Apectos conceituais e metodológicos de redes sociais e sua influência no estudo de fluxos de informação

As ligações e interações propiciadas pelas redes sociais permitem compreender como ocorrem os fluxos de informação entre indivíduos e instituições que unem esforços na busca de metas comuns. O artigo apresenta aspectos conceituais sobre redes e redes sociais ressaltando que a estrutura e as relações de interação e intermediação entre os elos da rede impulsionam mudanças nos fluxos de informação. Descreve a metodologia de Análise de Redes Sociais (ARS) sinalizando como esta pode ser utilizada na área da Ciência da Informação para compreender os fluxos de informação que se configuram e re-configuram nas redes sociais a partir da estrutura de relacionamento

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps

Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by edge maps to indicate the uncertainty involved in computing streamlines and topological structures.

ACTIVE NETWORKS THAT MAXIMIZE THE AMOUNT OF INFORMATION TRANSMISSION

This work clarifies the relationship between network circuit (topology) and behavior (information transmission and synchronization) in active networks, e. g. neural networks. As an application, we show how to determine a network topology that is optimal for information transmission. By optimal, we mean that the network is able to transmit a large amount of information, it possesses a large number of communication channels, and it is robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements) whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.

A rigorous approach to facilitate and guarantee the correctness of the genetic testing management in human genome information systems

Abstract Background Recent medical and biological technology advances have stimulated the development of new testing systems that have been providing huge, varied amounts of molecular and clinical data. Growing data volumes pose significant challenges for information processing systems in research centers. Additionally, the routines of genomics laboratory are typically characterized by high parallelism in testing and constant procedure changes. Results This paper describes a formal approach to address this challenge through the implementation of a genetic testing management system applied to human genome laboratory. We introduced the Human Genome Research Center Information System (CEGH) in Brazil, a system that is able to support constant changes in human genome testing and can provide patients updated results based on the most recent and validated genetic knowledge. Our approach uses a common repository for process planning to ensure reusability, specification, instantiation, monitoring, and execution of processes, which are defined using a relational database and rigorous control flow specifications based on process algebra (ACP). The main difference between our approach and related works is that we were able to join two important aspects: 1) process scalability achieved through relational database implementation, and 2) correctness of processes using process algebra. Furthermore, the software allows end users to define genetic testing without requiring any knowledge about business process notation or process algebra. Conclusions This paper presents the CEGH information system that is a Laboratory Information Management System (LIMS) based on a formal framework to support genetic testing management for Mendelian disorder studies. We have proved the feasibility and showed usability benefits of a rigorous approach that is able to specify, validate, and perform genetic testing using easy end user interfaces.

Comparison of flow cytometry and indirect immunofluorescence assay in the diagnosis and cure criterion after therapy of American tegumentary leishmaniasis by anti-live Leishmania (Viannia) braziliensis immunoglobulin G

The aim of this study was to compare the techniques of indirect immunofluorescence assay (IFA) and flow cytometry to clinical and laboratorial evaluation of patients before and after clinical cure and to evaluate the applicability of flow cytometry in post-therapeutic monitoring of patients with American tegumentary leishmaniasis (ATL). Sera from 14 patients before treatment (BT), 13 patients 1 year after treatment (AT), 10 patients 2 and 5 years AT were evaluated. The results from flow cytometry were expressed as levels of IgG reactivity, based on the percentage of positive fluorescent parasites (PPFP). The 1:256 sample dilution allowed us to differentiate individuals BT and AT. Comparative analysis of IFA and flow cytometry by ROC (receiver operating characteristic curve) showed, respectively, AUC (area under curve) = 0.8 (95% CI = 0.64–0.89) and AUC = 0.90 (95% CI = 0.75–0.95), demonstrating that the flow cytometry had equivalent accuracy. Our data demonstrated that 20% was the best cut-off point identified by the ROC curve for the flow cytometry assay. This test showed a sensitivity of 86% and specificity of 77% while the IFA had a sensitivity of 78% and specificity of 85%. The after-treatment screening, through comparative analysis of the technique performance indexes, 1, 2 and 5 years AT, showed an equal performance of the flow cytometry compared with the IFA. However, flow cytometry shows to be a better diagnostic alternative when applied to the study of ATL in the cure criterion. The information obtained in this work opens perspectives to monitor cure after treatment of ATL.