CYTOGENETIC STUDIES OF CHROMOSOME REPLICATION IN ZEA MAYS L.: I. Analysis of quantitative autoradiographic data using Principal Components Analysis

1973 ◽  
Vol 15 (3) ◽  
pp. 417-426 ◽  
Author(s):  
George R. Douglas ◽  
David B. Walden
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Euclidean Distance ◽  
Primary Root ◽  
Autoradiographic Study ◽  
Test Results ◽  
Additional Chromosome ◽  
The Mean ◽  
Components Analysis ◽  
Silver Grains

A quantitative, autoradiographic study of 3H-TdR uptake at 18 °C was undertaken in the nuclei of primary root meristems of a highly inbred stock of maize. Plots of the mean number of silver grains over nuclei vs. hours after removal from 3H-TdR (replication profiles) revealed fluctuation in DNA replication. Wilcoxon's signed-ranks test showed that some chromosome arms had replication profiles different from the profile of the nucleus. A Principal Components Analysis (PCA) was used to characterize empirically and further compare the replication of chromosome arms. The PCA results confirm the Wilcoxon's signed-ranks test results and also suggest unique replication behavior for some additional chromosome arms. To quantitatively compare the position of individuals in the scatter diagram of the first three factor axes from the PCA, the standardized Euclidean distance of each individual from the centroid was employed. Since some chromosome arms were found to be different from the others, at least limited autonomy of replication among chromosome arms is proposed.

Planktonic Biomass Trajectories In Lake Ecosystems

1983 ◽  
Vol 40 (10) ◽  
pp. 1752-1760 ◽  
Author(s):  
Michael A. Gates ◽  
Ann P. Zimmerman ◽  
W. Gary Sprules ◽  
Roy Knoechel
Keyword(s):  
Hypothesis Testing ◽  
Principal Components Analysis ◽  
Biomass Allocation ◽  
Principal Components ◽  
Primary Component ◽  
Size Category ◽  
Data Set ◽  
Lake Ecosystems ◽  
The Mean ◽  
Components Analysis

We introduce a method, based on principal components analysis, for studying temporal changes in biomass allocation among 16 size–category compartments of lake plankton. Applied to data from a series of 12 Ontario lakes over three sampling seasons, the technique provides a simple means of visualizing shifts in patterns of biomass allocation, and it allows comparative analyses of biomass fluctuations in different lakes. Each of the primary component axes is interpretable. Furthermore, a large proportion of the variance in both the mean position of a lake and its movement along these axes is interpreted as a function of lake physicochemistry. The analysis also provides weighted scores for use in hypothesis testing which are an improvement over mean biomass values alone, because they take into account the structure of variation in the data set.

Robust Statistics: A Survey and Some Prescriptions

1976 ◽  
Vol 1 (4) ◽  
pp. 285-312 ◽  
Author(s):  
Howard Wainer
Keyword(s):  
Factor Analysis ◽  
Multivariate Analysis ◽  
Standard Deviation ◽  
Principal Components Analysis ◽  
Analysis Of Variance ◽  
Principal Components ◽  
Robust Statistics ◽  
Robust Methods ◽  
The Mean ◽  
Components Analysis

It is noted that the usual estimators that are optimal under a Gaussian assumption are very vulnerable to the effects of outliers. A survey of robust alternatives to the mean, standard deviation, product moment correlation, t-test, and analysis of variance is offered. Robust methods of factor analysis, principal components analysis and multivariate analysis of variance are also surveyed, as are schemes for outlier detection.

The Leyton Obsessional Inventory: an analysis of the responses of 73 obsessional patients

1979 ◽  
Vol 9 (2) ◽  
pp. 305-311 ◽  
Author(s):  
R. M. Murray ◽  
J. E. Cooper ◽  
A. Smith
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Normal Subjects ◽  
Obsessive Compulsive ◽  
The Mean ◽  
Components Analysis

SynopsisThe Leyton Obsessional Inventory was administered to 73 obsessive-compulsive neurotics, and their responses compared with those of 100 normal subjects. The ratio of the mean patient to normal scores ranged from 2·4: 1 for obsessional traits and 3·2: 1 for symptoms to 6·2: 1 for resistance and 12·5: 1 for interference with other activities. A principal components analysis on the patients' replies produced 3 unitary components (household order, personal contamination, and doubting) plus 2 bipolar components (checking/parsimony and desire for closure/unpleasant ruminations). These appeared to be more definitive representations of components identified from a similar analysis on normal subjects, suggesting that obsessional neurotics differ from normal subjects quantitatively rather than qualitatively. A cluster analysis on the patients' responses produced 3 subgroups. Thirty-two patients were predominantly hesitant and indecisive (‘doubters’), 30 were concerned with bodily and clothing contamination (‘contaminators’), and 7 were preoccupied with checking (‘checkers’).

scholarly journals RANCANG BANGUN SISTEM PENGENALAN WAJAH DENGAN METODE PRINCIPAL COMPONENT ANALYSIS

2016 ◽  
Vol 1 (2) ◽  
pp. 59-75
Author(s):  
Salamun Salamun ◽  
Firman Wazir
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Physiological Measures ◽  
Test Results ◽  
Analysis Method ◽  
The Face ◽  
Components Analysis

The face is one of the easiest physiological measures and is often used to distinguish individual identities from one another. This facial recognition process uses raw information from pixel images generated through a camera which is then represented in the Principal Components Analysis method. The Principal Components Analysis method works by calculating the average flatvector pixel of images that have been stored in a database, from the average flatvector will get the value of each image eigenface and then the nearest eigenface value of the image will be found and then the nearest eigenface value of the image will be found the image of the face you want to recognize. The test results showed an overall success rate of face recognition of 82.27% with face data of 130 images.

Reliability and Validity of a Swedish Language Version of the Resilience Scale

2004 ◽  
Vol 12 (3) ◽  
pp. 169-178 ◽  
Author(s):  
Björn Nygren ◽  
Kerstin Björkman Randström ◽  
Anna K. Lejonklou ◽  
Beril Lundman
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Reliability And Validity ◽  
Internal Consistency Reliability ◽  
Self Esteem ◽  
Varimax Rotation ◽  
Language Version ◽  
Resilience Scale ◽  
The Mean ◽  
Components Analysis

The purpose of this study was to test the reliability and validity of the Swedish language version of the Resilience Scale (RS). Participants were 142 adults between 19–85 years of age. Internal consistency reliability, stability over time, and construct validity were evaluated using Cronbach’s alpha, principal components analysis with varimax rotation and correlations with scores on the Sense of Coherence Scale (SOC) and the Rosenberg Self-Esteem Scale (RSE). The mean score on the RS was 142 (SD = 15). The possible scores on the RS range from 25 to 175, and scores higher than 146 are considered high. The test-retest correlation was .78. Correlations with the SOC and the RSE were .41 (p < 0.01) and .37 (p < 0.01), respectively. Personal Assurance and Acceptance of Self and Life emerged as components from the principal components analysis. These findings provide evidence for the reliability and validity of the Swedish language version of the RS.

Delirium in elderly people: a study of a psychiatric liaison service in north India

2011 ◽  
Vol 24 (1) ◽  
pp. 117-127 ◽  
Author(s):  
Sandeep Grover ◽  
Natasha Kate ◽  
Munish Agarwal ◽  
Surendra Kumar Mattoo ◽  
Ajit Avasthi ◽  
...  
Keyword(s):  
Principal Components Analysis ◽  
Factor Structure ◽  
Principal Components ◽  
Rating Scale ◽  
North India ◽  
Physical Disorder ◽  
Inpatient Stay ◽  
The Mean ◽  
Components Analysis ◽  
Three Factor

ABSTRACTBackground: Very few studies from India have studied the phenomenology of delirium. The aim of the present study was to study the phenomenology as measured using the Delirium Rating Scale-Revised-98 (DRS-R98), the associated etiologies and the outcome of delirium among the elderly participants seen by the consultation-liaison psychiatric service in India. In addition, an attempt was made to study the factor structure of symptoms using principal components analysis.Methods: The case notes of 109 elderly patients referred to psychiatry liaison services were reviewed.Results: The mean age of the sample was 73.35 years (SD: 7.44; range 65–95 years) and two-thirds of the sample had hospital emergent delirium. The mean DRS-R98 severity score was 18.77 and the DRS-R98 total score was 24.81. In 15 patients the DRS-R98 scores were in the subsyndromal range. Among the various symptoms present, most patients had sleep-wake cycle disturbance, disturbance in orientation, attention and short-term memory impairments, fluctuation of symptoms, temporal onset of symptoms and a physical disorder. Principal components analysis identified three factors which explained 43.5% of variance of symptomatology and it yielded a three-factor structure. Endocrine/metabolic disturbances were the commonest associated etiological category with delirium. The mean hospital stay after being referred to psychiatry referral services was 8.89 days, after which delirium improved in 58.7% of cases. The mortality rate during the inpatient stay was 16.5%.Conclusions: Results suggest that the symptoms of delirium as assessed by DRS-R98 separate out into a three-factor structure. Delirium is commonly associated with metabolic endocrine disturbances and about one-sixth of the patients die during the short inpatient stay.

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