Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

Liquid chromatography-mass spectrometry (LC-MS) datasets can be compared or combined following chromatographic alignment. Here we describe a simple solution to the specific problem of aligning one LC-MS dataset and one LC-MS/MS dataset, acquired on separate instruments from an enzymatic digest of a protein mixture, using feature extraction and a genetic algorithm. First, the LC-MS dataset is searched within a few ppm of the calculated theoretical masses of peptides confidently identified by LC-MS/MS. A piecewise linear function is then fitted to these matched peptides using a genetic algorithm with a fitness function that is insensitive to incorrect matches but sufficiently flexible to adapt to the discrete shifts common when comparing LC datasets. We demonstrate the utility of this method by aligning ion trap LC-MS/MS data with accurate LC-MS data from an FTICR mass spectrometer and show how hybrid datasets can improve peptide and protein identification by combining the speed of the ion trap with the mass accuracy of the FTICR, similar to using a hybrid ion trap-FTICR instrument. We also show that the high resolving power of FTICR can improve precision and linear dynamic range in quantitative proteomics. The alignment software, msalign, is freely available as open source.

Extraction and determination of ellagic acid content in chestnut bark and fruit

Chestnuts are an important economic resource in the chestnut growing regions, not only for the fruit, but also for the wood. The content of ellagic acid (EA), a naturally occurring inhibitor of carcinogenesis, was determined in chestnut fruits and bark. EA was extracted with methanol and free ellagic acid was determined by HPLC with UV detection, both in the crude extract and after hydrolysis. The concentration of EA was generally increased after hydrolysis due to the presence of ellagitannins in the crude extract. The concentration varied between 0.71 and 21.6 ing g(-1) (d.w.) in un-hydrolyzed samples, and between 2.83 and 18.4 mg g(-1) (d.w.) ill hydrolyzed samples. In chestnut fruits, traces of EA were present in the seed, with higher concentrations in the pellicle and pericarp. However, all fruit tissues had lower concentrations of EA than had the bark. The concentration of EA in the hydrolyzed samples showed a non-linear correlation with the concentration in the unhydrolyzed extracts. (C) 2008 Elsevier Ltd. All rights reserved.

Effect of a cellulase treatment on extraction of antioxidant phenols from black currant (Ribes nigrum L.) pomace

The effect of a commercial cellulase preparation on phenol liberation and extraction from black currant pomace was studied. The enzyme used, which was from Trichoderma spp., was an effective "cellulase-hemicellulase" blend with low P-glucosidase activity and various side activities. Enzyme treatment significantly increased plant cell wall polysaccharide degradation as well as increasing the availability of phenols for subsequent methanolic extraction. The release of anthocyanins and other phenols was dependent on reaction parameters, including enzyme dosage, temperature, and time. At 50 degrees C, anthocyanin yields following extraction increased by 44% after 3 h and by 60% after 1.5 h for the lower and higher enzyme/substrate ratio (E/S), respectively. Phenolic acids were more easily released in the hydrolytic mixture (supernatant) and, although a short hydrolysis time was adequate to release hydroxybenzoic acids (HBA), hydroxycinnamic acids (HCA) required longer times. The highest E/S value of 0.16 gave a significant increase of flavonol yields in all samples. The antioxidant capacity of extracts, assessed by scavenging of 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radical cation, the oxygen radical absorbance capacity, and the ferric reducing antioxidant potential depended on the concentration and composition of the phenols present.

Extraction of polyphenols from processed black currant (Ribes nigrum L.) residues

The total phenol and anthocyanin contents of black currant pomace and black currant press residue (BPR) extracts, extracted with formic acid in methanol or with methanol/water/acetic acid, were studied. Anthocyanins and other phenols were identified by means of reversed phase HPLC, and differences between the two plant materials were monitored. In all BPR extracts, phenol levels, determined by the Folin-Ciocalteu method, were 8-9 times higher than in the pomace extracts. Acid hydrolysis liberated a much higher concentration of phenols from the pomace than from the black currant press residue. HPLC analysis revealed that delphinidin-3-O-glucoside, delphinidin-3-O-rutinoside, cyanidin-3-O-glucoside, and cyanidin-3-O-rutinoside were the major anthocyanins and constituted the main phenol class (approximate to 90%) in both types of black currant tissues tested. However, anthocyanins were present in considerably lower amounts in the pomace than in the BPR. In accordance with the total phenol content, the antioxidant activity determined by scavenging of 2,2'-azinobis(3-ethylbenzothiazoline-6- sulfonic acid) radical cation, the ABTS(center dot+) assay, showed that BPR extracts prepared by solvent extraction exhibited significantly higher (7-10 times) radical scavenging activity than the pomace extracts, and BPR anthocyanins contributed significantly (74 and 77%) to the observed high radical scavenging capacity of the corresponding extracts.

Recovery of norbixin from a raw extraction solution of annatto pigments using colloidal gas aphrons (CGAs)

Annatto dyes are widely used in food and are finding increasing interest also for their application in the pharmaceutical and cosmetics industry. Bixin is the main pigment extracted from annatto seeds and accounts for 80% of the carotenoids in the outer coat of the seeds; norbixin being the water-soluble form of the bixin. Typically annatto dyes are extracted from the seeds by mechanical means or solutions of alkali, edible oil or organic solvents, or a combination of the two depending on the desired final product. In this work CGAs are investigated as an alternative separation method for the recovery of norbixin from a raw extraction solution of annatto pigments in KOH. A volume of CGAs generated from a cationic surfactant (CTAB) solution is mixed with a volume of annatto solution and when the mixture is allowed to settle it separates into the top aphron phase and the bottom liquid phase. Potassium norbixinate presented in the annatto solution will interact with the surfactant in the aphron phase, which results in the effective separation of norbixin. Recovery= 94% was achieved at a CTAB to norbixin molar ratio of 3.3. In addition a mechanism of separation is proposed here based on the separation results with the cationic surfactant and an anionic surfactant (bis-2-ethyl hexyl sulfosuccinate, AOT) and measurements of surfactant to norbixin ratio in the aphron phase; electrostatic interactions between the surfactant and norbixin molecules result in the fort-nation of a coloured complex and effective separation of norbixin. (c) 2005 Elsevier B.V. All rights reserved.

Antecedent priming at trace positions in children's sentence processing

The present study examines whether children reactivate a moved constituent at its gap position and how children's more limited working memory span affects the way they process filler-gap dependencies. 46 5-7 year-old children and 54 adult controls participated in a cross-modal picture priming experiment and underwent a standardized working memory test. The results revealed a statistically significant interaction between the participants' working memory span and antecedent reactivation: High-span children (n = 19) and high-span adults (n = 22) showed evidence of antecedent priming at the gap site, while for low-span children and adults, there was no such effect. The antecedent priming effect in the high-span participants indicates that in both children and adults, dislocated arguments access their antecedents at gap positions. The absence of an antecedent reactivation effect in the low-span participants could mean that these participants required more time to integrate the dislocated constituent and reactivated the filler later during the sentence.

Gaps in second language sentence processing

Four groups of second language (L2) learners of English from different language backgrounds (Chinese, Japanese, German, and Greek) and a group of native speaker controls participated in an online reading time experiment with sentences involving long-distance whdependencies. Although the native speakers showed evidence of making use of intermediate syntactic gaps during processing, the L2 learners appeared to associate the fronted wh-phrase directly with its lexical subcategorizer, regardless of whether the subjacency constraint was operative in their native language. This finding is argued to support the hypothesis that nonnative comprehenders underuse syntactic information in L2 processing.

Unifying syntactic theory and sentence processing difficulty through a connectionist minimalist parser

Syntactic theory provides a rich array of representational assumptions about linguistic knowledge and processes. Such detailed and independently motivated constraints on grammatical knowledge ought to play a role in sentence comprehension. However most grammar-based explanations of processing difficulty in the literature have attempted to use grammatical representations and processes per se to explain processing difficulty. They did not take into account that the description of higher cognition in mind and brain encompasses two levels: on the one hand, at the macrolevel, symbolic computation is performed, and on the other hand, at the microlevel, computation is achieved through processes within a dynamical system. One critical question is therefore how linguistic theory and dynamical systems can be unified to provide an explanation for processing effects. Here, we present such a unification for a particular account to syntactic theory: namely a parser for Stabler's Minimalist Grammars, in the framework of Smolensky's Integrated Connectionist/Symbolic architectures. In simulations we demonstrate that the connectionist minimalist parser produces predictions which mirror global empirical findings from psycholinguistic research.

Facial feature extraction and selection by Gabor wavelets and boosting

In this paper, we present a feature selection approach based on Gabor wavelet feature and boosting for face verification. By convolution with a group of Gabor wavelets, the original images are transformed into vectors of Gabor wavelet features. Then for individual person, a small set of significant features are selected by the boosting algorithm from a large set of Gabor wavelet features. The experiment results have shown that the approach successfully selects meaningful and explainable features for face verification. The experiments also suggest that for the common characteristics such as eyes, noses, mouths may not be as important as some unique characteristic when training set is small. When training set is large, the unique characteristics and the common characteristics are both important.

Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners' creative processes. (C) 2009 Published by Elsevier B.V.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.