Unraveling birth weight determinants: Integrating machine learning, spatial analysis, and district-level mapping.

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.

Further Clarification of Pain Management Complexity in Radiotherapy: Insights from Modern Statistical Approaches.

BACKGROUND: The primary objective of this study was to assess the adequacy of analgesic care in radiotherapy (RT) patients, with a secondary objective to identify predictive variables associated with pain management adequacy using a modern statistical approach, integrating the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and the Classification and Regression Tree (CART) analysis. METHODS: This observational, multicenter cohort study involved 1387 patients reporting pain or taking analgesic drugs from 13 RT departments in Italy. The Pain Management Index (PMI) served as the measure for pain control adequacy, with a PMI score < 0 indicating suboptimal management. Patient demographics, clinical status, and treatment-related factors were examined to discern the predictors of pain management adequacy. RESULTS: Among the analyzed cohort, 46.1% reported inadequately managed pain. Non-cancer pain origin, breast cancer diagnosis, higher ECOG Performance Status scores, younger patient age, early assessment phase, and curative treatment intent emerged as significant determinants of negative PMI from the LASSO analysis. Notably, pain management was observed to improve as RT progressed, with a greater discrepancy between cancer (33.2% with PMI < 0) and non-cancer pain (73.1% with PMI < 0). Breast cancer patients under 70 years of age with non-cancer pain had the highest rate of negative PMI at 86.5%, highlighting a potential deficiency in managing benign pain in younger patients. CONCLUSIONS: The study underscores the dynamic nature of pain management during RT, suggesting improvements over the treatment course yet revealing specific challenges in non-cancer pain management, particularly among younger breast cancer patients. The use of advanced statistical techniques for analysis stresses the importance of a multifaceted approach to pain management, one that incorporates both cancer and non-cancer pain considerations to ensure a holistic and improved quality of oncological care.

Modeling habitat suitability for the lesser-known populations of endangered mountain nyala (Tragelaphus buxtoni) in the Arsi and Ahmar Mountains, Ethiopia.

Habitat suitability models have become a valuable tool for wildlife conservation and management, and are frequently used to better understand the range and habitat requirements of rare and endangered species. In this study, we employed two habitat suitability modeling techniques, namely Boosted Regression Tree (BRT) and Maximum Entropy (Maxent) models, to identify potential suitable habitats for the endangered mountain nyala (Tragelaphus buxtoni) and environmental factors affecting its distribution in the Arsi and Ahmar Mountains of Ethiopia. Presence points, used to develop our habitat suitability models, were recorded from fecal pellet counts (n = 130) encountered along 196 randomly established transects in 2015 and 2016. Predictor variables used in our models included major landcover types, Normalized Difference Vegetation Index (NDVI), greenness and wetness tasseled cap vegetation indices, elevation, and slope. Area Under the Curve model evaluations for BRT and Maxent were 0.96 and 0.95, respectively, demonstrating high performance. Both models were then ensembled into a single binary output highlighting an area of agreement. Our results suggest that 1864 km2 (9.1%) of the 20,567 km2 study area is suitable habitat for the mountain nyala with land cover types, elevation, NDVI, and slope of the terrain being the most important variables for both models. Our results highlight the extent to which habitat loss and fragmentation have disconnected mountain nyala subpopulations. Our models demonstrate the importance of further protecting suitable habitats for mountain nyala to ensure the species' conservation.

Deciphering carbon dioxide fluxes and interactions in the Ganga river Basin of South Asia.

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.

Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestine.

PURPOSE: This study utilized data mining and machine learning (ML) techniques to identify new patterns and classifications of the associations between nutrient intake and anemia among university students. METHODS: We employed K-means clustering analysis algorithm and Decision Tree (DT) technique to identify the association between anemia and vitamin and mineral intakes. We normalized and balanced the data based on anemia weighted clusters for improving ML models' accuracy. In addition, t-tests and Analysis of Variance (ANOVA) were performed to identify significant differences between the clusters. We evaluated the models on a balanced dataset of 755 female participants from the Hebron district in Palestine. RESULTS: Our study found that 34.8% of the participants were anemic. The intake of various micronutrients (i.e., folate, Vit A, B5, B6, B12, C, E, Ca, Fe, and Mg) was below RDA/AI values, which indicated an overall unbalanced malnutrition in the present cohort. Anemia was significantly associated with intakes of energy, protein, fat, Vit B1, B5, B6, C, Mg, Cu and Zn. On the other hand, intakes of protein, Vit B2, B5, B6, C, E, choline, folate, phosphorus, Mn and Zn were significantly lower in anemic than in non-anemic subjects. DT classification models for vitamins and minerals (accuracy rate: 82.1%) identified an inverse association between intakes of Vit B2, B3, B5, B6, B12, E, folate, Zn, Mg, Fe and Mn and prevalence of anemia. CONCLUSIONS: Besides the nutrients commonly known to be linked to anemia-like folate, Vit B6, C, B12, or Fe-the cluster analyses in the present cohort of young female university students have also found choline, Vit E, B2, Zn, Mg, Mn, and phosphorus as additional nutrients that might relate to the development of anemia. Further research is needed to elucidate if the intake of these nutrients might influence the risk of anemia.

Dissecting Alzheimer's Disease Risk in Asian American Elders: A Classification and Regression Tree Approach.

Background: Alzheimer's disease (AD) poses a growing public health challenge, particularly with an aging population. While extensive research has explored the relationships between AD, socio-demographic factors, and cardiovascular risk factors, a notable gap exists in understanding these connections within the Asian American elderly population. Objective: This study aims to address this gap by employing the Classification and Regression Tree (CART) approach to investigate the intricate interplay of socio-demographic variables, cardiovascular risk factors, sleep patterns, prior antidepressant use, and AD among Asian American elders. Methods: Data from the 2017 Uniform Data Set, provided by the National Alzheimer's Coordinating Center, were analyzed, focusing on a sample of Asian American elders (n = 4,343). The analysis utilized the Classification and Regression Tree (CART) approach. Results: CART analysis identified critical factors, including levels of independence, specific age thresholds (73.5 and 84.5 years), apnea, antidepressant use, and body mass index, as significantly associated with AD risk. Conclusions: These findings have far-reaching implications for future research, particularly in examining the roles of gender, cultural nuances, socio-demographic factors, and cardiovascular risk elements in AD within the Asian American elderly population. Such insights can inform tailored interventions, improved healthcare access, and culturally sensitive policies to address the complex challenges posed by AD in this community.

A Day 14 Endpoint for Acute GVHD Clinical Trials.

The overall response rate (ORR) 28 days after treatment has been adopted as the primary endpoint for clinical trials of acute graft versus host disease (GVHD). However, physicians often need to modify immunosuppression earlier than day (D) 28, and non-relapse mortality (NRM) does not always correlate with ORR at D28. We studied 1144 patients that received systemic treatment for GVHD in the Mount Sinai Acute GVHD International Consortium (MAGIC) and divided them into a training set (n=764) and a validation set (n=380). We used a recursive partitioning algorithm to create a Mount Sinai model that classifies patients into favorable or unfavorable groups that predicted 12 month NRM according to overall GVHD grade at both onset and D14. In the Mount Sinai model grade II GVHD at D14 was unfavorable for grade III/IV GVHD at onset and predicted NRM as well as the D28 standard response model. The MAGIC algorithm probability (MAP) is a validated score that combines the serum concentrations of suppression of tumorigenicity 2 (ST2) and regenerating islet-derived 3-alpha (REG3α) to predict NRM. Inclusion of the D14 MAP biomarker score with the D14 Mount Sinai model created three distinct groups (good, intermediate, poor) with strikingly different NRM (8%, 35%, 76% respectively). This D14 MAGIC model displayed better AUC, sensitivity, positive and negative predictive value, and net benefit in decision curve analysis compared to the D28 standard response model. We conclude that this D14 MAGIC model could be useful in therapeutic decisions and may offer an improved endpoint for clinical trials of acute GVHD treatment.

Classifying clinical phenotypes of functional recovery for acute traumatic spinal cord injury. An observational cohort study.

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.

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.

GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh.

The frequency of landslides and related economic and environmental damage has increased in recent decades across the hilly areas of the world, no exception is Bangladesh. Considering the first step in landslide disaster management, different methods have been applied but no methods found as best one. As a result, landslide assessment using different methods in different geographical regions has significant importance. The research aims to prepare and evaluate landslide susceptibility maps (LSMs) of the Chattogram district using three machine learning algorithms of Logistic Regression (LR), Random forest (RF) and Decision and Regression Tree (DRT). Sixteen landslide conditioning factors were determined considering topographic, hydro-climatic, geologic and anthropogenic influence. The landslide inventory database (255 locations) was randomly divided into training (80 %) and testing (20 %) sets. The LSMs showed that almost 9-12 % of areas of the Chattogram district are highly susceptible to landslides. The highly susceptible zones cover the Chattogram district's hill ranges where active morphological processes (erosion and denudation) are dominant. The ROC values for training data were 0.943, 0.917 and 0.947 and testing data were 0.963, 0.934 and 0.905 for LR, RF and DRT models, respectively. The accuracy is higher than the previous research in comparison to the extent of the study area and the size of the inventory. Among the models, LR showed the highest prediction rate and DRT showed the highest success rate. According to susceptibility zones, DRT is the more realistic model followed by LR. The maps can be applied at the local scale for landslide hazard management.

Investigation of the evolved pyrolytic products and energy potential of Bagasse: experimental, kinetic, thermodynamic and boosted regression trees analysis.

This study explored bagasse's energy potential grown using treated industrial wastewater through various analyses, experimental, kinetic, thermodynamic, and machine learning boosted regression tree methods. Thermogravimetry was employed to determine thermal degradation characteristics, varying the heating rate from 10 to 30 °C/min. The primary pyrolysis products from bagasse are H2, CH4, H2O, CO2, and hydrocarbons. Kinetic parameters were estimated using three model-free methods, yielding activation energies of approximately 245.98 kJ mol-1, 247.58 kJ mol-1, and 244.69 kJ mol-1. Thermodynamic parameters demonstrated the feasibility and reactivity of pyrolysis with ΔH ≈ 240.72 kJ mol-1, ΔG ≈ 162.87 kJ mol-1, and ΔS ≈ 165.35 J mol-1 K-1. The distribution of activation energy was analyzed using the multiple distributed activation energy model. Lastly, boosted regression trees predicted thermal degradation successfully, with an R2 of 0.9943. Therefore, bagasse's potential as an eco-friendly alternative to fossil fuels promotes waste utilization and carbon footprint reduction.

Assessing Derawan Island's Coral Reefs over Two Decades: A Machine Learning Classification Perspective.

This study aims to understand the dynamic changes in the coral reef habitats of Derawan Island over two decades (2003, 2011, and 2021) using advanced machine learning classification techniques. The motivation stems from the urgent need for accurate, detailed environmental monitoring to inform conservation strategies, particularly in ecologically sensitive areas like coral reefs. We employed non-parametric machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), to assess spatial and temporal changes in coral habitats. Our analysis utilized high-resolution data from Landsat 9, Landsat 7, Sentinel-2, and Multispectral Aerial Photos. The RF algorithm proved to be the most accurate, achieving an accuracy of 71.43% with Landsat 9, 73.68% with Sentinel-2, and 78.28% with Multispectral Aerial Photos. Our findings indicate that the classification accuracy is significantly influenced by the geographic resolution and the quality of the field and satellite/aerial image data. Over the two decades, there was a notable decrease in the coral reef area from 2003 to 2011, with a reduction to 16 hectares, followed by a slight increase in area but with more heterogeneous densities between 2011 and 2021. The study underscores the dynamic nature of coral reef habitats and the efficacy of machine learning in environmental monitoring. The insights gained highlight the importance of advanced analytical methods in guiding conservation efforts and understanding ecological changes over time.

Interactions of genetic variations in FAS, GJB2 and PTPRN2 are associated with noise-induced hearing loss: a case-control study in China.

BACKGROUND: This study aimed to screen and validate noise-induced hearing loss (NIHL) associated single nucleotide polymorphisms (SNPs), construct genetic risk prediction models, and evaluate higher-order gene-gene, gene-environment interactions for NIHL in Chinese population. METHODS: First, 83 cases and 83 controls were recruited and 60 candidate SNPs were genotyped. Then SNPs with promising results were validated in another case-control study (153 cases and 252 controls). NIHL-associated SNPs were identified by logistic regression analysis, and a genetic risk model was constructed based on the genetic risk score (GRS), and classification and regression tree (CART) analysis was used to evaluate interactions among gene-gene and gene-environment. RESULTS: Six SNPs in five genes were significantly associated with NIHL risk (p < 0.05). A positive dose-response relationship was found between GRS values and NIHL risk. CART analysis indicated that strongest interaction was among subjects with age ≥ 45 years and cumulative noise exposure ≥ 95 [dB(A)·years], without personal protective equipment, and carried GJB2 rs3751385 (AA/AB) and FAS rs1468063 (AA/AB) (OR = 10.038, 95% CI = 2.770, 47.792), compared with the referent group. CDH23, FAS, GJB2, PTPRN2 and SIK3 may be NIHL susceptibility genes. CONCLUSION: GRS values may be utilized in the evaluation of the cumulative effect of genetic risk for NIHL based on NIHL-associated SNPs. Gene-gene, gene-environment interaction patterns play an important role in the incidence of NIHL.

A combined molecular approach utilizing microbial DNA and microRNAs in a qPCR multiplex for the classification of five forensically relevant body fluids.

Body fluid identification is an essential step in the forensic biology workflow that can assist DNA analysts in determining where to collect DNA evidence. Current presumptive tests lack the specificity that molecular techniques can achieve; therefore, molecular methods, including microRNA (miRNA) and microbial signature characterization, have been extensively researched in the forensic community. Limitations of each method suggest combining molecular markers to increase the discrimination efficiency of multiple body fluids from a single assay. While microbial signatures have been successful in identifying fluids with high bacterial abundances, microRNAs have shown promise in fluids with low microbial abundance (blood and semen). This project synergized the benefits of microRNAs and microbial DNA to identify multiple body fluids using DNA extracts. A reverse transcription (RT)-qPCR duplex targeting miR-891a and let-7g was validated, and miR-891a differential expression was significantly different between blood and semen. The miRNA duplex was incorporated into a previously reported qPCR multiplex targeting 16S rRNA genes of Lactobacillus crispatus, Bacteroides uniformis, and Streptococcus salivarius to presumptively identify vaginal/menstrual secretions, feces, and saliva, respectively. The combined classification regression tree model resulted in the presumptive classification of five body fluids with 94.6% overall accuracy, now including blood and semen identification. These results provide proof of concept that microRNAs and microbial DNA can classify multiple body fluids simultaneously at the quantification step of the current forensic DNA workflow.

Developing radiology diagnostic tools for pulmonary fibrosis using machine learning methods.

BACKGROUND: Accurate and prompt diagnosis of the different patterns for pulmonary fibrosis is essential for patient management. However, accurate diagnosis of the specific pattern is challenging due to overlapping radiographic characteristics. MATERIALS AND METHODS: We conducted a retrospective chart review utilizing two machine learning methods, classification and regression tree and Bayesian additive regression tree, to select the most important radiographic features for diagnosing the three most common fibrosis patterns and created an online diagnostic app for convenient implementation. RESULTS: Four hundred patients (median age of 67 with inter quartile range 58-73; 200 males) were included in the study. Peripheral distribution, homogeneity, lower lobe predominance and mosaic attenuation of fibrosis are the four most important features identified. Bayesian additive regression tree demonstrates better performance than classification and regression tree in diagnosis prediction and provides the predicted probability of each diagnosis with uncertainty intervals for each combination of features. CONCLUSION: The model and app built with Bayesian additive regression tree can be used as an effective tool in assisting radiologists in the diagnostic process of pulmonary fibrosis pattern recognition.

Development of a clinical prediction rule for mobility status at discharge in patients with total knee arthroplasty: Using a decision tree model.

BACKGROUND: Total knee arthroplasty (TKA) is an effective treatment to improve mobility in patients with severe knee osteoarthritis. However, some patients continue to have poor mobility after surgery. The preoperative identification of patients with poor mobility after TKA allows for better treatment selection and appropriate goal setting. The purpose of this study was to develop a clinical prediction rule (CPR) to predict mobility after TKA. METHODS: This study included patients undergoing primary TKA. Predictors of outcome included patient characteristics, physical function, and psychological factors, which were measured preoperatively. The outcome measure was the Timed Up and Go test, which was measured at discharge. Patients with a score of ≥11 s were considered having a low-level of mobility. The classification and regression tree methodology of decision tree analysis was used for developing a CPR. RESULTS: Of the 101 cases (mean age, 72.2 years; 71.3 % female), 26 (25.7 %) were classified as low-mobility. Predictors were the modified Gait Efficacy Scale, age, knee pain on the operated side, knee extension range of motion on the non-operated side, and Somatic Focus, a subscale of the Tampa Scale for Kinesiophobia (short version). The model had a sensitivity of 50.0 %, a specificity of 98.7 %, a positive predictive value of 92.9 %, a positive likelihood ratio of 37.5, and an area under the receiver operating characteristic curve of 0.853. CONCLUSION: We have developed a CPR that, with some accuracy, predicts the mobility outcomes of patients after TKA. This CPR may be useful for predicting postoperative mobility and clinical goal setting.

Modelling groundwater vulnerability in a vulnerable deltaic coastal region of Sundarban Biosphere Reserve, India.

Groundwater is the most reliable source of freshwater for human well-being. Significant toxic contamination in groundwater, particularly in the aquifers of the Ganges delta, has been a substantial source of arsenic (As). The Sundarban Biosphere Reserve (SBR), located in the southwestern part of the world's largest Ganges delta, suffers from As contamination in groundwater. Therefore, assessment of groundwater vulnerability is essential to ensure the safety of groundwater quality in SBR. Three data-driven algorithms, i.e. "logistic regression (LR)", "random forest (RF)", and "boosted regression tree (BRT)", were used to assess groundwater vulnerability. Groundwater quality and hydrogeochemical characteristics were evaluated by Piper, United States Salinity Laboratory (USSL), and Wilcox's diagram. The result of this study indicates that among the applied models, BRT (AUC = 0.899) is the best-fit model, followed by RF (AUC = 0.882) and LR (AUC = 0.801) to assess groundwater vulnerability. In addition, the result also indicates that the general quality of the groundwater in this area is not very good for drinking purposes. The applied methods of this study can be used to evaluate the groundwater vulnerability of the other aquifer systems.

Risk profiles for smoke behavior in COVID-19: a classification and regression tree analysis approach.

BACKGROUND: COVID-19 pandemic emerged worldwide at the end of 2019, causing a severe global public health threat, and smoking is closely related to COVID-19. Previous studies have reported changes in smoking behavior and influencing factors during the COVID-19 period, but none of them explored the main influencing factor and high-risk populations for smoking behavior during this period. METHODS: We conducted a nationwide survey and obtained 21,916 valid data. Logistic regression was used to examine the relationships between each potential influencing factor (sociodemographic characteristics, perceived social support, depression, anxiety, and self-efficacy) and smoking outcomes. Then, variables related to smoking behavior were included based on the results of the multiple logistic regression, and the classification and regression tree (CART) method was used to determine the high-risk population for increased smoking behavior during COVID-19 and the most profound influencing factors on smoking increase. Finally, we used accuracy to evaluated the performance of the tree. RESULTS: The strongest predictor of smoking behavior during the COVID-19 period is acceptance degree of passive smoking. The subgroup with a high acceptation degree of passive smoking, have no smokers smoked around, and a length of smoking of ≥ 30 years is identified as the highest smoking risk (34%). The accuracy of classification and regression tree is 87%. CONCLUSION: The main influencing factor is acceptance degree of passive smoking. More knowledge about the harm of secondhand smoke should be promoted. For high-risk population who smoke, the "mask protection" effect during the COVID-19 pandemic should be fully utilized to encourage smoking cessation.

A machine learning-based generalized approach for predicting unauthorized immigration flow considering dynamic border security nexus.

Unauthorized immigration has been a long-standing and contentious challenge for developed and developing countries. Numerous continually evolving push and pull factors across international borders, such as economy, employment, population density, unrest, corruption, and climate have driven this migration. Large-scale pandemics such as COVID-19, causing further instability in countries' financial well-being, can initiate or alter emigration flow from different countries. In light of such a complex confluence of factors, climate change, and demographic shifts in migrant communities, it is high time to shift toward machine learning-reinforced generalized approaches from the traditional parametric approaches based on migrant community-specific localized surveys. To our best knowledge, no literature has explored the nonparametric approach and developed a comprehensive database independent of localized surveys to analyze unauthorized migration. This article fills this gap by deploying nine nonparametric machine learning algorithms for predicting unauthorized immigration flow considering the dynamic border security nexus. This framework considers the Seasonal Autoregressive Integrated Moving Average model as the null model. The proposed novel framework removes the dependency on localized survey-based studies and provides a more cost-effective, faster, and big data-friendly approach. This study finds the Bayesian Additive Regression Tree model as the best predictive model.

Methodological approaches for the assessment of bisphenol A exposure.

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.