Multiple Linear Stepwise Regression Models Research Articles

Estimating the Total and Regional Body Fat of Physically Active Men Is Not Appropriate for Sedentary Men

Objectives. The reliability of predictive body fat equations remains unclear due to their inappropriate use across different subject cohorts and conditions. The objective of this study was to validate and cross-validate equations to predict total and regional body fat in young physically active males. Material and methods. Three hundred and five young male participants were divided into the following groups: active validation (n=165), active cross-validation (n=70), or sedentary cross-validation ones (n=70). The study used a stratified random sampling based on weekly physical activity level. The total and regional body fat mass were measured using dual-energy X-ray absorptiometry (DEXA) after an overnight fast. Simultaneous measurements of height, body mass, skinfold thickness, body mass index, and body circumferences were taken. The total and regional body fat predictive equations were generated using multiple linear stepwise regression models. The coefficient of determination (R²) and standard error of estimation (SEE) were calculated to examine the accuracy of the predictive equations. Furthermore, cross-validation groups were analysed. Results. The percentage of total body fat, trunk fat, legs fat, arms fat, and body mass index of active cross-validation were found to be significantly lower than in the sedentary cross-validation groups. The total body fat percentage was highly associated with abdominal skinfold thickness (R=0.68-0.74, P<0.001), body mass index (R=0.55, P<0.001), and suprailiac skinfold thickness (R=0.67-0.71, P<0.001) in the active validation group. The predictive total and regional body fat equations of physically active men showed adjusted R² values ranging from 0.35 to 0.66, with standard error of estimation values between 2.74 to 4.35%. The standard error of estimation for the predictive percentage of total and regional body fat in the active cross-validation group was lower than in the sedentary cross-validation group. Conclusions. The findings demonstrate that new predictive total and regional body fat equations can be used to accurately estimate body fat in healthy young active males under fast conditions.

Spatial and variety distributions, risk assessment, and prediction model for heavy metals in rice grains in China.

In this study, 6229 brown rice grains from three major rice-producing regions were collected to investigate the spatial and variety distributions of heavy metals in rice grains in China. The potential sources of heavy metals in rice grains were identified using the Pearson correlation matrix and principal component analysis, and the health risks of dietary exposure to heavy metals via rice consumption were assessed using the hazard index (HI) and total carcinogenic risk (TCR) method, respectively. Moreover, 48 paired soil and rice samples from 11 cities were collected to construct a predicting model for Cd accumulation in rice grains using the multiple linear stepwise regression analysis. The results indicated that Cd and Ni were the main heavy metal pollutants in rice grains in China, with approximately 10% of samples exceeding their corresponding maximum allowable limits. The Yangtze River basin had heavier pollution of heavy metals than the Southeast Coastal Region and Northeast Plain, and the indica rice varieties had higher heavy metal accumulation abilities compared with the japonica rice. The Cu, Pb, and Cd mainly originated from anthropogenic sources, while As, Hg, Cr, and Ni originated from both natural and anthropogenic sources. The mean HI and TCR values of dietary exposure to heavy metals via rice consumption ranged from 2.92 to 4.31 and 9.74 × 10-3 to 1.44 × 10-2, respectively, much higher than the acceptable range, and As and Ni were the main contributor to the HI and TCR for Chinese adults and children, respectively. The available Si (ASi), total Cd (TCd), available Mo (AMo), and available S (AS) were the main soil factors determining grain Cd accumulation. A multiple linear stepwise regression model was constructed based on ASi, TCd, AMo, and AS in soils with good accuracy and precision, which could be applied to predict Cd accumulation in rice grains and guide safe rice production in contaminated paddy fields.

Effects of urban green spaces landscape pattern on carbon sink among urban ecological function areas at the appropriate scale: A case study in Xi’an

At appropriate scales, optimizing landscape pattern in urban green spaces is shown to augment carbon sink. However, existing research primarily concentrates on regional or city-wide areas, often overlooking the nuanced effects of landscape pattern on carbon sink within specialized urban ecological function areas. Focusing on the city of Xi'an and emphasizing the forest within green spaces as the principal subject, this study investigated the spatial interplay between forest landscape pattern and carbon sink across the Ecological Control Area, the Urban Ecological Construction Buffer Area, and the Tsinling Mountains Ecological Protection Area. Grounded in field surveys, this study employed GF1-WFV remote sensing imagery to ascertain the 2021 land-use data in Xi'an and further classified forests into five distinct types: deciduous broad-leaved forest, evergreen coniferous forest, evergreen broad-leaved forest, coniferous and broad-leaved mixed forest, and evergreen-deciduous broad-leaved mixed forest. Employing the Carnegie-Ames-Stanford Approach model and landscape metrics methods, the study evaluated the carbon sink and landscape pattern at multi-scale. Through semi-variance analyses, we discerned the appropriate grid scales. Finally, multiple linear stepwise regression model elucidated the significant landscape metrics, while geographically weighted regression model were employed to scrutinize spatial relationships. Our findings suggested that grid scales of 5 km for the Ecological Control Area, 3 km for the Urban Ecological Construction Buffer Area, and 6 km for the Tsinling Mountains Ecological Protection Area are most appropriate for effective management and planning. Metrics related to area, edge, and fragmentation were pivotal in enhancing carbon sink, and their influence was spatially variable. This study furnished an in-depth analysis of how landscape pattern and carbon sink interact spatially within various urban ecological function areas at appropriate grid scales, thereby providing a scientific framework for the sustainable planning and management of green spaces.

Sexual health literacy and its related factors among couples: A population-based study in Iran.

Sexual health literacy is one of the factors that affect sexual health. Several factors can influence sexual health literacy. As a result, the current study was carried out to determine sexual health literacy and its associated factors among Iranian couples. In 2022, 410 couples of reproductive age were referred to comprehensive health service centers and private clinics in Kerman city for the descriptive-analytical study. The study questionnaires included sexual health literacy for adults, sexual knowledge and attitude scale, marital intimacy scale, sexual intimacy scale, depression anxiety stress scale, sexual self-efficacy, the multidimensional scale of perceived social support, and socioeconomic status. Finally, multiple linear stepwise regression models were used to determine the factors related to sexual health literacy using the SPSS software version 22. According to the findings of this study, the mean(SD) of sexual health literacy in couples was 68.76(12.96), which was a desirable level. Furthermore, the findings revealed that sexual self-efficacy (B = 0.649، P<0.001), sexual knowledge and attitude (B = 0.217، P<0.001), the ability to identify reliable and non-reliable sources (B = -3.116، P<0.001), feelings of shame and embarrassment for obtaining sexual information (B = 1.860، P = 0.011), social support (B = 0.127، P<0.001) and the obscenity of sexual issues in family (B = 1.764، P = 0.015) were the final predictors of sexual health literacy in couples. It is suggested that researchers and health managers consider these factors when designing interventions to promote sexual health literacy.

Variations, controls and predictions of soil saturated hydraulic conductivity under different land use types in the alpine region of Tibet, China

Understanding the variations and controls of soil saturated hydraulic conductivity (Ks) under different land use types in the alpine region of Tibet is fundamental for predicting Ks in high-altitude regions where sample points are difficult to obtain and is important for establishing the regional soil hydrological model. In this study, soils samples under farmland, forestland, and grassland were collected from an east-west transect in southeastern Tibet, China. Basic soil properties and Ks were measured to analyze the influencing factors of Ks under different land use types and to establish suitable pedotransfer functions (PTFs) for predicting Ks in the alpine region. The average value of Ks followed the decreasing order of grassland (2.33 cm h−1) > farmland (2.12 cm h−1) > forestland (1.59 cm h−1). Ks of grassland (0.25–9.19 cm h−1) decreased gradually with increasing soil depth. Ks of farmland (0.11–8.82 cm h−1) increased initially and then decreased with increasing soil depth, whereas Ks of forestland (0.12–4.46 cm h−1) exhibited the opposite trend. There were significant differences in the main controlling factors of Ks under different land use types. In farmland, soil bulk density (BD), non-capillary porosity (Pn), clay and gravel content (GC) were the main drivers of Ks (P < 0.05). The dominant influencing factors of Ks in forestland were BD and total porosity (Pt) (P < 0.05), while Pn and GC were the main factors controlling Ks in grassland (P < 0.05). Compared with the multiple linear stepwise regression models and published PTFs, the BP neural network transfer model had better prediction accuracy for Ks in the alpine region. Our results not only provide hydraulic parameters for regional hydrological modeling and but also lay a foundation to construct Ks prediction models for other alpine regions in the world.

The effects of trunk kinematics and EMG activity of wheelchair racing T54 athletes on wheelchair propulsion speeds.

The purpose of this study is to examine the impact of trunk kinematic characteristics and trunk muscle electromyography (EMG) activity on propulsion speeds in wheelchair racing T54 athletes. The Vicon infrared high-speed 3D motion capture system was utilized to acquire kinematic data of the shoulders, elbows, wrists, and trunk from twelve T54 athletes at four different speeds (5.55 m/s, 6.94 m/s, 8.33 m/s, and personal maximum speed). Additionally, the Trigno Wireless EMG system was employed to collect synchronous surface electromyography (EMG) data from the rectus abdominis and erector spinae muscles. The kinematics and EMG data of the trunk were compared across various wheelchair propulsion speeds while also examining the correlation coefficient between wheelchair propulsion speeds and: (1) the range of motion of upper limb joints as well as the trunk; (2) the maximum angular velocities of the upper limbs joints as well as the trunk; and (3) rectus abdominis and erector spinae EMG activity. Two multiple linear stepwise regression models were utilized to examine the impact of variables that had been identified as significant through correlation coefficient tests (1) and (2) on propulsion speed, respectively. There were significant differences in the range of motion (p<0.01) and angular velocity (p<0.01) of the athlete's trunk between different propulsion speeds. The range of motion (p<0.01, r=0.725) and angular speed (p<0.01, r=0.882) of the trunk showed a stronger correlation with propulsion speed than did upper limb joint movements. The multiple linear stepwise regression model revealed that the standardized β values of trunk motion range and angular velocity in athletes were greater than those of other independent variables in both models. In terms of the EMG variables, four of six variables from the rectus abdominis showed differences at different speeds (p<0.01), one of six variables from the erector spinae showed differences at different speeds (p<0.01). All six variables derived from the rectus abdominis exhibited a significant correlation with propulsion speed (p<0.05, r>0.3), while one variable derived from the erector spinae was found to be significantly correlated with propulsion speed (p<0.01, r=0.551). The movement of the trunk plays a pivotal role in determining the propulsion speed of wheelchair racing T54 athletes. Athletes are advised to utilize trunk movements to enhance their wheelchair's propulsion speed while also being mindful of the potential negative impact on sports performance resulting from excessive trunk elevation. The findings of this study indicate that it would be beneficial for wheelchair racing T54 athletes to incorporate trunk strength training into their overall strength training regimen, with a specific emphasis on enhancing the flexion and extension muscles of the trunk.

Polymorphisms of pharmacogenetic candidate genes affect etomidate anesthesia susceptibility.

Purpose: Etomidate is widely used in general anesthesia and sedation, and significant individual differences are observed during anesthesia induction. This study aimed to explore the molecular mechanisms of different etomidate susceptibility at the genetic level. Methods: 128 patients were enrolled in the study. The bispectral index (BIS), mean arterial pressure (MAP) and heart rate (HR) were recorded when the patients entered the operating room for 5 min, before the administration of etomidate, 30 s, 60 s, 90 s, 120 s and 150 s after the administration of etomidate, and the corresponding single nucleotide polymorphisms (SNPs) were analyzed. Results: Significant individual differences were observed in etomidate anesthesia. The results of two-way ANOVA showed that CYP2C9 rs1559, GABRB2 rs2561, GABRA2 rs279858, GABRA2 rs279863 were associated with the BIS value during etomidate anesthesia; UGT1A9 rs11692021 was associated with the Extended Observer’s Assessment of Alertness and Sedation (EOAA/S) score during etomidate anesthesia; GABRB2 rs2561 was associated with MAP. Multiple linear stepwise regression model results showed that CYP2C9 rs1559, GABRA2 rs279858 and GABRB2 rs2561 were associated with the BIS value and UGT1A9 rs11692021 was associated with the EOAA/S score; GABRB2 rs2561 was associated with MAP. Conclusion: GABRA2 rs279858, GABRB2 rs2561, CYP2C9 rs1559 and UGT1A9 rs11692021 are the SNPs with individual differences during etomidate anesthesia. This is the first to study the SNPs of etomidate, which can provide certain evidence for the future use of etomidate anesthesia and theoretical basis for precision anesthesia.

A potential link between plasma short-chain fatty acids, TNF-α level and disease progression in non-alcoholic fatty liver disease: A retrospective study.

The onset and progression of non-alcoholic fatty liver disease (NAFLD) remains unclear, but short-chain fatty acids (SCFAs) in circulation may participate in its pathogenesis by acting as inflammation inhibitors. The aim of this retrospective study was to investigate plasma concentrations of general SCFAs in healthy individuals and in patients with distinct stages of NAFLD. Three main SCFAs (including acetate, propionate and butyrate) were analyzed by gas chromatography. The plasma TNF-α concentration was measured by ELISA. One-way ANOVA, Spearman's correlation and Pearson's correlation analysis were performed to estimate the associations between SCFAs, TNF-α and disease progression. Multiple linear stepwise regression was computed to explore the predictor variables of TNF-α in circulation. A total of 71 patients with NAFLD [including 27 patients with NAFL, 20 patients with non-alcoholic steatohepatitis (NASH) and 24 patients with NAFLD-related cirrhosis (NAFLD-cirrhosis)] and 9 healthy control (HC) subjects were enrolled for analysis. Although not statistically significant, plasma SCFAs were elevated in patients with NAFL compared with HC subjects, whereas the vast majority of SCFAs were statistically reduced in patients with NASH or NAFLD-cirrhosis compared with patients with NAFL. Plasma SCFAs had no significant differences in NASH or NAFLD-cirrhosis patients compared with HC subjects. In addition, significant negative correlations were observed between TNF-α and SCFAs. The progression of NAFLD (β=0.849; P<0.001) and the decline of the total three SCFA concentrations (β=-0.189; P<0.001) were recognized as independent risk variables related to the elevated peripheral TNF-α in the multiple linear stepwise regression model. Plasma SCFA concentrations may alter with the development of NAFLD and may have a potential link to TNF-α and the progression of NAFLD, which may serve a protective role toward disease advancement. Further mechanistic studies, such as analysis of gastrointestinal microecology, signaling pathways and functions involved in TNF-α, need to be performed. Also, therapeutic supplementation of SCFAs for NASH and NAFLD-cirrhosis needs further research and verification.

Initial and Final Cell Concentrations Significantly Influence the Maximum Growth Rate of Listeria monocytogenes in Published Literature Data for Whole Intact Fresh Produce

Listeria monocytogenes has shown the ability to grow on fresh uncut produce; however, the factors that control growth are not well understood. Peer-reviewed journal articles (n = 29) meeting the inclusion criteria and related to the growth of L. monocytogenes on fresh produce were found through university library databases and Google Scholar searches. Growth models were fit to each of the extracted 130 data sets to estimate log CFU per day rates of growth by using the DMFit tool. Multiple linear stepwise regression models for factors influencing growth rate were developed using R software. Factors included were temperature, nutrient level of inoculation buffer, initial cell concentration, final cell concentration, inoculation method, container permeability, and surface characteristics. The full model produced adjusted R2, Akaike information criterion, and root mean square error values of 0.41, 488, and 1.61, respectively. Stepwise regression resulted in a reduced model with parameters for incubation temperature, inoculation buffer type, initial and final cell concentrations, container characteristics, and produce surface characteristics. Model fit statistics improved slightly in the reduced model. A further reduced three-parameter model included storage temperature and initial and final cell concentrations, with interaction terms. This three-parameter model had adjusted R2, Akaike information criterion, and root mean square error values of 0.66, 417, and 1.24, respectively. Incubation temperature (P = 1.00E-09) initial cell concentration (P = 3.05E-12), and final cell concentration (P = 4.17E-09) all had highly significant effects on maximum growth rate. Our findings show the importance of inoculum concentration and produce microbial carrying capacity on the estimated growth rate and highlight the overall importance that temperature has on growth rate. Future experiments should consider initial inoculum concentration carefully when conducting growth studies for L. monocytogenes on whole produce.

Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis

Sentinel-2A multi-spectral remote sensing image data underwent high-efficiency differential processing to extract spectral information, which was then matched to soil organic matter (SOM) laboratory test values from field samples. From this, multiple-linear stepwise regression (MLSR) and partial least square (PLSR) models were established based on a differential algorithm for surface SOM modeling. The original spectra were subjected to basic transformations with first- and second-derivative processing. MLSR and PLSR models were established based on these methods and the measured values, respectively. The results show that Sentinel-2A remote sensing imagery and SOM content correlated in some bands. The correlation between the spectral value and SOM content was significantly improved after mathematical transformation, especially square-root transformation. After differential processing, the multi-band model had better predictive ability (based on fitting accuracy) than single-band and unprocessed multi-band models. The MLSR and PLSR models of SOM had good prediction functionality. The reciprocal logarithm first-order differential MLSR regression model had the best prediction and inversion results (i.e., most consistent with the real-world data). The MLSR model is more stable and reliable for monitoring SOM content, and provides a feasible method and reference for SOM content-mapping of the study area.

Mental health status and influential factors of employees of Chinese enterprises in Ethiopia

Objective To understand the mental health status and its influential factors of staff of Chinese enterprises in Ethiopia. Methods This study selected employees of Chinese enterprises still in Ethiopia to conduct the mental health questionnaire survey from June 27 to 28, 2020, and multiple linear stepwise regression model is performed to analyze the influential factors of the mental health. Results Among 671 employees, 38.75% had mental health problems. The anxiety and depression of the employees in the last 2 weeks was relatively prominent. The results of multivariate logistic regression analysis showed that the epidemic had an impact on the plan of returning home (OR=3.293, 95%CI: 1.897–5.714), employees worried about being in Africa and unable to take care of their family members in China (OR=2.329, 95%CI: 1.198–4.528), and the company did not take any measures (OR=4.697, 95%CI: 1.080–20.435) were risk factors of mental health. Employees had no physical symptoms in the past month (OR=0.305, 95%CI: 0.171–0.543), worked normally (OR=0.517, 95%CI: 0.322–0.831), the company guaranteed the necessities of life supply (OR=0.609, 95%CI: 0.371–1.000) and conducted COVID-19 knowledge promotion (OR=0.335, 95%CI: 0.183–0.614) were the protective factors of mental health. Conclusion The mental health status of employees of Chinese enterprises in Ethiopia is poor, and their mental health is affected by various factors. We should attach importance to their mental health and take various mental health interventions to reduce their anxiety and depression and other negative emotions.

Long-term variation of boundary layer height and possible contribution factors: A global analysis

Boundary layer height (BLH) plays an important role in regulating global weather/climate, as well as the dispersion and transportation of pollutants. Until now, however, the attribution and contributions of different controlling factors to BLH long-term variability and trends have not been quantified on a global scale. The long-term radiosonde dataset was used in this study to retrieve global BLH climatology; seasonal, diurnal, long-term variation and trends were analyzed over a 39-year period (1980–2018). Statistical results show that the global distribution of the BLH and its trend have apparent day-night differences. BLH during daytime is deeper during clear-sky conditions compared to cloudy sky conditions, indicating a significant effect of clouds; BLH during nighttime is deeper under cloudy conditions. BLH was also found to vary over different land types; dry and hot soil exhibits a deeper BLH than those of wet and cool soil. The long-term variation and trend of BLH are highly influenced by near-surface meteorological parameters. In particular, based on multiple linear stepwise regression models and the contribution calculation method, this investigation initiatively quantifies the influences of meteorological parameters on global BLH long-term variation and trend. Our results emphasized that a 10 m wind speed (WS) and low tropospheric stability (LTS) have significant contributions to long-term BLH variation; WS and LTS anomalies alternately dominated the contribution of the diurnal cycle of the BLH anomaly. Annual BLH recorded an average increasing trend (38.9–42.1 m/decade), and LTS is more dominant than WS from a contribution perspective, especially for increased BLH anomaly. Contributions from near-surface temperature (T) and relative humidity (RH) also play important roles. However, a decreasing WS trend dominated the decreased trends of BLH anomaly, accounting for nearly 40% of the total contribution.

Construction and Application Optimization of the Chl-a Forecast Model ARIMA for Lake Taihu

As an important indicator of phytoplankton biomass in lakes, the chlorophyll-a (Chl-a) concentration reflects the abundance and variation of phytoplankton in the water. Based on the monthly monitoring data of Chl-a and environmental factors in Lake Taihu from December 1999 to August 2019, key environmental factors related to Chl-a and their relationships were found using the principal component analysis (PCA) method. A multiple linear stepwise regression model and an auto-regressive integrated moving average (ARIMA) model were developed to predict the monthly Chl-a concentrations. The results showed that the Chl-a concentrations in Lake Taihu exhibited clear seasonal change characteristics and an overall trend of a gradual increase. The changes in total phosphorus (TP), the permanganate index, monthly average temperature (MAT), and monthly rainfall (MR) matched the Chl-a concentrations relatively well, whereas the changes in total nitrogen (TN) and ammonium nitrogen (NH4+-N) lagged significantly. The PCA results showed that the increased phytoplankton biomass and consequent algae outbreaks in Lake Taihu were not limited to the effect of a single factor such as TN or TP, but were comprehensively affected by multiple factors such as TN, NH4+-N, TP, the permanganate index, MR, and MAT. Through further validation, the ARIMA model of Chl-a concentrations was proved to be significantly better than the multiple linear stepwise regression model, especially when considering the key environmental factors as independent variables and optimizing their values. The established ARIMA (0,1,1) (0,1,1) model would be helpful for forecasting algae blooms in Lake Taihu and provide useful suggestions for water environmental management, such as water resources dispatch and regulation.

Models for factors influencing pathogen survival in low water activity foods from literature data are highly significant but show large unexplained variance

Factors that control pathogen survival in low water activity foods are not well understood and vary greatly from food to food. A literature search was performed to locate data on the survival of foodborne pathogens in low-water activity (<0.70) foods held at temperatures <37 °C. Data were extracted from 67 publications and simple linear regression models were fit to each data set to estimate log linear rates of change. Multiple linear stepwise regression models for factors influencing survival rate were developed. Subset regression modeling gave relatively low adjusted R2 values of 0.33, 0.37, and 0.48 for Salmonella, E. coli and L. monocytogenes respectively, but all subset models were highly significant (p < 1.0e-9). Subset regression models showed that Salmonella survival was significantly (p < 0.05) influenced by temperature, serovar and strain type, water activity, inoculum preparation method, and inoculation method. E. coli survival was significantly influenced by temperature, water activity, and inoculum preparation. L. monocytogenes survival was significantly influenced by temperature, serovar and strain type, and inoculum preparation method. While many factors were highly significant (p < 0.001), the high degrees of variability show that there is still much to learn about the factors which govern pathogen survival in low water activity foods.

Estimating the field capacity and permanent wilting point at the regional scale for the Hexi Corridor in China using a state-space modeling approach

The field capacity (FC) and permanent wilting point (PWP) are important soil hydraulic properties that determine the maximum available water for plants, and they are crucial parameters for biophysical models and irrigation management. However, previous estimates of the FC and PWP failed to consider their spatial correlations at the regional scale. Therefore, we estimated the FC and PWP using the state-space equation by considering the spatial correlations between soil properties. We estimated the FC and PWP using a first order autoregressive state-space equation based on the elevation (Elev), bulk density (BD), soil texture (Clay, Silt, Sand), soil organic carbon (SOC), and land use (LU) with a data set obtained from 104 in situ sampling sites across the entire Hexi Corridor. The results indicated that the distributions of the FC and PWP exhibited moderate variations in the Hexi Corridor, with values of 0.127 ± 0.060 and 0.075 ± 0.034 g/g (mean ± 1 standard deviation (SD)), respectively. According to t tests, the autocorrelation coefficients for FC, PWP, Elev, LU, Silt, Sand, and SOC as well as the cross-correlation coefficients between FC, PWP, and pertinent variables were significant with one lag distance (approximately 40 km) (p ˂ 0.05). Calculations of the coefficient of determination (R2) and root mean square error (RMSE) showed that the state-space models performed better at estimating FC and PWP than multiple linear stepwise regression models (SMLRs). Bivariate state-space equations based on Silt and LU were the optimal models for estimating FC (R2 = 0.999, RMSE = 0.002 g/g) and PWP (R2 = 0.997, RMSE = 0.002 g/g). According to the coefficients in the optimal state-space models, soil texture and LU were the dominant factors that affected the spatial variability in FC and PWP. After neglecting the spatial correlations between variables, the SMLRs showed that BD and soil texture were the best variables for estimating FC and PWP. The state-space approach is recommended as a useful tool for quantifying larger-scale spatial patterns in soil properties.

Spatial predictions of the permanent wilting point in arid and semi-arid regions of Northeast China

The permanent wilting point (PWP) is the minimal point of soil moisture a plant requires not to wilt. If moisture decreases to this, or any lower point, a plant wilts, and can no longer recover its turgidity when placed in a saturated atmosphere for 12 h. The PWP is an essential physical property that has a powerful influence on other soil properties and is important for agricultural production, including irrigation water use efficiency and crop yield. However, there are few published datasets on the PWP of cultivated land in the arid and semi-arid regions of Northeast China. Direct measurements of PWP are time-consuming and expensive; therefore, the aim of this study was to evaluate how environmental variables could be used in spatial predictions of PWP in an arid and semi-arid region based on boosted regression tree (BRT) and multiple linear stepwise regression (MLSR) analyses. Seventy soil samples and nine covariates (including topography and vegetation variables) were collected and analyzed. Cross-validation procedure was used to evaluate model performance and uncertainty. Accuracy evaluation results showed that the BRT model had better predictive performance than the MLSR. PWP content decreased from the southwest to the northeast of the study area, and average values of both models were 26%. BRT and MLSR models should be compared and calibrated to obtain the best prediction effect of PWP spatial distribution in similar areas. Elevation, slope gradient, profile curvature, topographic wetness index, and land-use variables are the main environmental indicators that should be included when generating PWP maps of regions with a dry, continental climate. Such information could aid local land managers and government agencies with evaluating soil quality and water sequestration potential. In conclusion, this study could be used as a reference for predicting the hydrological parameters of the topsoil in other similar ecological environments.

Quantifying Urban Land Sprawl and its Driving Forces in Northeast China from 1990 to 2015

Drastic urbanization has resulted in numerous problems worldwide, and many studies were devoted to individual cities. There is an urgent need to quantify urbanization patterns and illustrate their driving forces in the regional area on a large scale over a longer time period. This study produced a land cover dataset to characterize sequential urban land expansion in Northeast China from 1990 to 2015 using object-based backdating classification and calculating the expansion index. The drivers were investigated using Pearson correlation analysis and the multiple linear stepwise regression model. The results revealed that the total area of urban land increased by 43.15% from 1990 to 2015, mainly in the middle part of the study area, and especially in the coastal area of Liaoning. Liaoning had the fastest growth rate, while Heilongjiang showed a decrease in growth rate rankings. Urban land expanded northward and southward within Harbin City, towards the west and east within Changchun City, and relatively equally in all directions within Shenyang City. Expansion patterns changed from edge expansion (42%) to outlying expansion (47%). Urban land of Liaoning and the Eastern Inner Mongolia Autonomous region expanded with similar patterns as the entire area, but that of Heilongjiang grew in an opposite pattern, and Jilin maintained outlying major expansion. The influence of factors on urban land sprawl varied temporally. Tertiary industry product, gross domestic product, secondary industry product, total population and urban population were driving factors of urban land sprawl in Northeast China from 1990 to 2015. This research provides quantitative methods for better understanding urban land dynamics and devising feasible strategies for sustainable urban development.

Association between obesity-related biomarkers and cognitive and motor development in infants

BackgroundThis study aimed to verify the association between obesity-related biomarkers and cognitive and motor development in infants between 6 and 24 months of age. MethodsA cross-sectional study was conducted with 50 infants and plasma levels of leptin, adiponectin, resistin, soluble tumor necrosis factor receptors 1 and 2 (sTNFR1 and sTNFR2), chemokines, brain-derived neurotrophic factor (BDNF), serum cortisol and redox status were measured. The Bayley-III test was utilized to evaluate cognitive and motor development, and multiple linear stepwise regression models were performed to verify the association between selected biomarkers and cognitive and motor development. ResultsA significant association was found among plasma leptin and sTNFR1 levels with cognitive composite scores, and these two independents variables together explained 37% of the variability of cognitive composite scores (p=0.001). Only plasma sTNFR1 levels were associated and explained 24% of the variability of motor composite scores (p=0.003). ConclusionsPlasma levels of sTNFR1 were associated with the increase in cognitive and motor development scores in infants between 6 and 24 months of age through a mechanism not directly related to excess body weight. Moreover, increase in plasma levels of leptin reduced the cognitive development in this age range.

EMPIRICAL MODELS FOR PERFORMANCE OF DRIPPERS APPLYING CASHEW NUT PROCESSING WASTEWATER

ABSTRACT: The objective of this work was to develop empirical models for hydraulic performance of drippers operating with cashew nut processing wastewater depending on operating time, operating pressure and effluent quality. The experiment consisted of two factors, types of drippers (D1=1.65 L h-1, D2=2.00 L h-1 and D3=4.00 L h-1), and operating pressures (70, 140, 210 and 280 kPa), with three replications. The flow variation coefficient (FVC), distribution uniformity coefficient (DUC) and the physicochemical and biological characteristics of the effluent were evaluated every 20 hours until complete 160 hours of operation. Data were interpreted through simple and multiple linear stepwise regression models. The regression models that fitted to the FVC and DUC as a function of operating time were square root, linear and quadratic, with 17%, 17% and 8%, and 17%, 17% and 0%, respectively. The regression models that fitted to the FVC and DUC as a function of operating pressures were square root, linear and quadratic, with 11%, 22% and 0% and the 0%, 22% and 11%, respectively. Multiple linear regressions showed that the dissolved solids content is the main wastewater characteristic that interfere in the FVC and DUC values of the drip units D1 (1.65 L h-1) and D3 (4.00 L h-1), operating at work pressure of 70 kPa (P1).

Spatial prediction of soil properties in two contrasting physiographic regions in Brazil

This study compared the performance of ordinary kriging (OK) and regression kriging (RK) to predict soil physical-chemical properties in topsoil (0-15 cm). Mean prediction of error and root mean square of prediction error were used to assess the prediction methods. Two watersheds with contrasting soil-landscape features were studied, for which the prediction methods were performed differently. A multiple linear stepwise regression model was performed with RK using digital terrain models (DTMs) and remote sensing images in order to choose the best auxiliary covariates. Different pedogenic factors and land uses control soil property distributions in each watershed, and soil properties often display contrasting scales of variability. Environmental covariables and predictive methods can be useful in one site study, but inappropriate in another one. A better linear correlation was found at Lavrinha Creek Watershed, suggesting a relationship between contemporaneous landforms and soil properties, and RK outperformed OK. In most cases, RK did not outperform OK at the Marcela Creek Watershed due to lack of linear correlation between covariates and soil properties. Since alternatives of simple OK have been sought, other prediction methods should also be tested, considering not only the linear relationships between covariate and soil properties, but also the systematic pattern of soil property distributions over that landscape.