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

© 2016 The Author(s).Background: A 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. Methods: Total 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. Results: The 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 (R2PRESS = 0.838). Correlation between the reference method and the NM was 0.934 (95%CI: 0.880-0.964, Sy·x = 0.325 L). Conclusions: A 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

Out-of-focus Blur: Image De-blurring

Image de-blurring is important in many cases of imaging a real scene or object by a camera. This project focuses on de-blurring an image distorted by an out-of-focus blur through a simulation study. A pseudo-inverse filter is first explored but it fails because of severe noise amplification. Then Tikhonov regularization methods are employed, which produce greatly improved results compared to the pseudo-inverse filter. In Tikhonov regularization, the choice of the regularization parameter plays a critical rule in obtaining a high-quality image, and the regularized solutions possess a semi-convergence property. The best result, with the relative restoration error of 8.49%, is achieved when the prescribed discrepancy principle is used to decide an optimal value. Furthermore, an iterative method, Conjugated Gradient, is employed for image de-blurring, which is fast in computation and leads to an even better result with the relative restoration error of 8.22%. The number of iteration in CG acts as a regularization parameter, and the iterates have a semi-convergence property as well.Comment: 11 page

Descriptive statistics for variables reporting inter-individual variability on DXA assessments on trunk, upper limbs and lower limbs (n = 53).

<p>Descriptive statistics for variables reporting inter-individual variability on DXA assessments on trunk, upper limbs and lower limbs (n = 53).</p

Means and standard deviations by group and results of ANOVA to test the effect of sport participation on DXA assessments on trunk, upper limbs and lower limbs.

<p>Means and standard deviations by group and results of ANOVA to test the effect of sport participation on DXA assessments on trunk, upper limbs and lower limbs.</p

High-density lipoprotein-cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides and high-sensitivity C-reactive protein (hsCRP) in male controls (white bars), swimmers (grey bars) and basketball players (black bars).

<p>* indicates difference between the groups (p<0.05).</p

Whole body bone mineral content (BMC), trunk BMC, upper limbs BMC, lower limbs BMC in male controls (white bars), swimmers (grey bars) and basketball players (black bars) adjusted by chorological age, maturity offset, vitamin D intake and weekly training load.

<p>* indicates difference between the groups (p<0.05).</p

Mean difference between groups on DXA assessments on trunk, upper limbs and lower limbs.

<p>Mean difference between groups on DXA assessments on trunk, upper limbs and lower limbs.</p

Descriptive statistics on chronological age, maturation, training experience, parameters of training experience, indicators of lipid profile plus inflammatory biomarker, anthropometry of the overall body size and outputs of whole body DXA assessments for the total sample (n = 53).

<p>Descriptive statistics on chronological age, maturation, training experience, parameters of training experience, indicators of lipid profile plus inflammatory biomarker, anthropometry of the overall body size and outputs of whole body DXA assessments for the total sample (n = 53).</p

Means and standard deviations by group and results of ANOVA to test the effect of sport participation on chronological age, maturation, training experience, indicators of lipid profile plus inflammatory biomarker, anthropometry of the overall body size and outputs of whole body DXA assessments.

<p>Means and standard deviations by group and results of ANOVA to test the effect of sport participation on chronological age, maturation, training experience, indicators of lipid profile plus inflammatory biomarker, anthropometry of the overall body size and outputs of whole body DXA assessments.</p

Mean difference between groups on chronological age, maturation, training experience, parameters of training load, anthropometry of the overall body size, indicators of lipid profile plus inflammatory biomarker and outputs of whole body DXA.

<p>Mean difference between groups on chronological age, maturation, training experience, parameters of training load, anthropometry of the overall body size, indicators of lipid profile plus inflammatory biomarker and outputs of whole body DXA.</p