Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs-a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.

Identification of protein-coding and non-coding RNA expression profiles in CD34(+) and in stromal cells in refractory anemia with ringed sideroblasts

Background: Myelodysplastic syndromes (MDS) are a group of clonal hematological disorders characterized by ineffective hematopoiesis with morphological evidence of marrow cell dysplasia resulting in peripheral blood cytopenia. Microarray technology has permitted a refined high-throughput mapping of the transcriptional activity in the human genome. Non-coding RNAs (ncRNAs) transcribed from intronic regions of genes are involved in a number of processes related to post-transcriptional control of gene expression, and in the regulation of exon-skipping and intron retention. Characterization of ncRNAs in progenitor cells and stromal cells of MDS patients could be strategic for understanding gene expression regulation in this disease. Methods: In this study, gene expression profiles of CD34(+) cells of 4 patients with MDS of refractory anemia with ringed sideroblasts (RARS) subgroup and stromal cells of 3 patients with MDS-RARS were compared with healthy individuals using 44 k combined intron-exon oligoarrays, which included probes for exons of protein-coding genes, and for non-coding RNAs transcribed from intronic regions in either the sense or antisense strands. Real-time RT-PCR was performed to confirm the expression levels of selected transcripts. Results: In CD34(+) cells of MDS-RARS patients, 216 genes were significantly differentially expressed (q-value <= 0.01) in comparison to healthy individuals, of which 65 (30%) were non-coding transcripts. In stromal cells of MDS-RARS, 12 genes were significantly differentially expressed (q-value <= 0.05) in comparison to healthy individuals, of which 3 (25%) were non-coding transcripts. Conclusions: These results demonstrated, for the first time, the differential ncRNA expression profile between MDS-RARS and healthy individuals, in CD34(+) cells and stromal cells, suggesting that ncRNAs may play an important role during the development of myelodysplastic syndromes.

Time course proteomic profiling of human myocardial infarction plasma samples: An approach to new biomarker discovery

Background: The aim of this study was to identify novel candidate biomarker proteins differentially expressed in the plasma of patients with early stage acute myocardial infarction (AMI) using SELDI-TOF-MS as a high throughput screening technology. Methods: Ten individuals with recent acute ischemic-type chest pain (< 12 h duration) and ST-segment elevation AMI (1STEMI) and after a second AMI (2STEMI) were selected. Blood samples were drawn at six times after STEMI diagnosis. The first stage (T(0)) was in Emergency Unit before receiving any medication, the second was just after primary angioplasty (T(2)), and the next four stages occurred at 12 h intervals after T(0). Individuals (n = 7) with similar risk factors for cardiovascular disease and normal ergometric test were selected as a control group (CG). Plasma proteomic profiling analysis was performed using the top-down (i.e. intact proteins) SELDI-TOF-MS, after processing in a Multiple Affinity Removal Spin Cartridge System (Agilent). Results: Compared with the CG, the 1STEMI group exhibited 510 differentially expressed protein peaks in the first 48 h after the AMI (p < 0.05). The 2STEMI group, had similar to 85% fewer differently expressed protein peaks than those without previous history of AMI (76, p < 0.05). Among the 16 differentially-regulated protein peaks common to both STEMI cohorts (compared with the CG at T(0)), 6 peaks were persistently down-regulated at more than one time-stage, and also were inversed correlated with serum protein markers (cTnI, CK and CKMB) during 48 h-period after IAM. Conclusions: Proteomic analysis by SELDI-TOF-MS technology combined with bioinformatics tools demonstrated differential expression during a 48 h time course suggests a potential role of some of these proteins as biomarkers for the very early stages of AMI, as well as for monitoring early cardiac ischemic recovery. (C) 2011 Elsevier B.V. All rights reserved.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Bioinformatics construction of the human cell surfaceome

Cell surface proteins are excellent targets for diagnostic and therapeutic interventions. By using bioinformatics tools, we generated a catalog of 3,702 transmembrane proteins located at the surface of human cells (human cell surfaceome). We explored the genetic diversity of the human cell surfaceome at different levels, including the distribution of polymorphisms, conservation among eukaryotic species, and patterns of gene expression. By integrating expression information from a variety of sources, we were able to identify surfaceome genes with a restricted expression in normal tissues and/or differential expression in tumors, important characteristics for putative tumor targets. A high-throughput and efficient quantitative real-time PCR approach was used to validate 593 surfaceome genes selected on the basis of their expression pattern in normal and tumor samples. A number of candidates were identified as potential diagnostic and therapeutic targets for colorectal tumors and glioblastoma. Several candidate genes were also identified as coding for cell surface cancer/testis antigens. The human cell surfaceome will serve as a reference for further studies aimed at characterizing tumor targets at the surface of human cells.

A general hazard model for lifetime data in the presence of cure rate

Historically, the cure rate model has been used for modeling time-to-event data within which a significant proportion of patients are assumed to be cured of illnesses, including breast cancer, non-Hodgkin lymphoma, leukemia, prostate cancer, melanoma, and head and neck cancer. Perhaps the most popular type of cure rate model is the mixture model introduced by Berkson and Gage [1]. In this model, it is assumed that a certain proportion of the patients are cured, in the sense that they do not present the event of interest during a long period of time and can found to be immune to the cause of failure under study. In this paper, we propose a general hazard model which accommodates comprehensive families of cure rate models as particular cases, including the model proposed by Berkson and Gage. The maximum-likelihood-estimation procedure is discussed. A simulation study analyzes the coverage probabilities of the asymptotic confidence intervals for the parameters. A real data set on children exposed to HIV by vertical transmission illustrates the methodology.

On the generalized eigenvalue method for energies and matrix elements in lattice field theory

We discuss the generalized eigenvalue problem for computing energies and matrix elements in lattice gauge theory, including effective theories such as HQET. It is analyzed how the extracted effective energies and matrix elements converge when the time separations are made large. This suggests a particularly efficient application of the method for which we can prove that corrections vanish asymptotically as exp(-(E(N+1) - E(n))t). The gap E(N+1) - E(n) can be made large by increasing the number N of interpolating fields in the correlation matrix. We also show how excited state matrix elements can be extracted such that contaminations from all other states disappear exponentially in time. As a demonstration we present numerical results for the extraction of ground state and excited B-meson masses and decay constants in static approximation and to order 1/m(b) in HQET.

Abundances in the Galactic bulge: results from planetary nebulae and giant stars

Context. Our understanding of the chemical evolution (CE) of the Galactic bulge requires the determination of abundances in large samples of giant stars and planetary nebulae (PNe). Studies based on high resolution spectroscopy of giant stars in several fields of the Galactic bulge obtained with very large telescopes have allowed important progress. Aims. We discuss PNe abundances in the Galactic bulge and compare these results with those presented in the literature for giant stars. Methods. We present the largest, high-quality data-set available for PNe in the direction of the Galactic bulge (inner-disk/bulge). For comparison purposes, we also consider a sample of PNe in the Large Magellanic Cloud (LMC). We derive the element abundances in a consistent way for all the PNe studied. By comparing the abundances for the bulge, inner-disk, and LMC, we identify elements that have not been modified during the evolution of the PN progenitor and can be used to trace the bulge chemical enrichment history. We then compare the PN abundances with abundances of bulge field giant. Results. At the metallicity of the bulge, we find that the abundances of O and Ne are close to the values for the interstellar medium at the time of the PN progenitor formation, and hence these elements can be used as tracers of the bulge CE, in the same way as S and Ar, which are not expected to be affected by nucleosynthetic processes during the evolution of the PN progenitors. The PN oxygen abundance distribution is shifted to lower values by 0.3 dex with respect to the distribution given by giants. A similar shift appears to occur for Ne and S. We discuss possible reasons for this PNe-giant discrepancy and conclude that this is probably due to systematic errors in the abundance derivations in either giants or PNe (or both). We issue an important warning concerning the use of absolute abundances in CE studies.

An incremental space to visualize dynamic data sets

In Information Visualization, adding and removing data elements can strongly impact the underlying visual space. We have developed an inherently incremental technique (incBoard) that maintains a coherent disposition of elements from a dynamic multidimensional data set on a 2D grid as the set changes. Here, we introduce a novel layout that uses pairwise similarity from grid neighbors, as defined in incBoard, to reposition elements on the visual space, free from constraints imposed by the grid. The board continues to be updated and can be displayed alongside the new space. As similar items are placed together, while dissimilar neighbors are moved apart, it supports users in the identification of clusters and subsets of related elements. Densely populated areas identified in the incSpace can be efficiently explored with the corresponding incBoard visualization, which is not susceptible to occlusion. The solution remains inherently incremental and maintains a coherent disposition of elements, even for fully renewed sets. The algorithm considers relative positions for the initial placement of elements, and raw dissimilarity to fine tune the visualization. It has low computational cost, with complexity depending only on the size of the currently viewed subset, V. Thus, a data set of size N can be sequentially displayed in O(N) time, reaching O(N (2)) only if the complete set is simultaneously displayed.

Tempo de vida útil de propelentes base-simples

The purpose of this work was to determine the safe shelf life of single-base propellants. The kinetic parameters relative to the consumption of the stabilizer diphenylamine (DPA) added to the propellant were determined as a function of the storage and ageing time. High Performance Liquid Chromatography (HPLC) with spectrophotometric detection was used to determine the DPA percentage before and after the artificial ageing at 60, 70 and 80 ºC. The experimental data were very well adjusted to a pseudo-first order kinetic model and the respective kinetic constants are 8.0-10-3 day-1 (60 ºC); 1.9-10-2 day-1 (70 ºC); 1.2-10-1 day-1 (80 ºC). The activation energy was calculated as 130 kJ mol-1 and the half-time for depletion of the DPA at the hypothetical temperature of 40 ºC of storage was estimated as being 6 years.

Tendência temporal e desigualdades na mortalidade por diarreias em menores de 5 anos

OBJETIVO: Analisar a tendência da mortalidade por diarreia entre menores de 5 anos, no município de Osasco (SP), entre 1980 e 2000. MÉTODOS: Trata-se de estudo observacional com dois delineamentos. Um descritivo, que toma o indivíduo como unidade do estudo, e outro ecológico, analisando agregado populacional que incluiu análise de séries temporais. A fonte de dados foi o sistema de informação de mortalidade do Estado de São Paulo e censos de 1980, 1991 e 2000. Descreveu-se a variação sazonal e para a análise de tendência aplicaram-se modelos log lineares de regressão polinomiais, utilizando-se variáveis sociodemográficas da criança e da mãe. Foram analisadas a evolução de indicadores sociodemográficos do município de 1980 a 2000, as taxas médias de mortalidade por diarreia nos menores de 5 anos e seus diferenciais por distrito nos anos 90. RESULTADOS: Dos 1.360 óbitos, 94,3 e 75,3% atingiram, respectivamente, menores de 1 ano e de 6 meses. O declínio da mortalidade foi de 98,3%, com deslocamento da sazonalidade do verão para o outono. A mediana da idade elevou-se de 2 meses nos primeiros períodos para 3 meses no último. O resíduo de óbitos manteve-se entre filhos de mães de 20 a 29 anos e escolaridade < 8 anos. O risco relativo entre o distrito mais atingido e a taxa média do município diminuiu de 3,4 para 1,3 do primeiro para o segundo quinquênio dos anos 90. CONCLUSÃO: Nossos resultados apontam uma elevação da idade mais vulnerável e a provável mudança do agente mais frequentemente associado ao óbito por diarreia.

Tendência temporal e desigualdades na mortalidade por diarreias em

Objetivo: Analisar a tendência da mortalidade por diarreia entre menores de 5 anos, no município de Osasco (SP), entre 1980 e 2000. Métodos: Trata-se de estudo observacional com dois delineamentos. Um descritivo, que toma o indivíduo como unidade do estudo, e outro ecológico, analisando agregado populacional que incluiu análise de séries temporais. A fonte de dados foi o sistema de informação de mortalidade do Estado de São Paulo e censos de 1980, 1991 e 2000. Descreveu-se a variação sazonal e para a análise de tendência aplicaram-se modelos log lineares de regressão polinomiais, utilizando-se variáveis sociodemográficas da criança e da mãe. Foram analisadas a evolução de indicadores sociodemográficos do município de 1980 a 2000, as taxas médias de mortalidade por diarreia nos menores de 5 anos e seus diferenciais por distrito nos anos 90. Resultados: Dos 1.360 óbitos, 94,3 e 75,3% atingiram, respectivamente, menores de 1 ano e de 6 meses. O declínio da mortalidade foi de 98,3%, com deslocamento da sazonalidade do verão para o outono. A mediana da idade elevou-se de 2 meses nos primeiros períodos para 3 meses no último. O resíduo de óbitos manteve-se entre filhos de mães de 20 a 29 anos e escolaridade < 8 anos. O risco relativo entre o distrito mais atingido e a taxa média do município diminuiu de 3,4 para 1,3 do primeiro para o segundo quinquênio dos anos 90. Conclusão: Nossos resultados apontam uma elevação da idade mais vulnerável e a provável mudança do agente mais frequentemente associado ao óbito por diarreia

Inatividade física e fatores associados em adultos, São Paulo, Brasil

OBJETIVO: Analisar prevalências de inatividade física e fatores associados, e exercícios e esportes praticados segundo escolaridade em 2.050 adultos de 18 a 59 anos de idade - Estado de São Paulo, Brasil. MÉTODOS: Estudo transversal de base populacional com amostra estratificada e em múltiplos estágios. A inatividade física global foi aferida pelo International Physical Activity questionary - IPAQ short version, e por questão sobre prática regular de atividade física no lazer. A análise dos dados levou em conta o desenho amostral. RESULTADOS: A prevalência de inatividade física no lazer foi maior entre as mulheres. Já a inatividade física pelo IPAQ foi maior entre os homens. Modelos de regressão múltipla de Poisson indicaram, nos homens, menor inatividade física pelo IPAQ nos solteiros e separados, estudantes e aqueles que não possuíam carro. A inatividade física no lazer foi maior nos homens acima de 40 anos e com menor escolaridade ou apenas estudantes. A inatividade física pelo IPAQ, nas mulheres, foi mais prevalente entre as com maior escolaridade, ocupações menos qualificadas e viúvas; a inatividade física no lazer diminuiu com o aumento da idade e da escolaridade. Entre as modalidades praticadas no lazer, a caminhada foi a mais prevalente nas mulheres e o futebol nos homens. A maioria das modalidades foi diretamente associada à escolaridade; aproximadamente 25% dos indivíduos com mais de 12 anos de estudo praticava caminhada. CONCLUSÕES: Estes resultados sugerem que intervenções e políticas públicas de promoção da atividade física devem considerar diferenças socioeconômicas, de gênero, bem como as modalidades e o contexto em que a atividade física é praticadA

Cross-National Analysis of the Associations between Traumatic Events and Suicidal Behavior: Findings from the WHO World Mental Health Surveys

Background: Community and clinical data have suggested there is an association between trauma exposure and suicidal behavior (i.e., suicide ideation, plans and attempts). However, few studies have assessed which traumas are uniquely predictive of: the first onset of suicidal behavior, the progression from suicide ideation to plans and attempts, or the persistence of each form of suicidal behavior over time. Moreover, few data are available on such associations in developing countries. The current study addresses each of these issues. Methodology/Principal Findings: Data on trauma exposure and subsequent first onset of suicidal behavior were collected via structured interviews conducted in the households of 102,245 (age 18+) respondents from 21 countries participating in the WHO World Mental Health Surveys. Bivariate and multivariate survival models tested the relationship between the type and number of traumatic events and subsequent suicidal behavior. A range of traumatic events are associated with suicidal behavior, with sexual and interpersonal violence consistently showing the strongest effects. There is a dose-response relationship between the number of traumatic events and suicide ideation/attempt; however, there is decay in the strength of the association with more events. Although a range of traumatic events are associated with the onset of suicide ideation, fewer events predict which people with suicide ideation progress to suicide plan and attempt, or the persistence of suicidal behavior over time. Associations generally are consistent across high-, middle-, and low-income countries. Conclusions/Significance: This study provides more detailed information than previously available on the relationship between traumatic events and suicidal behavior and indicates that this association is fairly consistent across developed and developing countries. These data reinforce the importance of psychological trauma as a major public health problem, and highlight the significance of screening for the presence and accumulation of traumatic exposures as a risk factor for suicide ideation and attempt.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.