Kinetic resolution of a drug precursor by Burkholderia cepacia lipase immobilized by different methodologies on superparamagnetic nanoparticles

Burkholderia cepacia lipase was immobilized on superparamagnetic nanoparticles using three different methodologies (adsorption, chemisorption with carboxibenzaldehyde and chemisorption with glutaraldehyde) and employed in the kinetic resolution of a chiral drug precursor, (RS)-2-bromo-1-(phenyl)ethanol, via enantioselective acetylation reaction. An excellent improvement of lipase catalytical performance was observed. Free B. cepacia lipase gave the ester (S)-2 with poor E-value <30, and after its immobilization to magnetic nanoparticles the E-value was up to >200. The effect of several reaction parameters in the kinetic resolution was studied. The best results for kinetic resolution were obtained using vinyl acetate as acetyl donor and toluene as solvent, typically yielding the ester in high enantiomeric excess (>99%) and E-value (E > 200). Of the three tested immobilization methods, chemisorption with glutaraldehyde was the best one in terms of temperature stability and yield product. (C) 2010 Elsevier B.V. All rights reserved.

Enantioselective transesterification catalysis by Candida antarctica lipase immobilized on superparamagnetic nanoparticles

Lipase B from Candida antarctica can be directly immobilized onto functionalized superparamagnetic nanoparticles, preserving its enzymatic activity in the enantioselective transesterification of secondary alcohols, with excellent results in terms of enantiomeric discrimination. The immobilized enzyme can be easily recovered with a magnet, allowing its reuse with negligible loss of activity. (C) 2009 Elsevier Ltd. All rights reserved

Catalytic properties of thioredoxin immobilized on superparamagnetic nanoparticles

Thioredoxin (Trx1), a very important protein for regulating intracellular redox reactions, was immobilized on iron oxide superparamagnetic nanoparticles previously coated with 3-aminopropyltriethoxysilane (APTS) via covalent coupling using the EDC (1-ethyl-3-{3-dimethylaminopropyl}carbodiimide) method. The system was extensively characterized by atomic force microscopy, vibrational and magnetic techniques. In addition, gold nanoparticles were also employed to probe the exposed groups in the immobilized enzyme based on the SERS (surface enhanced Raman scattering) effect, confirming the accessibility of the cysteines residues at the catalytic site. For the single coated superparamagnetic nanoparticle, by monitoring the enzyme activity with the Ellman reagent, DTNB=5,5`-dithio-bis(2-15 nitrobenzoic acid), an inhibitory effect was observed after the first catalytic cycle. The inhibiting effect disappeared after the application of an additional silicate coating before the AFTS treatment, reflecting a possible influence of unprotected iron-oxide sites in the redox kinetics. In contrast, the doubly coated system exhibited a normal in-vitro kinetic activity, allowing a good enzyme recovery and recyclability. (C) 2011 Elsevier Inc. All rights reserved.

Magnetic coupled electrochemistry: Exploring the use of superparamagnetic nanoparticles for capturing, transporting and concentrating trace amounts of analytes

We propose the use of functionalized superparamagnetic nanoparticles for capturing, and transporting analytes, in association with an external miniature magnet to deposit such nanocarrier species at the electrode surface. This approach can be employed for the electroanalytical determination of chemical species capable of interacting with the nanoparticles, or in the opposite case, to block their response at the electrode surface. The concept was successfully demonstrated by using aminofunctionalized nanoparticles to block the discharge of hexacyanoferrate(II) ions, and to enhance the signals of aquapentacyanoferrate(II) ions via coordination to the surface amino groups. Selective analysis was also performed for silver ions, surpassing the stripping methods in terms of versatility and usefulness. (C) 2010 Elsevier B.V. All rights reserved.

Clustering Lab value working with medical data

Data mining is a relatively new field of research that its objective is to acquire knowledge from large amounts of data. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available [27]. On the one hand, practitioners are expected to use all this data in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. A major objective of this thesis is to evaluate data mining tools in medical and health care applications to develop a tool that can help make rather accurate decisions. In this thesis, the goal is finding a pattern among patients who got pneumonia by clustering of lab data values which have been recorded every day. By this pattern we can generalize it to the patients who did not have been diagnosed by this disease whose lab values shows the same trend as pneumonia patients does. There are 10 tables which have been extracted from a big data base of a hospital in Jena for my work .In ICU (intensive care unit), COPRA system which is a patient management system has been used. All the tables and data stored in German Language database.

Utilizando conceitos como descritores de textos para o processo de identificação de conglomerados (clustering) de documentos

A descoberta e a análise de conglomerados textuais são processos muito importantes para a estruturação, organização e a recuperação de informações, assim como para a descoberta de conhecimento. Isto porque o ser humano coleta e armazena uma quantidade muito grande de dados textuais, que necessitam ser vasculhados, estudados, conhecidos e organizados de forma a fornecerem informações que lhe dêem o conhecimento para a execução de uma tarefa que exija a tomada de uma decisão. É justamente nesse ponto que os processos de descoberta e de análise de conglomerados (clustering) se insere, pois eles auxiliam na exploração e análise dos dados, permitindo conhecer melhor seu conteúdo e inter-relações. No entanto, esse processo, por ser aplicado em textos, está sujeito a sofrer interferências decorrentes de problemas da própria linguagem e do vocabulário utilizado nos mesmos, tais como erros ortográficos, sinonímia, homonímia, variações morfológicas e similares. Esta Tese apresenta uma solução para minimizar esses problemas, que consiste na utilização de “conceitos” (estruturas capazes de representar objetos e idéias presentes nos textos) na modelagem do conteúdo dos documentos. Para tanto, são apresentados os conceitos e as áreas relacionadas com o tema, os trabalhos correlatos (revisão bibliográfica), a metodologia proposta e alguns experimentos que permitem desenvolver determinados argumentos e comprovar algumas hipóteses sobre a proposta. As conclusões principais desta Tese indicam que a técnica de conceitos possui diversas vantagens, dentre elas a utilização de uma quantidade muito menor, porém mais representativa, de descritores para os documentos, o que torna o tempo e a complexidade do seu processamento muito menor, permitindo que uma quantidade muito maior deles seja analisada. Outra vantagem está no fato de o poder de expressão de conceitos permitir que os usuários analisem os aglomerados resultantes muito mais facilmente e compreendam melhor seu conteúdo e forma. Além do método e da metodologia proposta, esta Tese possui diversas contribuições, entre elas vários trabalhos e artigos desenvolvidos em parceria com outros pesquisadores e colegas.

Contribuições aos Processos de Clustering com Base em Métricas não-Euclidianas

In this work we present a new clustering method that groups up points of a data set in classes. The method is based in a algorithm to link auxiliary clusters that are obtained using traditional vector quantization techniques. It is described some approaches during the development of the work that are based in measures of distances or dissimilarities (divergence) between the auxiliary clusters. This new method uses only two a priori information, the number of auxiliary clusters Na and a threshold distance dt that will be used to decide about the linkage or not of the auxiliary clusters. The number os classes could be automatically found by the method, that do it based in the chosen threshold distance dt, or it is given as additional information to help in the choice of the correct threshold. Some analysis are made and the results are compared with traditional clustering methods. In this work different dissimilarities metrics are analyzed and a new one is proposed based on the concept of negentropy. Besides grouping points of a set in classes, it is proposed a method to statistical modeling the classes aiming to obtain a expression to the probability of a point to belong to one of the classes. Experiments with several values of Na e dt are made in tests sets and the results are analyzed aiming to study the robustness of the method and to consider heuristics to the choice of the correct threshold. During this work it is explored the aspects of information theory applied to the calculation of the divergences. It will be explored specifically the different measures of information and divergence using the Rényi entropy. The results using the different metrics are compared and commented. The work also has appendix where are exposed real applications using the proposed method

Uma Aplicação do Algoritmo QT Clustering para Marcação Colaborativa de Pontos Perigosos em Vias Públicas

This work proposes a collaborative system for marking dangerous points in the transport routes and generation of alerts to drivers. It consisted of a proximity warning system for a danger point that is fed by the driver via a mobile device equipped with GPS. The system will consolidate data provided by several different drivers and generate a set of points common to be used in the warning system. Although the application is designed to protect drivers, the data generated by it can serve as inputs for the responsible to improve signage and recovery of public roads

Hybrid evolutionary algorithm for the Capacitated Centered Clustering Problem

The Capacitated Centered Clustering Problem (CCCP) consists of defining a set of p groups with minimum dissimilarity on a network with n points. Demand values are associated with each point and each group has a demand capacity. The problem is well known to be NP-hard and has many practical applications. In this paper, the hybrid method Clustering Search (CS) is implemented to solve the CCCP. This method identifies promising regions of the search space by generating solutions with a metaheuristic, such as Genetic Algorithm, and clustering them into clusters that are then explored further with local search heuristics. Computational results considering instances available in the literature are presented to demonstrate the efficacy of CS. (C) 2010 Elsevier Ltd. All rights reserved.

Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems

One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.