Predicted Residual Sum Of Squares Research Articles

Resting metabolic rate in healthy Singaporeans: Performance of the Harris-Benedict equation and a new predictive model

Background Prediction equations for resting metabolic rate (RMR) are valuable in managing patients’ weight; however, no accurate equation exists for Singaporeans. Objective To develop and cross-validate a predictive regression equation for RMR in Singaporeans, using indirect calorimetry as the reference method. Methods 104 healthy Singaporeans (34.3 ± 12.2 years) participated, comprising 34 men and 70 women. Anthropometric measurements and demographics information were obtained from participants. RMR was measured via indirect calorimetry (TrueOne 2400 system). Stepwise regression analysis was used to develop the most parsimonious predictive equation. Performance of the equation was evaluated using ordinary least products (OLP) regression and Bland–Altman analysis, whilst internal cross-validation was performed by use of the predicted residual sum of squares (PRESS) method. To compare the new equation with existing ones, the performance of the Harris-Benedict equation was also evaluated. Results The best predictive equation takes the form RMR(kcal) = 918 + 16.5(weight)-135.7(gender) - 1152(Waist-to-height-ratio) +0.014(International Physical Activity Questionnaire Score), where gender = 1 (female) or 0 (male). OLP regression revealed no systematic bias for the new equation. Bland–Altman analysis showed that its total (systematic and random) error was 212 kcal. Internal model validation using the PRESS method revealed minimal reduction in predictive accuracy. In contrast, OLP regression showed a significant pattern of over-prediction by the Harris-Benedict equation (y-intercept = −280 kcal; 95%CI, −100 to −461 kcal). Conclusions Our new equation outperformed the Harris-Benedict equation in accurately predicting RMR in Singaporeans. Comprising easily obtained anthropometric and self-reported measures, we envisage its potential relevance in clinical and epidemiological settings.

Design of New Chlorochalcone Derivatives as Potential Breast Anticancer Compound Based on QSAR Analysis and Molecular Docking Study

Quantitative structure-activity relationships (QSAR) proposes a model that relates the biological activities of drugs to their chemical structures, and the interaction between the drug and its target enzyme is revealed by molecular docking research. These studies were conducted on chalcone to produce a model that could design highly potent breast anticancer MCF7 cells. The compounds were optimized using ab initio using a basis set 6-31G, then their descriptors calculated using this method. Genetic Function Algorithm (GFA) was used to select descriptors and build the model. One of the six models generated was found to be the best with internal and external squared correlation coefficient (R2) of 0.743 and 0.744, respectively, adjusted squared correlation coefficient (adjusted R2) of 0.700, Standard estimate of error (SEE) of 0.198, Fcalc/Ftable of 6.423, and Predicted residual sum of squares (PRESS) of 1.177. The QSAR equation is pIC50 = 3.869 + (1.427 x qC1) + (4. 027 x qC10) + (0.856 x qC15) - (35.900 x ELUMO) + (0.208 x Log P). Hence, it can predict the breast anticancer activities of new chlorochalcones A-F. The compound with the best prediction was chlorochalcone A with pIC50 2.65 and IC50 value of 2.26 μM. The chlorochalcones A-F were able to bind to the main amino acid residues, namely Arg120 and Tyr355, on the active site of the COX-2 enzyme. These results could serve as a model for designing novel chlorochalcone as inhibitors of COX-2 with higher breast anticancer activities. Keywords: Chlorochalcone, COX-2, QSAR, MCF-7, Molecular docking

Cardiodynamic variables measured by impedance cardiography during a 6-minute walk test are reliable predictors of peak oxygen consumption in young healthy adults.

Accurate prediction of aerobic capacity is necessary to guide appropriate exercise prescription. It is common to use 6-minute walk distance (6MWD) to predict peak oxygen uptake (VO2peak) in the clinical environment. The aim of this study was to determine whether prediction of VO2peak can be improved by the inclusion of cardiovascular indices derived by impedance cardiography (ICG) during the 6MWT. A total of 62 healthy university students aged 21±1 years completed in separate days, a cardiopulmonary exercise test (CPET) and two 6MWTs (30 min apart), during which heart rate (HR), stroke volume (SV) and cardiac output (CO) were measured by ICG (PhysioFlow® PF07 EnduroTM). The CPET was conducted with the Ergoselect 200 Ergoline and oxygen consumption measured by a MasterScreenTM CPX breath-by-breath metabolic cart. Multiple regression analyses were conducted to generate VO2peak prediction equations using 6MWD with, or without the cardiovascular indices recorded at the end of the best performed 6MWT as predictor variables. The mean peak HR (bpm), SV (ml) and CO (L/min) recorded during 6MWT were 156±18, 95.6±9, 15±2.8 and during CPET were 176±16, 91.3±8, 16.2±2.7, respectively. Analyses revealed the following VO2peak prediction equation: VO2peak = 100.297+(0.019x6MWD)+(-0.598xHR6MWT)+(-1.236xSV6MWT) + (8.671 x CO6MWT). This equation has a squared multiple correlation (R2) of 0.866, standard error of the estimate (SEE) of 2.28 mL/kg/min and SEE:VO2peak (SEE%) of 7.2%. Cross-validation of equation stability using predicted residual sum of squares (PRESS) statistics showed a R2 (Rp2), SEE (SEEp) and SEEp% of 0.842, 2.38 mL/kg/min and 7.6% respectively. The minimal shrinkage of R2 implied regression model stability. Correlation between measured and predicted VO2peak using this equation was strong (r = 0.931, p<0.001). When 6MWD alone was used as the predictor for VO2peak, the generated equation had a lower R2 (0.549), and a higher SEE (4.08 mL/kg/min) and SEE% (12.9%). This is the first study which included cardiac indices during a 6MWT as variables for VO2peak prediction. Our results suggest that inclusion of cardiac indices measured during the 6MWT more accurately predicts VO2peak than using 6MWD data alone.

Retention time prediction of polycyclic aromatic hydrocarbons in gas chromatography–mass spectrometry using QSPR based on random forests and artificial neural network

In this study, a quantitative structure–property relationship (QSPR) was proposed using the random forests (RF) and artificial neural network (ANN) for determining the retention time (RT) of 123 polycyclic aromatic hydrocarbons (PAHs) in tire fire products. The data containing the RT of the PAHs were obtained by gas chromatography–mass spectrometry (GC–MS) method. The optimum number of trees (nt) and the number of randomly selected variables to split each node (m) related to the RF model were found 100 and 101, respectively. Different algorithms of ANN were studied, and Levenberg–Marquardt (LM) algorithm with a minimum mean square error (MSE) was selected as the best algorithm. Also, leave-one-out cross-validation (LOOCV), including correlation determination (R2), standard error of prediction (SEP), mean absolute error (MAE), relative error prediction (REP), mean squared error (MSE), mean relative error (MRE), and predicted residual sum of squares (PRESS), was applied to investigate the validity of statistical models. The R2 of the RF, stepwise regression artificial neural network (SR-ANN), and RF-ANN models were achieved 0.985, 0.983, and 0.959, respectively. An effective model, which does not require experimental steps, was proposed for the RT prediction of new compounds.

Simultaneous Spectrophotometric Quantification of Metamizole Sodium, Pitofenone Hydrochloride, and Fenpiverinium Bromide in a Pharmaceutical Product Using Multivariate Calibration Methods

A spectrophotometric method was developed for the simultaneous determination of metamizole sodium, pitofenone hydrochloride, and fenpiverinium bromide in the ternary synthetic mixtures and pharmaceutical products. Partial least squares (PLS-1 and PLS-2 methods) combined with ultraviolet-visible spectrophotometry was applied for the quantitative determination of pharmaceuticals. The linear dynamic ranges for determination of the content of metamizole sodium, pitofenone hydrochloride, and fenpiverinium bromide were between 1 and 50 mg/L, 1 and 15 mg/L, and 60 and 1000 mg/L, respectively. The predicted residual sum of squares (PRESS) and root mean square error (RMSE) statistical parameters for the proposed PLS models were calculated to characterize the prediction abilities of the regression models. The results indicate that the highest prediction error was obtained for fenpiverinium bromide content in both regression models, probably due to its low molar extinction coefficient. This procedure was successfully applied in the control laboratory in the pharmaceutical industry for the rapid estimation of the content of drugs in a pharmaceutical product to avoid expensive and complex chromatography procedures.

Application of quantitative structure-activity relationship (QSAR) model in comprehensive human health risk assessment of PAHs, and alkyl-, nitro-, carbonyl-, and hydroxyl-PAHs laden in urban road dust

The carcinogenic human health risks (CHHR) posed by the exposure to PAHs and transformed PAH products (TPPs) are currently inconclusive due to the lack of toxicity equivalency factors (TEFs) for most TPPs although some of these pollutants are more potent carcinogens. The applicability of quantitative structure-activity relationship (QSAR) model in predicting TEF of PAHs and TPPs to holistically evaluate the CHHR posed by the exposure to these pollutants in road dust from Gold Coast, Australia was examined. Statistical evaluation via ten metrics shows that partial least-squares regression (PLSR1) model has more statistical power in predicting TEF than multiple linear regression (MLR) within relevant applicability domain. For instance, the predicted residual sum of squares (PRESS) and standard deviation of error of prediction (SDEP) for PLSR is closer to zero than that of MLR. The total cancer risk estimated using the QSAR model derived TEFs and original TEFs for outliers gives a more holistic incremental lifetime cancer risk in relation to children and adults. Potential cancer risk exists for adults with this approach whereas reliance on only the originally available TEFs lead to a negligible risk diagnosis. The application of QSAR model in assessing CHHR due to PAHs and TPPs exposures is very viable.

Model Selection Criteria on Beta Regression for Machine Learning

Beta regression models are a class of supervised learning tools for regression problems with univariate and limited response. Current fitting procedures for beta regression require variable selection based on (potentially problematic) information criteria. We propose model selection criteria that take into account the leverage, residuals, and influence of the observations, both to systematic linear and nonlinear components. To that end, we propose a Predictive Residual Sum of Squares (PRESS)-like machine learning tool and a prediction coefficient, namely P 2 statistic, as a computational procedure. Monte Carlo simulation results on the finite sample behavior of prediction-based model selection criteria P 2 are provided. We also evaluated two versions of the R 2 criterion. Finally, applications to real data are presented. The new criterion proved to be crucial to choose models taking into account the robustness of the maximum likelihood estimation procedure in the presence of influential cases.

A PRESS statistic for working correlation structure selection in generalized estimating equations

ABSTRACTGeneralized estimating equations (GEE) is one of the most commonly used methods for regression analysis of longitudinal data, especially with discrete outcomes. The GEE method accounts for the association among the responses of a subject through a working correlation matrix and its correct specification ensures efficient estimation of the regression parameters in the marginal mean regression model. This study proposes a predicted residual sum of squares (PRESS) statistic as a working correlation selection criterion in GEE. A simulation study is designed to assess the performance of the proposed GEE PRESS criterion and to compare its performance with its counterpart criteria in the literature. The results show that the GEE PRESS criterion has better performance than the weighted error sum of squares SC criterion in all cases but is surpassed in performance by the Gaussian pseudo-likelihood criterion. Lastly, the working correlation selection criteria are illustrated with data from the Coronary Artery Risk Development in Young Adults study.

PRESS-based EFOR algorithm for the dynamic parametrical modeling of nonlinear MDOF systems

In response to the identification problem concerning multi-degree of freedom (MDOF) nonlinear systems, this study presents the extended forward orthogonal regression (EFOR) based on predicted residual sums of squares (PRESS) to construct a nonlinear dynamic parametrical model. The proposed parametrical model is based on the non-linear autoregressive with exogenous inputs (NARX) model and aims to explicitly reveal the physical design parameters of the system. The PRESS-based EFOR algorithm is proposed to identify such a model for MDOF systems. By using the algorithm, we built a common-structured model based on the fundamental concept of evaluating its generalization capability through cross-validation. The resulting model aims to prevent over-fitting with poor generalization performance caused by the average error reduction ratio (AERR)-based EFOR algorithm. Then, a functional relationship is established between the coefficients of the terms and the design parameters of the unified model. Moreover, a 5-DOF nonlinear system is taken as a case to illustrate the modeling of the proposed algorithm. Finally, a dynamic parametrical model of a cantilever beam is constructed from experimental data. Results indicate that the dynamic parametrical model of nonlinear systems, which depends on the PRESS-based EFOR, can accurately predict the output response, thus providing a theoretical basis for the optimal design of modeling methods for MDOF nonlinear systems.

Concurrent agreement between an anthropometric model to predict thigh volume and dual-energy X-Ray absorptiometry assessment in female volleyball players aged 14-18 years.

BackgroundA variety of performance outputs are strongly determined by lower limbs volume and composition in children and adolescents. The current study aimed to examine the validity of thigh volume (TV) estimated by anthropometry in late adolescent female volleyball players. Dual-energy X-ray absorptiometry (DXA) measures were used as the reference method.MethodsTotal and regional body composition was assessed with a Lunar DPX NT/Pro/MD+/Duo/Bravo scanner in a cross-sectional sample of 42 Portuguese female volleyball players aged 14–18 years (165.2 ± 0.9 cm; 61.1 ± 1.4 kg). TV was estimated with the reference method (TV-DXA) and with the anthropometric method (TV-ANTH). Agreement between procedures was assessed with Deming regression. The analysis also considered a calibration of the anthropometric approach.ResultsThe equation that best predicted TV-DXA was: -0.899 + 0.876 × log10 (body mass) + 0.113 × log10 (TV-ANTH). This new model (NM) was validated using the predicted residual sum of squares (PRESS) method (R2 PRESS = 0.838). Correlation between the reference method and the NM was 0.934 (95%CI: 0.880–0.964, Sy∙x = 0.325 L).ConclusionsA new and accurate anthropometric method to estimate TV in adolescent female volleyball players was obtained from the equation of Jones and Pearson alongside with adjustments for body mass.Electronic supplementary materialThe online version of this article (doi:10.1186/s12887-016-0730-7) contains supplementary material, which is available to authorized users.

Selecting best-fit models for estimating the body mass from 3D data of the human calcaneus

Body mass (BM) estimation could facilitate the interpretation of skeletal materials in terms of the individual's body size and physique in forensic anthropology. However, few metric studies have tried to estimate BM by focusing on prominent biomechanical properties of the calcaneus. The purpose of this study was to prepare best-fit models for estimating BM from the 3D human calcaneus by two major linear regression analysis (the heuristic statistical and all-possible-regressions techniques) and validate the models through predicted residual sum of squares (PRESS) statistics. A metric analysis was conducted based on 70 human calcaneus samples (29 males and 41 females) taken from 3D models in the Digital Korean Database and 10 variables were measured for each sample. Three best-fit models were postulated by F-statistics, Mallows’ Cp, and Akaike information criterion (AIC) and Bayes information criterion (BIC) for each available candidate models. Finally, the most accurate regression model yields lowest %SEE and 0.843 of R2. Through the application of leave-one-out cross validation, the predictive power was indicated a high level of validation accuracy. This study also confirms that the equations for estimating BM using 3D models of human calcaneus will be helpful to establish identification in forensic cases with consistent reliability.

Be aware of error measures. Further studies on validation of predictive QSAR models

Validation is the most crucial concept for development and application of quantitative structure–activity relationship (QSAR) models. The validation process confirms the reliability of the developed QSAR models along with the acceptability of each step during model development such as assessing the quality of input data, dataset diversity, predictability on an external set, domain of applicability and mechanistic interpretability. External validation or validation using an independent test set is usually considered as the gold standard in evaluating the quality of predictions from a QSAR model. The external predictivity of QSAR models is commonly described by employing various validation metrics, which can be broadly categorized into two major classes, viz., R2 based metrics namely R2test, Q2(ext_F1), and Q2(ext_F2), and purely error based measures like predicted residual sum of squares (PRESS), root mean square error (RMSE), and mean absolute error (MAE). The problem associated with the error based measures is the absence of any well-defined threshold for determining the quality of predictions making the R2 based metrics more suitable for use due to easy comprehension. However, in this paper, we show the problems associated with the R2 based validation metrics commonly used in QSAR studies, since their values are highly dependent on the range of the response values of the test set compounds and their distribution pattern around the training/test set mean. We also propose a guideline for determining the quality of predictions based on MAE and its standard deviation computed from the test set predictions after omitting 5% high residual data points in order to obviate the influence of any rarely occurring high prediction errors that may significantly obscure the quality of predictions for the whole test set. In this manner, we try to evaluate the prediction performance of a model on most (95%) of the data points present in the external set. An online tool (XternalValidationPlus) for computing the suggested MAE based criteria (along with other conventional metrics) for external validation has been made available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/. The MAE based criteria suggested here along with other commonly used validation metrics may be applied to evaluate predictive performance of QSAR models with a greater degree of confidence.

A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy

The ChemCam instrument on the Mars Curiosity rover is generating thousands of LIBS spectra and bringing interest in this technique to public attention. The key to interpreting Mars or any other types of LIBS data are calibrations that relate laboratory standards to unknowns examined in other settings and enable predictions of chemical composition. Here, LIBS spectral data are analyzed using linear regression methods including partial least squares (PLS-1 and PLS-2), principal component regression (PCR), least absolute shrinkage and selection operator (lasso), elastic net, and linear support vector regression (SVR-Lin). These were compared against results from nonlinear regression methods including kernel principal component regression (K-PCR), polynomial kernel support vector regression (SVR-Py) and k-nearest neighbor (kNN) regression to discern the most effective models for interpreting chemical abundances from LIBS spectra of geological samples. The results were evaluated for 100 samples analyzed with 50 laser pulses at each of five locations averaged together. Wilcoxon signed-rank tests were employed to evaluate the statistical significance of differences among the nine models using their predicted residual sum of squares (PRESS) to make comparisons. For MgO, SiO2, Fe2O3, CaO, and MnO, the sparse models outperform all the others except for linear SVR, while for Na2O, K2O, TiO2, and P2O5, the sparse methods produce inferior results, likely because their emission lines in this energy range have lower transition probabilities. The strong performance of the sparse methods in this study suggests that use of dimensionality-reduction techniques as a preprocessing step may improve the performance of the linear models. Nonlinear methods tend to overfit the data and predict less accurately, while the linear methods proved to be more generalizable with better predictive performance. These results are attributed to the high dimensionality of the data (6144 channels) relative to the small number of samples studied. The best-performing models were SVR-Lin for SiO2, MgO, Fe2O3, and Na2O, lasso for Al2O3, elastic net for MnO, and PLS-1 for CaO, TiO2, and K2O. Although these differences in model performance between methods were identified, most of the models produce comparable results when p≤0.05 and all techniques except kNN produced statistically-indistinguishable results. It is likely that a combination of models could be used together to yield a lower total error of prediction, depending on the requirements of the user.

Estimation of influential points in any data set from coefficient of determination and its leave-one-out cross-validated counterpart

Coefficient of determination (R (2)) and its leave-one-out cross-validated analogue (denoted by Q (2) or R cv (2) ) are the most frequantly published values to characterize the predictive performance of models. In this article we use R (2) and Q (2) in a reversed aspect to determine uncommon points, i.e. influential points in any data sets. The term (1 - Q (2))/(1 - R (2)) corresponds to the ratio of predictive residual sum of squares and the residual sum of squares. The ratio correlates to the number of influential points in experimental and random data sets. We propose an (approximate) F test on (1 - Q (2))/(1 - R (2)) term to quickly pre-estimate the presence of influential points in training sets of models. The test is founded upon the routinely calculated Q (2) and R (2) values and warns the model builders to verify the training set, to perform influence analysis or even to change to robust modeling.

Allometric Scaling of Wingate Anaerobic Power Test Scores in Men

This study examined the appropriate magnitude of allometric scaling of the Wingate anaerobic test (WAnT) power data for body mass (BM) and established normative data for the WAnT for adult men. Eighty-three men completed a standard WAnT using 0.1 kg·kg(-1) BM resistance. Allometric exponents and percentile ranks for 1-second peak power (PP), 5-second PP, and mean power (MP) were established. The Predicted Residual Sum of Squares (PRESS) procedure was used to assess external validity while avoiding data splitting. The mean 1-second PP, 5-second PP, and MP were 1,049.1 ± 168.8 W, 1,013.4 ± 158.6 W, and 777.9 ± 105.0 W, respectively. Allometric exponents for 1-second PP, 5-second PP, and MP scaled for BM were b = 0.89, 0.88, and 0.86, respectively. Correlations between allometrically scaled 1-second PP, 5-second PP, and MP, and BM were r = -0.03, -0.03, and -0.02, respectively, suggesting that the allometric exponents derived were effective in partialling out the effect of BM on WAnT values. The PRESS procedure values resulted in small decreases in R² (0.03, 0.04, and 0.02 for 1-second PP, 5-second PP, and MP, respectively) suggesting acceptable levels of external validity when applied to independent samples. The allometric exponents and normative values provide a useful tool for comparing WAnT scores in college-aged females without the confounding effect of BM. It is suggested that exponents of b = 0.89 (1-second PP), b = 0.88 (5-second PP), and b = 0.86 (MP) be used for allometrically scaling WAnT power values in healthy adult men and that the confidence limits for these allometric exponents be considered as 0.66-1.0 for PP and 0.69-1.0 for MP. The use of these exponents in allometric scaling of male WAnT power values provide coaches and practitioners with valid means for comparing power production between individuals without the confounding influence of BM.

Prediction of the concentration of standardized ileal digestible amino acids in distillers dried grains with solubles1

Values for the concentration of standardized ileal digestible (SID) CP, Lys, Met, Thr, and Trp from 34 sources of distillers dried grains with solubles (DDGS) were obtained from a series of 5 experiments with the objective of predicting the concentration of SID AA from physical and chemical assays. The concentration of NDF, ADF, hemicellulose, acid detergent insoluble CP (ADICP), and KOH soluble protein (SolCP) were measured and calculated in all DDGS sources. Likewise, particle size was measured and color of each source of DDGS was determined with a Minolta colorimeter and HunterLab spectrometer and was expressed as lightness (L*), redness (a*), and yellowness (b*). The HunterLab spectrometer also provided optical density that was recorded between 400 and 700 nm. Front face fluorescence was measured at 360 nm excitation and recorded from 380 to 600 nm. Multiple linear regression and principal components analyses were performed to predict the concentration of SID AA among DDGS sources, and predicted means as well as predicted residual sums of squares (PRESS) were calculated to estimate accuracy and precision of the model. Some correlations (P < 0.05) were observed between ADF, hemicellulose, ADICP, and SolCP with SID CP and AA but were generally low (r < 0.51). There was a greater association (R(2) = 0.40; P < 0.05) between L* and SID Lys among DDGS sources when L* was less than 50 than when samples had L* values greater than 50. In addition, a* was negatively correlated (P < 0.05) with SID CP (r = -0.41), Lys (r = -0.59), and Met (r = -0.50) whereas b* tended to be positively correlated (P < 0.10) with SID Lys (r = 0.31) and Trp (r = 0.30) and was correlated (P = 0.05) with SID Met (r = 0.43) and Thr (r = 0.36). There were no correlations between NDF or particle size with SID CP and AA. Optical density, along with CP, was highly predictive of SID Lys (R(2) = 0.97; PRESS = 0.05), Thr (R(2) = 0.94; PRESS = 0.06), and Trp (R(2) = 0.93; PRESS = 0.004) but not SID Met (R(2) = 0.39; PRESS = 0.12). Front face fluorescence was also highly predictive of SID Lys (R(2) = 0.99; PRESS = 0.07), Met (R(2) = 0.95; PRESS = 0.05), Thr (R(2) = 0.99; PRESS = 0.008), and Trp (R(2) = 0.99; PRESS = 0.006). In conclusion, correlations between ADICP, SolCP, NDF, particle size, and color measurements with SID AA concentrations were poor, but optical density and front face fluorescence methods appear to provide good predictions of SID AA concentrations in DDGS. However, these prediction equations need to be validated using samples of DDGS from a separate data set.

A study on relationships between durum wheat semolina properties, technological mixing parameters and the properties of dough after mixing

SummaryPartial least square regression analysis was used to study the correlation between X variables (semolina quality, hydration level and mixing time) and Y variables, which were, in a first model, dough consistency during mixing, and, in a second model, dough properties after mixing (strength, elasticity, density) and leavening (maximum volume). The first model showed a predictive residual sum of squares (PRESS) of 2.98 and a predictive R2 (Q2) of 0.92, and highlighted the key role of hydration and mixing time on dough consistency. The second model had the best PRESS (8.25) and Q2 (0.94) values for dough volume and indicated that the volume increased with increasing mixing time until the dough consistency decreased of 20–30%. Dough volume was primarily affected by hydration. The model indicated that maximum volume after leavening, corresponding to optimum mixing time, was obtained with a soft and elastic dough, with a low‐density value.

The Association between Cognition and Academic Performance in Ugandan Children Surviving Malaria with Neurological Involvement

BackgroundThe contribution of different cognitive abilities to academic performance in children surviving cerebral insult can guide the choice of interventions to improve cognitive and academic outcomes. This study's objective was to identify which cognitive abilities are associated with academic performance in children after malaria with neurological involvement.Methods62 Ugandan children with a history of malaria with neurological involvement were assessed for cognitive ability (working memory, reasoning, learning, visual spatial skills, attention) and academic performance (reading, spelling, arithmetic) three months after the illness. Linear regressions were fit for each academic score with the five cognitive outcomes entered as predictors. Adjusters in the analysis were age, sex, education, nutrition, and home environment. Exploratory factor analysis (EFA) and structural equation models (SEM) were used to determine the nature of the association between cognition and academic performance. Predictive residual sum of squares was used to determine which combination of cognitive scores was needed to predict academic performance.ResultsIn regressions of a single academic score on all five cognitive outcomes and adjusters, only Working Memory was associated with Reading (coefficient estimate = 0.36, 95% confidence interval = 0.10 to 0.63, p<0.01) and Spelling (0.46, 0.13 to 0.78, p<0.01), Visual Spatial Skills was associated with Arithmetic (0.15, 0.03 to 0.26, p<0.05), and Learning was associated with Reading (0.06, 0.00 to 0.11, p<0.05). One latent cognitive factor was identified using EFA. The SEM found a strong association between this latent cognitive ability and each academic performance measure (P<0.0001). Working memory, visual spatial ability and learning were the best predictors of academic performance.ConclusionAcademic performance is strongly associated with the latent variable labelled “cognitive ability” which captures most of the variation in the individual specific cognitive outcome measures. Working memory, visual spatial skills, and learning together stood out as the best combination to predict academic performance.

QSAR study of amidino bis-benzimidazole derivatives as potent anti-malarial agents against Plasmodium falciparum

AbstractA data set of amidino bis-benzimidazoles, in particular 2′-arylsubstituted-1H,1′H-[2,5′]bisbenzimidazolyl-5-carboximidine derivatives with anti-malarial activity against Plasmodium falciparum was employed in investigating the quantitative structure-activity relationship (QSAR). Quantum chemical and molecular descriptors were obtained from B3LYP/6-31g(d) calculations and Dragon software, respectively. Significant variables, which included total energy (E T), highest occupied molecular orbital (HOMO), Moran autocorrelation-lag3/weighted by atomic masses (MATS3m), Geary autocorrelation-lag8/weighted by atomic masses (GATS8m), and 3D-MoRSEsignal 11/weighted by atomic Sanderson electronegativities (Mor11e), were used in the construction of QSAR models using multiple linear regression (MLR) and artificial neural network (ANN). The results indicated that the predictive models for both the MLR and ANN approaches using leave-one-out cross-validation afforded a good performance in modelling the anti-malarial activity against P. falciparum as observed by correlation coefficients of leave-one-out cross-validation (R LOO-CV) of 0.9760 and 0.9821, respectively, root mean squared error of leave-one-out cross-validation (RMSELOO-CV) of 0.1301 and 0.1102, respectively, and predictivity of leave-one-out cross-validation (Q LOO-CV2) of 0.9526 and 0.9645, respectively. Model validation was performed using an external testing set and the results suggested that the model provided good predictivity for both MLR and ANN models with correlation coefficient of the external set (R Ext) values of 0.9978 and 0.9844, respectively, root mean squared error of the external set (RMSEExt) of 0.0764 and 0.1302 respectively, and predictivity of the external set (Q Ext2) of 0.9956 and 0.9690, respectively. Furthermore, the robustness of the QSAR models is corroborated by a number of statistical parameters, comprising adjusted correlation coefficient (R Adj2), standard deviation (s), predicted residual sum of squares (PRESS), standard error of prediction (SDEP), total sum of squares deviation (SSY), and quality factor (Q). The QSAR models so constructed provide pertinent insights for the future design of anti-malarial agents.

Prediction of Maximal Oxygen Uptake From a 3-Minute Walk Based on Gender, Age, and Body Composition

The purpose was to develop new maximal oxygen uptake (VO2max) prediction models using a perceptually regulated 3-minute walk test. VO2max was measured with a maximal incremental cycle test in 283 Japanese adults. A 3-minute walk test was conducted at a self-regulated intensity corresponding to ratings of perceived exertion (RPE) 13. A 3-minute walk distance (3MWD) was significantly related to VO2max (r = .60, P < .001). Three prediction models were developed by multiple regression to estimate VO2max using data on gender, age, 3MWD, and either BMI [BMI model, multiple correlation coefficients (R) = .78, standard error of estimate (SEE) = 5.26 ml·kg-1·min-1], waist circumference (WC model, R = .80, SEE = 5.04 ml·kg-1·min-1), or body fat percentage (%Fat model, R = .84, SEE = 4.57 ml·kg-1·min-1), suggesting that the %Fat model is the best model [VO2max = 37.501 + 0.463 × Gender (0 = women, 1 = men) - 0.195 × Age - 0.589 × %Fat + 0.053 × 3MWD]. Cross-validation by using the predicted residual sum of squares (PRESS) procedures demonstrated a high level of cross-validity of all prediction models. The new VO2max prediction models are reasonably applicable to estimating VO2max in Japanese adults and represent a quick, low-risk, and convenient means for estimating VO2max in the field.