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Regression Algorithm (regression + algorithm)
Selected AbstractsCapillary sieving electrophoresis and micellar electrokinetic capillary chromatography produce highly correlated separation of tryptic digestsELECTROPHORESIS, Issue 14 2010Jane A. Dickerson Abstract We perform 2-D capillary electrophoresis on fluorescently labeled proteins and peptides. Capillary sieving electrophoresis (CSE) was performed in the first dimension and MEKC was performed in the second. A cellular homogenate was labeled with the fluorogenic reagent FQ and separated using the system. This homogenate generated a pair of ridges; the first had essentially constant migration time in the CSE dimension, while the second had essentially constant migration time in the MEKC dimension. In addition, a few spots were scattered through the electropherogram. The same homogenate was digested using trypsin, and then labeled and subjected to the 2-D separation. In this case, the two ridges observed from the original 2-D separation disappeared and were replaced by a set of spots that fell along the diagonal. Those spots were identified using a local-maximum algorithm and each was fit using a 2-D Gaussian surface by an unsupervised nonlinear least squares regression algorithm. The migration times of the tryptic digest components were highly correlated (r=0.862). When the slowest migrating components were eliminated from the analysis, the correlation coefficient improved to r=0.956. [source] Effects of pore aspect ratios on velocity prediction from well-log dataGEOPHYSICAL PROSPECTING, Issue 3 2002Jun Yan ABSTRACT We develop a semi-empirical model which combines the theoretical model of Xu and White and the empirical formula of Han, Nur and Morgan in sand,clay environments. This new model may be used for petrophysical interpretation of P- and S-wave velocities. In particular, we are able to obtain an independent estimation of aspect ratios based on log data and seismic velocity, and also the relationship between velocities and other reservoir parameters (e.g. porosity and clay content), thus providing a prediction of shear-wave velocity. To achieve this, we first use Kuster and Toksöz's theory to derive bulk and shear moduli in a sand,clay mixture. Secondly, Xu and White's model is combined with an artificial neural network to invert the depth-dependent variation of pore aspect ratios. Finally these aspect ratio results are linked to the empirical formula of Han, Nur and Morgan, using a multiple regression algorithm for petrophysical interpretation. Tests on field data from a North Sea reservoir show that this semi-empirical model provides simple but satisfactory results for the prediction of shear-wave velocities and the estimation of reservoir parameters. [source] X-ray fluorescence holography: a novel treatment for crystal structure determinationACTA CRYSTALLOGRAPHICA SECTION A, Issue 2 2003F. N. Chukhovskii It is shown that it is possible to use a linear regression algorithm direct method to solve crystal structures from X-ray fluorescence holography (XFH) data. It is found that, in contrast to conventional X-ray structure determination methods, which do not always work unambiguously, the sustainable method utilizing the XFH data generally provides the unique phase-retrieval structure solution and is able, in many cases, to replace the above for determining both the absolute values (moduli) and phases of structure factors. The XFH scan with a fluorescing Cu atom from a spherical cluster of a Cu3Au single crystal, at an energy of 10,keV for the incident unpolarized plane-wave X-radiation, is numerically simulated to test the performance of the method in finding a unique solution for the structure factors involved in the restoration procedure using the linear regression algorithm. [source] Fluorescence-based soft-sensor for monitoring ,-lactoglobulin and ,-lactalbumin solubility during thermal aggregationBIOTECHNOLOGY & BIOENGINEERING, Issue 3 2008Rand Elshereef Abstract A soft-sensor for monitoring solubility of native-like ,-lactalbumin (,-LA) and ,-lactoglobulin (,-LG) and their aggregation behavior following heat treatment of mixtures under different treatment conditions was developed using fluorescence spectroscopy data regressed with a multivariate Partial Least Squares (PLS) regression algorithm. PLS regression was used to correlate the concentrations of ,-LA and ,-LG to the fluorescence spectra obtained for their mixtures. Data for the calibration and validation of the soft sensor was derived from fluorescence spectra. The process of thermal induced aggregation of ,-LG and ,-LA protein in mixtures, which involves the disappearance of native-like proteins, was studied under various treatment conditions including different temperatures, pH, total initial protein concentration and proportions of ,-LA and ,-LG. It was demonstrated that the multivariate regression models used could effectively deconvolute multi-wavelength fluorescence spectra collected under a variety of process conditions and provide a fairly accurate quantification of respective native-like proteins despite the significant overlapping between their emission profiles. It was also demonstrated that a PLS model can be used as a black-box prediction tool for estimating protein aggregation when combined with simple mass balances. Bioeng. 2008;99: 567,577. © 2007 Wiley Periodicals, Inc. [source] | ||||||||||