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.

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.

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.

Utilizing Random Effects Machine Learning Algorithms for Identifying Vulnerability to Depression

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.

Volumetric regional MRI and neuropsychological predictors of motor task variability in cognitively unimpaired, Mild Cognitive Impairment, and probable Alzheimer's disease older adults

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.

Neighborhood, built environment and resilience in transportation during the COVID-19 pandemic

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.

The use of regression tree method for Sentinel-2 satellite data to mapping percent tree cover in different forest types

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.

Predicting medical usage rate at mass gathering events in Belgium: development and validation of a nonlinear multivariable regression model

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 R² of 0.68 (95% confidence interval 0.60–0.75). For TTHR, the most determining predictor variables were mass gathering category and predicted PPR (R² = 0.48). External validation indicated limited predictive value for other events (R² = 0.02 for PPR; R² = 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-12580-8.

Regression tree modelling to predict total average extra costs in household spending during COVID-19 pandemic

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.

Efficacy, safety and factors associated with disease progression in patients with unresectable (stage III) or distant metastatic (stage IV) BRAF V600-mutant melanoma: An open label, non-randomized, phase IIIb study of trametinib in combination with dabrafenib

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.

Surgical planning of horizontal strabismus using multiple output regression tree

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.

Using farmer observations for animal health syndromic surveillance: Participation and performance of an online enhanced passive surveillance system

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.

Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis

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.

Diffusive flux of CH4 and N2O from agricultural river networks: Regression tree and importance analysis

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 (CH₄) nitrous oxide (N₂O) diffusion occurs in all the four reaches (W1, W2, W3 and W4) of Tuojia river networks. Among them, W1 contributed lowest CH₄ (22.55 μg C m^-2 h^-1) and N₂O (5.00 μg N m^-2 h^-1) diffusive flux than the other three (P < 0.05), while W4 offered highest CH₄ (166.15 μg C m^-2 h^-1) and N₂O (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 CH₄ (14.55 kg C ha^-1 yr^-1) and N₂O (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 CH₄ and N₂O. 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.

Intraseasonal variation of E. coli and environmental covariates in two irrigation ponds in Maryland, USA

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 R² 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.

Identifying street design elements associated with vehicle-to-pedestrian collision reduction at intersections in New York City

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.

Regression Trees for Longitudinal Data with Baseline Covariates

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.

A regression model including fetal orbit measurements to predict parturition in Standardbred mares with normal pregnancy

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.

Spatial pattern of heavy metals accumulation risk in urban soils of Beijing and its influencing factors

Accumulations of heavy metals in urban soils are highly spatial heterogeneity and affected by multiple factors including soil properties, land use and pattern, population and climatic conditions. We studied accumulation risks of Cd, Cu, Pb and Zn in unban soils of Beijing and their influencing based on the regression tree analysis and a GIS-based overlay model. Result shows that Zinc causes the most extensive soil pollution and Cu result in the most acute soil pollution. The soil's organic carbon content and CEC and population growth are the most significant factors affecting heavy metal accumulation. Other influence factors in land use pattern, urban landscape, and wind speed also contributed, but less pronounced. The soils in areas with higher degree of urbanization and surrounded by intense vehicular traffics have higher accumulation risk of Cd, Cu, Pb, and Zn.

Importance of Natural and Anthropogenic Environmental Factors to Fish Communities of the Fox River in Illinois

The dominant environmental determinants of aquatic communities have been a persistent topic for many years. Interactions between natural and anthropogenic characteristics within the aquatic environment influence fish communities in complex ways that make the effect of a single characteristic difficult to ascertain. Researchers are faced with the question of how to deal with a large number of variables and complex interrelationships. This study utilized multiple approaches to identify key environmental variables to fish communities of the Fox River Basin in Illinois: Pearson and Spearman correlations, an algorithm based on information theory called mutual information, and a measure of variable importance built into the machine learning algorithm Random Forest. The results are based on a dataset developed for this study, which uses a fish index of biological integrity (IBI) and its ten component metrics as response variables and a range of environmental variables describing geomorphology, stream flow statistics, climate, and both reach-scale and watershed-scale land use as independent variables. Agricultural land use and the magnitude and duration of low flow events were ranked by the algorithms as key factors for the study area. Reach-scale characteristics were dominant for native sunfish, and stream flow metrics were rated highly for native suckers. Regression tree analyses of environmental variables on fish IBI identified breakpoints in percent agricultural land in the watershed (~64%), duration of low flow pulses (~12 days), and 90-day minimum flow (~0.13 cms). The findings should be useful for building predictive models and design of more effective monitoring systems and restoration plans.

Seasonality of absolute humidity explains seasonality of influenza-like illness in Vietnam

BACKGROUND

Experimental and ecological studies have shown the role of climatic factors in driving the epidemiology of influenza. In particular, low absolute humidity (AH) has been shown to increase influenza virus transmissibility and has been identified to explain the onset of epidemics in temperate regions. Here, we aim to study the potential climatic drivers of influenza-like illness (ILI) epidemiology in Vietnam, a tropical country characterized by a high diversity of climates. We specifically focus on quantifying and explaining the seasonality of ILI.

METHODS

We used 18 years (1993-2010) of monthly ILI notifications aggregated by province (52) and monthly climatic variables (minimum, mean, maximum temperatures, absolute and relative humidities, rainfall and hours of sunshine) from 67 weather stations across Vietnam. Seasonalities were quantified from global wavelet spectra, using the value of the power at the period of 1 year as a measure of the intensity of seasonality. The 7 climatic time series were characterized by 534 summary statistics which were entered into a regression tree to identify factors associated with the seasonality of AH. Results were extrapolated to the global scale using simulated climatic times series from the NCEP/NCAR project.

RESULTS

The intensity of ILI seasonality in Vietnam is best explained by the intensity of AH seasonality. We find that ILI seasonality is weak in provinces experiencing weak seasonal fluctuations in AH (annual power <17.6), whereas ILI seasonality is strongest in provinces with pronounced AH seasonality (power >17.6). In Vietnam, AH and ILI are positively correlated.

CONCLUSIONS

Our results identify a role for AH in driving the epidemiology of ILI in a tropical setting. However, in contrast to temperate regions, high rather than low AH is associated with increased ILI activity. Fluctuation in AH may be the climate factor that underlies and unifies the seasonality of ILI in both temperate and tropical regions. Alternatively, the mechanism of action of AH on disease transmission may be different in cold-dry versus hot-humid settings.

Climatic, Edaphic Factors and Cropping History Help Predict Click Beetle (Coleoptera: Elateridae) (Agriotes spp.) Abundance

It is assumed that the abundance of Agriotes wireworms (Coleoptera: Elateridae) is affected by agro-ecological factors such as climatic and edaphic factors and the crop/previous crop grown at the sites investigated. The aim of this study, conducted in three different geographic counties in Croatia from 2007 to 2009, was to determine the factors that influence the abundance of adult click beetle of the species Agriotes brevis Cand., Agriotes lineatus (L.), Agriotes obscurus (L.), Agriotes sputator (L.), and Agriotes ustulatus Schall. The mean annual air temperature, total rainfall, percentage of coarse and fine sand, coarse and fine silt and clay, the soil pH, and humus were investigated as potential factors that may influence abundance. Adult click beetle emergence was monitored using sex pheromone traps (YATLORf and VARb3). Exploratory data analysis was preformed via regression tree models and regional differences in Agriotes species' abundance were predicted based on the agro-ecological factors measured. It was found that the best overall predictor of A. brevis abundance was the previous crop grown. Conversely, the best predictor of A. lineatus abundance was the current crop being grown and the percentage of humus. The best predictor of A. obscurus abundance was soil pH in KCl. The best predictor of A. sputator abundance was rainfall. Finally, the best predictors of A. ustulatus abundance were soil pH in KCl and humus. These results may be useful in regional pest control programs or for predicting future outbreaks of these species.

Investigating the role of feedstock properties and process conditions on products formed during the hydrothermal carbonization of organics using regression techniques

The purpose of this study is to develop regression models that describe the role of process conditions and feedstock chemical properties on carbonization product characteristics. Experimental data were collected and compiled from literature-reported carbonization studies and subsequently analyzed using two statistical approaches: multiple linear regression and regression trees. Results from these analyses indicate that both the multiple linear regression and regression tree models fit the product characteristics data well. The regression tree models provide valuable insight into parameter relationships. Relative weight analyses indicate that process conditions are more influential to the solid yields and liquid and gas-phase carbon contents, while feedstock properties are more influential on the hydrochar carbon content, energy content, and the normalized carbon content of the solid.