scholarly journals Phenotypic Variation in Freshwater Murrel,Channa punctatus(Bloch, 1793) from Northern and Eastern Regions of India Using Truss Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Ankur Kashyap ◽  
Madhu Awasthi ◽  
Mohammad Serajuddin
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Significant Variation ◽  
Function Analysis ◽  
Principal Component ◽  
Group Classification ◽  
Channa Punctatus ◽  
Morphometric Variation ◽  
Freshwater Murrel ◽  
Confidence Ellipses

Morphometric variation study was carried out using truss analysis in freshwater murrel,Channa punctatus(Bloch, 1793), to explore different phenotypic stocks ofC. punctatus.Significant variation in the morphometric analysis was observed using the truss network and multivariate analysis of the three subpopulations of freshwater murrel,C. punctatus, from the two different regions of India. Principal component analysis accounted for a total variation of 91.95% by the first three principal components. PC 1 represented the variation due to size and PC 2 and PC 3 represented variations due to shape. The 95% confidence ellipses depicted the clear isolation between the samples in scatter graph of PC 1 on PC 2. The combined groups plot (DF 1 on DF 2) based on discriminant function analysis (DFA) also validated the existence of three different subpopulations. In group classification using DFA, 100 percent of all the samples were correctly classified into their original subpopulation. These results support the existence of multiracial composition ofC. punctatusacross the different geographical isolated sites and, thus, these findings indicate the presence of three different phenotypic stocks ofC. punctatus.

scholarly journals A postura locomotora de Protolipterna ellipsodontoides Cifelli, 1983 (Mammalia: Litopterna: Protolipternidae) da Bacia de São José de Itaboraí, Rio de Janeiro (Paleoceno superior)

2007 ◽  
Vol 30 (1) ◽  
pp. 58-66
Author(s):  
Ana Carolina Fortes Bastos ◽  
Lílian Paglarelli Bergqvist
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Rio De Janeiro ◽  
Function Analysis ◽  
Principal Component ◽  
Postcranial Morphology ◽  
Foot Posture ◽  
Fissure Fill ◽  
Large Groups ◽  
Janeiro State

The Litopterna is a group of endemic South America ungulates that lived from Late Paleocene (Itaboraiense) to Late Pleistocene (Lujanense). The order is divided in two large groups based on dental features: the Bunolipterna, in which the Protolipternidae is placed, is composed by taxa with primitive bunodont teeth; and the Lopholipterna, grouping taxa with derived lophodont teeth. In both the postcranial morphology is derived and uniform since the early forms. The Itaborai Basin, located at São José district, Itaboraí city, Rio de Janeiro state, is filled with different kinds of limestones, cut vertically by fissure fill deposits, where most of the fossils were collected. Protolipterna ellipsodontoides was described in 1983 based on dental features, but later postcranial bones were associated to this species. The main goal of this article is to infer the foot posture of P. ellipsodontoides. The material studied consists of femora, astragali, calcanea and metatarsals III, comprising 165 bones. All fossils were deposited in the fossil mammal collection of Departamento Nacional da Produção Mineral, in Rio de Janeiro state, Brasil. The methodology employed consisted of 15 linear and curvilinear measurements, which were submitted to a multivariate analysis (Principal Component Analysis -PCA and Discriminant Function Analysis -DFA). The results suggested a digitigrade posture to P. ellipsodontoides. Other morphological features of the skeleton, associated with a digitigrades posture, are suggestive of a cursorial locomotion, but with probable saltatory habits.

scholarly journals The Development of Athlete Performance Capabilities Index (APCI) Model for Male U12 Player Selection using Multivariate Analysis

2018 ◽  
Vol 7 (4.34) ◽  
pp. 97
Author(s):  
Mohamad Razali Abdullah ◽  
Hafizan Juahir ◽  
N. Mohamad Shukri ◽  
N. A. Fuat ◽  
N. A. Mohd Ros ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Factor Score ◽  
Principal Component ◽  
Factor Loading ◽  
Group Classification ◽  
Performance Group ◽  
Two Factors ◽  
Low Performance ◽  
High Factor Loading ◽  
Athlete Performance

This study develops an Athlete Performance Capabilities Index (APCI) model using multivariate analysis for selecting the best player of under twelve (U12).  Measurement of anthropometrics and physical fitness were evaluated among 178 male players aged 12±0.52 years. Factor score derived by Principal Component Analysis were used to obtain a model for APCI and Discriminant Analysis (DA) were conducted to validate the correctness of group classification by APCI. Result was found two factors with eigenvalues greater than 1 were extracted which accounted for 62.00% of the variations present in the original variables. The two factors were used to obtain the factor score coefficients explained by 35.72% and 26.67% of the variations in athlete performance respectively. Factor 1 revealed high factor loading on fitness compared to Factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing APCI ≥ 75%, 25% ≤ APCI < 75% and APCI < 25% as high, moderate and low performance group respectively. Three discriminated variables out of thirteen variables were obtained using Forward and Backward stepwise mode of DA, which were weight, standing broad jump, and 40 meters’ speed. Such variables were established as essential indicator for selecting the best player among male U12.   

scholarly journals Use of Highly Discriminating Morphological Characters and Isozymes in the Study of Spanish Chestnut Cultivars

2006 ◽  
Vol 131 (6) ◽  
pp. 770-779 ◽  
Author(s):  
Santiago Pereira-Lorenzo ◽  
María Belén Díaz-Hernández ◽  
Ana María Ramos-Cabrer
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Castanea Sativa ◽  
Principal Component ◽  
Market Value ◽  
Morphological Characters ◽  
Minor Importance ◽  
Breeding Programs ◽  
And Cluster Analysis

Morphological characters (six traits) and isozymes (four systems, five loci) were used to discriminate between Spanish chestnut cultivars (Castanea sativa Mill.) from the Iberian Peninsula. A total of 701 accessions (representing 168 local cultivars) were analyzed from collections made between 1989 and 2003 in the main chestnut growing areas: 31 were from Andalucía (12 cultivars), 293 from Asturias (65 cultivars), 25 from Castilla-León (nine cultivars), four from Extremadura (two cultivars) and 348 from Galicia (80 cultivars). Data were synthesized using multivariate analysis, principal component analysis, and cluster analysis. A total of 152 Spanish cultivars were verified: 58 cultivars of major importance and 94 of minor importance, of which 18 had high intracultivar variation. Thirty-seven cultivars were clustered into 14 synonymous groups. Six of these were from Galicia, one from Castilla-León (El Bierzo), four from Asturias, one from Asturias and Castilla-León (El Bierzo), and two from Asturias, Castilla-León (El Bierzo), and Galicia. The chestnut cultivars from Galicia and Asturias were undifferentiated in genetic terms, indicating that they are not genetically isolated. Overall, chestnut cultivars from southern Spain showed the least variation. Many (58%) of Spanish cultivars produced more than 100 nuts/kg; removing this low market-value character will be a high priority. The data obtained will be of use in chestnut breeding programs in Spain and elsewhere.

scholarly journals Inferência Exploratória de Dados Espaço-Temporal da Precipitação Pluviométrica no Nordeste Brasileiro

2020 ◽  
Vol 13 (5) ◽  
pp. 2019
Author(s):  
Jhon Lennon Bezerra da Silva ◽  
Geber Barbosa De Albuquerque Moura ◽  
Marcos Vinícios Da Silva ◽  
Roni Valter De Souza Guedes ◽  
Pabrício Marcos Oliveira Lopes ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Temporal Variability ◽  
Principal Component ◽  
Component Analysis ◽  
Northeastern Brazil ◽  
Annual Rainfall ◽  
Intermittent Rivers ◽  
Rainfall Patterns ◽  
Brazilian Northeast

A gestão eficiente dos recursos hídricos no Nordeste brasileiro torna-se fundamental diante do regime hidrológico dos rios intermitentes, dos quais muitos são extremamente críticos. Todavia estes dependem de um regime pluviométrico irregular, tanto em escala de tempo mensal quanto anual. Objetivou-se determinar a variabilidade espaço-temporal da precipitação pluviométrica total anual, averiguando-se, também, as regiões com padrões de precipitação semelhantes por técnicas de análise multivariada (clusters e componentes principais) no Nordeste do Brasil. Foram analisados dados de precipitação pluviométrica total anual, entre os anos de 1995 e 2016, de 37 diferentes estações meteorológicas do INMET, estas situadas nos limites territoriais dos nove estados do Nordeste brasileiro. A análise de clusters verificou a formação de quatro grupos distintos, com padrões semelhantes de precipitação nas regiões dentro dos grupos, conforme também observado na análise de componentes principais. A padronização e/ou variabilidade espaço-temporal da precipitação pluviométrica dos municípios analisados mostrou-se está intimamente associada as condições das estações do ano e anomalias climatológicas, aos fatores de uso e ocupação do solo, condições de altitude e relevo, tais quais favorecem na formação e estabilidade de chuvas menores ou maiores no Nordeste brasileiro. A análise multivariada de cluster e componentes principal identificaram padrões e semelhanças pluviométricas de grupos, nos diferentes estados do Nordeste do Brasil entre os anos de 1995 e 2016. Exploratory Inference of Spatial-Temporal Data of Rainfall in the Brazilian Northeast ABSTRACTThe efficient management of water resources in the Northeast of Brazil is essential in view of the hydrological regime of intermittent rivers, of which many are extremely critical, as they depend on an irregular rainfall regime, both on a monthly and annual time scale. The objective of this study was to determine the spatial and temporal variability of the annual total rainfall, also investigating the regions with similar rainfall patterns by multivariate analysis techniques (clusters and principal components) in Brazilian Northeast. Data from total annual rainfall between the years 1995 and 2016, of 37 different INMET weather stations were analyzed, located within the territorial limit of the nine states of Brazilian Northeast. Cluster analysis verified the formation of four distinct groups, with similar precipitation patterns in the regions within the groups as also observed in the principal component analysis. The pattern and/or spatial-temporal variability of rainfall in the municipalities analyzed was shown to be intimately associated with the conditions of the year and climatic anomalies stations, and the factors of land use and occupation, altitude and relief conditions, such as favoring the formation and stability of minor or major rain in the Brazilian Northeast. Multivariate cluster and principal component analysis identified rainfall patterns and similarities of groups, in the different states of Northeastern Brazil between the years 1995 and 2016.Keywords: multivariate analysis, climate change, semiarid, regional climate patterns.

scholarly journals What Do Global Metrics Tell Us about the World?

2019 ◽  
Vol 8 (5) ◽  
pp. 136
Author(s):  
John Rennie Short ◽  
Justin Vélez-Hagan ◽  
Leah Dubots
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Principal Component ◽  
Component Analysis ◽  
Group Classification ◽  
Social Progress ◽  
The World ◽  
Global Indicators ◽  
Underlying Dimension ◽  
Degree Of Overlap

There are now a wide variety of global indicators that measure different economic, political and social attributes of countries in the world. This paper seeks to answer two questions. First, what is the degree of overlap between these different measures? Are they, in fact, measuring the same underlying dimension? To answer this question, we employ a principal component analysis (PCA) to 15 indices across 145 countries. The results demonstrate that there is one underlying dimension that combines economic development and social progress with state stability. Second, how do countries score on this dimension? The results of the PCA allow us to produce categorical divisions of the world. The threefold division identifies a world composed of what we describe and map as rich, poor and middle countries. A five-group classification provided a more nuanced categorization described as: The very rich, free and stable; affluent and free; upper middle; lower middle; poor and not free.

scholarly journals Morphological and molecular identification of hairtail (Trichiurus spp.) caught in Pangandaran Waters

2020 ◽  
Vol 147 ◽  
pp. 02021
Author(s):  
Tia Aprianti Lestari ◽  
Murwantoko Murwantoko ◽  
Eko Setyobudi
Keyword(s):  
Principal Component Analysis ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Molecular Identification ◽  
Principal Component ◽  
Component Analysis ◽  
Rrna Gene ◽  
Morphometric Variation ◽  
Dorsal Fin ◽  
Fish Samples

This study aimed to identify the species of hairtail caught in Pengandaran waters based on morphological, meristic character and molecular approach. In total 135 fish samples were collected from Pangandaran Waters, during March-April 2017. Each sample was identified, measured on 22 morphometric and 4 meristic characters, then analyzed using Principal Component Analysis (PCA). Molecular identification was conducted by sequenced of 16S rRNA gene. The result of the research showed that hairtail characterized by III spines and 125-140 soft rays of dorsal fin (D.III, 125-140), the anal fin situated below 38th to 41th of dorsal-fin soft ray, I spine and 10 soft rays of pectoral fin (P.I.10), and I spine and 91 to 112 spinules of anal fin (A.I.91-112). Based on the morphological identification, the hairtail was belonged to Trichiurus lepturus. Principal Component Analysis showing the morphometric variation was presented in the caudal peduncle length. Molecular analysis of mitochondrial DNA of the partial 16S rRNA gene confirmed the hairtail as T. lepturus with similarity 98-99% based on previously published data. Phylogenetic analysis showed that T. lepturus from Pangandaran were closely similar to related species caught from the Southern Coast of Yogyakarta Special Territory (Indian Ocean) and Hainan China (Pacific Ocean).

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