. Different combinations of four transfer functions with four machine learning algorithms, including K-nearest neighbour, multiple linear regression, support vector machine, and regression tree, were used to build several variants of the proposed algorithm. QSAR models constructed by each version were verified by internal and external validations. The best variants were selected based on a method called sum of ranking differences.

Background: Computerized adaptive testing (CAT) has been shown to deliver short, accurate, and personalized versions of the CLEFT-Q patient-reported outcome measure for children and young adults born with a cleft lip and/or palate. Decision trees may integrate clinician-reported data (eg, age, gender, cleft type, and planned treatments) to make these assessments even shorter and more accurate., Objective: We aimed to create decision tree models incorporating clinician-reported data into adaptive CLEFT-Q assessments and compare their accuracy to traditional CAT models., Methods: We used relevant clinician-reported data and patient-reported item responses from the CLEFT-Q field test to train and test decision tree models using recursive partitioning. We compared the prediction accuracy of decision trees to CAT assessments of similar length. Participant scores from the full-length questionnaire were used as ground truth. Accuracy was assessed through Pearson's correlation coefficient of predicted and ground truth scores, mean absolute error, root mean squared error, and a two-tailed Wilcoxon signed-rank test comparing squared error., Results: Decision trees demonstrated poorer accuracy than CAT comparators and generally made data splits based on item responses rather than clinician-reported data., Conclusions: When predicting CLEFT-Q scores, individual item responses are generally more informative than clinician-reported data. Decision trees that make binary splits are at risk of underfitting polytomous patient-reported outcome measure data and demonstrated poorer performance than CATs in this study., (©Conrad J Harrison, Chris J Sidey-Gibbons, Anne F Klassen, Karen W Y Wong Riff, Dominic Furniss, Marc C Swan, Jeremy N Rodrigues. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.07.2021.)

Strabismus is an eye disease that affects about 0.12%-9.86% of the population, which can cause irreversible sensory damage to vision and psychological problems. The most severe cases require surgical intervention, despite other less invasive techniques being available for a more conservative approach. As for surgeries, the treatment goal is to align the eyes to recover binocular vision, which demands knowledge, training, and experience. One of the leading causes of failure is human error during the measurement of deviation. Thus, this work proposes a new method based on the Decision Tree Regressor algorithms to assist in the surgical planning for horizontal strabismus to predict recoil and resection measures in the lateral and medial rectus muscles. In the presented method, two application approaches were taken, being in the form of multiple single target models, one procedure at a time, and the form of one multiple target model or all surgical procedures together. The method's efficiency is indicated by the average difference between the value indicated by the method and the physician's value. In our most accurate model, an average error of 0.66 mm was obtained for all surgical procedures, both for resection and recoil in the indication of the horizontal strabismus surgical planning. The results present the feasibility of using Decision Tree Regressor algorithms to perform the planning of strabismus surgeries, making it possible to predict correction values for surgical procedures based on medical data analysis and exceeding state-of-art., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Predicting the long-term functional outcome after traumatic spinal cord injury (TSCI) is needed to adapt medical strategies and plan an optimized rehabilitation. This study investigates the use of regression trees for the development of predictive models based on acute clinical and demographic predictors. This prospective study was performed on 172 patients hospitalized after TSCI. Functional outcome was quantified using the Spinal Cord Independence Measure (SCIM) collected within the first-year post-injury. Age, delay before surgery, and Injury Severity Score (ISS) were considered as continuous predictors whereas energy of injury, trauma mechanisms, neurological level of injury, injury severity, occurrence of early spasticity, urinary tract infection, pressure ulcer, and pneumonia were coded as categorical inputs. A simplified model was built using only American Spinal Injury Association Impairment Scale grade, neurological level, energy, and age as predictor and was compared to a more complex model considering all 11 predictors mentioned above. The models built using 4 and 11 predictors were found to explain 51.4% and 62.3% of the variance of the SCIM total score after validation, respectively. Severity of the neurological deficit at admission was found to be the most important predictor. Other important predictors were the ISS, age, neurological level, and delay before surgery. Regression trees offer promising performances for predicting the functional outcome after a TSCI. It could help to determine the number and type of predictors leading to a prediction model of the functional outcome that can be used clinically in the future.

The challenge of animal health surveillance is to provide the information necessary to appropriately inform disease prevention and control activities within the constraints of available resources. Syndromic surveillance of farmers' disease observations can improve animal health data capture from extensive livestock farming systems, especially where data are not otherwise being systematically collected or when data on confirmed aetiological diagnoses are unavailable at the disease level. As it is rarely feasible to recruit a truly random sample of farmers to provide observational reports, directing farmer sampling to align with the surveillance objectives is a reasonable and practical approach. As long as potential bias is recognised and managed, farmers who will report reliably can be desirable participants in a surveillance system. Thus, one early objective of a surveillance program should be to identify characteristics associated with reporting behaviour. Knowledge of the demographic and managerial characteristics of good reporters can inform efforts to recruit additional farms into the system or aid understanding of potential bias of system reports. We describe the operation of a farmer syndromic surveillance system in Victoria, Australia, over its first two years from 2014 to 2016. Survival analysis and classification and regression tree analysis were used to identify farm level factors associated with 'reliable' participation (low non-response rates in longitudinal reporting). Response rate and timeliness were not associated with whether farmers had disease to report, or with different months of the year. Farmers keeping only sheep were the most reliable and timely respondents. Farmers < 43 years of age had lower response rates than older farmers. Farmers with veterinary qualifications and those working full-time on-farm provided less timely reports than other educational backgrounds and farmers who worked part-time on-farm. These analyses provide a starting point to guide recruitment of participants for surveillance of farmers' observations using syndromic surveillance, and provide examples of strengths and weaknesses of syndromic surveillance systems for extensively-managed livestock. Once farm characteristics associated with reliable participation are known, they can be incorporated into surveillance system design in accordance with the objectives of the system., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

This study determined the association between Myf5, CAST and MSTN genes and fattening performance traits in 228 (Holstein = 103 and Brown = 125) cattle breeds. Classification and regression tree (CART) was used to determine association between genes and fattening performance. The allele frequencies of Holstein cattle in terms of Myf5, CAST and MSTN genes were A:0.30, B:0.70; A:0.43, G:0.56; A:0.97, B:0.03, respectively, whereas in the Brown Swiss cattle were A:0.36, B:0.64; A:0.65, G:0.34; A:0.88, B:0.12 respectively. The Myf5, CAST, and MSTN genes were found to be in the Hardy-Weinberg equilibrium ( p  > 0.05) in both of the cattle breeds. As a result, the association between Myf5, CAST and MSTN genes and fattening performance traits were found to be significant according to CART analysis.

Background: Prevention of coronavirus (COVID-19) regarding households has many aspects, such as buying mask, hand sanitizer, face shield, and many others. As a result of buying the previous items, the household spending per month will increase during the COVID-19 pandemic period. This study aimed to calculate the average costs of each extra item involved in households spending during COVID-19 pandemic and to predict the total average extra costs spending by households., Results: Most of the respondents were females (81%) and aged between 30 and 40 (56.3%). About 63.1% of families had the same monthly income while 35.4% had a decrease in monthly income. A significant reduction in days of leaving home before and after COVID-19 pandemic was observed (before; median = 6, after; median = 5, P  =  < 0.001). The extra spending in grocery was the dominated item compared to other items (mean = 707.2 L.E./month, SD = 530.7). Regarding regression tree, the maximum average extra costs due to COVID-19 pandemic were 1386 L.E./month (around 88.56$/month (1$-> 15.65L.E.)) while the minimum average extra costs were 217 L.E./month (around 13.86$/month)., Conclusions: The effect of COVID-19 pandemic in households spending varies largely between households, it depends on what they do to prevent COVID-19., Competing Interests: Competing interestsThe author declares that there is no competing interests., (© The Author(s) 2021.)

Exposure to high concentration levels of radon gas constitutes a major health hazard, being nowadays the second-leading cause of lung cancer after smoking. Facing this situation, the last years have seen a clear trend towards the search for methodologies that allow an efficient prevention of the potential risks derived from the presence of harmful radon gas concentration levels in buildings. With that, it is intended to establish preventive and corrective actions that might help to reduce the impact of radon exposure on people, especially in places where workers and external users must stay for long periods of time, as it may be the case of healthcare buildings. In this paper, a new methodology is developed and applied to the prevention of the risks derived from the exposure to radon gas in indoor spaces. Such methodology is grounded in the concurrent use of expert systems and regression trees that allows producing a diagram with recommendations associated to the exposure risk. The presented methodology has been implemented by means of a software application that supports the definition of the expert systems and the regression algorithm. Finally, after proving its applicability with a case study and discussing its contributions, it may be claimed that the benefits of the new methodology might lead on to an innovation in this field of study., Competing Interests: The authors declare no conflict of interest.

Coronavirus disease 2019 (COVID-19) has developed into a global pandemic, affecting every nation and territory in the world. Machine learning-based approaches are useful when trying to understand the complexity behind the spread of the disease and how to contain its spread effectively. The unsupervised learning method could be useful to evaluate the shortcomings of health facilities in areas of increased infection as well as what strategies are necessary to prevent disease spread within or outside of the country. To contribute toward the well-being of society, this paper focusses on the implementation of machine learning techniques for identifying common prevailing public health care facilities and concerns related to COVID-19 as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation. Regression tree, random forest, cluster analysis and principal component machine learning techniques are used to analyze the global COVID-19 data of 133 countries obtained from the Worldometer website as of April 17, 2020. The analysis revealed that there are four major clusters among the countries. Eight countries having the highest cumulative infected cases and deaths, forming the first cluster. Seven countries, United States, Spain, Italy, France, Germany, United Kingdom, and Iran, play a vital role in explaining the 60% variation of the total variations by us of the first component characterized by all variables except for the rate variables. The remaining countries explain only 20% of the variation of the total variation by use of the second component characterized by only rate variables. Most strikingly, the analysis found that the variable number of tests by the country did not play a vital role in the prediction of the cumulative number of confirmed cases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Khan and Hossain.)

To use salmon protein hydrolysates as food ingredients and to mask the fish odor, Maillard reactions were associated with enzymatic production of hydrolysates. The study explored an original approach based on regression trees (RT) and random forest (RF) methodologies to predict hydrolysate odor profiles from volatile compounds. An experimental design with four factors: enzyme/substrate ratio, quantity of xylose, hydrolysis and cooking times was used to create a range of enzymatic hydrolysates. Twenty samples were submitted to a trained panel for sensory descriptions of odor. Hydrolysate volatile compounds were extracted by means of Headspace Solid Phase MicroExtraction (HS-SPME) and analyzed using gas chromatography/mass spectrometry (GC-MS). The results showed that RT and RF methodologies can be useful tools for predicting an entire sensory profile from volatile compounds. Four main volatile compounds made it possible to separate hydrolysates into five groups according to their specific sensory profile. 2,5-dimethylpyrazine, 1-hydroxy-2-propanone and 3-hydroxy-2-pentanone were identified as the main predictors of the roasted odor, whereas methanethiol was associated with a mud odor. These results also suggest the appropriate process conditions for obtaining a typical roasted odor., Competing Interests: Declaration of Competing Interest 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., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

This study aimed to (1) investigate the effect of birth and weaning body weight (BW) on performance indicators of grow-finisher pigs and (2) estimate birth and weaning BW cut-off values in order to identify slow growing pigs (SGP). Pigs (n = 144) were classified as SMALL (0.9 ± 0.13 kg) or BIG (1.4 ± 0.20 kg) at birth and re-classified as SMALL (5.4 ± 1.6 kg) or BIG (6.3 ± 1.91 kg) at weaning. Individual BW was recorded bi-weekly, and feed intake was recorded on a daily basis. Average daily gain (ADG) and feed intake, feed conversion ratio (FCR) and days to target slaughter weight (TSW) were calculated. SMALL-SMALL pigs had lower ADG (p < 0.05) requiring 167.1 days (i.e., 14.2 extra days) to TSW (p < 0.05) compared with BIG pigs at birth and/or weaning. However, FCR was similar between groups (p > 0.05). Pigs weaned at <3.7 kg BW would likely be SGP. Pigs born at ≥1.1 kg BW or weaned at ≥6.4 kg BW are more likely to reach TSW at 22 weeks of age. The results suggest that birth BW might not be the best predictor for subsequent performance, as some small-born pigs were able to catch up with their bigger counterparts. The cut-off values identified could be used to design specific management and nutritional strategies for SGP., Competing Interests: The authors declare no conflict of interest.

The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting 201 countries and territories around the globe. As of April 4, 2020, it has caused a pandemic outbreak with more than 11,16,643 confirmed infections and more than 59,170 reported deaths worldwide. The main focus of this paper is two-fold: (a) generating short term (real-time) forecasts of the future COVID-19 cases for multiple countries; (b) risk assessment (in terms of case fatality rate) of the novel COVID-19 for some profoundly affected countries by finding various important demographic characteristics of the countries along with some disease characteristics. To solve the first problem, we presented a hybrid approach based on autoregressive integrated moving average model and Wavelet-based forecasting model that can generate short-term (ten days ahead) forecasts of the number of daily confirmed cases for Canada, France, India, South Korea, and the UK. The predictions of the future outbreak for different countries will be useful for the effective allocation of health care resources and will act as an early-warning system for government policymakers. In the second problem, we applied an optimal regression tree algorithm to find essential causal variables that significantly affect the case fatality rates for different countries. This data-driven analysis will necessarily provide deep insights into the study of early risk assessments for 50 immensely affected countries., 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., (© 2020 Elsevier Ltd. All rights reserved.)

River networks in subtropical agricultural hilly region become an inconvenient greenhouse gas (GHG, methane and nitrous oxide) source because of the influence of human activities, which has caused large uncertainties for refinement of national GHG inventories and their global budget. Based on field monitoring experiments at high temporal resolution, we employed regression tree and importance analysis to identify quantitatively factors that influence the diffusive flux of GHGs to provide a scientific basis for reducing GHG emissions and controlling regional carbon and nitrogen losses. The results indicate that significant spatiotemporal variation of methane (CH 4 ) nitrous oxide (N 2 O) diffusion occurs in all the four reaches (W1, W2, W3 and W4) of Tuojia river networks. Among them, W1 contributed lowest CH 4 (22.55 μg C m -2  h -1 ) and N 2 O (5.00 μg N m -2  h -1 ) diffusive flux than the other three (P < 0.05), while W4 offered highest CH 4 (166.15 μg C m -2  h -1 ) and N 2 O (30.47 μg N m -2  h -1 ) diffusive flux but with no statistically significant difference between W2 and W3 due to homogeneous extraneous nutrition loading into the two reaches. W4 also contributed largest cumulative flux of CH 4 (14.55 kg C ha -1  yr -1 ) and N 2 O (2.69 kg N ha -1  yr -1 ) in Tuojia River networks (P < 0.05). Furthermore, the regression tree and importance analysis indicate that, in the anaerobic environment, dissolved oxygen saturation controlled the production and diffusion for both CH 4 and N 2 O. The findings of this investigation highlighted that decision support tools provide an effective pathway to enhance the GHG mitigation technology research in agroecosystems and simultaneously shed light on the global campaign on refinement of national GHG inventories as well as regional nutrient management., Competing Interests: Declaration of competing interest All the authors have seen and approved the final version of this manuscript, and they are aware of and accept responsibility for the manuscript. Therefore, there is no conflict of interest to declare., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Multiple imputation by chained equations (MICE) has emerged as a leading strategy for imputing missing epidemiological data due to its ease of implementation and ability to maintain unbiased effect estimates and valid inference. Within the MICE algorithm, imputation can be performed using a variety of parametric or nonparametric methods. Literature has suggested that nonparametric tree-based imputation methods outperform parametric methods in terms of bias and coverage when there are interactions or other nonlinear effects among the variables. However, these studies fail to provide a fair comparison as they do not follow the well-established recommendation that any effects in the final analysis model (including interactions) should be included in the parametric imputation model. We show via simulation that properly incorporating interactions in the parametric imputation model leads to much better performance. In fact, correctly specified parametric imputation and tree-based random forest imputation perform similarly when estimating the interaction effect. Parametric imputation leads to slightly higher coverage for the interaction effect, but it has wider confidence intervals than random forest imputation and requires correct specification of the imputation model. Epidemiologists should take care in specifying MICE imputation models, and this paper assists in that task by providing a fair comparison of parametric and tree-based imputation in MICE., (© 2020 John Wiley & Sons, Ltd.)

Acid phosphatase produced by plants and microbes plays a fundamental role in the recycling of soil phosphorus (P). A quantification of the spatial variation in potential acid phosphatase activity (AP) on large spatial scales and its drivers can help to reduce the uncertainty in our understanding of bio-availability of soil P. We applied two machine-learning methods (Random forests and back-propagation artificial networks) to simulate the spatial patterns of AP across Europe by scaling up 126 site observations of potential AP activity from field samples measured in the laboratory, using 12 environmental drivers as predictors. The back-propagation artificial network (BPN) method explained 58% of AP variability, more than the regression tree model (49%). In addition, BPN was able to identify the gradients in AP along three transects in Europe. Partial correlation analysis revealed that soil nutrients (total nitrogen, total P, and labile organic P) and climatic controls (annual precipitation, mean annual temperature, and temperature amplitude) were the dominant factors influencing AP variations in space. Higher AP occurred in regions with higher mean annual temperature, precipitation and higher soil total nitrogen. Soil TP and Po were non-monotonically correlated with modeled AP for Europe, indicating diffident strategies of P utilization by biomes in arid and humid area. This study helps to separate the influences of each factor on AP production and to reduce the uncertainty in estimating soil P availability. The BPN model trained with European data, however, could not produce a robust global map of AP due to the lack of representative measurements of AP for tropical regions. Filling this data gap will help us to understand the physiological basis of P-use strategies in natural soils., (Copyright © 2020 Sun, Goll, Ciais, Peng, Margalef, Asensio, Sardans and Peñuelas.)

One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers and other visual display terminals for a prolonged time may have negative effects on vision, leading to a greater risk of Computer Vision Syndrome (CVS) among their users. In this study, the importance of ocular and visual symptoms related to CVS was evaluated, and the factors associated with CVS were studied, with the help of an algorithm based on regression trees and genetic algorithms. The performance of this proposed model was also tested to check its ability to predict how prone a worker is to suffering from CVS. The findings of the present research confirm a high prevalence of CVS in healthcare workers, and associate CVS with a longer duration of occupation and higher daily computer usage.

The microbial quality of irrigation water is typically assessed by measuring the concentrations of E. coli in irrigation water reservoirs that are variable in space and time. E. coli concentrations are affected by water quality parameters that co-vary with E. coli concentrations and may be easily measured with currently available sensors. The objective of this work was to identify the most influential environmental covariates affecting E. coli concentrations during a three-month biweekly monitoring period within two irrigation ponds in Maryland during the summer of 2017. E. coli levels as well as sensor-based water quality parameters including turbidity, pH, dissolved oxygen, dissolved fluorescent organic matter, conductivity, and chlorophyll were measured at 23 and 34 locations in ponds 1 and 2, respectively. Regression tree analyses were used to determine the most influential water quality parameters for the prediction of E. coli levels. Correlations between E. coli and water quality covariates were not strong and were inconsistently significant. Shoreline sample locations had higher E. coli concentrations than interior pond samples and significant differences were observed when comparing these two groups. Regression trees provided fairly accurate predictions of E. coli levels based on water quality parameters with R 2 values ranging from 0.70 to 0.93. Factors identified via the regression trees varied by sampling date but common leading covariates included cyanobacteria, organic matter, and turbidity. Results indicated environmental covariates, sensed either remotely or in situ, could be useful to delineate areas with different E. coli survival conditions across irrigation ponds and potentially other water bodies such as lakes, rivers, or bays., (Published by Elsevier B.V.)

Background: Patients with chronic diseases, like patients with end-stage renal disease (ESRD), have long history of care driven by multiple determinants (medical, social, economic, etc.). Although in most epidemiological studies, analyses of health care determinants are computed on single health care events using classical multivariate statistical regression methods. Only few studies have integrated the concept of treatment trajectories as a whole and studied their determinants., Methods: All 18- to 80-year-old incident ESRD patients who started dialysis in Ile-de-France or Bretagne between 2006 and 2009 and could be followed for a period of 48 months after initiation of a renal replacement therapy were included ( n  = 5568). Their care trajectories were defined as categorical state sequences. Associations between patients' characteristics and care trajectories were assessed using a regression tree model together with a discrepancy analysis., Results: On average, each patient experienced 1.56 different renal replacement therapies (min = 1; max = 5) during the 48 months of follow-up. About 55% of patients never changed treatment and only 1% tried three or more renal replacement therapy modalities. Twelve homogeneous care trajectory groups were identified. Covariates explained 12% of the discrepancy between groups, particularly age, regions and initiation of hemodialysis with a catheter., Conclusions: Regression tree analysis of categorical state sequence highlighted geographical disparities in the care trajectory of French patients with ESRD that cannot be observed when focusing on a single outcome, such as survival. This method is an original tool to visualize and characterize care trajectories, notably in the context of chronic condition like ESRD.

Increasingly frequent urban waterlogging disasters, which are mainly caused by the increase in impervious surfaces due to rapid urbanization, have attracted public attention. Green roofs are conducive to increasing the urban pervious surface area to control sources of runoff, which has great significance for the ecological environment. This study uses the green roof of the administrative building of Jinling Primary School in Nanjing as the study area. 76 rainfall-runoff events collected over 17 months (2016-06-2017-10) were used to calculate the comprehensive runoff control ability and factors influencing the green roof in the context of the site scale. Based on life cycle assessment theory, the benefits of stormwater regulation over its 30-year life cycle were quantitatively evaluated. The results show that:① The average retention of the green roof was 62.7%, which could have a significant impact on runoff and peak flow, reducing the runoff time and delaying the flood peak. ② The green roof has a strong ability to retain runoff during small and medium rainfall; however, this ability becomes low when the retention capacity is saturated or not fully recovered, even in small rainfall-runoff events. ③ The main factors affecting the retention ability of the green roof are the total rainfall, rainfall intensity, and water content of the growth substrate soil. ④ The green roof has great economic benefits, with a construction cost of about 12.51 yuan·m -3 and a return on investment of 0.41. The results of this study can provide an important scientific basis and decision-making reference for the planning and construction of green roofs and the promotion of related policies.

In the mare, foaling is a critical unpredictable event due to a wide range of gestational length and the absence of clear signs of impending parturition. To predict foaling, pH, inversion sodium potassium and increase of calcium concentration in mammary secretions are used. The aim of this study was to find how many days are left until parturition knowing mare's age (A) and parity (P) combined with ultrasonographic measurements of the fetal orbit in Standardbred mares with normal pregnancy. Eighty healthy Standardbred mares with normal pregnancy were hospitalized for attended delivery. Information about mare's age, parity and breeding date were recorded at admission. Transrectal ultrasonography were routinely performed at admission and every 10 days until parturition using a B-mode real time portable unit equipped with a 5-7.5 MHz linear transducer. The images of the fetal orbit were acquired when cornea, anterior and posterior chamber, vitreous body, lens and optic nerve were visible. Longitudinal diameter (LD) was considered as the distance between the two ocular poles, within the vitreous body; transverse diameter (TD), perpendicular to LD and bisecting the lens, was measured as the distance between cornea and retina. At delivery, length of pregnancy and gestational age at each exam were registered. For each ultrasound examination, days before parturition (DBP) were calculated. Seventy-eight Standardbred mares with normal pregnancies were included in the study. Mares' mean age was 9 ± 5 years old (range 4-20 years) and mean gestation length was 341 ± 7 days (range 327-366 days). Thirty-three mares were primiparous and 45 mares were multiparous. Data were analyzed using a regression tree: P, A, LD and TD were used as covariates. DBP was used as the variable of interest. Nine terminal nodes were identified based on the selected covariates. The first split is produced by the TD: fetuses with TD greater or equal than 2.97 cm are further split according to LD, with a threshold of 3.28 cm. The next split is dictated by A; after a further split on LD, the first terminal node is built, containing 34 fetuses with average DBP equal to 10 days. If the A is ≥ 9.5 years a further split is on P: when mares are multiparous, the TD built two different nodes. Since prediction of mare's foaling date is an important factor in stud farm management, the regression model developed may help the veterinarian to estimate the DBP in Standardbred mares with normal pregnancy., (Copyright © 2018. Published by Elsevier Inc.)

Predictive modeling is the development of a model that is best able to predict an outcome based on given input variables. Model algorithms are different processes that are used to define functions that transform the data within models. Common algorithms include logistic regression (LR), linear discriminant analysis (LDA), classification and regression trees (CART), naïve Bayes (NB), and k-nearest neighbor (KNN). Data preprocessing option, such as feature extraction and reduction, and model algorithms are commonly selected empirically in epidemiological studies even though these decisions can significantly affect model performance. Accordingly, full model selection (FMS) methods were developed to provide a systematic approach to select predictive modeling methods; however, current limitations of FMS, such as its dependency on user-selected hyperparameters, have prevented their routine incorporation into analyses for model performance optimization. Here we present the use of regression trees as an innovative method to apply FMS. Regression tree FMS (rtFMS) requires the development of a model for every combination of predictive modeling method options under consideration. The iterated, cross-validation performances of these models are then passed through a regression tree for selection of a final model. We demonstrate the benefits of rtFMS using a milk Fourier transform infrared spectroscopy dataset, wherein we build prediction models for two blood metabolic health parameters in dairy cows, nonesterified fatty acids (NEFA) and β-hydroxybutyrate acid (BHBA). The goal for building NEFA and BHBA prediction models is to provide a milk-based screening tool for metabolic health in dairy cattle that can be incorporated automatically in milk analysis routines. These models could be used in conjunction with physical exams, cow side tests, and other indications to initiate medical intervention. In contrast to previously reported FMS methods, rtFMS is not a black box, is simple to implement and interpret, it does not have hyperparameters, and it illustrates the relative importance of modeling options. Additionally, rtFMS allows for indirect comparisons among models developed using different datasets. Finally, rtFMS eliminates user bias due to personal preference for certain methods and rtFMS removes the dependency on published comparisons of methods. Thus, rtFMS provides clear benefits over the empirical selection of data preprocessing options and model algorithms., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objective: We evaluated associations between the installation of eleven street design elements, between 2007 and 2015, and subsequent changes in vehicle-to-pedestrian collisions in New York City., Methods: Collision data were from Accident Location Information System in the New York State Department of Transportation. Safety improvement projects at 118 intersections were reviewed and their implemented street design elements were identified. First, we assessed potential regression-to-the-mean effects using historic trends of pedestrian collision count at the intersection project locations. Second, we used a two-group pretest-posttest design to assess individual element's associations with pedestrian collision reduction after installations. Pedestrian collision count and pedestrian- and vehicle-based pedestrian collision rates were examined. Third, regression trees were used to classify the intersections with design elements as independent variables for the target variables of collision outcomes, to identify street design element combinations associated with pedestrian collision reductions., Results: Treatments with pedestrian refuge island or pedestrian plaza had reductions in pedestrian collision count and pedestrian-based collision rate while their comparisons had no changes. Treatments with pedestrian refuge island had a larger reduction in pedestrian collision when combined with lane removal or narrowing. Treatment with curb extension or pedestrian plaza had reductions in vehicle-based pedestrian collision rate while their comparisons had no changes. Other studied elements showed no, small, or insignificant associations with post-project pedestrian collision reductions., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Longitudinal changes in a population of interest are often heterogeneous and may be influenced by a combination of baseline factors. In such cases, traditional linear mixed effects models (Laird and Ware, 1982) assuming common parametric form for the mean structure may not be applicable. We show that the regression tree methodology for longitudinal data can identify and characterize longitudinally homogeneous subgroups. Most of the currently available regression tree construction methods are either limited to a repeated measures scenario or combine the heterogeneity among subgroups with the random inter-subject variability. We propose a longitudinal classification and regression tree (LongCART) algorithm under conditional inference framework (Hothorn, Hornik and Zeileis, 2006) that overcomes these limitations utilizing a two-step approach. The LongCART algorithm first selects the partitioning variable via a parameter instability test and then finds the optimal split for the selected partitioning variable. Thus, at each node, the decision of further splitting is type-I error controlled and thus it guards against variable selection bias, over-fitting and spurious splitting. We have obtained the asymptotic results for the proposed instability test and examined its finite sample behavior through simulation studies. Comparative performance of LongCART algorithm were evaluated empirically via simulation studies. Finally, we applied LongCART to study the longitudinal changes in choline levels among HIV-positive patients.

Classification of particles as gunshot residues (GSRs) is conducted using a semiautomatic approach in which the system first classifies particles based on an automatic elemental analysis, and then, examiners manually analyze particles having compositions which are characteristic of or consistent with GSRs. Analyzing all the particles in the second stage is time consuming with many particles classified by the initial automated system as being potentially GSRs excluded as such by the forensic examiner. In this paper, a new algorithm is developed to improve the initial classification step. The algorithm is based on a binary tree that was trained on almost 16,000 particles from 43 stubs used to sample hands of suspects. The classification algorithm was tested on 5,900 particles from 23 independent stubs and performed very well in terms of false positive and false negative rates. A routine use of the new algorithm can reduce significantly the analysis time of GSRs., (© 2017 American Academy of Forensic Sciences.)

Background: The Seychelles Child Development Study has been examining the relationship between prenatal methylmercury (MeHg) exposure from consuming fish during pregnancy and child development. This study re-analyzes seven outcomes in the 17 year Main Cohort data to determine if there are nonlinear or non-homogeneous (subgroup) associations that were not identified in the linear analysis., Methods: We adopted two statistical approaches. First, we carried out an additive nonlinear analysis assuming homogeneous prenatal MeHg-outcome relationships to explore overall associations. Second, we applied the regression tree to the Woodcock-Johnson Calculation subtest (it was significantly associated in earlier analyses) and identified 4 clusters based on covariates. Then we used additive models to assess the prenatal MeHg association in each of the four clusters for all seven outcomes. This approach assumes nonlinear associations in each cluster and non-homogeneous associations between clusters., Results: The additive nonlinear analysis yielded prenatal MeHg curves similar to the linear analysis. For the regression tree analysis, the curves relating prenatal MeHg to outcomes between the 4 clusters differed and some crossed at higher prenatal MeHg levels, suggesting non-homogeneity in the upper range of exposure. Additionally, some of the curves suggested a possible non-linear relationship within the range of exposure we studied., Conclusion: This non-linear analysis supports the findings from the linear analysis. It shows little evidence to support an adverse association of prenatal MeHg exposure through maternal consumption of fish contaminated with natural background levels. However, the tree analysis suggests that the prenatal exposure/outcome relationship may not be homogeneous across all individuals and that some subpopulations may have an adverse association in the upper range of the exposures studied. More robust data in the higher levels of exposure in this cohort are needed to confirm this finding., Competing Interests: Conflicts of Interest: We wish to confirm that there are no known conflicts of interest associated with this paper and there has been no significant financial support for this work that could have influenced its outcome.