Infrared microspectroscopy of inflammatory process and colon tumors.

According to the last global burden of disease published by the World Health Organization, tumors were the third leading cause of death worldwide in 2004. Among the different types of tumors, colorectal cancer ranks as the fourth most lethal. To date, tumor diagnosis is based mainly on the identification of morphological changes in tissues. Considering that these changes appears after many biochemical reactions, the development of vibrational techniques may contribute to the early detection of tumors, since they are able to detect such reactions. The present study aimed to develop a methodology based on infrared microspectroscopy to characterize colon samples, providing complementary information to the pathologist and facilitating the early diagnosis of tumors. The study groups were composed by human colon samples obtained from paraffin-embedded biopsies. The groups are divided in normal (n=20), inflammation (n=17) and tumor (n=18). Two adjacent slices were acquired from each block. The first one was subjected to chemical dewaxing and H&E staining. The infrared imaging was performed on the second slice, which was not dewaxed or stained. A computational preprocessing methodology was employed to identify the paraffin in the images and to perform spectral baseline correction. Such methodology was adapted to include two types of spectral quality control. Afterwards the preprocessing step, spectra belonging to the same image were analyzed and grouped according to their biochemical similarities. One pathologist associated each obtained group with some histological structure based on the H&E stained slice. Such analysis highlighted the biochemical differences between the three studied groups. Results showed that severe inflammation presents biochemical features similar to the tumors ones, indicating that tumors can develop from inflammatory process. A spectral database was constructed containing the biochemical information identified in the previous step. Spectra obtained from new samples were confronted with the database information, leading to their classification into one of the three groups: normal, inflammation or tumor. Internal and external validation were performed based on the classification sensitivity, specificity and accuracy. Comparison between the classification results and H&E stained sections revealed some discrepancies. Some regions histologically normal were identified as inflammation by the classification algorithm. Similarly, some regions presenting inflammatory lesions in the stained section were classified into the tumor group. Such differences were considered as misclassification, but they may actually evidence that biochemical changes are in course in the analyzed sample. In the latter case, the method developed throughout this thesis would have proved able to identify early stages of inflammatory and tumor lesions. It is necessary to perform additional experiments to elucidate this discrepancy between the classification results and the morphological features. One solution would be the use of immunohistochemistry techniques with specific markers for tumor and inflammation. Another option includes the recovering of the medical records of patients who participated in this study in order to check, in later times to the biopsy collection, whether they actually developed the lesions supposedly detected in this research.

Empirical study of the anaphoric accessibility space in Spanish dialogues

This paper shows an empirical study about the anaphoric accessibility space in Spanish dialogues. According to this study, antecedents of pronominal and adjectival anaphors can almost always (95.9%) be found in the noun phrases set taken from spaces defined using a structure based on adjacency pairs. Furthermore, a proposal of a reliable annotation scheme for Spanish dialogues is presented in order to define this anaphoric accessibility space. Using this annotation scheme, anaphora resolution algorithms can locate the adequate set of anaphor antecedent candidates.

Enzymatic characterization and homology model of a catalytically active recombinant West Nile virus NS3 protease

West Nile Virus (WNV) is a mosquito-borne flavivirus with a rapidly expanding global distribution. Infection causes severe neurological disease and fatalities in both human and animal hosts. The West Nile viral protease (NS2B-NS3) is essential for post-translational processing in host-infected cells of a viral polypeptide precursor into structural and functional viral proteins, and its inhibition could represent a potential treatment for viral infections. This article describes the design, expression, and enzymatic characterization of a catalytically active recombinant WNV protease, consisting of a 40-residue component of cofactor NS2B tethered via a noncleavable nonapeptide (G(4)SG(4)) to the N-terminal 184 residues of NS3. A chromogenic assay using synthetic para-nitroanilide (pNA) hexapeptide substrates was used to identify optimal enzyme-processing conditions (pH 9.5, I < 0.1 M, 30% glycerol, 1 mM CHAPS), preferred substrate cleavage sites, and the first competitive inhibitor (Ac-FASGKR- H, IC50 &SIM; 1 μM). A putative three-dimensional structure of WNV protease, created through homology modeling based on the crystal structures of Dengue-2 and Hepatitis C NS3 viral proteases, provides some valuable insights for structure-based design of potent and selective inhibitors of WNV protease.

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

Selecting optimal instantiations of data models - Theory and validation of an ex ante approach

The schema of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. Obtaining quickly the appropriate data increases the likelihood that an organization will make good decisions and respond adeptly to challenges. This research presents and validates a methodology for evaluating, ex ante, the relative desirability of alternative instantiations of a model of data. In contrast to prior research, each instantiation is based on a different formal theory. This research theorizes that the instantiation that yields the lowest weighted average query complexity for a representative sample of information requests is the most desirable instantiation for end-user queries. The theory was validated by an experiment that compared end-user performance using an instantiation of a data structure based on the relational model of data with performance using the corresponding instantiation of the data structure based on the object-relational model of data. Complexity was measured using three different Halstead metrics: program length, difficulty, and effort. For a representative sample of queries, the average complexity using each instantiation was calculated. As theorized, end users querying the instantiation with the lower average complexity made fewer semantic errors, i.e., were more effective at composing queries. (c) 2005 Elsevier B.V. All rights reserved.

Prediction of HLA-DQ3.2 beta ligands: evidence of multiple registers in class II binding peptides

Motivation: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. Methods: We describe a structure-based prediction model for modeling peptide-DQ3.2 beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2 beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2 beta binding and non-binding peptides obtained from biochemical and functional studies. Results: Our model predicts DQ3.2 beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2 beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.

CAPITALISMO COMO RELIGIÃO: Uma crítica a seus fundamentos mítico-teológicos.

Seria possível compreender o capitalismo como religião? Nos marcos categoriais da Modernidade, baseada na racionalização e na secularização, relacionar economia e religião é um contrassenso. O capitalismo é sistema econômico secular, portanto sem relação com religião. Entretanto, se a crítica do capitalismo como religião não se reduz a uma simples metáfora, é necessário encontrar conceitos alternativos que captem a força teórica desta articulação. Que tipo de quadro analítico desvela os limites da razão instrumental em explicitar o funcionamento religioso do capitalismo? A profundidade crítica de capitalismo como religião advém justamente da junção intrigante entre a análise racional do funcionamento estrutural do capitalismo (fetiche) com a dimensão subjetiva que o impulsiona como motivação (espírito). Mesmo sendo um sistema racional e não-religioso, que submete a vida humana a suas leis internas desprovidas de qualquer sentido humano, o capitalismo desenvolve não-intencionalmente na interação humana uma estrutura de funcionamento com fundamento mítico-religioso sacrificial. As relações humanas são mediadas pelas mercadorias, em que o consumo adquire um aspecto central na significação da vida e na reprodução simbólica da sociedade. Na produção e distribuição de mercadorias, o processo de violência que explora, exclui e mata é o mesmo que gera fascínio e adesão. A expressão visível deste espírito não está mais nas tradicionais instituições religiosas, mas no próprio capitalismo. Benjamin afirma que o capitalismo substitui a religião. É uma crítica de um sistema de culpabilização das vítimas e dos próprios capitalistas, na medida em que estes nunca acumulam de modo infinito e pleno. É uma denúncia dos elementos míticos que geram legitimação religiosa para o fascínio que oculta a barbárie. Os teólogos da Escola do DEI também articulam sua teoria com finalidade crítica, numa abordagem teológica que procura discernir e criticar a idolatria no mundo de hoje. Buscam entender os mecanismos de produção de morte com a culpabilização das vítimas como sacrifício necessário em nome da esperança de redenção. O discernimento teológico de idolatria do capital supõe um tipo de razão teológica de caráter não-confessional que, superando os limites da epistemologia moderna, explicite a contradição dos pressupostos da civilização moderna ocidental. Revela o papel do pensamento mítico-teológico na ocultação do caráter sacrificial e sedutor do espírito do capitalismo. Ao mesmo tempo, enfatiza a necessária superação da interpretação positivista da religião ao criticar o reducionismo da epistemologia moderna na identificação da razão instrumental com a racionalidade humana. Renova o instrumental analítico da configuração espiritual do Capitalismo e vislumbra as brechas de sua superação.

ANÁLISE DAS MUDANÇAS DOS FATORES DETERMINANTES DA ESTRUTURA DE CAPITAL EM FUNÇÃO DO CICLO DE VIDA DAS EMPRESAS BRASILEIRAS SÃO BERNARDO DO CAMPO

As várias teorias acerca da estrutura de capital despertam interesse motivando diversos estudos sobre o assunto sem, no entanto, ter um consenso. Outro tema aparentemente pouco explorado refere-se ao ciclo de vida das empresas e como ele pode influenciar a estrutura de capital. Este estudo teve como objetivo verificar quais determinantes possuem maior relevância no endividamento das empresas e se estes determinantes alteram-se dependendo do ciclo de vida da empresa apoiada pelas teorias Trade Off, Pecking Order e Teoria da Agência. Para alcançar o objetivo deste trabalho foi utilizado análise em painel de efeito fixo sendo a amostra composta por empresas brasileiras de capital aberto, com dados secundários disponíveis na Economática® no período de 2005 a 2013, utilizando-se os setores da BM&FBOVESPA. Como resultado principal destaca-se o mesmo comportamento entre a amostra geral, alto e baixo crescimento pelo endividamento contábil para o determinante Lucratividade apresentando uma relação negativa, e para os determinantes Oportunidade de Crescimento e Tamanho, estes com uma relação positiva. Para os grupos de alto e baixo crescimento alguns determinantes apresentaram resultados diferentes, como a singularidade que resultou significância nestes dois grupos, sendo positiva no baixo crescimento e negativa no alto crescimento, para o valor colateral dos ativos e benefício fiscal não dívida apresentaram significância apenas no grupo de baixo crescimento. Para o endividamento a valor de mercado foi observado significância para o Benefício fiscal não dívida e Singularidade. Este resultado reforça o argumento de que o ciclo de vida influência a estrutura de capital

Toward the discovery of vaccine adjuvants:coupling in silico screening and in vitro analysis of antagonist binding to human and mouse CCR4 receptors

Background Adjuvants enhance or modify an immune response that is made to an antigen. An antagonist of the chemokine CCR4 receptor can display adjuvant-like properties by diminishing the ability of CD4+CD25+ regulatory T cells (Tregs) to down-regulate immune responses. Methodology Here, we have used protein modelling to create a plausible chemokine receptor model with the aim of using virtual screening to identify potential small molecule chemokine antagonists. A combination of homology modelling and molecular docking was used to create a model of the CCR4 receptor in order to investigate potential lead compounds that display antagonistic properties. Three-dimensional structure-based virtual screening of the CCR4 receptor identified 116 small molecules that were calculated to have a high affinity for the receptor; these were tested experimentally for CCR4 antagonism. Fifteen of these small molecules were shown to inhibit specifically CCR4-mediated cell migration, including that of CCR4+ Tregs. Significance Our CCR4 antagonists act as adjuvants augmenting human T cell proliferation in an in vitro immune response model and compound SP50 increases T cell and antibody responses in vivo when combined with vaccine antigens of Mycobacterium tuberculosis and Plasmodium yoelii in mice.

HLA-DP2 binding prediction by molecular dynamics simulations

Major histocompatibility complex (MHC) II proteins bind peptide fragments derived from pathogen antigens and present them at the cell surface for recognition by T cells. MHC proteins are divided into Class I and Class II. Human MHC Class II alleles are grouped into three loci: HLA-DP, HLA-DQ, and HLA-DR. They are involved in many autoimmune diseases. In contrast to HLA-DR and HLA-DQ proteins, the X-ray structure of the HLA-DP2 protein has been solved quite recently. In this study, we have used structure-based molecular dynamics simulation to derive a tool for rapid and accurate virtual screening for the prediction of HLA-DP2-peptide binding. A combinatorial library of 247 peptides was built using the "single amino acid substitution" approach and docked into the HLA-DP2 binding site. The complexes were simulated for 1 ns and the short range interaction energies (Lennard-Jones and Coulumb) were used as binding scores after normalization. The normalized values were collected into quantitative matrices (QMs) and their predictive abilities were validated on a large external test set. The validation shows that the best performing QM consisted of Lennard-Jones energies normalized over all positions for anchor residues only plus cross terms between anchor-residues.

Molecular modelling assisted design and synthesis of cyclothialidine derivatives as DNA gyrase inhibitors

Since cyclothialidine was discovered as the most active DNA gyrase inhibitor in 1994, enormous efforts have been devoted to make it into a commercial medicine by a number of pharmaceutical companies and research groups worldwide. However, no serious breakthrough has been made up to now. An essential problem involved with cyclothialidine is that though it demonstrated the potent inhibition of DNA gyrase, it showed little activity against bacteria. This probably is attributable to its inability to penetrate bacterial cell walls and membranes. We applied the TSAR programme to generate a QSAR equation to the gram-negative organisms. In that equation, LogP is profoundly indicated as the key factor influencing the cyclothialidine activity against bacteria. However, the synthesized new analogues have failed to prove that. In the structure based drug design stage, we designed a group of open chain cyclothialidine derivatives by applying the SPROUT programme and completed the syntheses. Improved activity is found in a few analogues and a 3D pharmacophore of the DNA gyrase B is proposed to lead to synthesis of the new derivatives for development of potent antibiotics.

Relieving the first bottleneck in the drug discovery pipeline:using array technologies to rationalize membrane protein production

The slow down in the drug discovery pipeline is, in part, owing to a lack of structural and functional information available for new drug targets. Membrane proteins, the targets of well over 50% of marketed pharmaceuticals, present a particular challenge. As they are not naturally abundant, they must be produced recombinantly for the structural biology that is a prerequisite to structure-based drug design. Unfortunately, however, obtaining high yields of functional, recombinant membrane proteins remains a major bottleneck in contemporary bioscience. While repeated rounds of trial-and-error optimization have not (and cannot) reveal mechanistic details of the biology of recombinant protein production, examination of the host response has provided new insights. To this end, we published an early transcriptome analysis that identified genes implicated in high-yielding yeast cell factories, which has enabled the engineering of improved production strains. These advances offer hope that the bottleneck of membrane protein production can be relieved rationally.

Systematic identification of small molecule adjuvants

Introduction: Adjuvants potentiate immune responses, reducing the amount and dosing frequency of antigen required for inducing protective immunity. Adjuvants are of special importance when considering subunit, epitope-based or more unusual vaccine formulations lacking significant innate immunogenicity. While numerous adjuvants are known, only a few are licensed for human use; principally alum, and squalene-based oil-in-water adjuvants. Alum, the most commonly used, is suboptimal. There are many varieties of adjuvant: proteins, oligonucleotides, drug-like small molecules and liposome-based delivery systems with intrinsic adjuvant activity being perhaps the most prominent. Areas covered: This article focuses on small molecules acting as adjuvants, with the author reviewing their current status while highlighting their potential for systematic discovery and rational optimisation. Known small molecule adjuvants (SMAs) can be synthetically complex natural products, small oligonucleotides or drug-like synthetic molecules. The author provides examples of each class, discussing adjuvant mechanisms relevant to SMAs, and exploring the high-throughput discovery of SMAs. Expert opinion: SMAs, particularly synthetic drug-like adjuvants, are amenable to the plethora of drug-discovery techniques able to optimise the properties of biologically active small molecules. These range from laborious synthetic modifications to modern, rational, effort-efficient computational approaches, such as QSAR and structure-based drug design. In principal, any property or characteristic can thus be designed in or out of compounds, allowing us to tailor SMAs to specific biological functions, such as targeting specific cells or pathways, in turn affording the power to tailor SMAs to better address different diseases.