scholarly journals Intragroup variation in the Pre-Columbian Cuba population: A perspective from cranial morphology

2021 ◽  
Vol 84 (3) ◽  
pp. 233-255
Author(s):  
Taisiya Syutkina ◽  
Mario Juan Gordillo Pérez ◽  
Silvia Teresita Hernández Godoy ◽  
Carlos Arredondo Antúnez ◽  
Armando Rangel Rivero
Keyword(s):  
Principal Component Analysis ◽  
Biological Diversity ◽  
Principal Component ◽  
Morphological Diversity ◽  
Component Analysis ◽  
Mantel Test ◽  
Cranial Morphology ◽  
Shape Variation ◽  
The Moment ◽  
Metric Traits

Abstract The paper aims to study intragroup variation inside the two pre-Columbian Cuban populations: the aceramic Archaic and the ceramic Taino groups, based on their cranial morphology. The latter applied artificial cranial deformation to all its members, so the groups are referred to as “non-deformed” and “deformed” samples here. Studies across different disciplines suggest evidence of cultural and biological diversity inside the non-deformed group, while local variations of applying the deforming device can be responsible for shape variation across the deformed group. Cranial metrics and non-metric cranial traits of the 92 crania of Cuban origin were analyzed, although the sample size varied between the analyses due to the incompleteness of the crania. Geometric morphometrics was applied to the deformed crania to study the shape variation across the sample. Three deformed crania from the Dominican Republic were analyzed together with the deformed Cuban sample to test the variability of the practice between the islands. Principal component analysis and the Mantel test did not reveal any geographic differences in the cranial metric traits. No morphological differences associated with the antiquity of materials could be seen either based on the available data. The principal component analysis of the Procrustes coordinates of the cranial vault outline in the lateral norm revealed continuous variability of cranial shapes from the ones with more flattened frontal and occipital bones to the more curved outlines, which is probably explained by individual variation. Non-metric traits variation revealed bilateral asymmetry in the expression of the occipito-mastoidal ossicles among the deformed crania. In conclusion, the study did not support assumptions about morphological diversity inside the studied samples or proved the impossibility of available craniological data to reflect possible intragroup differentiation at the moment.

Evaluation of the morphological diversity of tea roses (Rosa x odorata) based on phenotypic traits

2020 ◽  
Vol 18 (3) ◽  
pp. 149-158
Author(s):  
Bixuan Cheng ◽  
Chao Yu ◽  
Heling Fu ◽  
Lijun Zhou ◽  
Le Luo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Morphological Traits ◽  
Significant Positive Correlation ◽  
Flower Colour ◽  
Principal Component ◽  
Morphological Diversity ◽  
Component Analysis ◽  
Germplasm Resources ◽  
Trait Correlation

AbstractRosa x odorata (sect. Chinenses, Rosaceae) is an important species distributed only in Yunnan Province, China. There is an abundance of wild variation within the species. Using 22 germplasm resources collected from the wild, as well as R. chinensis var. spontanea, R. chinensis ‘Old Blush’ and R. lucidissima, this study involved morphological variation analysis, inter-trait correlation analysis, principal component analysis and clustering analysis based on 16 morphological traits. This study identified a high degree of morphological diversity in R. x odorata germplasm resources and the variation coefficients had a distribution range from 18.00 to 184.04%. The flower colour had the highest degree of variation, while leaflet length/width had the lowest degree of variation. Inter-trait correlation analysis revealed that there was an extremely significant positive correlation between leaflet length and leaflet width. There was also a significant positive correlation between the number of petals and duration of blooming, and the L* and a* values of flower colour were significantly negatively correlated. Principal component analysis screened five principal components with the highest cumulative contribution rate (81.679%) to population variance. Among the 16 morphological traits, style length, sepal width, flower diameter, flower colour, leaflet length and leaflet width were important indices that influenced the morphology of R. x odorata. This study offers guidance for the further development and utilization of R. x odorata germplasm resources.

scholarly journals Digital Morphometrics: A Tool for Leaf Morpho- Taxonomical Studies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-7
Author(s):  
Kavi K. Oza ◽  
Rinku J. Desai ◽  
Vinay M. Raole*
Keyword(s):  
Principal Component Analysis ◽  
Abiotic Factors ◽  
Leaf Shape ◽  
Principal Component ◽  
Component Analysis ◽  
Herbarium Specimens ◽  
Fourier Descriptors ◽  
Shape Variation ◽  
Quantitative Analysis Method ◽  
Elliptic Fourier Descriptors

Leaves are most important part of the plant and can be used for the identification of a taxon. An appropriate understanding of leaf development in terms of shape and responsible abiotic factors is necessary for improvement in plant. Leaf shape variation could be evaluated successfully, and the symmetrical and asymmetrical elements of the overall shape variation could be detected. The aim of the present study was to establish a quantitative analysis method of leaf shape by elliptic Fourier descriptors and principal component analysis (EF-PCA). EF-PCA describes an overall shape mathematically by transforming coordinate information concerning its contours into elliptic Fourier descriptors (EFDs) and summarizing the EFDs by principal component analysis. We can be able to extract six variables by using leaf specimen images from field and herbarium specimens. In the present study, total leaf area with respect to notch area is more variable within species. Within a species the major source of the symmetrical elements may be governed by genotypic features and the asymmetrical elements are strongly affected by the environment. We could discuss the value of morphometrics to detect subtle morphological variation which may be undetectable by human eye.

scholarly journals The Variation in 3D Face Shapes of Dutch Children for Mask Design

2021 ◽  
Vol 11 (15) ◽  
pp. 6843
Author(s):  
Lyè Goto ◽  
Wonsup Lee ◽  
Toon Huysmans ◽  
Johan F. M. Molenbroek ◽  
Richard H. M. Goossens
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Shape Variation ◽  
Anthropometric Data ◽  
Mask Design ◽  
The Face ◽  
3D Scans ◽  
Dutch Children ◽  
Close Fit

The use of 3D anthropometric data of children’s heads and faces has great potential in the development of protective gear and medical products that need to provide a close fit in order to function well. Given the lack of detailed data of this kind, the aim of this study is to map the size and shape variation of Dutch children’s heads and faces and investigate possible implications for the design of a ventilation mask. In this study, a dataset of heads and faces of 303 Dutch children aged six months to seven years consisting of traditional measurements and 3D scans were analysed. A principal component analysis (PCA) of facial measurements was performed to map the variation of the children’s face shapes. The first principal component describes the overall size, whilst the second principal component captures the more width related variation of the face. After establishing a homology between the 3D scanned face shapes, a second principal component analysis was done on the point coordinates, revealing the most prominent variations in 3D shape within the sample.

scholarly journals Morphological diversity in growth characteristics of Jatropha curcas l. accessions from South- West Nigeria

2021 ◽  
Vol 38 (1) ◽  
pp. 109-119
Author(s):  
G.A. Adebusuyi ◽  
O.F. Oyedeji ◽  
V.I. Alaje ◽  
I.L. Sowunmi ◽  
Y.A. Dunmade
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Total Variation ◽  
Jatropha Curcas ◽  
Principal Component ◽  
Morphological Diversity ◽  
Component Analysis ◽  
Southwestern Nigeria ◽  
Breeding Programmes ◽  
Cluster 2

Jatropha curcas is a multi-purpose tree with significant economic importance that has not been fully exploited due to lack of adequate breeding programme in Nigeria. Consequently upon this, 31 accessions collected from 4 states in Southwestern Nigeria were assessed for their morphological diversity in order to establish this as a bed rock for further breeding programmes. Data were collected on plant height, numbers of leaves and collar diameter; these were subjected to analysis of variance, principal component analysis and cluster analysis using Minitab version 17. The results showed significant differences (p≤0.05) among the 31 accessions assessed. Principal component analysis indicated that the first three axes contributed 97.8% of the total variation observed. The first axis accounted for 68% of the total variation while the second and third axes accounted for 24.7% and 5.1%, respectively, of the total variation recorded. Cluster analysis as well as the dendrogram revealed three distinct clusters of genetic similarities and differences. High genetic similarities were observed among accessions collected from the different states whereas some accessions collected from similar regions had low genetic similarities. Cluster 1 consisted of 21 genotypes with their characters falli ng below the grand mean. Cluster 2 had nine genotypes, they produced the highest values for all the characters assessed. Cluster 3 with only one genotype has its values below the ground mean. Members of cluster 2 have proven to be superior. The existence of morphological diversity offers potential for selection among the accessions in the breeding of J. curcas from southwestern Nigeria.

scholarly journals The Classification of Primitive-Shaped Patterns by Using Principal Component Analysis Method

2019 ◽  
Vol 20 (2) ◽  
pp. 12
Author(s):  
IGA Widagda ◽  
Hery Suyanto
Keyword(s):  
Principal Component Analysis ◽  
Predictive Accuracy ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Moment Invariant ◽  
Pca Method ◽  
The Moment

Abstrak – The recognition or classification of patterns is a major problem in computer vision. Many methods have been applied such as: moment invariant, Artificial Neural Networks (ANN), K-mean, Support Vector Machine (SVM) and others. These methods have a few limitations. The moment invariant fashion is highly vulnerable to noise. ANN methods require a long computing time (especially multi-layer ANN) during the training process. On the other hand, the dimensions of the features generated from the methods are relatively high, which requires large storage space (memory). In addition, this leads to the long computing time when the testing process is carried out. Based on these facts, this research makes use of methods that being able to reduce the feature dimensions, namely the Principal Component Analysis (PCA). In the PCA method the dimensions of the sample image are converted to principal components (face space), whose dimensions are much smaller than the dimensions of the sample image itself. Our works exhibit that the PCA method is highly effective in carrying out the pattern classification process. This can be indicated by the relatively high values of Predictive Accuracy, Precision and Recall (close to 1) while the FP Rate is low (close to 0). Moreover, the location of the point coordinates (FP Rate, TP Rate) in ROC graphs is fallen in the upper left region (approaching the perfect classifier region).

scholarly journals Molecular Distributions of the Disk/Envelope System of L483: Principal Component Analysis for the Image Cube Data

2021 ◽  
Vol 923 (2) ◽  
pp. 168
Author(s):  
Yuki Okoda ◽  
Yoko Oya ◽  
Shotaro Abe ◽  
Ayano Komaki ◽  
Yoshimasa Watanabe ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Molecular Line ◽  
Large Correlation ◽  
Complex Organic Molecule ◽  
Molecular Lines ◽  
Characteristic Features ◽  
The Moment ◽  
Complex Organic

Abstract Unbiased understanding of molecular distributions in a disk/envelope system of a low-mass protostellar source is crucial for investigating physical and chemical evolution processes. We have observed 23 molecular lines toward the Class 0 protostellar source L483 with ALMA and have performed principal component analysis (PCA) for their cube data (PCA-3D) to characterize their distributions and velocity structures in the vicinity of the protostar. The sum of the contributions of the first three components is 63.1%. Most oxygen-bearing complex organic molecule lines have a large correlation with the first principal component (PC1), representing the overall structure of the disk/envelope system around the protostar. Contrary, the C18O and SiO emissions show small and negative correlations with PC1. The NH2CHO lines stand out conspicuously at the second principal component (PC2), revealing more compact distribution. The HNCO lines and the high-excitation line of CH3OH have a similar trend for PC2 to NH2CHO. On the other hand, C18O is well correlated with the third principal component (PC3). Thus, PCA-3D enables us to elucidate the similarities and the differences of the distributions and the velocity structures among molecular lines simultaneously, so that the chemical differentiation between the oxygen-bearing complex organic molecules and the nitrogen-bearing ones is revealed in this source. We have also conducted PCA for the moment 0 maps (PCA-2D) and that for the spectral line profiles (PCA-1D). While they can extract part of characteristics of the molecular line data, PCA-3D is essential for comprehensive understandings. Characteristic features of the molecular line distributions are discussed on NH2CHO.

scholarly journals Processing of X-ray Microcalorimeter Data with Pulse Shape Variation using Principal Component Analysis

2016 ◽  
Vol 184 (1-2) ◽  
pp. 397-404 ◽  
Author(s):  
D. Yan ◽  
T. Cecil ◽  
L. Gades ◽  
C. Jacobsen ◽  
T. Madden ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Pulse Shape ◽  
Principal Component ◽  
Component Analysis ◽  
Shape Variation ◽  
X Ray
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