Exploring Developmental and Behavioral Heterogeneity among Preschoolers with ASD: A Cluster Analysis on Principal Components

2020 ◽  
Vol 13 (5) ◽  
pp. 796-809 ◽  
Author(s):  
Shuting Zheng ◽  
Kara A. Hume ◽  
Harriet Able ◽  
Somer L. Bishop ◽  
Brian A. Boyd
Keyword(s):  
Cluster Analysis ◽  
Principal Components ◽  
Behavioral Heterogeneity

Chemistry research in Europe: A publication analysis (2006–2016)

2019 ◽  
pp. 016555151986549
Author(s):  
Hakan Kaygusuz
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Polymer Science ◽  
Chemistry Research ◽  
Research Outcome ◽  
Components Analysis ◽  
Number Of Publications

In this article, chemistry research in 51 different European countries between years 2006 and 2016 was studied using statistical methods. This study consists of two parts: In the first part, different economical, institutional and citation parameters were correlated with the number of publications, citations and chemical industry numbers using principal components analysis and hierarchical cluster analysis. The results of the first part indicated that economical and geographical parameters directly affect the chemistry research outcome. In the second part, research in branches of chemistry and related disciplines such as analytical chemistry, polymer science and physical chemistry were analysed using principal components analysis and hierarchical cluster analysis for each country. Publication data were collected as the number of chemistry publications (in Science Citation Index–Expanded (SCI-E)) between years 2006 and 2016 in different chemistry subdisciplines and related scientific areas. Results of the second part of the study produced geographical and economical clusters of countries, interestingly, without addition of any geographical data.

Potential of random amplified microsatellites (RAMS) to typify and discriminate varieties of Physalis ixocarpa Brot. ex Hornem.

2018 ◽  
pp. 396
Author(s):  
Eli Amanda Delgado-Alvarado, Norma Almaraz-Abarca ◽  
Cirenio Escamirosa- Tinoco ◽  
Jose Natividad Uribe-Soto, Jose Antonio Avila-Reyes ◽  
Rene Torres-Ricario, Ana Isabel Chaidez-Ayala
Keyword(s):  
Cluster Analysis ◽  
Genetic Variability ◽  
South America ◽  
Principal Components ◽  
Genetic Relationships ◽  
Important Species ◽  
Physalis Peruviana ◽  
Edible Species ◽  
Components Analysis ◽  
Genetic Closeness

Physalis ixocarpa is an edible species of Solanaceae. This is one of the few cultivated and economically important species of the genus in Mesoamerica. In Mexico, several varieties and landraces have been developed, which have not been molecularly characterized. In the current study, five RAMS primers were used to characterize and assess the genetic variability of two varieties and three landraces of this species. The capacity of these markers to discriminate between them was also evaluated. With comparative aims, Physalis peruviana, the most economically important species of the genus in South America, was analyzed in the same manner. The results revealed that the varieties and landraces of P. ixocarpa conserve important levels of genetic variability (21.75% > Polymorphism < 42.75%), which were higher than that found for P. peruviana (10.75% Polymorphism). RAMS were useful specific markers, as P. peruviana and P. ixocarpa were clearly distinguished one from each other by both cluster analysis and principal components analysis. Close genetic relationships were found between the landraces San Isidro Chihuiro and Verde Puebla, and between the varieties Diamante and Rendidora. In spite of the genetic closeness, the RAMS amplification profiles had a clear varietal-specific tendency, in such a way that they may represent varietal fingerprints, which can be used as authentication tool for varieties and landraces of P. ixocarpa.

scholarly journals Determination of genetic divergence in pointed gourd by principal component and non-hierarchical euclidean cluster analysis

2017 ◽  
Vol 9 (4) ◽  
pp. 2421-2426
Author(s):  
Priyanka Verma ◽  
S. K. Maurya ◽  
Hridesh Yadav ◽  
Ankit Panchbhaiya
Keyword(s):  
Cluster Analysis ◽  
Principal Components ◽  
Genetic Divergence ◽  
Principal Component ◽  
Female Flower ◽  
Research Centre ◽  
Cluster Number ◽  
Wide Range ◽  
Total Divergence ◽  
Pointed Gourd

The present investigation was carried out at Vegetable Research Centre, Pantnagar to estimate the ge-netic divergence using Mahalanobis D2 statistics for twelve characters on 35 genotypes of pointed gourd. Cluster analysis and principal component analysis (PCA) were used to identify the most discerning trait responsible for greater variability in the lines and on the basis of mean performance, genotypes were classified into different groups. Five principal components (PC) have been extracted using the mean performance of the genotypes and 83.23 per cent variation is yielded by the first five principal components, among them high mean positive value or higher weight age was obtained was obtained for days to first female flower anthesis and days to first fruit harvest among all the vectors, indicates that these traits are important component of genetic divergence in pointed gourd. Non- hierarchical Euclidean cluster analysis grouped the genotypes into seven clusters and the highest number of genotypes were found in cluster number IV i.e. eleven whereas maximum inter-cluster distance was found between the cluster III and VI i.e. 74.250, it indicates that a wide range of genetic divergence is present between the genotypes present among these two clusters. And as per contribution toward total divergence, traits like fruit yield per hectare and number of fruit per plant contributed 92.64% toward total divergence. The high diversity found in the genotypes showed its great potential for improving qualitative as well as quantitative traits in pointed gourd.

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