scholarly journals Establishment of Prediction Equations of Lean Body Mass Suitable for Chinese Adults

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Jinhua Li ◽  
Jingjie Shang ◽  
Bin Guo ◽  
Jian Gong ◽  
Hao Xu
Keyword(s):  
Lean Body Mass ◽  
Body Mass ◽  
Predictive Ability ◽  
Female Group ◽  
Chinese Adults ◽  
Multilinear Regression ◽  
Predictive Equations ◽  
Large Sample ◽  
Multilinear Regression Analysis ◽  
Southern Chinese

Aim. To develop predictive equations of lean body mass (LBM) suitable for healthy southern Chinese adults with a large sample. LBM measured by dual-energy X-ray absorptiometry (DXA) are considered as the standard ones. Methods. Retrospective analysis was conducted on the consecutive people who did total body measurement with DXA from July 2005 to October 2015. People with diseases that might affect LBM were excluded and overall 12,194 subjects were included in this study. Information about the 10,683 subjects (2,987 males and 7,696 females) from July 2005 to November 2014 was used to establish equations. These subjects were grouped by sex and then subdivided according to their body mass index (BMI). The female group was divided into another two subgroups: the premenopausal and postmenopausal subgroups. Equations were developed through stepwise multilinear regression analysis of height, weight, age, and BMI. Information about the 1,511 subjects (395 males and 1116 females) from December 2014 to October 2015 was used to verify the established equations. Results. BMI, height, weight, and age were introduced into the equations as independent variables in the male group, while age was proved to have no influence on LBM in the female group. Regrouping according to BMI or menopause did not increase the predictive ability of equations. Good agreement between LBM evaluated by equation (LBM_PE) and LBM measured by DXA (LBM_DXA) was observed in both the male and female groups. Conclusion. Predictive equations of LBM suitable for healthy southern Chinese adults are established with a large sample. BMI was related to LBM content; however, there is no need for further group based on BMI or menopause while developing LBM questions.

scholarly journals Sex-specific lean body mass predictive equations are accurate in the obese paediatric population

2015 ◽  
Vol 43 (5) ◽  
pp. 417-422 ◽  
Author(s):  
Lanier B. Jackson ◽  
Melissa H. Henshaw ◽  
Janet Carter ◽  
Shahryar M. Chowdhury
Keyword(s):  
Lean Body Mass ◽  
Body Mass ◽  
Paediatric Population ◽  
Predictive Equations

Monitoring the Temperature of Dilute Aqueous Solutions Using Near-Infrared Water Absorption

2003 ◽  
Vol 57 (6) ◽  
pp. 661-666 ◽  
Author(s):  
Eugenio H. Otal ◽  
Fernando A. Iñón ◽  
Francisco J. Andrade
Keyword(s):  
Aqueous Solutions ◽  
Water Absorption ◽  
Prediction Error ◽  
Near Infrared ◽  
Matrix Effects ◽  
Signal To Noise Ratio ◽  
Predictive Ability ◽  
Multilinear Regression ◽  
Multilinear Regression Analysis ◽  
Dissolved Ions

An alternative spectroscopic approach for monitoring the temperature of aqueous solutions is presented. The method is based upon the temperature-induced spectral changes undergone by the second overtone (around 960 nm) of the near-infrared (NIR) water absorption band. Single and multilinear regression analysis are tested in order to evaluate the predictive ability of temperature. A linear dependence is found when measurements are performed at a single wavelength, but a lower prediction error is obtained when multilinear models are applied. No matrix effects produced by moderately concentrated common dissolved ions are found in a broad range of pH. A signal-to-noise ratio allows a precision of 0.5 °C for temperature monitoring. A prediction error of 0.77 °C (single linear regression) and 0.25 °C (multilinear approach) are achieved in a range from 15 to 90 °C. Advantages in terms of instrumentation and data analysis required are discussed.

Vocal Acoustic Analysis

2022 ◽  
pp. 612-628
Author(s):  
João Paulo Teixeira ◽  
Nuno Alves ◽  
Paula Odete Fernandes
Keyword(s):  
Acoustic Analysis ◽  
Low Cost ◽  
Principal Component ◽  
Female Group ◽  
Multilinear Regression ◽  
Non Invasive ◽  
Reduction Techniques ◽  
Multilinear Regression Analysis ◽  
And Control ◽  
Dimension Reduction Techniques

Vocal acoustic analysis is becoming a useful tool for the classification and recognition of laryngological pathologies. This technique enables a non-invasive and low-cost assessment of voice disorders, allowing a more efficient, fast, and objective diagnosis. In this work, ANN and SVM were experimented on to classify between dysphonic/control and vocal cord paralysis/control. A vector was made up of 4 jitter parameters, 4 shimmer parameters, and a harmonic to noise ratio (HNR), determined from 3 different vowels at 3 different tones, with a total of 81 features. Variable selection and dimension reduction techniques such as hierarchical clustering, multilinear regression analysis and principal component analysis (PCA) was applied. The classification between dysphonic and control was made with an accuracy of 100% for female and male groups with ANN and SVM. For the classification between vocal cords paralysis and control an accuracy of 78,9% was achieved for female group with SVM, and 81,8% for the male group with ANN.

scholarly journals Vocal Acoustic Analysis

2020 ◽  
Vol 11 (1) ◽  
pp. 37-51
Author(s):  
João Paulo Teixeira ◽  
Nuno Alves ◽  
Paula Odete Fernandes
Keyword(s):  
Acoustic Analysis ◽  
Low Cost ◽  
Principal Component ◽  
Female Group ◽  
Multilinear Regression ◽  
Non Invasive ◽  
Reduction Techniques ◽  
Multilinear Regression Analysis ◽  
And Control ◽  
Dimension Reduction Techniques

Vocal acoustic analysis is becoming a useful tool for the classification and recognition of laryngological pathologies. This technique enables a non-invasive and low-cost assessment of voice disorders, allowing a more efficient, fast, and objective diagnosis. In this work, ANN and SVM were experimented on to classify between dysphonic/control and vocal cord paralysis/control. A vector was made up of 4 jitter parameters, 4 shimmer parameters, and a harmonic to noise ratio (HNR), determined from 3 different vowels at 3 different tones, with a total of 81 features. Variable selection and dimension reduction techniques such as hierarchical clustering, multilinear regression analysis and principal component analysis (PCA) was applied. The classification between dysphonic and control was made with an accuracy of 100% for female and male groups with ANN and SVM. For the classification between vocal cords paralysis and control an accuracy of 78,9% was achieved for female group with SVM, and 81,8% for the male group with ANN.

scholarly journals Nutritional status and body composition in individuals with overweight or obesity using usual and unusual indicators

2021 ◽  
Vol 10 (1) ◽  
pp. e4910111339
Author(s):  
Heloá Costa Borim Christinelli ◽  
Greice Westphal ◽  
Nelson Nardo Junior ◽  
Maria Luiza Costa Borim ◽  
Maria Antonia Ramos Costa ◽  
...  
Keyword(s):  
Body Composition ◽  
Nutritional Status ◽  
Lean Body Mass ◽  
Body Mass ◽  
Female Group ◽  
Fat Percentage ◽  
Anthropometric Parameters ◽  
Composition Methods ◽  
Inferential Statistic ◽  
Overweight Or Obesity

Objective: the aim of this study is to explore the nutritional status in adults according to usual and non-usual components of body composition. Methods: this is a descriptive study involving a sample of 274 Brazilians adults from the south of the country. The participants had their body composition assessed by an octapolar bioimpedanciometer (InBody 520 - Biospace) and the anthropometric parameters according to the WHO standard. The data was analyzed by the descriptive and inferential statistic with the software SPSS version 22.0. Results: The participants were grouped by nutritional status and sex. There were statistical differences between the groups (both nutritional status and sex) in all the variables, except age and stature. The correlation among the variables were also significant, except for the Lean Body Mass and Body Fat Percentage (LBM vs BFP) and for the Lean-to-Fat-Ratio and Lean Body Mass (LFR vs LBM). Final considerations: The results has shown that the usual and the non-usual parameters of body composition are related (most of them) and were more strongly related in the female group.

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