Étude sur l'utilisation de liquides ioniques à base imidazolium pour l'extraction sélective de phosphopeptides

La phosphorylation des protéines constitue l’une des plus importantes modifications post-traductionnelles (PTMs) et intervient dans de multiples processus physiologiques tels, la croissance, la différenciation cellulaire, l’apoptose, etc. En dépit de son importance, l’analyse des phosphoprotéines demeure une tâche difficile en raison de leur nature dynamique (car la phosphorylation des protéines est un processus réversible) et de leur faible abondance relative. En effet, la détermination des sites de phosphorylation est souvent difficile car les phosphopeptides sont souvent difficiles à détecter par des méthodes d’analyse chromatographique classique et par spectrométrie de masse (MS). De récentes études ont démontré que les nombreuses méthodes d’enrichissement de phosphopeptides existantes ne sont pas complètes, et que le nombre total de phosphopeptides détectés ne chevauchent pas complètement ces méthodes. C’est pour cela qu’il existe une nécessité de combler les lacunes des méthodes d’enrichissement existantes afin d’avoir des analyses phosphoprotéomiques plus complètes. Dans cette étude, nous avons utilisé les liquides ioniques (LI), plus particulièrement les sels d’imidazolium, comme une technique d’enrichissement alternative, dans le but de favoriser une extraction sélective de phosphopeptides présents en solution. Les sels d’imidazolium ont donc été utilisés en raison de leurs propriétés physico-chimiques "facilement" ajustables selon la nature des substituants sur le noyau imidazolium et la nature de l’anion. Les sels de monoimidazolium et de bis-imidazolium possédant respectivement des chaînes linéaires à 4, 12 et 16 atomes de carbone et ayant différents anions ont été synthétisés et utilisés pour effectuer des extractions liquide-liquide et solide-liquide des phosphopeptides en solution. Dans un premier temps, des extractions liquide-liquide ont été réalisées en utilisant un liquide ionique (LI) ayant une chaine linéaire de 4 atomes de carbone. Ces extractions réalisées avec le bis(trifluoromethanesulfonyl) amide de 3-butyl-1-methylimidazolium (BMIM-NTf2) et l’hexafluorophosphate de 3-butyl-1-methylimidazolium (BMIM-PF6) n’ont pas montré une extraction notable du PPS comparativement au PN. Dans un deuxième temps, des extractions solide-liquide ont été réalisées en fonctionnalisant des particules solides avec des sels d’imidazolium possédant des chaines linéaires de 12 ou 16 atomes de carbone. Ces extractions ont été faites en utilisant un phosphopentapeptide Ac-Ile-pTyr-Gly-Glu-Phe-NH2 (PPS) en présence de 2 analogues acides non-phosphorylés. Il a été démontré que les sels d’imidazolium à chaine C12 étaient meilleurs pour extraire le PPS que les deux autres peptides PN (Ac-Ile-Tyr-Gly-Glu-Phe-NH2) et PE (Ac-Glu-Tyr-Gly-Glu-Phe-NH2) L’électrophorèse capillaire (CE) et la chromatographie liquide à haute performance couplée à la spectrométrie de masse (LC-MS) ont été utilisées pour quantifier le mélange des trois peptides avant et après extraction ; dans le but de mesurer la sélectivité et l’efficacité d’extraction de ces peptides par rapport à la composition chimique du liquide ionique utilisé.

OFFLINE AND ONLINE SOLID PHASE EXTRACTION/PRECONCENTRATION OF INORGANICS

Solid phase extraction (SPE) is a powerful technique for preconcentration/removal or separation of trace and ultra trace amounts of toxic and nutrient elements. SPE effectively simplifies the labour intensive sample preparation, increase its reliability and eliminate the clean up step by using more selective extraction procedures. The synthesis of sorbents with a simplified procedure and diminution of the risks of errors shows the interest in the areas of environmental monitoring, geochemical exploration, food, agricultural, pharmaceutical, biochemical industry and high purity metal designing, etc. There is no universal SPE method because the sample pretreatment depends strongly on the analytical demand. But there is always an increasing demand for more sensitive, selective, rapid and reliable analytical procedures. Among the various materials, chelate modified naphthalene, activated carbon and chelate functionalized highly cross linked polymers are most important. In the biological and environmental field, large numbers of samples are to be analysed within a short span of time. Hence, online flow injection methods are preferred as they allow extraction, separation, identification and quantification of many numbers of analytes. The flow injection online preconcentration flame AAS procedure developed allows the determination of as low as 0.1 µg/l of nickel in soil and cobalt in human hair samples. The developed procedure is precise and rapid and allows the analysis of 30 samples per hour with a loading time of 60 s. The online FI manifold used in the present study permits high sampling, loading rates and thus resulting in higher preconcentration/enrichment factors of -725 and 600 for cobalt and nickel respectively with a 1 min preconcentration time compared to conventional FAAS signal. These enrichment factors are far superior to hitherto developed on line preconcentration procedures for inorganics. The instrumentation adopted in the present study allows much simpler equipment and low maintenance costs compared to costlier ICP-AES or ICP-MS instruments.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH RECOGNITION

Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more natu-rally and efficiently. In this work, a speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech and hence feature extraction method plays an important role in speech recognition. Here, front end processing for extracting the features is per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose. After classification using Naive Bayes classifier, DWT produced a recognition accuracy of 83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Relative proportions of polycyclic aromatic hydrocarbons differ between accumulation bioassays and chemical methods to predict bioavailability

Chemical methods to predict the bioavailable fraction of organic contaminants are usually validated in the literature by comparison with established bioassays. A soil spiked with polycyclic aromatic hydrocarbons (PAHs) was aged over six months and subjected to butanol, cyclodextrin and tenax extractions as well as an exhaustive extraction to determine total PAH concentrations at several time points. Earthworm (Eisenia fetida) and rye grass root (Lolium multiflorum) accumulation bioassays were conducted in parallel. Butanol extractions gave the best relationship with earthworm accumulation (r2 ≤ 0.54, p ≤ 0.01); cyclodextrin, butanol and acetone–hexane extractions all gave good predictions of accumulation in rye grass roots (r2 ≤ 0.86, p ≤ 0.01). However, the profile of the PAHs extracted by the different chemical methods was significantly different (p < 0.01) to that accumulated in the organisms. Biota accumulated a higher proportion of the heavier 4-ringed PAHs. It is concluded that bioaccumulation is a complex process that cannot be predicted by measuring the bioavailable fraction alone. The ability of chemical methods to predict PAH accumulation in Eisenia fetida and Lolium multiflorum was hindered by the varied metabolic fate of the different PAHs within the organisms.

Fate of prions in soil: Detergent extraction of PrP from soils

The transmissible spongiform encephalopathies (TSEs) are caused by infectious agents whose structures have not been fully characterized but include abnormal forms of the host protein PrP, designated PrPSc, which are deposited in infected tissues. The transmission routes of scrapie and chronic wasting disease (CWD) seem to include environmental spread in their epidemiology, yet the fate of TSE agents in the environment is poorly understood. There are concerns that, for example, buried carcasses may remain a potential reservoir of infectivity for many years. Experimental determination of the environmental fate requires methods for assessing binding/elution of TSE infectivity, or its surrogate marker PrPSc, to and from materials with which it might interact. We report a method using Sarkosyl for the extraction of murine PrPSc, and its application to soils containing recombinant ovine PrP (recPrP). Elution properties suggest that PrP binds strongly to one or more soil components. Elution from a clay soil also required proteinase K digestion, suggesting that in the clay soil binding occurs via the N-terminal of PrP to a component that is absent from the sandy soils tested.

Evaluating the efficiency of sampling methods in assessing soil macrofauna communities in arable systems

The soil fauna is often a neglected group in many large-scale studies of farmland biodiversity due to difficulties in extracting organisms efficiently from the soil. This study assesses the relative efficiency of the simple and cheap sampling method of handsorting against Berlese-Tullgren funnel and Winkler apparatus extraction. Soil cores were taken from grassy arable field margins and wheat fields in Cambridgeshire, UK, and the efficiencies of the three methods in assessing the abundances and species densities of soil macroinver-tebrates were compared. Handsorting in most cases was as efficient at extracting the majority of the soil macrofauna as the Berlese-Tullgren funnel and Winkler bag methods, although it underestimated the species densities of the woodlice and adult beetles. There were no obvious biases among the three methods for the particular vegetation types sampled and no significant differences in the size distributions of the earthworms and beetles. Proportionally fewer damaged earthworms were recorded in larger (25 x 25 cm) soil cores when compared with smaller ones (15 x 15 cm). Handsorting has many benefits, including targeted extraction, minimum disturbance to the habitat and shorter sampling periods and may be the most appropriate method for studies of farmland biodiversity when a high number of soil cores need to be sampled. (C) 2008 Elsevier Masson SAS. All rights reserved.

Extraction and coordination studies of the unexplored bifunctional ligand carbamoyl methyl sulfoxide (CMSO) with uranium(VI) and cerium(III) nitrates. Synthesis and structures of [UO2(NO3)(2)((PhSOCH2CONBu2)-Bu-i)] and [Ce(NO3)(3)(PhSOCH2CONBu2)(2)]

The bifunctional carbamoyl methyl sulfoxide ligands, PhCH2SOCH2CONHPh (L-1), PhCH2SOCH2CONHCH2Ph (L-2), (PhSOCH2CONPr2)-Pr-i (L-3), PhSOCH2CONBu2 (L-4), (PhSOCH2CONBu2)-Bu-i (L-5) and PhSOCH2CON(C8H17)(2) (L-6) have been synthesized and characterized by spectroscopic methods. The selected coordination chemistry of L-1, L-3, L-4 and L-5 with [UO2(NO3)(2)] and [Ce(NO3)(3)] has been evaluated. The structures of the compounds [UO2(NO3)(2)((PhSOCH2CONBu2)-Bu-i)] (10) and [Ce(NO3)(3)(PhSOCH2CONBu2)(2)] (12) have been determined by single crystal X-ray diffraction methods. Preliminary extraction studies of ligand L-6 with U(VI), Pu(IV) and Am(III) in tracer level showed an appreciable extraction for U(VI) and Pu(IV) in up to 10 M HNO3 but not for Am(III). Thermal studies on compounds 8 and 10 in air revealed that the ligands can be destroyed completely on incineration. The electron spray mass spectra of compounds 8 and 10 in acetone show that extensive ligand distribution reactions occur in solution to give a mixture of products with ligand to metal ratios of 1 : 1 and 2 : 1. However, 10 retains its solid state structure in CH2Cl2.

Coordination and extraction studies of an unexplored bi-functional ligand, carbamoyl methyl pyrazole (CMPz) with uranium(VI), lanthanum(III) and cerium(III) nitrates

The bi-functional carbamoyl methyl pyrazole ligands, C5H7N2CH2CONBu2 (L-1), (C5H7N2CH2CONBu2)-Bu-i (L-2), C3H3N2CH2CONBu2 (L-3), (C3H3N2CH2CONBu2)-Bu-i (L-4) and C5H7N2CH2CON(C8H17)(2) (L-5) were synthesized and characterized by spectroscopic and elemental analysis methods. The selected coordination chemistry of L-1 to L-4 with [UO2(NO3)(2)center dot 6H(2)O], [La(NO3)(3)center dot 6H(2)O] and [Ce(NO3)(3)center dot 6H(2)O] has been evaluated. Structures for the compounds [UO2(NO3)(2) C5H7N2CH2CONBu2] (6) [UO2(NO3)(2) (C5H7N2CHCONBu2)-Bu-i] (7) and [Ce(NO3)(3){C(3)H(3)N(2)CH(2)CON(i)Bu2}(2)] (11) have been determined by single crystal X-ray diffraction methods. Preliminary extraction studies of the ligand L-5 with U(VI) and Pu(IV) in tracer level showed an appreciable extraction for U(VI) and Pu(TV) up to 10 M HNO3 but not for Am(III). Thermal studies of the compounds 6 and 7 in air revealed that the ligands can be destroyed completely on incineration. (c) 2007 Elsevier Ltd. All rights reserved.

Fecal water as a non-invasive biomarker in nutritional intervention: comparison of preparation methods and refinement of different endpoints

The assessment of cellular effects by the aqueous phase of human feces (fecal water, FW) is a useful biomarker approach to study cancer risks and protective activities of food. In order to refine and develop the biomarker, different protocols of preparing FW were compared. Fecal waters were prepared by 3 methods: (A) direct centrifugation; (B) extraction of feces in PBS before centrifugation; and (C) centrifugation of lyophilized and reconstituted feces. Genotoxicity was determined in colon cells using the Comet assay. Selected samples were investigated for additional parameters related to carcinogenesis. Two of 7 FWs obtained by methods A and B were similarly genotoxic. Method B, however, yielded higher volumes of FW, allowing sterile filtration for long-term culture experiments. Four of 7 samples were non-genotoxic when prepared according to all 3 methods. FW from lyophilized feces and from fresh samples were equally genotoxic. FWs modulated cytotoxicity, paracellular permeability, and invasion, independent of their genotoxicity. All 3 methods of FW preparation can be used to assess genotoxicity. The higher volumes of FWobtained by preparation method B greatly enhance the perspectives of measuring different types of biological parameters and using these to disclose activities related to cancer development.

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 comparison of automated keyphrase extraction techniques and of automatic evaluation vs. human evaluation

Keyphrases are added to documents to help identify the areas of interest they contain. However, in a significant proportion of papers author selected keyphrases are not appropriate for the document they accompany: for instance, they can be classificatory rather than explanatory, or they are not updated when the focus of the paper changes. As such, automated methods for improving the use of keyphrases are needed, and various methods have been published. However, each method was evaluated using a different corpus, typically one relevant to the field of study of the method’s authors. This not only makes it difficult to incorporate the useful elements of algorithms in future work, but also makes comparing the results of each method inefficient and ineffective. This paper describes the work undertaken to compare five methods across a common baseline of corpora. The methods chosen were Term Frequency, Inverse Document Frequency, the C-Value, the NC-Value, and a Synonym based approach. These methods were analysed to evaluate performance and quality of results, and to provide a future benchmark. It is shown that Term Frequency and Inverse Document Frequency were the best algorithms, with the Synonym approach following them. Following these findings, a study was undertaken into the value of using human evaluators to judge the outputs. The Synonym method was compared to the original author keyphrases of the Reuters’ News Corpus. The findings show that authors of Reuters’ news articles provide good keyphrases but that more often than not they do not provide any keyphrases.

Implementation of an interior point source in the ultra weak variational formulation through source extraction

The Ultra Weak Variational Formulation (UWVF) is a powerful numerical method for the approximation of acoustic, elastic and electromagnetic waves in the time-harmonic regime. The use of Trefftz-type basis functions incorporates the known wave-like behaviour of the solution in the discrete space, allowing large reductions in the required number of degrees of freedom for a given accuracy, when compared to standard finite element methods. However, the UWVF is not well disposed to the accurate approximation of singular sources in the interior of the computational domain. We propose an adjustment to the UWVF for seismic imaging applications, which we call the Source Extraction UWVF. Differing fields are solved for in subdomains around the source, and matched on the inter-domain boundaries. Numerical results are presented for a domain of constant wavenumber and for a domain of varying sound speed in a model used for seismic imaging.

Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification.