Computer-assisted prediction of small peptide sequencing in reversed-phase liquid chromatography

Summary A computer-assisted prediction system for small peptide sequencing has been constructed based on the retention prediction approach in reversed-phase liquid chromatography. The system has the basic function to predict retention of small peptides at any chromatographic conditions and this function can be useful for the prediction of sequencing. The outline of the system construction and the performance is discussed.     

Threshold bootstrap target factor analysis study of neodymium with pyridine 2,4 dicarboxylic acid N-oxide-an investigation of traceability

Threshold bootstrap computer-assisted target factor analysis (TB CAT) has been applied to the evaluation of UV-Vis spectra of Nd(III) in aqueous solutions containing varying amounts of pyridine 2,4-dicarboxylic acid N-oxide (dipyr NO). Using the total uncertainty budget concept, probability distributions of the conditional formation constant(s) of the respective Nd(III) dipyr NO species have been evaluated. The experimental data are compatible with two different systems: a single Nd(III) dipyr NO species with logbeta(11)=2.8+/-0.11or, alternatively, a two species system with logbeta(11)=3.7+/-0.35and logbeta(12)=5.2+/-0.8. The results seem to favor the single species interpretation. However, a discussion of the influence of spectral correlation indicates that the situation should be considered with caution. The results are compared to a previous TB CAT study on Nd(III) picolinic acid N-oxide interaction illustrating the advantage of arguing in terms of probability density functions. The pyridine N-oxides with substituted carboxylic acids are structurally poorly investigated. Hence, this study calls for more structural information on rare earth pyridine N-oxide derivates.     

Computer-aided design and optimization of an on-line gas chromatographic procedure for the analysis of purgeable compounds in waste water

This report describes the application of a computer-aided column design and optimization program to the analysis of purgeable compounds in waste water. Series-coupled columns with dissimilar phases can be “selectivity-tuned” to provide the maximum separation factors, thus facilitating the most rapid analysis. A computer program calculates the column lengths and operating conditions which are required to provide the desired resolution at a specified flow rate.     

Are signal intensity and homogeneity useful parameters for distinguishing between benign and malignant soft tissue masses on MR images?: Objective evaluation by means of texture analysis

Objectives

To objectively identify possible differences in the signal characteristics of benign and malignant soft tissue masses (STM) on magnetic resonance (MR) images by means of texture analysis and to determine the value of these differences for computer-assisted lesion classification.

Method

Fifty-eight patients with histologically proven STM (benign, n=30; malignant, n=28) were included. STM texture was analyzed on routine T1-weighted, T2-weighted and short tau inversion recovery (STIR) images obtained with heterogeneous acquisition protocols. Fisher coefficients (F) and the probability of classification error and average correlation coefficients (POE+ACC) were calculated to identify the most discriminative texture features for separation of benign and malignant STM. F>1 indicated adequate discriminative power of texture features. Based on the texture features, computer-assisted classification of the STM by means of k-nearest-neighbor (k-NN) and artificial neural network (ANN) classification was performed, and accuracy, sensitivity and specificity were calculated.

Results

Discriminative power was only adequate for two texture features, derived from the gray-level histogram of the STIR images (first and 10th gray-level percentiles). Accordingly, the best results of STM classification were achieved using texture information from STIR images, with an accuracy of 75.0% (sensitivity, 71.4%; specificity, 78.3%) for the k-NN classifier, and an accuracy of 90.5% (sensitivity, 91.1%; specificity, 90.0%) for the ANN classifier.

Conclusion

Texture analysis revealed only small differences in the signal characteristics of benign and malignant STM on routine MR images. Computer-assisted pattern recognition algorithms may aid in the characterization of STM, but more data is necessary to confirm their clinical value.