The complete poetical works of Bret Harte.

Mode of access: Internet.

The poetical works of Bret Harte.

Mode of access: Internet.

The writings of Bret Harte.

Index included in vol. 19.

The writings of Bret Harte.

Mode of access: Internet.

The poetical works of Bret Harte.

Marked "End of vol. I." on last page; a reissue of v. 1 of the Riverside edition, omitting the prose drama, "Two men of Sandy Bar".

The complete poetical works of Bret Harte.

Mode of access: Internet.

The complete poetical works of Bret Harte.

Includes indexes.

Description and critical appraisal of principal components analysis (PCA) methodology applied to pulsed-field gel electrophoresis profiles of methicillin-resistant Staphylococcus aureus isolates

Principal components analysis (PCA) has been described for over 50 years; however, it is rarely applied to the analysis of epidemiological data. In this study PCA was critically appraised in its ability to reveal relationships between pulsed-field gel electrophoresis (PFGE) profiles of methicillin- resistant Staphylococcus aureus (MRSA) in comparison to the more commonly employed cluster analysis and representation by dendrograms. The PFGE type following SmaI chromosomal digest was determined for 44 multidrug-resistant hospital-acquired methicillin-resistant S. aureus (MR-HA-MRSA) isolates, two multidrug-resistant community-acquired MRSA (MR-CA-MRSA), 50 hospital-acquired MRSA (HA-MRSA) isolates (from the University Hospital Birmingham, NHS Trust, UK) and 34 community-acquired MRSA (CA-MRSA) isolates (from general practitioners in Birmingham, UK). Strain relatedness was determined using Dice band-matching with UPGMA clustering and PCA. The results indicated that PCA revealed relationships between MRSA strains, which were more strongly correlated with known epidemiology, most likely because, unlike cluster analysis, PCA does not have the constraint of generating a hierarchic classification. In addition, PCA provides the opportunity for further analysis to identify key polymorphic bands within complex genotypic profiles, which is not always possible with dendrograms. Here we provide a detailed description of a PCA method for the analysis of PFGE profiles to complement further the epidemiological study of infectious disease. © 2005 Elsevier B.V. All rights reserved.

Neuropathological changes in ten cases of neuronal intermediate filament inclusion disease (NIFID):A study using α-internexin immunohistochemistry and principal components analysis (PCA)

Ten cases of neuronal intermediate filament inclusion disease (NIFID) were studied quantitatively. The α-internexin positive neurofilament inclusions (NI) were most abundant in the motor cortex and CA sectors of the hippocampus. The densities of the NI and the swollen achromatic neurons (SN) were similar in laminae II/III and V/VI but glial cell density was greater in V/VI. The density of the NI was positively correlated with the SN and the glial cells. Principal components analysis (PCA) suggested that PC1 was associated with variation in neuronal loss in the frontal/temporal lobes and PC2 with neuronal loss in the frontal lobe and NI density in the parahippocampal gyrus. The data suggest: 1) frontal and temporal lobe degeneration in NIFID is associated with the widespread formation of NI and SN, 2) NI and SN affect cortical laminae II/III and V/VI, 3) the NI and SN affect closely related neuronal populations, and 4) variations in neuronal loss and in the density of NI were the most important sources of pathological heterogeneity. © Springer-Verlag 2005.

Statnote 27: Principal components analysis (PCA)

In Statnotes 24 and 25, multiple linear regression, a statistical method that examines the relationship between a single dependent variable (Y) and two or more independent variables (X), was described. The principle objective of such an analysis was to determine which of the X variables had a significant influence on Y and to construct an equation that predicts Y from the X variables. ‘Principal components analysis’ (PCA) and ‘factor analysis’ (FA) are also methods of examining the relationships between different variables but they differ from multiple regression in that no distinction is made between the dependent and independent variables, all variables being essentially treated the same. Originally, PCA and FA were regarded as distinct methods but in recent times they have been combined into a single analysis, PCA often being the first stage of a FA. The basic objective of a PCA/FA is to examine the relationships between the variables or the ‘structure’ of the variables and to determine whether these relationships can be explained by a smaller number of ‘factors’. This statnote describes the use of PCA/FA in the analysis of the differences between the DNA profiles of different MRSA strains introduced in Statnote 26.

Data analysis methods in optometry Part 8: Principal components analysis (PCA) and factor analysis (FA)

PCA/FA is a method of analyzing complex data sets in which there are no clearly defined X or Y variables. It has multiple uses including the study of the pattern of variation between individual entities such as patients with particular disorders and the detailed study of descriptive variables. In most applications, variables are related to a smaller number of ‘factors’ or PCs that account for the maximum variance in the data and hence, may explain important trends among the variables. An increasingly important application of the method is in the ‘validation’ of questionnaires that attempt to relate subjective aspects of a patients experience with more objective measures of vision.