Introduction of 4-chloro-alpha-cyanocinnamic acid liquid matrices for high sensitivity UV-MALDI MS

Matrix-assisted laser desorption/ionization (MALDI) is a key ionization technique in mass spectrometry (MS) for the analysis of labile macromolecules. An important area of study and improvements in relation to MALDI and its application in high-sensitivity MS is that of matrix design and sample preparation. Recently, 4-chloro-alpha-cyanocinnamic acid (ClCCA) has been introduced as a new rationally designed matrix and reported to provide an improved analytical performance as demonstrated by an increase in sequence coverage of protein digests obtained by peptide mass mapping (PMM) (Jaskolla, T. W.; et al. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 12200-12205). This new matrix shows the potential to be a superior alternative to the commonly used and highly successful alpha-cyano-4-hydroxycinnamic acid (CHCA). We have taken this design one step further by developing and optimizing an ionic liquid matrix (ILM) and liquid support matrix (LSM) using ClCCA as the principle chromophore and MALDI matrix compound. These new liquid matrices possess greater sample homogeneity and a simpler morphology. The data obtained from our studies show improved sequence coverage for BSA digests compared to the traditional CHCA crystalline matrix and for the ClCCA-containing ILM a similar performance to the ClCCA crystalline matrix down to 1 fmol of BSA digest prepared in a single MALDI sample droplet with current sensitivity levels in the attomole range. The LSMs show a high tolerance to contamination such as ammonium bicarbonate, a commonly used buffering agent.

Small towns as 'sub-poles' in English rural development: Investigating rural-urban linkages using sub-regional social accounting matrices

This paper uses spatial economic data from four small English towns to measure the strength of economic integration between town and hinterland and to estimate the magnitude of town-hinterland spill-over effects. Following estimation of local integration indicators and inter-locale flows, sub-regional social accounting matrices (SAMs) are developed to estimate the strength of local employment and output multipliers for various economic sectors. The potential value of a town as a 'sub-pole' in local economic development is shown to be dependent on structural differences in the local economy, such as the particular mix of firms within towns. Although the multipliers are generally small, indicating a low level of local linkages, some sectors, particularly financial services and banking, show consistently higher multipliers for both output and employment. (c) 2007 Elsevier Ltd. All rights reserved.

Influence-matrix diagnostic of a data assimilation system

The influence matrix is used in ordinary least-squares applications for monitoring statistical multiple-regression analyses. Concepts related to the influence matrix provide diagnostics on the influence of individual data on the analysis - the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for signal) in any sub-set of the analysed data. In this paper, the corresponding concepts have been derived in the context of linear statistical data assimilation in numerical weather prediction. An approximate method to compute the diagonal elements of the influence matrix (the self-sensitivities) has been developed for a large-dimension variational data assimilation system (the four-dimensional variational system of the European Centre for Medium-Range Weather Forecasts). Results show that, in the boreal spring 2003 operational system, 15% of the global influence is due to the assimilated observations in any one analysis, and the complementary 85% is the influence of the prior (background) information, a short-range forecast containing information from earlier assimilated observations. About 25% of the observational information is currently provided by surface-based observing systems, and 75% by satellite systems. Low-influence data points usually occur in data-rich areas, while high-influence data points are in data-sparse areas or in dynamically active regions. Background-error correlations also play an important role: high correlation diminishes the observation influence and amplifies the importance of the surrounding real and pseudo observations (prior information in observation space). Incorrect specifications of background and observation-error covariance matrices can be identified, interpreted and better understood by the use of influence-matrix diagnostics for the variety of observation types and observed variables used in the data assimilation system. Copyright © 2004 Royal Meteorological Society

A practical approach to a multi-level analysis with a sparse binary outcome within a large surgical trial

Objectives: To assess the potential source of variation that surgeon may add to patient outcome in a clinical trial of surgical procedures. Methods: Two large (n = 1380) parallel multicentre randomized surgical trials were undertaken to compare laparoscopically assisted hysterectomy with conventional methods of abdominal and vaginal hysterectomy; involving 43 surgeons. The primary end point of the trial was the occurrence of at least one major complication. Patients were nested within surgeons giving the data set a hierarchical structure. A total of 10% of patients had at least one major complication, that is, a sparse binary outcome variable. A linear mixed logistic regression model (with logit link function) was used to model the probability of a major complication, with surgeon fitted as a random effect. Models were fitted using the method of maximum likelihood in SAS((R)). Results: There were many convergence problems. These were resolved using a variety of approaches including; treating all effects as fixed for the initial model building; modelling the variance of a parameter on a logarithmic scale and centring of continuous covariates. The initial model building process indicated no significant 'type of operation' across surgeon interaction effect in either trial, the 'type of operation' term was highly significant in the abdominal trial, and the 'surgeon' term was not significant in either trial. Conclusions: The analysis did not find a surgeon effect but it is difficult to conclude that there was not a difference between surgeons. The statistical test may have lacked sufficient power, the variance estimates were small with large standard errors, indicating that the precision of the variance estimates may be questionable.

Liquid matrices for analyses by UV-MALDI mass spectrometry

Data are presented for a pH-adjustable liquid UV-matrix-assisted laser desorption ionization (MALDI) matrix for mass spectrometry analysis. The liquid matrix system possesses high analytical sensitivity within the same order of magnitude as that achievable by the commonly used solid UV-MALDI matrices such as 2,5-dihydroxybenzoic acid but with improved spot homogeneity and reproducibility. The pH of the matrix has been adjusted by the addition of up to 0.35% trifluoroacetic acid and up to 200 mM ammonium bicarbonate, achieving an on-target pH range of 3.5-8.6. Alteration of the pH does not seem to affect the overall sample signal intensity or signal-to-noise ratio achievable, nor does it affect the individual peptide ion signals from a mixture of peptides with varying isoelectric points (p1). In addition, the pH adjustment has allowed for the performance of a tryptic digest within the diluted pH-optimized liquid matrix.

Reactions of hydroxyl radicals with trichloroethene and tetrachloroethene in argon matrices at 12 K

Irradiation of argon matrices at 12 K containing hydrogen peroxide and tetrachloroethene using the output from a medium-pressure mercury lamp gives rise to the carbonyl compound trichloroacetyl chloride (CCl3CClO). Similarly trichloroethene gives dichloroacetyl chloride ( CCl2HCClO) - predominantly in the gauche form - under the same conditions. It appears that the reaction is initiated by homolysis of the O-O bond of H2O2 to give OH radicals, one of which adds to the double bond of an alkene molecule. The reaction then proceeds by abstraction of the H atom of the hydroxyl group and Cl-atom migration. This mechanism has been explored by the use of DFT calculations to back up the experimental findings. The mechanism is analogous to that shown by the simple hydrocarbon alkenes.

A sparse kernel density estimation algorithm using forward constrained regression

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.

Sparse kernel modelling: a unified approach

A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.