Microgeographic genetic variation of Sitka spruce in southeastern Alaska

1989 ◽  
Vol 19 (8) ◽  
pp. 1004-1013 ◽  
Author(s):  
Robert K. Campbell ◽  
William A. Pawuk ◽  
Arland S. Harris
Keyword(s):  
Genetic Variation ◽  
Standard Deviation ◽  
Principal Components ◽  
Principal Component ◽  
Extreme Case ◽  
Common Garden ◽  
Sitka Spruce ◽  
Slope Aspect ◽  
Factor Scores ◽  
Additive Genetic Variation

Microgeographic genetic variation among populations of Sitka spruce on Mitkof Island in southeastern Alaska is described. In two common-garden environments, we evaluated genotypes of 208 parent trees from 114 locations in a 17 000-ha area. Two principal components accounted for most of the variation among locations in 11 traits measured to evaluate growth vigor and rhythm of 2-year-old seedlings. Regression analyses of factor scores derived from principal components revealed genetic gradients associated with elevation, slope, aspect, and west–east and north–south direction. Large amounts of additive genetic variation in factor scores occurred among trees within locations. When this variation within locations was used as a scale, variation among locations was also large. In an extreme case, locations differed in factor scores of the first principal component by about 3.0 units of the standard deviation of additive genetic variation in factor scores. Of the total differentiation in this case, elevational range (600 m) contributed 0.7 units of standard deviation, aspect contributed 0.9 units, and distance (16 km) from north central to southeastern parts of the island contributed 1.4 units.

A Real-Time Combustion Control With Reconstructed In-Cylinder Pressure by Principal Component Analysis for a CRDI Diesel Engine

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
Jaesung Chung ◽  
Junhyeong Oh ◽  
Myoungho Sunwoo
Keyword(s):  
Principal Component Analysis ◽  
Standard Deviation ◽  
Real Time ◽  
Principal Components ◽  
Control Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Cylinder Pressure ◽  
Combustion Control ◽  
Pca Method

This paper proposes a real-time combustion control algorithm using reconstructed in-cylinder pressure traces by principal component analysis (PCA). The PCA method reconstructs the in-cylinder pressure traces using the principal components of the in-cylinder pressure traces. It was shown that using only five principal components, we were able to reconstruct the in-cylinder pressure traces within 1% root mean squared percent error. Furthermore, the reconstructed in-cylinder pressure traces were validated to effectively reduce the cycle-to-cycle variations caused by the noise signals. As a result, the standard deviation of MFB50 which was calculated from the reconstructed in-cylinder pressure was reduced by 45%. Furthermore, this combustion parameter was applied to a real-time combustion control. Since variations of the control variables for the real-time combustion control were reduced, the control performances were enhanced.

scholarly journals Scalable probabilistic PCA for large-scale genetic variation data

2019 ◽  
Author(s):  
Aman Agrawal ◽  
Alec M. Chiu ◽  
Minh Le ◽  
Eran Halperin ◽  
Sriram Sankararaman
Keyword(s):  
Principal Component Analysis ◽  
Population Structure ◽  
Genetic Variation ◽  
Principal Components ◽  
Large Scale ◽  
Principal Component ◽  
Uk Biobank ◽  
Genome Wide ◽  
Variation Data ◽  
The Uk

AbstractPrincipal component analysis (PCA) is a key tool for understanding population structure and controlling for population stratification in genome-wide association studies (GWAS). With the advent of large-scale datasets of genetic variation, there is a need for methods that can compute principal components (PCs) with scalable computational and memory requirements. We present ProPCA, a highly scalable method based on a probabilistic generative model, which computes the top PCs on genetic variation data efficiently. We applied ProPCA to compute the top five PCs on genotype data from the UK Biobank, consisting of 488,363 individuals and 146,671 SNPs, in less than thirty minutes. Leveraging the population structure inferred by ProPCA within the White British individuals in the UK Biobank, we scanned for SNPs that are not well-explained by the PCs to identify several novel genome-wide signals of recent putative selection including missense mutations in RPGRIP1L and TLR4.Author SummaryPrincipal component analysis is a commonly used technique for understanding population structure and genetic variation. With the advent of large-scale datasets that contain the genetic information of hundreds of thousands of individuals, there is a need for methods that can compute principal components (PCs) with scalable computational and memory requirements. In this study, we present ProPCA, a highly scalable statistical method to compute genetic PCs efficiently. We systematically evaluate the accuracy and robustness of our method on large-scale simulated data and apply it to the UK Biobank. Leveraging the population structure inferred by ProPCA within the White British individuals in the UK Biobank, we identify several novel signals of putative recent selection.

Landscape patterns of phenotypic variation and population structuring in a selfing grass, Elymus glaucus (blue wildrye)

2004 ◽  
Vol 82 (12) ◽  
pp. 1776-1789 ◽  
Author(s):  
Vicky J Erickson ◽  
Nancy L Mandel ◽  
Frank C Sorensen
Keyword(s):  
Genetic Variation ◽  
Principal Component ◽  
Common Garden ◽  
Primary Objective ◽  
Landscape Patterns ◽  
Clinal Variation ◽  
Phenotypic Variance ◽  
Seed Transfer ◽  
Wide Range ◽  
Elymus Glaucus

Source-related phenotypic variance was investigated in a common garden study of populations of Elymus glaucus Buckley (blue wildrye) from the Blue Mountain Ecological Province of northeastern Oregon and adjoining Washington. The primary objective of this study was to assess geographic patterns of potentially adaptive differentiation in this self-fertile allotetraploid grass, and use this information to develop a framework for guiding seed movement and preserving adaptive patterns of genetic variation in ongoing restoration work. Progeny of 188 families were grown for 3 years under two moisture treatments and measured for a wide range of traits involving growth, morphology, fecundity, and phenology. Variation among seed sources was analyzed in relation to physiographic and climatic trends, and to various spatial stratifications such as ecoregions, watersheds, edaphic classifications, etc. Principal component (PC) analysis extracted four primary PCs that together accounted for 67% of the variance in measured traits. Regression and cluster analyses revealed predominantly ecotypic or stepped-clinal distribution of genetic variation. Three distinct geographic groups of locations accounted for over 84% of the variation in PC-1 and PC-2 scores; group differences were best described by longitude and ecoregion. Clinal variation in PC-3 and PC-4 scores was present in the largest geographic group. Four geographic subdivisions were proposed for delimiting E. glaucus seed transfer in the Blue Mountains.Key words: Elymus glaucus, morphological variation, local adaptation, seed transfer, seed zones, polyploid.

Comparative ecology of Quebec boreal forests: a numerical approach to modelling

1972 ◽  
Vol 50 (11) ◽  
pp. 2357-2379 ◽  
Author(s):  
Pierre Bellefleur ◽  
Allan N. Auclair
Keyword(s):  
Principal Components ◽  
Tree Species ◽  
Boreal Forests ◽  
Meteorological Parameters ◽  
Principal Component ◽  
Numerical Approach ◽  
Slope Aspect ◽  
Comparative Ecology ◽  
Environmental Models ◽  
Geographical Distances

Climatic and species–environmental models for seven regions of Quebec boreal and boreal-transition forest were developed using principal components factor analysis. Climatic models based on growth-relevant meteorological parameters indicated that despite large geographical distances, all regions were very similar. The first principal component in the climatic model accounted for 73% of the total variation in the original climatic variables. Two regions were relatively dissimilar, coinciding with differences in predominant air mass during the growth season.Analytical models of tree species and tree species with environmental variables were based on 1668 forest plots. Compositional and environmental models were similar as were major ecological species–environment relationships among the seven regions. Air and moisture drainage related to catena, natural stand disturbances related to specific edaphic parameters, and microclimate related to slope aspect accounted for 29.7%, 21.5%, and 17.4% of the variance on first, second, and third principal components, respectively. Validity of the models and problems for further investigation are discussed.

Structuring Assessments of Psychopathology

2006 ◽  
Vol 27 (2) ◽  
pp. 87-92 ◽  
Author(s):  
Willem K.B. Hofstee ◽  
Dick P.H. Barelds ◽  
Jos M.F. Ten Berge
Keyword(s):  
Principal Components ◽  
Personality Assessment ◽  
Clinical Sample ◽  
Principal Component ◽  
Normal Sample ◽  
Normative Sample ◽  
Assessment Data ◽  
Obsessive Compulsive ◽  
Oblique Rotation ◽  
Two Samples

Hofstee and Ten Berge (2004a) have proposed a new look at personality assessment data, based on a bipolar proportional (-1, .. . 0, .. . +1) scale, a corresponding coefficient of raw-scores likeness L = ΢XY/N, and raw-scores principal component analysis. In a normal sample, the approach resulted in a structure dominated by a first principal component, according to which most people are faintly to mildly socially desirable. We hypothesized that a more differentiated structure would arise in a clinical sample. We analyzed the scores of 775 psychiatric clients on the 132 items of the Dutch Personality Questionnaire (NPV). In comparison to a normative sample (N = 3140), the eigenvalue for the first principal component appeared to be 1.7 times as small, indicating that such clients have less personality (social desirability) in common. Still, the match between the structures in the two samples was excellent after oblique rotation of the loadings. We applied the abridged m-dimensional circumplex design, by which persons are typed by their two highest scores on the principal components, to the scores on the first four principal components. We identified five types: Indignant (1-), Resilient (1-2+), Nervous (1-2-), Obsessive-Compulsive (1-3-), and Introverted (1-4-), covering 40% of the psychiatric sample. Some 26% of the individuals had negligible scores on all type vectors. We discuss the potential and the limitations of our approach in a clinical context.

Comparison of Principal Component Solutions in Two Populations

Methodology ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Gregor Sočan
Keyword(s):  
Simulation Study ◽  
Principal Components ◽  
Common Factor ◽  
Principal Component ◽  
Component Model ◽  
Bootstrap Procedure ◽  
Factor Loadings ◽  
Component Loadings ◽  
Principal Component Model ◽  
Two Populations

Abstract. When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

Genetic Analysis of Chinese Cucumber Collections in the U.S. National Germplasm Collection

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 534e-534 ◽  
Author(s):  
J. Staub ◽  
Felix Sequen ◽  
Tom Horejsi ◽  
Jin Feng Chen
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Genetic Variation ◽  
Genetic Analysis ◽  
Allelic Variation ◽  
Germplasm Collection ◽  
Principal Component ◽  
Isozyme Loci ◽  
National Plant Germplasm System ◽  
The U.S

Genetic variation in cucumber accessions from China was assessed by examining variation at 21 polymorphic isozyme loci. Principal component analysis of allelic variation allowed for the depiction of two distinct groupings of Chinese accessions collected in 1994 and 1996 (67 accessions). Six isozyme loci (Gpi, Gr, Mdh-2, Mpi-2, Pep-gl, and Pep-la) were important in elucidating these major groups. These groupings were different from a single grouping of Chinese 146 accessions acquired before 1994. Allelic variation in Chinese accessions allowed for comparisons with other accessions in the U.S. National Plant Germplasm System (U.S. NPGS) collection grouped by continent and sub-continent. When Chinese accessions taken collectively were compared with an array of 853 C. sativus U.S. NPGS accessions examined previously, relationships differed between accessions grouped by country or subcontinent. Data indicate that acquisition of additional Chinese and Indian cucumber accessions would be strategically important for increasing genetic diversity in the U.S. NPGS cucumber collection.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Evaluation functions for integral mapping

2017 ◽  
Vol 921 (3) ◽  
pp. 24-29 ◽  
Author(s):  
S.I. Lesnykh ◽  
A.K. Cherkashin
Keyword(s):  
Principal Component Analysis ◽  
Environmental Factors ◽  
Principal Components ◽  
Principal Component ◽  
Evaluation Function ◽  
Final Value ◽  
Evaluation Functions ◽  
Geographical Environment ◽  
Environmental Background ◽  
Integral Mapping

The proposed procedure of integral mapping is based on calculation of evaluation functions on the integral indicators (II) taking into account the feature of the local geographical environment, when geosystems in the same states in the different environs have various estimates. Calculation of II is realized with application of a Principal Component Analysis for processing of the forest database, allowing to consider in II the weight of each indicator (attribute). The final value of II is equal to a difference of the first (condition of geosystem) and the second (condition of environmental background) principal components. The evaluation functions are calculated on this value for various problems of integral mapping. The environmental factors of variability is excluded from final value of II, therefore there is an opportunity to find the invariant evaluation function and to determine coefficients of this function. Concepts and functions of the theory of reliability for making the evaluation maps of the hazard of functioning and stability of geosystems are used.

scholarly journals COVID-19 Pandemic and Adaptive Shopping Patterns: An Insight from Indonesian Consumers

2021 ◽  
pp. 097215092110135
Author(s):  
Arif Hartono ◽  
Asma'i Ishak ◽  
Agus Abdurrahman ◽  
Budi Astuti ◽  
Endy Gunanto Marsasi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Demographic Characteristics ◽  
Depth Information ◽  
Factor Scores ◽  
Consumer Spending ◽  
Consumer Responses ◽  
Buying Behaviour ◽  
Manova Analysis

Although existing studies on consumers typology are extensively conducted, insights on consumers typology in adapting their shopping attitude and behaviour during the COVID-19 pandemic remain unexplored. Current studies on consumer responses to the COVID-19 pandemic tend to focus on the following themes: panic buying behaviour, consumer spending and consumer consumption. This study explores a typology of adaptive shopping patterns in response to the COVID-19 pandemic. The study involved a survey of 465 Indonesian consumers. Principal component analysis is used to identify the variables related to adaptive shopping patterns. Cluster analysis of the factor scores obtained on the adaptive shopping attitude and behaviour revealed the typology of Indonesian shoppers’ adaptive patterns. Multivariate Analysis of Variance (MANOVA) analysis is used to profile the identified clusters based on attitude, behaviour and demographic characteristics. Results revealed five adaptive shopping patterns with substantial differences among them. This study provides in-depth information about the profile of Indonesian shoppers’ adaptive patterns that would help retailers in understanding consumers and choosing their target group. The major contribution of this study is providing segmentation on shopping adaptive patterns in the context of the COVID-19 pandemic which presents interesting differences compared with previous studies. This study reveals new insights on shoppers’ adaptive attitude and behaviour as consumers coped with the pandemic.

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