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PubMed2000

Scores and principal components: the relationship between components due to subjects and to variables.

The main purpose of this article is: given a score matrix called S, find out the joint proportional contribution of factors due to persons (conditions, situations, and so forth) and factors due to variables, for any sij observed score, where i identifies persons, and j, variables. This approach makes it possible a) to show that the same score in a given variable may have a different quantitative interpretation in terms of persons or conditions, and b) to find out how subjects differ in the way in which they relate variables.

Confounding Factors, Epidemiologic↗
PubMed2000

Near infrared with principal component analysis as a novel analytical approach for nanoparticle technology.

PURPOSE: To progress in the characterization of a poly(MePEGcyanoacrylate-co-hexadecylcyanoacrylate) (poly(PEGCA-co-HDCA) copolymer and the nanoparticles formed from this copolymer. METHODS: Poly(PEGCA-co-HDCA) at a MePEG/hexadecyl ratio of 1:4 was investigated by 1H-NMR and near infrared spectroscopy. The nanoparticle suspensions, obtained by the methods of nanoprecipitation or emulsion--solvent evaporation, as well as the crude nanoparticles and their dispersion medium--were analyzed by MePEG measurement, 1H-NMR, and near infrared spectroscopy. RESULTS: The 1H-NMR results showed that the (poly(PEGCA-co-HDCA) copolymer obtained bore lateral hydrophilic MePEG chains and lateral hydrophobic hexadecyl chains in a final ratio of 1:4. However, this ratio, although reproducible from batch to batch, represented only a mean value for different molecular species. Indeed, our results demonstrated the formation of more hydrophobic poly(alkyl-cyanoacrylate) oligomers (with a higher content of hexadecyl chains) and other more hydrophilic oligomers (with a higher MePEG content). Only the more hydrophobic oligomers were able to form solid pegylated nanoparticles. As far as these nanoparticles were concerned, determination of their MePEG content allowed the calculation of a distance of 1.2 nm and 1.05 nm between 2 grafted MePEG chains at the nanoparticle surface, when obtained by nanoprecipitation and emulsion-solvent evaporation, respectively. Moreover, when the same copolymer batch was used, different nanoparticles were obtained according to the preparation method, as seen by near infrared spectroscopy. CONCLUSIONS: The nanoparticles obtained by nanoprecipitation or emulsion-solvent evaporation of poly(PEGCA-co-HDCA) 1:4 copolymer displayed a different supramolecular organization, as evidenced by the near infrared spectroscopy results. Moreover, these nanoparticles showed surface characteristics compatible with a long circulating carrier.

Colloids↗
PubMed1995

More about the difference between men and women: evidence from linear neural networks and the principal-component approach.

The ability of a statistical/neural network to classify faces by sex by means of a pixel-based representation has not been fully investigated. Simulations with pixel-based codes have provided sex-classification results that are less impressive than those reported for measurement-based codes. In no case, however, have the reported pixel-based simulations been optimized for the task of classifying faces by sex. A series of simulations is described in which four network models were applied to the same pixel-based face code. These simulations involved either a radial basis function network or a perceptron as a classifier, preceded or not by a preprocessing step of eigendecomposition. It is shown that performance comparable to that of the measurement-based models can be achieved with pixel-based input (90%) when the data are preprocessed. The effect of the eigendecomposition preprocessing of the faces is then compared with spatial-frequency analysis of face images and analyzed in terms of the perceptual information it captures. It is shown that such an examination may offer insight into the facial aspects important to the sex-classification process. Finally, the contribution of hair information to the performance of the model is evaluated. It is shown that, although the hair contributes to the sex-classification process, it is not the only important contributor.

Algorithms↗
PubMed1997

Prediction of protein side-chain conformations by principal component analysis for fixed main-chain atoms.

A method of side-chain prediction without calculating the potential function is introduced. It is based on the assumption that similar side-chain conformations have a similar structural environment around the side chains. The environment information is represented by vectors that were obtained from principle component analysis and represented by the variance of positions of main-chain atoms around side chains. This information was added to the side-chain library (rotamer library) made from X-ray structures. Side-chain conformations were constructed using this side-chain library without using potential functions. An optimal solution was determined by comparing environmental information with the backbone conformation around the side chain to be predicted and native ones in the library. The method was performed for 15 proteins whose structures were known. The result for the root-mean-square deviation between the predicted and X-ray side-chain conformations was approximately 1.5 A (the value for core residues was approximately 1.1 A) and the percentage of predicted chi 1 angles correct within 40 degrees was approximately 65% (75% for the core). The computational time was short (approximately 60 s for the prediction of proteins with 200 amino acid residues). About 70% of the side-chain conformations were constructed by location of the main-chain atoms around the central C beta atom and the average of r.m.s.d. was approximately 1.4 A (for core residues the average was approximately 1.0 A).

Animals↗