Complex networks: the key to systems biology

Though introduced recently, complex networks research has grown steadily because of its potential to represent, characterize and model a wide range of intricate natural systems and phenomena. Because of the intrinsic complexity and systemic organization of life, complex networks provide a specially promising framework for systems biology investigation. The current article is an up-to-date review of the major developments related to the application of complex networks in biology, with special attention focused on the more recent literature. The main concepts and models of complex networks are presented and illustrated in an accessible fashion. Three main types of networks are covered: transcriptional regulatory networks, protein-protein interaction networks and metabolic networks. The key role of complex networks for systems biology is extensively illustrated by several of the papers reviewed.

Neural networks and statistical analysis for classification of corneal videokeratography maps based on Zernike coefficients: a quantitative comparison

PURPOSE: The main goal of this study was to develop and compare two different techniques for classification of specific types of corneal shapes when Zernike coefficients are used as inputs. A feed-forward artificial Neural Network (NN) and discriminant analysis (DA) techniques were used. METHODS: The inputs both for the NN and DA were the first 15 standard Zernike coefficients for 80 previously classified corneal elevation data files from an Eyesys System 2000 Videokeratograph (VK), installed at the Departamento de Oftalmologia of the Escola Paulista de Medicina, São Paulo. The NN had 5 output neurons which were associated with 5 typical corneal shapes: keratoconus, with-the-rule astigmatism, against-the-rule astigmatism, "regular" or "normal" shape and post-PRK. RESULTS: The NN and DA responses were statistically analyzed in terms of precision ([true positive+true negative]/total number of cases). Mean overall results for all cases for the NN and DA techniques were, respectively, 94% and 84.8%. CONCLUSION: Although we used a relatively small database, results obtained in the present study indicate that Zernike polynomials as descriptors of corneal shape may be a reliable parameter as input data for diagnostic automation of VK maps, using either NN or DA.

Inequalities in access and utilization of dental services: a cross-sectional study in an area covered by the Family Health Strategy

This cross-sectional study aimed to investigate the presence of inequalities in the access and use of dental services for people living in the coverage area of the Family Health Strategy (FHS) in Ponta Grossa, Paraná State, Brazil, and to assess individual determinants related to them. The sample consisted of 747 individuals who answered a pre-tested questionnaire. Data analysis was performed by chi-square test and Poisson regression analysis, obtaining explanatory models for recent use and, by limiting the analysis to those who sought dental care, for effective access. Results showed that 41% of the sample had recent dental visits. The lowest visit rates were observed among preschoolers and elderly people. The subjects who most identified the FHS as a regular source of dental care were children. Besides age, better socioeconomic conditions and the presence of a regular source of dental care were positively associated to recent dental visits. We identified inequalities in use and access to dental care, reinforcing the need to promote incentives to improve access for underserved populations.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Insight into Anopheles (Nyssorhynchus) (Diptera: Culicidae) species from Brazil full access

Anopheles (Nyssorhynchus) benarrochi s.l., Anopheles (Nyssorhynchus) oswaldoi s.l., and Anopheles (Nyssorhynchus) konderi s.l. collected in Acrelândia, state of Acre, Brazil, were identified based on morphological characters of the male genitalia, fourth-instar larvae, and pupae. Morphological variation was observed in the male genitalia of these species in comparison with specimens from other localities in Brazil. DNA sequence from the nuclear ribosomal second internal transcribed spacer of individuals identified as An. benarrochi s.l. by using male genitalia characteristics showed that the various morphological forms are conspecific but are distinct from An. benarrochi B from Colombia. Anopheles konderi s.l. and An. oswaldoi s.l. both misidentified as An. oswaldoi s.s. (Peryassú) throughout Brazil, may actually comprise at least two undescribed species. Diagnostic morphological characteristics of the male genitalia are provided to distinguish Anopheles benarrochi s.l., Anopheles oswaldoi s.l., and Anopheles konderi s.l. from morphologically similar species. Incrimination of An. oswaldoi s.s. in malaria transmission in Brazil needs further investigation because other undescribed species from Acre may have been confounded with this taxon

Efetividade de programa de agentes comunitários na promoção da saúde bucal

OBJETIVO:Avaliar mudanças em conhecimentos, atitudes e acesso/utilização de serviços odontológicos decorrentes de um programa de promoção da saúde bucal com agentes comunitários de saúde. MÉTODOS:Um projeto de capacitação combinando ensino-aprendizagem, apoio e supervisão, foi desenvolvido entre os meses de julho de 2003 a agosto de 2004. As mudanças foram avaliadas por meio de entrevistas estruturadas em que participaram 36 agentes comunitários de saúde e uma amostra de 91 mulheres e mães, representativa de donas de casa com 25 a 39 anos de idade, alfabetizadas e residentes em domicílios de três a seis cômodos no município de Rio Grande da Serra (SP). Foram colhidos dados sobre conhecimentos de saúde-doença bucal, práticas e capacidades auto-referidas em relação ao auto-exame, higiene bucal, número de residentes e de escovas dentais individuais e coletivas em cada domicílio e acesso e uso de serviços odontológicos. Por meio do teste t de Student pareado, foram comparadas as médias dos valores obtidos antes e depois do programa para cada um dos grupos estudados. As respostas foram analisadas adotando-se um nível de significância de 5%. RESULTADOS: Foram observadas diferenças estatisticamente significativas para questões relativas ao conhecimento de saúde bucal entre os agentes e entre as mulheres antes e depois da capacitação (p<0,05). Desequilíbrio entre o número de escovas e de indivíduos em cada família diminuiu. A freqüência da escovação e do uso do fio dental se elevou depois da atuação dos agentes. Os valores de auto-avaliação da higiene bucal aumentaram. Modificação nas práticas e capacidades auto-referidas mostrou significativa elevação da auto-confiança. O acesso ao serviço foi mais fácil (p<0,000) e seu uso mais regular (p<0,000) entre mulheres. CONCLUSÕES: Houve mudanças positivas na percepção em relação a aspectos de saúde bucal, na auto-confiança e no acesso e uso de serviços odontológicos. Tais mudanças po

Classification of breast masses using a committee machine of artificial neural networks

This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area A. under the receiver operating characteristics (ROC) curve. The A, result for the committee machine is compared with the A, results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier Tests are carried out using the student's t-distribution. The committee machine classifier outperforms the MLP SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the A, values of the four methods are, in order 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of A(z) (A(z) = 0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result. (c) 2008 SPIE and IS&T.

Dynamical models for computer viruses propagation

Nowadays, digital computer systems and networks are the main engineering tools, being used in planning, design, operation, and control of all sizes of building, transportation, machinery, business, and life maintaining devices. Consequently, computer viruses became one of the most important sources of uncertainty, contributing to decrease the reliability of vital activities. A lot of antivirus programs have been developed, but they are limited to detecting and removing infections, based on previous knowledge of the virus code. In spite of having good adaptation capability, these programs work just as vaccines against diseases and are not able to prevent new infections based on the network state. Here, a trial on modeling computer viruses propagation dynamics relates it to other notable events occurring in the network permitting to establish preventive policies in the network management. Data from three different viruses are collected in the Internet and two different identification techniques, autoregressive and Fourier analyses, are applied showing that it is possible to forecast the dynamics of a new virus propagation by using the data collected from other viruses that formerly infected the network. Copyright (c) 2008 J. R. C. Piqueira and F. B. Cesar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Fully Connected PLL Networks: How Filter Determines the Number of Nodes

Synchronization plays an important role in telecommunication systems, integrated circuits, and automation systems. Formerly, the masterslave synchronization strategy was used in the great majority of cases due to its reliability and simplicity. Recently, with the wireless networks development, and with the increase of the operation frequency of integrated circuits, the decentralized clock distribution strategies are gaining importance. Consequently, fully connected clock distribution systems with nodes composed of phase-locked loops (PLLs) appear as a convenient engineering solution. In this work, the stability of the synchronous state of these networks is studied in two relevant situations: when the node filters are first-order lag-lead low-pass or when the node filters are second-order low-pass. For first-order filters, the synchronous state of the network shows to be stable for any number of nodes. For second-order filter, there is a superior limit for the number of nodes, depending on the PLL parameters. Copyright (C) 2009 Atila Madureira Bueno et al.

A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Background: Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges. Results: The results shown here are for an adapted subset of a Saccharomyces cerevisiae gene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions. Conclusion: Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses.

The effect of laparoscopy access and antibiotics on the outcome of severe bacterial peritonitis in rats

Introduction: Treatment of severe bacterial peritonitis, especially by videolaparoscopy, is still a matter of investigation. The aim of the present study was to evaluate the effect of videolaparoscopy and laparotomy access with or without antibiotics on the outcome of severe bacterial peritonitis in rats. Materials and Methods: Sixty-four male Wistar rats were equally assigned to 8 groups: Sham surgery (SHAM), SHAM+antibiotics (SHAM+AB), cecal ligation and puncture (CLP), CLP+AB, CLP+videolaparoscopy (VLAP), CLP+laparotomy (LAP), VLAP+AB, and LAP+AB. All treated animals were submitted to an evaluation of bacteremia, white cell counts, and cytokine determinations: interleukin (IL)-1, IL-6, and tumor necrosis factor-alpha (TNF-alpha). The groups treated with antibiotics received gentamicin and metronidazole. Survival was monitored over a period of 7 days. Results: Peritonitis induced by CLP was severe, with IL-1, IL-6, and TNF-alpha levels and lethality being significantly higher compared to the SHAM group. The IL-6 levels in the VLAP group were significantly higher compared to the CLP and VLAP+AB groups, and the TNF-alpha levels in the VLAP and LAP+AB groups were significantly higher compared to the LAP group. The survival time was significantly higher in the CLP+AB and VLAP+AB groups, when compared to the CLP group. There was no significant difference in bacteremia and lethality rates between the resources employed for treatment of peritonitis. Conclusions: Although the use of laparoscopic access itself exacerbates the inflammatory response, the combination with antibiotics minimizes this effect and increases the survival time. However, all of the resources used for treating severe peritonitis, when applied alone or in combination, have an equivalent influence on bacteremia and lethality rates.

The Missing Part of Seed Dispersal Networks: Structure and Robustness of Bat-Fruit Interactions

Mutualistic networks are crucial to the maintenance of ecosystem services. Unfortunately, what we know about seed dispersal networks is based only on bird-fruit interactions. Therefore, we aimed at filling part of this gap by investigating bat-fruit networks. It is known from population studies that: (i) some bat species depend more on fruits than others, and (ii) that some specialized frugivorous bats prefer particular plant genera. We tested whether those preferences affected the structure and robustness of the whole network and the functional roles of species. Nine bat-fruit datasets from the literature were analyzed and all networks showed lower complementary specialization (H(2)' = 0.3760.10, mean 6 SD) and similar nestedness (NODF = 0.5660.12) than pollination networks. All networks were modular (M=0.32 +/- 0.07), and had on average four cohesive subgroups (modules) of tightly connected bats and plants. The composition of those modules followed the genus-genus associations observed at population level (Artibeus-Ficus, Carollia-Piper, and Sturnira-Solanum), although a few of those plant genera were dispersed also by other bats. Bat-fruit networks showed high robustness to simulated cumulative removals of both bats (R = 0.55 +/- 0.10) and plants (R = 0.68 +/- 0.09). Primary frugivores interacted with a larger proportion of the plants available and also occupied more central positions; furthermore, their extinction caused larger changes in network structure. We conclude that bat-fruit networks are highly cohesive and robust mutualistic systems, in which redundancy is high within modules, although modules are complementary to each other. Dietary specialization seems to be an important structuring factor that affects the topology, the guild structure and functional roles in bat-fruit networks.

Social Networks Shape the Transmission Dynamics of Hepatitis C Virus

Hepatitis C virus (HCV) infects 170 million people worldwide, and is a major public health problem in Brazil, where over 1% of the population may be infected and where multiple viral genotypes co-circulate. Chronically infected individuals are both the source of transmission to others and are at risk for HCV-related diseases, such as liver cancer and cirrhosis. Before the adoption of anti-HCV control measures in blood banks, this virus was mainly transmitted via blood transfusion. Today, needle sharing among injecting drug users is the most common form of HCV transmission. Of particular importance is that HCV prevalence is growing in non-risk groups. Since there is no vaccine against HCV, it is important to determine the factors that control viral transmission in order to develop more efficient control measures. However, despite the health costs associated with HCV, the factors that determine the spread of virus at the epidemiological scale are often poorly understood. Here, we sequenced partial NS5b gene sequences sampled from blood samples collected from 591 patients in Sao Paulo state, Brazil. We show that different viral genotypes entered Sao Paulo at different times, grew at different rates, and are associated with different age groups and risk behaviors. In particular, subtype 1b is older and grew more slowly than subtypes 1a and 3a, and is associated with multiple age classes. In contrast, subtypes 1a and 3b are associated with younger people infected more recently, possibly with higher rates of sexual transmission. The transmission dynamics of HCV in Sao Paulo therefore vary by subtype and are determined by a combination of age, risk exposure and underlying social network. We conclude that social factors may play a key role in determining the rate and pattern of HCV spread, and should influence future intervention policies.

Characterization of subgraph relationships and distribution in complex networks

A network can be analyzed at different topological scales, ranging from single nodes to motifs, communities, up to the complete structure. We propose a novel approach which extends from single nodes to the whole network level by considering non-overlapping subgraphs (i.e. connected components) and their interrelationships and distribution through the network. Though such subgraphs can be completely general, our methodology focuses on the cases in which the nodes of these subgraphs share some special feature, such as being critical for the proper operation of the network. The methodology of subgraph characterization involves two main aspects: (i) the generation of histograms of subgraph sizes and distances between subgraphs and (ii) a merging algorithm, developed to assess the relevance of nodes outside subgraphs by progressively merging subgraphs until the whole network is covered. The latter procedure complements the histograms by taking into account the nodes lying between subgraphs, as well as the relevance of these nodes to the overall subgraph interconnectivity. Experiments were carried out using four types of network models and five instances of real-world networks, in order to illustrate how subgraph characterization can help complementing complex network-based studies.

PARTITIONED ANALYSIS FOR DIMENSIONALLY-HETEROGENEOUS HYDRAULIC NETWORKS

In this work an iterative strategy is developed to tackle the problem of coupling dimensionally-heterogeneous models in the context of fluid mechanics. The procedure proposed here makes use of a reinterpretation of the original problem as a nonlinear interface problem for which classical nonlinear solvers can be applied. Strong coupling of the partitions is achieved while dealing with different codes for each partition, each code in black-box mode. The main application for which this procedure is envisaged arises when modeling hydraulic networks in which complex and simple subsystems are treated using detailed and simplified models, correspondingly. The potentialities and the performance of the strategy are assessed through several examples involving transient flows and complex network configurations.