Investigation of liquid MALDI and optimization for instrument tuning and quantitative measurements

The success of Matrix-assisted laser desorption / ionisation (MALDI) in fields such as proteomics has partially but not exclusively been due to the development of improved data acquisition and sample preparation techniques. This has been required to overcome some of the short comings of the commonly used solid-state MALDI matrices such as - cyano-4-hydroxycinnamic acid (CHCA) and 2,5-dihydroxybenzoic acid (DHB). Solid state matrices form crystalline samples with highly inhomogeneous topography and morphology which results in large fluctuations in analyte signal intensity from spot to spot and positions within the spot. This means that efficient tuning of the mass spectrometer can be impeded and the use of MALDI MS for quantitative measurements is severely impeded. Recently new MALDI liquid matrices have been introduced which promise to be an effective alternative to crystalline matrices. Generally the liquid matrices comprise either ionic liquid matrices (ILMs) or a usually viscous liquid matrix which is doped with a UV lightabsorbing chromophore [1-3]. The advantages are that the droplet surface is smooth and relatively uniform with the analyte homogeneously distributed within. They have the ability to replenish a sampling position between shots negating the need to search for sample hot-spots. Also the liquid nature of the matrix allows for the use of additional additives to change the environment to which the analyte is added.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Use of the statistical properties of the wavelet-transform coefficients for optimization of integration time in Fourier transform spectrometry

We show that an analysis of the mean and variance of discrete wavelet coefficients of coaveraged time-domain interferograms can be used as a specification for determining when to stop coaveraging. We also show that, if a prediction model built in the wavelet domain is used to determine the composition of unknown samples, a stopping criterion for the coaveraging process can be developed with respect to the uncertainty tolerated in the prediction.

Quantitation of apolipoprotein B-48 in triacylglycerol-rich lipoproteins by a specific enzyme-linked immunosorbent assay

This paper describes the use of an antiserum, specific for apolipoprotein (apo) B-48, in a competitive, enzyme-linked immunosorbent assay (ELISA) for apo B-48 in triacylglycerol-rich lipoprotein (TRL) fractions prepared from fasting and post-prandial plasma samples. Previously we showed the antiserum to act as an effective immunoblotting agent following sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Its use in this ELISA indicates that the antiserum recognises the C-terminal region of the protein on the surface of lipoprotein particles. The ELISA had a sensitivity of less than 37 ng/ml and intra- and inter-assay coefficients of variation of 3.8% and 8.6%, respectively. There was no cross-reaction in the ELISA against serum albumin, ovalbumin, thyroglobulin, or apo B-100 (purified by immunoaffinity chromatography), and high lipid concentrations (as Intralipid) did not interfere. A low density lipoprotein fraction reacted in the ELISA but SDS-PAGE-Western blot analysis confirmed the presence, in the fraction, of a small amount of apo B-48, indicating the existence of low density dietary-derived lipoprotein particles. ELISA and SDS-PAGE-Western blot analysis were used to measure apo B-48 in 12 series of postprandial samples collected from 4 diabetic and 8 normal subjects, following test meals of varying fat content. The mean correlation between the two methods was r = 0.74. The mean fasting concentration of apo B-48 in the TRL fractions from 15 healthy men was 0.46 μg/ml of plasma.

Dynamic integrated system optimization and parameter estimation for discrete time optimal control of nonlinear systems

An algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimization and Parameter Estimation (DISOPE), which achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimization procedure. A version of the algorithm with a linear-quadratic model-based problem, implemented in the C+ + programming language, is developed and applied to illustrative simulation examples. An analysis of the optimality and convergence properties of the algorithm is also presented.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

Supramolecular structures of enzyme clusters

The structural characterization of subtilisin mesoscale clusters, which were previously shown to induce supramolecular order in biocatalytic self-assembly of Fmocdipeptides, was carried out by synchrotron small-angle X-ray, dynamic, and static light scattering measurements. Subtilisin molecules self-assemble to form supramolecular structures in phosphate buffer solutions. Structural arrangement of subtilisin clusters at 55 degrees Centigrade was found to vary systematically with increasing enzyme concentration. Static light scattering measurements showed the cluster structure to be consistent with a fractal-like arrangement, with fractal dimension varying from 1.8 to 2.6 with increasing concentration for low to moderate enzyme concentrations. This was followed by a structural transition around the enzyme concentration of 0.5 mg mL-1 to more compact structures with significantly slower relaxation dynamics, as evidenced by dynamic light scattering measurements. These concentration-dependent supramolecular enzyme clusters provide tunable templates for biocatalytic self-assembly.

Tendering: optimization and rationality

There have been various techniques published for optimizing the net present value of tenders by use of discounted cash flow theory and linear programming. These approaches to tendering appear to have been largely ignored by the industry. This paper utilises six case studies of tendering practice in order to establish the reasons for this apparent disregard. Tendering is demonstrated to be a market orientated function with many subjective judgements being made regarding a firm's environment. Detailed consideration of 'internal' factors such as cash flow are therefore judged to be unjustified. Systems theory is then drawn upon and applied to the separate processes of estimating and tendering. Estimating is seen as taking place in a relatively sheltered environment and as such operates as a relatively closed system. Tendering, however, takes place in a changing and dynamic environment and as such must operate as a relatively open system. The use of sophisticated methods to optimize the value of tenders is then identified as being dependent upon the assumption of rationality, which is justified in the case of a relatively closed system (i.e. estimating), but not for a relatively open system (i.e. tendering).

The platelet-surface thiol isomerase enzyme ERp57 modulates platelet function

Background: Thiol isomerases are a family of endoplasmic reticulum enzymes which orchestrate redox-based modifications of protein disulphide bonds. Previous studies have identified important roles for the thiol isomerases PDI and ERp5 in the regulation of normal platelet function. Objectives: Recently, we demonstrated the presence of a further five thiol isomerases at the platelet surface. In this report we aim to report the role of one of these enzymes - ERp57 in the regulation of platelet function. Methods/Results: Using enzyme activity function blocking antibodies, we demonstrate a role for ERp57 in platelet aggregation, dense granule secretion, fibrinogen binding, calcium mobilisation and thrombus formation under arterial conditions. In addition to the effects of ERp57 on isolated platelets, we observe the presence of ERp57 in the developing thrombus in vivo. Furthermore the inhibition of ERp57 function was found to reduce laser-injury induced arterial thrombus formation in a murine model of thrombosis. Conclusions: These data suggest that ERp57 is important for normal platelet function and opens up the possibility that the regulation of platelet function by a range of cell surface thiol isomerases may represent a broad paradigm for the regulation of haemostasis and thrombosis.

Production of angiotensin-I-converting enzyme (ACE) inhibitory activity in milk fermented with probiotic strains: Effects of calcium, pH and peptides on the ACE-inhibitory activity

Recently, probiotic fermented milk products have raised interest regarding their potential anti-hypertensive activity mainly due to the production of angiotensin-I-converting enzyme (ACE) inhibitory peptides. Ionic calcium released upon milk acidification during fermentation is also known to exert hypotensive activity. Thus, the main aim of this study was to screen probiotic strains for their ability to induce ACE-inhibitory activity upon fermentation of milk. The relationship of ACE-inhibitory activity percentage (ACEi%) with cell growth, pH, degree of hydrolysis and the concentration of ionic calcium released during the fermentation was also investigated. Compared with other lactic acid bacteria, Lactobacillus casei YIT 9029 and Bifidobacterium bifidum MF 20/5 were able to induce strong ACE-inhibitory activity. Furthermore, it was found that the ionic calcium released during milk fermentation could contribute to the ACE-inhibitory activity. These findings will contribute to the development of new probiotic dairy products with anti-hypertensive activity.

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

Temperature optimization of optical-phase conjugation in dye doped polymer-films

Experimental results of the temperature dependence of the nonlinear optical response of methyl red doped polymethylmethacrylate films in the range 20°C to 170°C are reported. It is found that the intensity of the phase conjugate signal resulting from degenerate four-wave mixing using pump and probe beams with parallel polarisation states increases dramatically on heating by a factor of ∼ 10, reaching a maximum at ∼ 100°C. The intensity of the phase conjugate signal for the case with crossed polarisation states of the pump and probe beams drops monotonically with increasing temperature. For both configurations the response time shortens with increasing temperature. The particular role of the polymer matrix in this temperature variation of the nonlinear optical response is discussed.