scholarly journals Assessment of body weight and morphological traits of two breed of grower pigs using principal component analysis

2021 ◽  
Vol 48 (5) ◽  
pp. 1-11
Author(s):  
P.O. Akporhuarho ◽  
O. Iriakpe
Keyword(s):  
Principal Component Analysis ◽  
Body Weight ◽  
Body Length ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Head Length ◽  
Large White ◽  
Poids Corporel ◽  
The Mean

The study aimed at explaining objectively the relationship between morphologic traits of two breeds of pigs (Large-white and Duroc) using principal component analysis to determine the body size of grower pigs of two different breeds with a view of identifying components that best define body conformation. Body weight and five biometric variables namely head length, body length, body girth, ham length and ear length. The descriptive statistics showed that the mean body weight of Large-white was 13.14kg while the body measurements were 24.61cm, 71.35cm, 65.12cm, 43.13cm and 21.94cm for head length, body length, body girth, ham length and ear length respectively at 5 – 24 weeks of age. The mean body weight of Duroc was 12.87kg while the body measurements were 23.70cm, 57.93cm, 47.93cm, 22.90cm, 19.26cm for head length, body length, body girth, ham length and ear length respectively. The coefficient of correlation ranges from 0.08-0.424 and 0.01-0.402 for Large-white and Duroc respectively. The association between and were the highest for Duroc, body length r=0.402 and Large-white, body girth 0.424. Two components were identified for Large-white while those of Duroc were three components. The ratios of variance were 53.55 and 71.07% for Large-white and Duroc, respectively. The first factor in each case accounted for the biggest percentage of the total variation, and was designated the general size, the other factors (indices of body shape) offer forms of variation independent of the general size. The principal component based regression models which were chosen for selecting animals for optimal balance accounted for 58 and 76% of the variation in the body weight for Large-white and Duroc respectively. The study concluded that the use of principal component analysis techniques tends to explore the interdependence in the original five parameters measured: head length, body length, body girth, ham length and ear length of Large-white and Duroc     L'étude explique objectivement la relation entre les traits morphologiques de deux races de porcs (gros blanc et de Duroc) à l'aide d'une analyse de composants principaux afin de déterminer la taille du corps des porcs de producteurs de deux races différentes en vue d'identifier les composants qui définissent le mieux la conformation corporelle. Poids corporel et cinq variables biométriques, nommément longueur de la tête, longueur du corps, circonférence du corps, longueur du jambon et longueur de l'oreille. Les statistiques descriptives ont montré que le poids corporel moyen de gros blanc était de 13,14 kg tandis que les mesures du corps étaient de 24,61 cm, 71,35 cm, 65,12 cm, 43,13 cm et 21,94 cm pour la longueur de la tête, la longueur du corps, la circonférence du corps, la longueur du jambon et la longueur de l'oreille respectivement à 5 - 24 semaines. Le poids corporel moyen de Duroc était de 12,87 kg tandis que les mesures du corps étaient de 23,70 cm, 57,93 cm, 47,93 cm, 22,90 cm, 19,26 cm pour la longueur de la tête, la longueur du corps, la circonférence du corps, la longueur du jambon et la longueur de l'oreille respectivement. Le coefficient de corrélation varie de 0,08 à 0,424 et de 0,01 à 0,402 pour les gros blancs et Duroc respectivement. L'association entre et étaient les plus élevées pour Duroc, la longueur du corps R = 0,402 et de gros blancs, la circonférence du corps 0,424. Deux composants ont été identifiés pour les gros blancs tandis que ceux de Duroc étaient trois composants. Les ratios de variance étaient respectivement de 53,55 et 71,07% pour les gros blancs et Duroc. Le premier facteur de chaque cas représentait le plus gros pourcentage de la variation totale et a été désigné la taille générale, les autres facteurs (indices de la forme du corps) offrent des formes de variation indépendantes de la taille générale. Les principaux modèles de régression basés sur les composants choisis pour sélectionner des animaux pour un solde optimal représentaient 58 et 76% de la variation du poids corporel pour les grands blancs et Duroc respectivement. L'étude a conclu que l'utilisation de techniques d'analyse des composants principaux a tendance à explorer l'interdépendance dans les cinq paramètres d'origines mesurées: longueur de la tête, longueur du corps, circonférence corporelle, longueur du jambon et longueur de l'oreille de grosse blanc et de Duroc

scholarly journals Voxelwise Principal Component Analysis of Dynamic [S-Methyl-11C]Methionine PET Data in Glioma Patients

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2342
Author(s):  
Corentin Martens ◽  
Olivier Debeir ◽  
Christine Decaestecker ◽  
Thierry Metens ◽  
Laetitia Lebrun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Added Value ◽  
Time Activity ◽  
Positron Emission ◽  
Activity Curve ◽  
The Mean ◽  
Mean Time ◽  
Met Pet

Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas.

scholarly journals Hyperspectral data compression based upon the principal component analysis

2021 ◽  
Vol 45 (2) ◽  
pp. 235-244
Author(s):  
A.S. Minkin ◽  
O.V. Nikolaeva ◽  
A.A. Russkov
Keyword(s):  
Principal Component Analysis ◽  
Data Compression ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Gas Absorption ◽  
Absorption Bands ◽  
Spectral Bands ◽  
The Mean ◽  
Retrieval Errors

The paper is aimed at developing an algorithm of hyperspectral data compression that combines small losses with high compression rate. The algorithm relies on a principal component analysis and a method of exhaustion. The principal components are singular vectors of an initial signal matrix, which are found by the method of exhaustion. A retrieved signal matrix is formed in parallel. The process continues until a required retrieval error is attained. The algorithm is described in detail and input and output parameters are specified. Testing is performed using AVIRIS data (Airborne Visible-Infrared Imaging Spectrometer). Three images of differently looking sky (clear sky, partly clouded sky, and overcast skies) are analyzed. For each image, testing is performed for all spectral bands and for a set of bands from which high water-vapour absorption bands are excluded. Retrieval errors versus compression rates are presented. The error formulas include the root mean square deviation, the noise-to-signal ratio, the mean structural similarity index, and the mean relative deviation. It is shown that the retrieval errors decrease by more than an order of magnitude if spectral bands with high gas absorption are disregarded. It is shown that the reason is that weak signals in the absorption bands are measured with great errors, leading to a weak dependence between the spectra in different spatial pixels. A mean cosine distance between the spectra in different spatial pixels is suggested to be used to assess the image compressibility.

scholarly journals Principal Component Analysis of Morphometric Traits of West African Dwarf Goats

2019 ◽  
Vol 1 ◽  
pp. 26-32 ◽  
Author(s):  
I O Dudusola ◽  
S O Oseni ◽  
M A Popoola ◽  
A Jenyo
Keyword(s):  
Principal Component Analysis ◽  
Body Weight ◽  
Total Variation ◽  
Age Groups ◽  
Principal Component ◽  
Component Analysis ◽  
West African ◽  
Age Group ◽  
Morphometric Traits ◽  
West African Dwarf

The study was conducted to evaluate the principal component analysis of phenotypic attributes of West African Dwarf (WAD) goat. Data collected on the live body weight and twelve morphometric traits of the goats which were categorised into four age groups based on their dentition. The age groups were: less than 2years old, 2- 3years old, 3-4 years old and 4 years old. The data were subjected to a PCA and Cluster analyses using the multivariate procedure components of SAS (2003). Result revealed that highest values of morphometric traits were obtained in goats that of 4 years old. The rate of increase in body weight and other morphometric traits was high in age group of ˂2 years to age 2-3years compared to differences observed in others across the age group. Heart Girth had the highest correlation with body weight. Foreleg, neck, ear and hind leg lengths; wither height and rump height were weakly correlated with the body weight of the goats. Result revealed that two Principal components were retained in the first age group (age group˂2years) which accounted for 72.99% of the total variation. The first PC alone accounted for 63.13% of the total variation while PC2 accounted for the remaining 9.86%. From this study, it was concluded that there is interdependence among body weight and morphometric traits and that morphometric traits can be used in predicting live weight of WAD goats; PCA and Cluster could be exploited in breeding and selection programmes to acquire highly coordinated animal bodies using fewer measurements.

Study of Body Conformation of Carpet Wool Type Chitarangi Sheep of India using Principal Component Analysis

2021 ◽  
Author(s):  
A.K. Mishra ◽  
Anand Jain ◽  
S. Singh ◽  
R.K. Pundir
Keyword(s):  
Principal Component Analysis ◽  
Body Weight ◽  
Total Variation ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Sheep Population ◽  
Body Conformation ◽  
Face Length ◽  
Minimum Number

Background: The principal component analysis is applied to identify minimum number of combined variables that account for maximum portion of the variance existing in all variables studied. Chitarangi is a lesser known carpet type wool sheep distributed in Fazilka and Muktsar districts of Punjab, Sri Ganganagar district of Rajasthan and the adjoining areas. The information on body biometry is a prerequisite to characterize the lesser known sheep population available in the country. Hence, it is important to describe the body conformation by recording minimum number of biometric traits. Methods: Body biometry traits of Chitarangi sheep, a lesser known carpet quality wool producing sheep population were studied using Principal Component Analysis. The traits studied were body length (BL), height at wither (HW), chest girth (CG), paunch girth (PG), ear length (EL), face length (FL), face width (FW), tail length (TL) and adult body weight (BW). The data were collected on 297 ewes in the breeding tract of Chitarangi sheep. The descriptive statistics were determined for all the traits. The phenotypic correlations between different body biometric traits were estimated using partial correlations. Principal components were estimated using correlation matrix. Principal component analysis (PCA), a multivariate approach, is used when the recorded traits are highly correlated. Rotation of principal components was through the transformation of the components to approximate a simple structure. Factor analysis using oblique (promax) rotation was used. All the analysis was carried out using the SPSS statistical package. Result: The averages for body weight and biometry traits confirmed large size of Chitarangi animals. Most of the phenotypic correlations amongst the studied traits were positive and significant (p less than 0.01). The three components extracted from nine principal components accounted for 69.06% of the total variance. The first component, which described body size of ewes, accounted for 43.68% of the total variation with high loading for BW, CG, PG, HW, BL and FL. The components two and three explained 13.54 and 11.83% of total variance, respectively. The communalities ranged from 0.490 (FL) to 0.888 (PG). The lower communalities for face length indicated lower contribution of the trait to explain the total variation than others. The study indicates that principal components provided a means of reduction in number of biometric traits to explain body confirmation of adult female Chitarangi sheep.

Shape of Western Australian dragon lizards (Agamidae)

2005 ◽  
Vol 26 (1) ◽  
pp. 73-85 ◽  
Author(s):  
Philip Withers ◽  
Graham Thompson
Keyword(s):  
Principal Component Analysis ◽  
Body Length ◽  
Size Effects ◽  
Principal Component ◽  
Component Analysis ◽  
Regression Residuals ◽  
Performance Traits ◽  
And Performance ◽  
Western Australian

AbstractFor 41 species of Western Australian agamid lizards, we found that most appendage lengths vary isometrically, so shape is largely independent of size. Of the three methods we used to quantitatively remove the effects of size on shape, the two that use principal component analysis (PCA; Jolicoeur, 1963; Somers, 1986; 1989) provided similar results, whereas regression residuals (against body length) provided a different interpretation. Somers' size-free PCA approach to remove the size-effects was the most useful because it provided 'size-free' scores for each species that were further analysed using other techniques, and its results seemed more biologically meaningful. Some, but not all, of the variation in size-free shape for these lizards could be related to phylogeny, retreat choice and performance traits.

Relationships between sites used in the interstate wheat variety trials

1981 ◽  
Vol 32 (5) ◽  
pp. 691 ◽  
Author(s):  
PN Fox ◽  
AJ Rathjen
Keyword(s):  
Principal Component Analysis ◽  
Wheat Variety ◽  
Principal Component ◽  
Component Analysis ◽  
Climatic Variables ◽  
Statistical Techniques ◽  
Strong Similarity ◽  
Variety Trials ◽  
Variety Trial ◽  
The Mean

A combination of statistical techniques was used to present useful information for breeders concerning the 197.5 Interstate Wheat Variety Trial. Grouping of sites was similar for all techniques, but was shown most clearly by the principal component analysis. Within three of the four groups of sites there was strong similarity between members. Some groups included widely geographically separated sites, which suggests that in the final stages of varietal testing, it might be possible to use widely separated sites as an alternative to testing over several years within a region. One group dominated the overall mean yields of the trial because it included more sites and because these sites were more uniform than sites within other groups. This domination, illustrated by regression and ranking techniques, may reduce the value to industry of the Interstate Wheat Variety Trials if these sites are not representative of extensive areas of wheat production. The differences in relative performances of varieties between sites could not be related either to differences in the mean yields at these sites or to edaphic or climatic variables. The need for such analysis of each year's data from the Interstate Wheat Variety Trials is stressed.

scholarly journals Bayesian principal component analysis of Nigerian indigenous normal feather chickens' body linear measurements

2020 ◽  
Vol 44 (1) ◽  
pp. 10-20
Author(s):  
A. E. Sonubi ◽  
A. S. Adenaike ◽  
A. A. Dauda ◽  
T. P. Alao ◽  
B. O. Shonubi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Body Weight ◽  
Principal Components ◽  
Body Shape ◽  
Principal Component ◽  
Component Analysis ◽  
Body Measurements ◽  
Indigenous Chicken ◽  
Body Dimension ◽  
Linear Measurements

The indigenous chicken is a store house of unique genes that could be used in other parts of the world for improving other breeds. This study was carried out using bayesian principal component analysis and aimed objectively at determining the effect of sex on Nigerian indigenous normal feather chickens' body dimension, describing their body shape, and predicting their body weights from body measurements using orthogonal conformation traits derived from the principal components score. The parameters measured at 16 weeks of age were body weight, body length, breast girth, thigh length, shank length, shank diameter, keel length, wing length, wing span, and tail length on 233 randomly selected adult chickens. Sexual dimorphism was observed in all the traits with higher values recorded for males. Bayesian correlations among body weight and biometric traits were positive (r = 0.09 to 0.651 and 0.017 to 0.579 in male and female chickens respectively). The descriptive statistics showed that the mean body weight was 1.8085 ± 0.263 kg for males and 1.403 ± 0.226 kg for females. The first two principal components (PCs) were extracted for the males, both PCs components account for 72.21%. For the females, three PCs were extracted and they account for 77%. The first PC in each case accounted for the greatest percentage of the total variation. The use of orthogonal body shape characteristics derived from components' scores was more appropriate than the use of original traits in body weight prediction as multi-collinearity problems were eliminated. This led to simultaneous analysis of these body measurements rather than on individual basis. These components could be used as selection criteria for improving body weight of indigenous Nigerian chickens.

scholarly journals On the fuzziness of circulation types derived from the application of obliquely rotated principal component analysis to a T-mode climatic field

2021 ◽  
Author(s):  
Chibuike Chiedozie Ibebuchi
Keyword(s):  
Principal Component Analysis ◽  
Frequency Distribution ◽  
Principal Component ◽  
Component Analysis ◽  
Threshold Values ◽  
Study Region ◽  
Probability Of Occurrence ◽  
Circulation Types ◽  
Mean Shape ◽  
The Mean

Abstract This study examined the separability of circulation types (CTs) classified from the application of principal component analysis (PCA) to the T-mode matrix (variable is time series and observation is grid points) of a climatic field that explains atmospheric circulation; in addition to the uncertainty introduced on (i) the probability of occurrence, (ii) the mean shape of the CTs, (iii) the trend in the annual frequency of occurrence, (iv) the frequency distribution of the CTs, by using varying threshold values within the range of 0.2–0.35 to assign days to a given CT. The study region is Africa, south of the equator. Some large clusters were classified with most days in the analysis period assigned to them; these classes are interpreted as the dominant states of the atmosphere and generally, their existence results in the poor separability of the CTs since their features overlap with other CTs. Qualitatively, the choice of the threshold values within the defined range has little or no influence on the overall structure of the probability of occurrence of the CTs, the mean shape of the CTs, and the year-to-year variations in the annual occurrence of the CTs. However, it significantly impacts the frequency distribution of the CTs and the statistical significance of the trend in the annual occurrence of the CTs. Stringent threshold values within the defined range might benefit studies that aim to isolate days when specific CTs are most expressed and analyze their mechanism using composite maps, without focus on the frequency distribution and annual occurrence of the CTs. Overall, for the study region, lower threshold values within the defined range might be recommended since relatively, they do not tend to further constrain the probability of group membership, and equally seem to reveal the mechanisms that might be consistent when a given CT occurred regardless of the strength of its signal at a given time.

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