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.

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.
METHODS: 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.

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.

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 (CO2) emissions. In this study, we computed the partial pressure of CO2 (pCO2) using the carbonate equilibria-based model (pCO2SYS) 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 pCO2 across the Ganga River. The pCO2 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 pCO2 and FCO2 levels (fugacity of CO2: 3.63 gm2d-1) compared to the middle and lower stretch, underscoring the intricate interplay between hydrochemistry and CO2 dynamics. In the context of pCO2 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 pCO2 variations across the Ganga River (n = 91). A robust negative correlation between BOD and FCO2 was also observed. The distinct longitudinal patterns of both parameters may induce a negative correlation between BOD and pCO2. Therefore, comprehensive studies are necessitated to decipher the underlying mechanisms governing this relationship. The present insights are instrumental in comprehending the potential of CO2 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.

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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Random effects machine learning models have the potential for high clinical utility and can be leveraged for interventions to reduce vulnerability to depression.

UNASSIGNED: Identify patient subgroups with different functional outcomes after SCI and study the association between functional status and initial ISNCSCI components.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
After a traumatic Spinal Cord Injury (SCI), early neurological examination using the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) is recommended to determine initial injury severity and prognosis.This study identified three initial ISNCSCI components defining four subgroups of SCI patients with different expectations in functional outcomes, namely the initial pinprick sensory score, the Lower Extremity Motor Score, and the Upper Extremity Motor Score.Clinicians could use these subgroups early after tSCI to plan rehabilitation and set realistic therapeutic goals regarding functional outcomes.In clinical practice, careful and accurate assessment of pinprick sensation early after the SCI is crucial when predicting function or stratifying patients based on the expected function.

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.
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.
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.
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.

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.

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.
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.
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.
Variability in motor task performance across practice trials may be valuable for understanding preclinical and early-stage AD.