Standard Error Of Estimate Values Research Articles

Adsorption of Coomassie Brilliant Blue on a Novel Eco‐Friendly Nanogel from Simulated Water: Equilibrium, Kinetic, and Thermodynamic Studies

AbstractHerein a novel gum acacia‐alginate nanogel (GAA) was synthesized via ultrasound‐assisted crosslinking by DPPH and applied for the removal of coomassie brilliant blue (CBB) dye. The fabrication of GAA and its interaction with CBB was specified via FTIR, XRD, SEM, and TEM techniques. The process parameters at which maximum adsorption efficiency, qe (80.12 mg/g), and % efficiency >94 % were achieved include dosage (0.8 g/L), time (80 min), initial dye concentration (20 mg/L), and pH (6). Ultrasound‐assisted uptake was greater than stirring‐assisted due to acoustic cavitation′s hydrodynamic and thermal processes. Pseudo‐second‐order model described the adsorption kinetics and the diffusion mechanism suggested that the rate‐determining step was controlled by both the intraparticle diffusion and the liquid film models. Isotherms showed an excellent fit to the Freundlich model based on higher R2 (0.998) and lower standard error of estimate values (SEE=0.367). The −ΔG° values (−8.152, −8.292, −8.433, −8.586 kJ/mol) indicated that the adsorption process was feasible and spontaneous. The removal of CBB ensued via hydrogen bonding, electrostatic, π‐π, and n‐π interactions. The maximum adsorption capacity of 182.096 mg/g, eco‐friendly characteristics along with regeneration competence depicted that GAA could be effective for scavenging CBB from wastewater.

Accuracy of Exercise-based Equations for Estimating Cardiorespiratory Fitness.

This study aimed to compare the accuracy of exercise-based prediction equations with directly measured CRF and evaluate their ability to classify an individual's CRF. The sample included 4871 tests from apparently healthy adults (38% female, age 44.4 ± 12.3 yr (mean ± SD)). Estimated CRF (eCRF) was determined from 2 nonexercise equations, 3 submaximal exercise equations, and 10 maximal exercise equations; all eCRF calculations were then compared with directly measured CRF, determined from a cardiopulmonary exercise test. Analysis included Pearson product-moment correlations, standard error of estimate values, intraclass correlation coefficients, Cohen κ coefficients, and the Benjamini-Hochberg procedure to compare eCRF with directly measured CRF. All eCRF values from the prediction equations were associated with directly measured CRF (P < 0.01), with intraclass correlation coefficient estimates ranging from 0.07 to 0.89. Although significant agreement was found when using eCRF to categorize participants into fitness tertiles, submaximal exercise equations correctly classified an average of only 51% (range, 37%-58%) and maximal exercise equations correctly classified an average of only 59% (range, 43%-76%). Despite significant associations between exercise-based prediction equations and directly measured CRF, the equations had a low degree of accuracy in categorizing participants into fitness tertiles, a key requirement when stratifying risk within a clinical setting. The present analysis highlights the limited accuracy of exercise-based determinations of eCRF and suggests the need to include cardiopulmonary measures with maximal exercise to accurately assess CRF within a clinical setting.

Comparison of non-exercise cardiorespiratory fitness prediction equations in apparently healthy adults.

A recent scientific statement suggests clinicians should routinely assess cardiorespiratory fitness using at least non-exercise prediction equations. However, no study has comprehensively compared the many non-exercise cardiorespiratory fitness prediction equations to directly-measured cardiorespiratory fitness using data from a single cohort. Our purpose was to compare the accuracy of non-exercise prediction equations to directly-measured cardiorespiratory fitness and evaluate their ability to classify an individual's cardiorespiratory fitness. The sample included 2529 tests from apparently healthy adults (42% female, aged 45.4 ± 13.1 years (mean±standard deviation). Estimated cardiorespiratory fitness from 28 distinct non-exercise prediction equations was compared with directly-measured cardiorespiratory fitness, determined from a cardiopulmonary exercise test. Analysis included the Benjamini-Hochberg procedure to compare estimated cardiorespiratory fitness with directly-measured cardiorespiratory fitness, Pearson product moment correlations, standard error of estimate values, and the percentage of participants correctly placed into three fitness categories. All of the estimated cardiorespiratory fitness values from the equations were correlated to directly measured cardiorespiratory fitness (p < 0.001) although the R2 values ranged from 0.25-0.70 and the estimated cardiorespiratory fitness values from 27 out of 28 equations were statistically different compared with directly-measured cardiorespiratory fitness. The range of standard error of estimate values was 4.1-6.2 ml·kg-1·min-1. On average, only 52% of participants were correctly classified into the three fitness categories when using estimated cardiorespiratory fitness. Differences exist between non-exercise prediction equations, which influences the accuracy of estimated cardiorespiratory fitness. The present analysis can assist researchers and clinicians with choosing a non-exercise prediction equation appropriate for epidemiological or population research. However, the error and misclassification associated with estimated cardiorespiratory fitness suggests future research is needed on the clinical utility of estimated cardiorespiratory fitness.

Modelling of critical power from road data

Background: Performance tests are an integral part of evaluating competitive cyclists. Despite all technological and physiological advances, limited research has been performed addressing the translation of standardized, relevant laboratory tests into the field and consequently into “real world” cycling (i.e. .(i.e. GonzAilez-Haro et al., 2007: British Journal of Sports Medicine, 41(3), 174–179;  Quod et al., 2010:InterInternational Journal of  Sports Medicine, 31(6), 397–401; Nimmerichter et al., 2010:  International  Journal of Sports Medicine, 31(3), 160- 166). For continuous activities between approximately 2 and 30 minutes, the assessment of Critical Power (CP) is one such relevant test. Compromising ecological validity, to date CP testing is mostly constrained to the laboratory. Purpose: To investigate a novel CP road testing protocol. Methods: Laboratory determined CP values using a 30 min intra-trial recovery period (Bishop & Jenkins, 1995: European Journal of Applied Physiology and Occupational Physiology, 72 (1-2), 115-120) were compared with those determined in the field, i.e. on the road. The experiment comprised of planned maximal efforts of 12 min, 7 min and 3 min with a 30 min recovery period between efforts. Linear regression was used to determine CP using the work-time equation (W = CPt + Wi‚¢). Results: There was no significant difference between laboratory and road CP values. The mean difference between the two environments was 0 ± 5.5 W. The standard error of estimates was 1.7% and limits of agreement were -10.8 – 10.8 W (Fig. 1). Discussion: Results suggests that CP can be tested on the road. Gonzales-Haro accepted their incremental velodrome field test as being valid with reported limits of agreement of 130 W to – 24 W and a random error of 13.9%. Our limits of agreement values are considerably higher and standard error of estimate values are considerably lower than those reported by Gonzales-Haro. The experimental protocol provides a practical and easy to use alternative to the conventional testing protocol for coaches and athletes when determining CP in on the road. Conclusion: The aforementioned research provides support for the acceptance of road CP performance testing using a 30 min inter-maximal effort recovery period.

Ionic dye adsorption by zinc oxide nanoparticles

The adsorption behaviour of Basic Red 12, Acid Orange 7 and Acid Blue 1 on zinc oxide nanoparticles (ZNP) has been investigated to understand the physicochemical process involved and to explore the possible use of nanoparticles in the treatment and management of textile waste matter. The dye removal capacity of ZNP towards Basic Red 12, Acid Orange 7 and Acid Blue 1 was found to be 15.64, 6.78 and 6.38 mg g−1, respectively. The adsorption process was pH dependent and optimum pH values of 9.0, 2.0 and 4.0 were obtained for Basic Red 12, Acid Orange 7 and Acid Blue 1, respectively. Equilibrium was established after 1.0 h for all dyes. Langmuir, Freundlich and Temkin isotherm models were applied to the system. The adsorbent ZNP was characterised using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) and Fourier transform infrared (FTIR) techniques. SEM analysis revealed the noticeable nanoporous morphology of the material. The results of FTIR spectroscopy showed that the process is driven by an electrostatic complexation mechanism. XRD studies revealed the nanocrystalline structure of ZNP. BET surface area measurement suggested a high pore volume and large surface area for the adsorbent. The kinetic measurements suggested pseudo-second-order kinetic processes with high regression coefficients and smaller standard error of estimate values and lower residual sum of squares. The thermodynamic measurements suggested that all processes were exothermic and accompanied by negative values for Δ G0, Δ S0 and Δ H0.

Estimating stature in South African populations using various measures of the sacrum

The accurate estimation of stature can meaningfully contribute to the positive identification of skeletal remains of unknown individuals. Regression equations are most commonly used to estimate stature and have been derived from numerous bones. Little is however known about stature estimation from the sacrum and therefore the aim of the current study was to evaluate whether it is possible to estimate stature from various sacral measurements in South African populations. A total of 210 sacra representing 50 black males, 51 white males, 58 black females and 51 white females were measured. These measurements included anterior sacral height, anterior sacral width, maximum transverse diameter of the base, anterior sacral height of individual sacral segments (S1, S2, S3, S4, S5) as well as combinations of anterior sacral heights of segments, (i.e. S1+S2). The correlation of these measures to stature was calculated using Pearson's correlation coefficient, followed by the calculation of linear least square and multiple regression equations for the estimation of stature. Statistical analyses indicated differences between sexes and population groups with multiple regressions being slightly more accurate than regression equations derived from single variables. Standard error of estimate values for the present study fall within the ranges stated by previous researchers, with smaller stature estimation intervals reported for black South Africans and females. This study suggests that regression equations derived from the sacrum of South Africans can be used to calculate reasonable estimates of stature, in the absence of bones that yield more accurate results.

Tracking fat-free mass changes in elderly men and women using single-frequency bioimpedance and dual-energy X-ray absorptiometry: a four-compartment model comparison

To compare single estimations of fat-free mass (FFM) and to track FFM using single-frequency bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) compared with a four-compartment (4C) model in healthy elderly Americans. Thirty-four men and thirty-eight women (Caucasian, ≥ 65 years) were included in the study. Subjects participated in either the control group or the exercise group. All testing and training took place during the 21-week investigation. Body composition assessments using nine BIA equations, DXA and a 4C model were performed during weeks 1, 12 and 24 of the study. Single estimations for DXA and BIA produced high r values (0.79-0.95) and low standard error of estimate values (1.62-3.3 kg), producing subjective ratings of 'ideal' for men and 'excellent' for women. Both DXA and two BIA equations revealed the same significance when comparing groups and times with the 4C model. Individual accuracy for tracking changes was similar among BIA equations and DXA compared with the 4C model, with a total agreement of 25% for BIA and 27% for DXA compared with the 4C model. The current data in combination with the reliability errors for both BIA and DXA FFM estimations suggest that individual results should be interpreted with caution if FFM changes are <5 kg. However, DXA and BIA are both valid methods that can be used interchangeably to estimate FFM at a single time point or for tracking changes in FFM in small groups (15-22) of healthy American older adults.

Estimating body fat in NCAA Division I female athletes: a five-compartment model validation of laboratory methods

The purpose of the present study was to determine the validity of various laboratory methods for estimating percent body fat (%fat) in NCAA Division I college female athletes (n = 29; 20 +/- 1 year). Body composition was assessed via hydrostatic weighing (HW), air displacement plethysmography (ADP), and dual-energy X-ray absorptiometry (DXA), and estimates of %fat derived using 4-compartment (C), 3C, and 2C models were compared to a criterion 5C model that included bone mineral content, body volume (BV), total body water, and soft tissue mineral. The Wang-4C and the Siri-3C models produced nearly identical values compared to the 5C model (r > 0.99, total error (TE) < 0.40%fat). For the remaining laboratory methods, constant error values (CE) ranged from -0.04%fat (HW-Siri) to -3.71%fat (DXA); r values ranged from 0.89 (ADP-Siri, ADP-Brozek) to 0.93 (DXA); standard error of estimate values ranged from 1.78%fat (DXA) to 2.19%fat (ADP-Siri, ADP-Brozek); and TE values ranged from 2.22%fat (HW-Brozek) to 4.90%fat (DXA). The limits of agreement for DXA (-10.10 to 2.68%fat) were the largest with a significant trend of -0.43 (P < 0.05). With the exception of DXA, all of the equations resulted in acceptable TE values (<3.08%fat). However, the results for individual estimates of %fat using the Brozek equation indicated that the 2C models that derived BV from ADP and HW overestimated (5.38, 3.65%) and underestimated (5.19, 4.88%) %fat, respectively. The acceptable TE values for both HW and ADP suggest that these methods are valid for estimating %fat in college female athletes; however, the Wang-4C and Siri-3C models should be used to identify individual estimates of %fat in this population.

MULTIVARIATE ANALYSIS FOR EVALUATING SESAME YIELD AND ITS CONTRIBUTING FACTORS

Two field experiments were carried out in a commercial field at Abo Rawash village, Giza governorate, Egypt during 2004 and 2005 seasons to compare five statistical procedures including: simple correlation, path analysis, multiple linear regression, stepwise regression and factor analysis in determining the relationship between sesame seed yield and its contributing traits. Thirty sesame genotypes were used for this purpose. The studied characters were: flowering date, plant height, number of fruiting branches, stem height to the first capsule, fruiting zone length, number of capsules on main stem, number of capsules per plant, capsule density on main stem, 1000-seed weight and seed yield per plant. The simple correlation coefficients and path analysis of yield components revealed that components with the highest positive correlation to yield also had the highest positive direct effect to yield i.e., number of capsules on main stem and number of capsules per plant. Path analysis showed that, the residual effect (0.433) was high in magnitude which shows that some other important yield contributing characters which contribute to yield have to be included. Stepwise multiple regression analysis showed that 77.25% of the total variation in seed yield could be explained by the variation in number of capsules per plant and flowering date in sesame. The linear regression equation was (Y) = 10.951 - 0.110 X1 + 0.114 X7, where Y, X1 and X7 represent seed yield per plant, flowering date and number of capsules per plant, respectively. Besides, coefficient of determination (R2), adjusted R-squared statistic and standard error of estimate values, mean absolute error (MAE) and Durbin-Watson (DW) statistic test showed no significant differences between the full model regression and stepwise multiple regression analysis technique. However, the efficiency expressed is due to the reduction in number of variables in the fitted model from all variables (full model regression) to two variables only (stepwise multiple regression). Factor analysis indicated that three factors could explain approximately 81.9% of the total variation. The first factor which accounted for about 41% of the variation was strongly associated with fruiting zone length, number of capsules on main stem, number of capsules per plant, and capsule density. The second factor which accounts for about 25% of the variation, was strongly associated and positive effects on days to flowering, 1000-seed weight, plant height and stem height to the first capsule, whereas the third factor had positive effects on number of fruiting branches only, which accounts for about 16% of the variation. Factor analysis technique was more efficient than other used statistical techniques. It provides more information about cluster of inter-correlated variables. It could be concluded that the five of statistical analysis techniques, agreed upon that high yield of sesame plants could be obtained by selecting breeding materials with high number of capsules on main stem, number of capsules per plant, plant height and increasing capsule density on the main stem.

A Practical Method for Estimating Percent Body Fat of Adult Mentally Retarded Males

The subjects were 170 moderately and severely mentally retarded men who were divided into two groups. The first group was used to formulate a new prediction equation and the second group was used to cross-validate and ascertain the stability of the derived equation. The prediction equation, employing waist and forearm circumferences, height and weight as predictors, and estimated percent body fat calculated by the generalized regression equation of Jackson and Pollock (1978) as the criterion measure, was formulated using a stepwise multiple regression analysis. A multiple R value of .86 was obtained for the derived equation with a standard error of estimate value of 3.35. The equation was cross-validated on the second sample to ascertain its stability. An r of .81 and a standard error of estimate of 4.41 was obtained between the subjects’ estimated percent body fat, using the new equation, and the criterion measure. This simplified equation provides practitioners with an accurate, reliable, and inexpensive method of estimating percent body fat for adult mentally retarded males.

Evaluation and interpretation of thematic mapper ratios in equations for estimating corn growth parameters

Reflectance and agronomic data were analyzed to determine possible data transformations for evaluating several plant parameters of corn. Three transformation forms were used: the ratio of two bands, logarithms of two-band ratios, and normalized differences of two bands. Normalized differences and logarithms of two-band ratios responded similarly in the equations for estimating the plant growth parameters evaluated in this study. Two-term equations were required to obtain the maximum predictability of percent ground cover, canopy moisture content, and total wet phytomass. Standard error of estimate values were 15–26% lower for two-term estimates of these parameters than for one-term estimates. The terms log(TM4/TM2) and (TM4/TM5) produced the maximum predictability for leaf area and dry green leaf weight, respectively. The middle infrared bands TM5 and TM7 are essential for maximizing predictability for all measured plant parameters except LAI. The estimating models were evaluated over bare soil to discriminate between equations which are statistically similar. Qualitative interpretations of the resulting prediction equations are consistent with general agronomic and remote sensing theory.

Noninvasive Doppler determination of cardiac output during submaximal and peak exercise.

We compared pulsed Doppler (PD) measurements of stroke volume (SV) and cardiac output (CO) as a function of work load with previously reported values that were obtained by standard invasive methods. Suprasternal notch measurements of Doppler-shifted frequency (delta f) were obtained from the ascending aorta and SV calculated with the Doppler equation and an independent measurement of aortic diameter. Motion artifacts were minimized with the aid of a restraining table cycle ergometer. Signal aliasing was accommodated with manual summation of delta f waveforms. A total of 207 determinations were made in 10 sitting subjects exercising to exhaustion. Linear regression analysis of CO vs. work load was significant (P less than 0.001). The correlation coefficient (r = 0.95) and standard error of estimate value (1.21 1/min) were similar to values from the literature. Absolute values of CO and SV underestimated the literature values across all work loads. Technical reproducibility was assessed by comparing with paired t tests the differences between 65 duplicate serial measurements of CO and SV at rest and exercise. No significant differences (P less than 0.001) were found. We concluded that PD-determined SV and CO are reproducible and correlate linearly with work load in a manner consistent with reported invasive techniques. Thus the PD method appears suitable for use during submaximal and peak exercise.

Errors in Determining Instar Numbers through Head Capsule Measurements of a Lepidopteran—a Laboratory Study and Critique

Larvae of Choristoneura viridis Freeman (Tortricidae) were reared individually on an artificial medium in the laboratory under controlled conditions. The larvae exhibited developmental polymorphism, i.e., some larvae had a total of 6 instars, others 7, and still others 8 instars. Except for instar I, all larval head capsules were recovered and widths measured for each larva in the study population. A frequency distribution curve of capsule widths suggested only 6 instars, when in fact over 57% of individuals showed 7 or more instars. Head capsule width ranges for successive instars, as suggested by the multimodal frequency distribution curve, were not in agreement with known values for the laboratory population, nor were they in agreement when values for the population were segregated by sex irrespective of larval instar group. Peaks in the curve could be interpreted only after the head capsule widths in the population were segregated by instar group. In larval growth regressions for the population as well as for head capsules broken down by sex and/or instar group, standard error of estimate values appeared to be more sensitive than R2 values in reflecting relative precisions of equations. Generated frequency distribution curves of head capsule widths support the argument that frequency distribution of head capsule widths cannot be used to assign instar numbers in lepidopterous species with developmental polymorphism.