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Academic Use (academic + use)
Selected AbstractsA MATLAB toolbox for solving acid-base chemistry problems in environmental engineering applicationsCOMPUTER APPLICATIONS IN ENGINEERING EDUCATION, Issue 4 2005Chetan T. Goudar Abstract A MATLAB toolbox incorporating several computer programs has been developed in an attempt to automate laborious calculations in acid-base chemistry. Such calculations are routinely used in several environmental engineering applications including the design of wastewater treatment systems and for predicting contaminant fate and transport in the subsurface. The computer programs presented in this study do not replace student thinking involved in formulating the problem solving strategy but are merely tools that simplify the actual problem solving process. They encompass a wide variety of acid-base chemistry topics including equilibrium constant calculations, construction of distribution diagrams for mono and multiprotic systems, ionic strength and activity coefficient calculations, and buffer index calculations. All programs are characterized by an intuitive graphical user interface where the user supplies input information. Program outputs are either numerical or graphical depending upon the nature of the problem. The application of this approach to solving actual acid-base chemistry problems is illustrated by computing the pH and equilibrium composition of a 0.1 M Na2CO3 system at 30°C using several programs in the toolbox. As these programs simplify lengthy computations such as ionization fraction and activity coefficient calculations, it is hoped they will help bring more complicated problems to the environmental engineering classroom and enhance student understanding of important concepts that are applicable to real-world systems. The programs are available free of charge for academic use from the authors. © 2005 Wiley Periodicals, Inc. Comput Appl Eng Educ 13: 257,265, 2005; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20051 [source] Identification of protein-coding genes in the genome of Vibrio cholerae with more than 98% accuracy using occurrence frequencies of single nucleotidesFEBS JOURNAL, Issue 15 2001Ju Wang The published sequence of the Vibrio cholerae genome indicates that, in addition to the genes that encode proteins of known and unknown function, there are 1577 ORFs identified as conserved hypothetical or hypothetical gene candidates. Because the annotation is not 100% accurate, it is not known which of the 1577 ORFs are true protein-coding genes. In this paper, an algorithm based on the Z curve method, with sensitivity, specificity and accuracy greater than 98%, is used to solve this problem. Twenty-fold cross-validation tests show that the accuracy of the algorithm is 98.8%. A detailed discussion of the mechanism of the algorithm is also presented. It was found that 172 of the 1577 ORFs are unlikely to be protein-coding genes. The number of protein-coding genes in the V. cholerae genome was re-estimated and found to be ,,3716. This result should be of use in microarray analysis of gene expression in the genome, because the cost of preparing chips may be somewhat decreased. A computer program was written to calculate a coding score called VCZ for gene identification in the genome. Coding/noncoding is simply determined by VCZ > 0/VCZ < 0. The program is freely available on request for academic use. [source] Epothilones: Quantitative Structure Activity Relations Studied by Support Vector Machines and Artificial Neural NetworksMOLECULAR INFORMATICS, Issue 7 2003Annalen Bleckmann Abstract In this paper the relation between the structure of epothilones (a new class of anti-tumour agents) and their potential to influence the tubulin-microtubule equilibrium is investigated. Insights into the character of the tubulin-epothilone interactions are derived as the accuracy and reliability of support vector machines and artificial neural networks to model such relations quantitatively is compared. Both methods are well qualified to model relationships between the structure of epothilone derivatives and their anti-tumour activities. Artificial neural networks achieve lower residual standard deviations (22%) compared to support vector machines (25%) and better classification results (89% compared to 75%). However, the reproducibility of the results is greater for support vector machines, which suggests a stronger convergence. The mapping of the influence of individual structural descriptors on the three-dimensional epothilone structure suggests one side of the rather flat molecule to be more important for its activity. The "LIBSVM" software which is used for simulating the support vector machines is freely available from www.csie.ntu.edu.tw/~cjlin/libsvm. The Program "Smart" which is used for simulating artificial neural networks is free for academic use and can be obtained together with the database of epothilones and their activities from www.jens-meiler.de. [source] Annotated regions of significance of SELDI-TOF-MS spectra for detecting protein biomarkersPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 23 2006Chuen Seng Tan Abstract Peak detection is a key step in the analysis of SELDI-TOF-MS spectra, but the current default method has low specificity and poor peak annotation. To improve data quality, scientists still have to validate the identified peaks visually, a tedious and time-consuming process, especially for large data sets. Hence, there is a genuine need for methods that minimize manual validation. We have previously reported a multi-spectral signal detection method, called RS for ,region of significance', with improved specificity. Here we extend it to include a peak quantification algorithm based on annotated regions of significance (ARS). For each spectral region flagged as significant by RS, we first identify a dominant spectrum for determining the number of peaks and the m/z region of these peaks. From each m/z region of peaks, a peak template is extracted from all spectra via the principal component analysis. Finally, with the template, we estimate the amplitude and location of the peak in each spectrum with the least-squares method and refine the estimation of the amplitude via the mixture model. We have evaluated the ARS algorithm on patient samples from a clinical study. Comparison with the standard method shows that ARS (i),inherits the superior specificity of RS, and (ii),gives more accurate peak annotations than the standard method. In conclusion, we find that ARS alleviates the main problems in the preprocessing of SELDI-TOF spectra. The R-package ProSpect that implements ARS is freely available for academic use at http://www.meb.ki.se/,yudpaw. [source] | ||||||||||