New magnetic nano-absorbent for the determination of n-octanol/water partition coefficients

A novel and generic miniaturization methodology for the determination of partition coefficient values of organic compounds in noctanol/water by using magnetic nanoparticles is, for the first time, described. We have successfully designed, synthesised and characterised new colloidal stable porous silica-encapsulated magnetic nanoparticles of controlled dimensions. These nanoparticles absorbing a tiny amount of n-octanol in their porous silica over-layer are homogeneously dispersed into a bulk aqueous phase (pH 7.40) containing an organic compound prior to magnetic separation. The small size of the particles and the efficient mixing allow a rapid establishment of the partition equilibrium of the organic compound between the solid supported n-octanol nano-droplets and the bulk aqueous phase. UV-vis spectrophotometry is then applied as a quantitative method to determine the concentration of the organic compound in the aqueous phase both before and after partitioning (after magnetic separation). log D values of organic compounds of pharmaceutical interest (0.65-3.50), determined by this novel methodology, were found to be in excellent agreement with the values measured by the shake-flask method in two independent laboratories, which are also consistent with the literature data. It was also found that this new technique gives a number of advantages such as providing an accurate measurement of log D value, a much shorter experimental time and a smaller sample size required. With this approach, the formation of a problematic emulsion, commonly encountered in shake-flask experiments, is eliminated. It is envisaged that this method could be applicable to the high throughput log D screening of drug candidates. (c) 2005 Elsevier B.V. All rights reserved.

Automatic internal calibration in liquid chromatography/Fourier transform ion cyclotron resonance mass spectrometry of protein digests

Accurately measured peptide masses can be used for large-scale protein identification from bacterial whole-cell digests as an alternative to tandem mass spectrometry (MS/MS) provided mass measurement errors of a few parts-per-million (ppm) are obtained. Fourier transform ion cyclotron resonance (FTICR) mass spectrometry (MS) routinely achieves such mass accuracy either with internal calibration or by regulating the charge in the analyzer cell. We have developed a novel and automated method for internal calibration of liquid chromatography (LC)/FTICR data from whole-cell digests using peptides in the sample identified by concurrent MS/MS together with ambient polydimethyl-cyclosiloxanes as internal calibrants in the mass spectra. The method reduced mass measurement error from 4.3 +/- 3.7 ppm to 0.3 +/- 2.3 ppm in an E. coli LC/FTICR dataset of 1000 MS and MS/MS spectra and is applicable to all analyses of complex protein digests by FTICRMS. Copyright (c) 2006 John Wiley & Sons, Ltd.

Gas-phase rate coefficients for reactions of NO3, OH, O-3 and O(P-3) with unsaturated alcohols and ethers: correlations and structure-activity relations (SARs)

Experimental difficulties sometimes force modellers to use predicted rate coefficients for reactions of oxygenated volatile organic compounds (oVOCs). We examine here methods for making the predictions for reactions of atmospheric initiators of oxidation, NO3, OH, O-3 and O(P-3), with unsaturated alcohols and ethers. Logarithmic correlations are found between measured rate coefficients and calculated orbital energies, and these correlations may be used directly to estimate rate coefficients for compounds where measurements have not been performed. To provide a shortcut that obviates the need to calculate orbital energies, structure-activity relations (SARs) are developed. Our SARs are tested for predictive power against compounds for which experimental rate coefficients exist, and their accuracy is discussed. Estimated atmospheric lifetimes for oVOCs are presented. The SARs for alkenols successfully predict key rate coefficients, and thus can be used to enhance the scope of atmospheric models incorporating detailed chemistry. SARs for the ethers have more limited applicability, but can still be useful in improving tropospheric models. (C) 2008 Elsevier Ltd. All rights reserved.

Gas-phase rate coefficients for the reactions of NO3, OH and O-3 with alpha,beta-unsaturated esters and ketones: structure-activity relations (SARs)

Gas-phase rate coefficients for the atmospherically important reactions of NO3, OH and O-3 are predicted for 55 alpha,beta-unsaturated esters and ketones. The rate coefficients were calculated using a correlation described previously [Pfrang, C., King, M.D., C. E. Canosa-Mas, C.E., Wayne, R.P., 2006. Atmospheric Environment 40, 1170-1179]. These rate coefficients were used to extend structure-activity relations for predicting the rate coefficients for the reactions of NO3, OH or O-3 with alkenes to include alpha,beta-unsaturated esters and ketones. Conjugation of an alkene with an alpha,beta-keto or alpha,beta-ester group will reduce the value of a rate coefficient by a factor of similar to 110, similar to 2.5 and similar to 12 for reaction with NO3, OH or O-3, respectively. The actual identity of the alkyl group, R, in -C(O)R or -C(O)OR has only a small influence. An assessment of the reliability of the SAR is given that demonstrates that it is useful for reactions involving NO3 and OH, but less valuable for those of O-3 or peroxy nitrate esters. (c) 2006 Elsevier Ltd. All rights reserved.

An analysis of real time implementation of Fourier transform-based frequency recognition algorithms

Frequency recognition is an important task in many engineering fields such as audio signal processing and telecommunications engineering, for example in applications like Dual-Tone Multi-Frequency (DTMF) detection or the recognition of the carrier frequency of a Global Positioning, System (GPS) signal. This paper will present results of investigations on several common Fourier Transform-based frequency recognition algorithms implemented in real time on a Texas Instruments (TI) TMS320C6713 Digital Signal Processor (DSP) core. In addition, suitable metrics are going to be evaluated in order to ascertain which of these selected algorithms is appropriate for audio signal processing(1).

Investigation of support vector machine for the detection of architectural distortion in mammographic images

This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma.

Optimal discrimination and classification of THz spectra in the wavelet domain

In rapid scan Fourier transform spectrometry, we show that the noise in the wavelet coefficients resulting from the filter bank decomposition of the complex insertion loss function is linearly related to the noise power in the sample interferogram by a noise amplification factor. By maximizing an objective function composed of the power of the wavelet coefficients divided by the noise amplification factor, optimal feature extraction in the wavelet domain is performed. The performance of a classifier based on the output of a filter bank is shown to be considerably better than that of an Euclidean distance classifier in the original spectral domain. An optimization procedure results in a further improvement of the wavelet classifier. The procedure is suitable for enhancing the contrast or classifying spectra acquired by either continuous wave or THz transient spectrometers as well as for increasing the dynamic range of THz imaging systems. (C) 2003 Optical Society of America.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

Finding the point on Bezier Curves with the normal vector passing an external point

Two algorithms for finding the point on non-rational/rational Bezier curves of which the normal vector passes through a given external point are presented. The algorithms are based on Bezier curves generation algorithms of de Casteljau's algorithm for non-rational Bezier curve or Farin's recursion for rational Bezier curve, respectively. Orthogonal projections from the external point are used to guide the directional search used in the proposed iterative algorithms. Using Lyapunov's method, it is shown that each algorithm is able to converge to a local minimum for each case of non-rational/rational Bezier curves. It is also shown that on convergence the distance between the point on curves to the external point reaches a local minimum for both approaches. Illustrative examples are included to demonstrate the effectiveness of the proposed approaches.

Fourier transform, null variety, and Laplacian's eigenvalues

We consider a quantity κ(Ω)—the distance to the origin from the null variety of the Fourier transform of the characteristic function of Ω. We conjecture, firstly, that κ(Ω) is maximised, among all convex balanced domains of a fixed volume, by a ball, and also that κ(Ω) is bounded above by the square root of the second Dirichlet eigenvalue of Ω. We prove some weaker versions of these conjectures in dimension two, as well as their validity for domains asymptotically close to a disk, and also discuss further links between κ(Ω) and the eigenvalues of the Laplacians.