A unified theory of carcinogenesis based on order–disorder transitions in DNA structure as studied in the human ovary and breast

Fourier transform-infrared/statistics models demonstrate that the malignant transformation of morphologically normal human ovarian and breast tissues involves the creation of a high degree of structural modification (disorder) in DNA, before restoration of order in distant metastases. Order–disorder transitions were revealed by methods including principal components analysis of infrared spectra in which DNA samples were represented by points in two-dimensional space. Differences between the geometric sizes of clusters of points and between their locations revealed the magnitude of the order–disorder transitions. Infrared spectra provided evidence for the types of structural changes involved. Normal ovarian DNAs formed a tight cluster comparable to that of normal human blood leukocytes. The DNAs of ovarian primary carcinomas, including those that had given rise to metastases, had a high degree of disorder, whereas the DNAs of distant metastases from ovarian carcinomas were relatively ordered. However, the spectra of the metastases were more diverse than those of normal ovarian DNAs in regions assigned to base vibrations, implying increased genetic changes. DNAs of normal female breasts were substantially disordered (e.g., compared with the human blood leukocytes) as were those of the primary carcinomas, whether or not they had metastasized. The DNAs of distant breast cancer metastases were relatively ordered. These findings evoke a unified theory of carcinogenesis in which the creation of disorder in the DNA structure is an obligatory process followed by the selection of ordered, mutated DNA forms that ultimately give rise to metastases.

Statistical methods for characterizing similarities and differences between semantic structures

This paper describes a variety of statistical methods for obtaining precise quantitative estimates of the similarities and differences in the structures of semantic domains in different languages. The methods include comparing mean correlations within and between groups, principal components analysis of interspeaker correlations, and analysis of variance of speaker by question data. Methods for graphical displays of the results are also presented. The methods give convergent results that are mutually supportive and equivalent under suitable interpretation. The methods are illustrated on the semantic domain of emotion terms in a comparison of the semantic structures of native English and native Japanese speaking subjects. We suggest that, in comparative studies concerning the extent to which semantic structures are universally shared or culture-specific, both similarities and differences should be measured and compared rather than placing total emphasis on one or the other polar position.

Identification of thermophilic species by the amino acid compositions deduced from their genomes

The global amino acid compositions as deduced from the complete genomic sequences of six thermophilic archaea, two thermophilic bacteria, 17 mesophilic bacteria and two eukaryotic species were analysed by hierarchical clustering and principal components analysis. Both methods showed an influence of several factors on amino acid composition. Although GC content has a dominant effect, thermophilic species can be identified by their global amino acid compositions alone. This study presents a careful statistical analysis of factors that affect amino acid composition and also yielded specific features of the average amino acid composition of thermophilic species. Moreover, we introduce the first example of a ‘compositional tree’ of species that takes into account not only homologous proteins, but also proteins unique to particular species. We expect this simple yet novel approach to be a useful additional tool for the study of phylogeny at the genome level.

Blind separation of auditory event-related brain responses into independent components

Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected—and undetected—target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.

Spatially independent activity patterns in functional MRI data during the Stroop color-naming task

A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a “map”) and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.