Conjugate-gradient method for computing the Moore-Penrose inverse and rank of a matrix

A conjugate-gradient method is developed for computing the Moore-Penrose generalized inverseA of a matrix and the associated projectors, by using the least-square characteristics of both the method and the inverseA . Two dual algorithms are introduced for computing the least-square and the minimum-norm generalized inverses, as well asA . It is shown that (i) these algorithms converge for any starting approximation; (ii) if they are started from the zero matrix, they converge toA ; and (iii) the trace of a sequence of approximations multiplied byA is a monotone increasing function converging to the rank ofA. A practical way of compensating the self-correcting feature in the computation ofA is devised by using the duality of the algorithms. Comparison with Ben-Israel's method is made through numerical examples. The conjugate-gradient method has an advantage over Ben-Israel's method.After having completed the present paper, the author received from Professor M. R. Hestenes his paper entitledPseudo Inverses and Conjugate Gradients. This paper treated the same subject and appeared in Communications of the ACM, Vol. 18, pp. 40–43, 1975.     

Simultaneous kinetic spectrophotometric determination of cyanide and thiocyanate using the partial least squares (PLS) regression

The partial least squares (PLS-1) calibration model based on spectrophotometric measurement, for the simultaneous determination of CN⁻ and SCN⁻ ions is described. The method is based on the difference in the rate of the reaction between CN⁻ and SCN⁻ ions with chloramine-T in a pH 4.0 buffer solution and at 30 °C. The produced cyanogen chloride (CNCl) reacts with pyridine and the product condenses with barbituric acid and forms a final colored product. The absorption kinetic profiles of the solutions were monitored by measuring absorbance at 578 nm in the time range 20-180 s after initiation of the reaction with 2 s intervals. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 31 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 10.0-900.0 and 50.0-1200.0 ng mL⁻¹ for CN⁻ and SCN⁻ ions, respectively. The proposed method was successfully applied to the simultaneous determination of cyanide and thiocyanate in water samples.     

Differential Pulse Voltammetric Simultaneous Determination of Four Anti‐Inflammatory Drugs by Using Soft Modelling

An application of a partial least squares calibration method for the simultaneous voltammetric determination of indomethacin, acemethacin, piroxicam and tenoxicam is suggested. It was shown that it is possible to resolve complex mixtures of analytes even when they have strongly overlapped signals. In order to check the proposed method, statistical analysis of the results was performed by mean of hypothesis tests. The method developed was applied to the electrochemical reduction region of four anti‐inflammatory drugs and allowed the drugs to be quantified at concentrations between 0.52 and 4.09 μg mL^?1 for acemethacin, 0.44 and 3.50 μg mL^?1 for indomethacin, 0.43 and 3.40 μg mL^?1 for piroxicam, and 0.42 and 3.30 μg mL^?1 for tenoxicam with good results. The average absolute value of relative errors was 2.25%, 4.31%, 1.68% and 2.49%, respectively.     

LapRLSR for NIR spectral modeling and its application to online monitoring of the column separation of Salvianolate

A novel near infrared (NIR) modeling method—Laplacian regularized least squares regression (LapRLSR) was presented, which can take the advantage of many unlabeled spectra to promote the prediction performance of the model even if there are only few calibration samples. Using LapRLSR modeling, NIR spectral analysis was applied to the online monitoring of the concentration of salvia acid B in the column separation of Salvianolate. The results demonstrated that LapRLSR outperformed partial least squares (PLS) significantly, and NIR online analysis was applicable.     

Prediction of intracellular storage polymers using quantitative image analysis in enhanced biological phosphorus removal systems

The present study focuses on predicting the concentration of intracellular storage polymers in enhanced biological phosphorus removal (EBPR) systems. For that purpose, quantitative image analysis techniques were developed for determining the intracellular concentrations of PHA (PHB and PHV) with Nile blue and glycogen with aniline blue staining. Partial least squares (PLS) were used to predict the standard analytical values of these polymers by the proposed methodology. Identification of the aerobic and anaerobic stages proved to be crucial for improving the assessment of PHA, PHB and PHV intracellular concentrations. Current Nile blue based methodology can be seen as a feasible starting point for further enhancement. Glycogen detection based on the developed aniline blue staining methodology combined with the image analysis data proved to be a promising technique, toward the elimination of the need for analytical off-line measurements.     

Exhaled breath condensate: Determination of non-volatile compounds and their potential for clinical diagnosis and monitoring. A review

Exhaled breath condensate is a promising, non-invasive, diagnostic sample obtained by condensation of exhaled breath. Starting from a historical perspective of early attempts of breath testing towards the contemporary state-of-the-art breath analysis, this review article focuses mainly on the progress in determination of non-volatile compounds in exhaled breath condensate. The mechanisms by which the aerosols/droplets of non-volatile compounds are formed in the airways are discussed with methodological consequences for sampling. Dilution of respiratory droplets is a major problem for correct clinical interpretation of the measured data and there is an urgent need for standardization of EBC. This applies also for collection instrumentation and therefore various commercial and in-house built devices are described and compared with regard to their design, function and collection parameters. The analytical techniques and methods for determination of non-volatile compounds as potential markers of oxidative stress and lung inflammation are scrutinized with an emphasis on method suitability, sensitivity and appropriateness. The relevance of clinical findings for each group of possible non-volatile markers of selected pulmonary diseases and methodological recommendations with emphasis on interdisciplinary collaboration that is essential for future development into a fully validated clinical diagnostic tool are given.     

Gauss–Seidel Estimation of Generalized Linear Mixed Models With Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) are often fit by computational procedures such as penalized quasi-likelihood (PQL). Special cases of GLMMs are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints make it difficult to apply these iterative procedures to datasets having a very large number of records.

We propose a computationally efficient strategy based on the Gauss–Seidel algorithm that iteratively fits submodels of the GLMM to collapsed versions of the data. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status, and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. For Poisson and binomial regression models, the Gauss–Seidel approach is found to substantially outperform existing methods in terms of maximum analyzable sample size. Remarkably, for both models, the average time per iteration and the total time until convergence of the Gauss–Seidel procedure are less than 0.3% of the corresponding times for the IWLS algorithm. Platform-independent pseudo-code for fitting GLMS, as well as the source code used to generate and analyze the datasets in the simulation studies, are available online as supplemental materials.