102 resultados para Data clustering
Resumo:
The bacterial diversity present in sediments of a well-preserved mangrove in Ilha do Cardoso, located in the extreme south of So Paulo State coastline, Brazil, was assessed using culture-independent molecular approaches (denaturing gradient gel electrophoresis (DGGE) and analysis of 166 sequences from a clone library). The data revealed a bacterial community dominated by Alphaproteobacteria (40.36% of clones), Gammaproteobacteria (19.28% of clones) and Acidobacteria (27.71% of clones), while minor components of the assemblage were affiliated to Betaproteobacteria, Deltaproteobacteria, Firmicutes, Actinobacteria and Bacteroidetes. The clustering and redundancy analysis (RDA) based on DGGE were used to determine factors that modulate the diversity of bacterial communities in mangroves, such as depth, seasonal fluctuations, and locations over a transect area from the sea to the land. Profiles of specific DGGE gels showed that both dominant (`universal` Bacteria and Alphaproteobacteria) and low-density bacterial communities (Betaproteobacteria and Actinobacteria) are responsive to shifts in environmental factors. The location within the mangrove was determinant for all fractions of the community studied, whereas season was significant for Bacteria, Alphaproteobacteria, and Betaproteobacteria and sample depth determined the diversity of Alphaproteobacteria and Actinobacteria.
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Allele frequency distributions and population data for 12 Y-chromosomal short tandem repeats (STRs) included in the PowerPlex (R) Y Systems (Promega) were obtained for a sample of 200 healthy unrelated males living in S (a) over tildeo Paulo State (Southeast of Brazil). A total of 192 haplotypes were identified, of which 184 were unique and 8 were found in 2 individuals. The average gene diversity of the 12 Y-STR was 0.6746 and the haplotype diversity was 0.9996. Pairwise analysis confirmed that our population is more similar with the Italy, North Portugal and Spain, being more distant of the Japan. (c) 2007 Elsevier Ireland Ltd. All rights reserved.
Resumo:
The Brazilian Network of Food Data Systems (BRASILFOODS) has been keeping the Brazilian Food Composition Database-USP (TBCA-USP) (http://www.fcf.usp.br/tabela) since 1998. Besides the constant compilation, analysis and update work in the database, the network tries to innovate through the introduction of food information that may contribute to decrease the risk for non-transmissible chronic diseases, such as the profile of carbohydrates and flavonoids in foods. In 2008, data on carbohydrates, individually analyzed, of 112 foods, and 41 data related to the glycemic response produced by foods widely consumed in the country were included in the TBCA-USP. Data (773) about the different flavonoid subclasses of 197 Brazilian foods were compiled and the quality of each data was evaluated according to the USDAs data quality evaluation system. In 2007, BRASILFOODS/USP and INFOODS/FAO organized the 7th International Food Data Conference ""Food Composition and Biodiversity"". This conference was a unique opportunity for interaction between renowned researchers and participants from several countries and it allowed the discussion of aspects that may improve the food composition area. During the period, the LATINFOODS Regional Technical Compilation Committee and BRASILFOODS disseminated to Latin America the Form and Manual for Data Compilation, version 2009, ministered a Food Composition Data Compilation course and developed many activities related to data production and compilation. (C) 2010 Elsevier Inc. All rights reserved.
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In a recent thought-provoking paper, Ball and Sheridan [Ball, L., Sheridan, N., 2005. Does inflation targeting matter? In: Bernanke, B.S., Woodford, M. (Eds.), The Inflation-Targeting Debate, University of Chicago Press] show that the available evidence for a group of developed economies does not lend credence to the belief that adopting an inflation targeting regime (IT) was instrumental in bringing inflation and inflation volatility down. Here, we extend Ball and Sheridan`s analysis for a subset of 36 emerging market economies and find that, for them, the story is quite different. Compared to non-targeters, developing countries adopting the IT regime not only experienced greater drops in inflation, but also in growth volatility, thus corroborating the view that the regime`s ""constrained flexibility"" to deal with adverse shocks delivered concrete welfare gains. (c) 2006 Elsevier B.V. All rights reserved.
Resumo:
A graph clustering algorithm constructs groups of closely related parts and machines separately. After they are matched for the least intercell moves, a refining process runs on the initial cell formation to decrease the number of intercell moves. A simple modification of this main approach can deal with some practical constraints, such as the popular constraint of bounding the maximum number of machines in a cell. Our approach makes a big improvement in the computational time. More importantly, improvement is seen in the number of intercell moves when the computational results were compared with best known solutions from the literature. (C) 2009 Elsevier Ltd. All rights reserved.
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In this article we present a complete (1)H and (13)C NMR spectral analysis of three 7,7`-dihydroarylnaphthalene lignan lactones using modern NMR techniques such as COSY, HSQC, HMBC and NOE experiments. Complete assignment and homonuclear hydrogen coupling constant measurements were performed. Copyright (C) 2009 John Wiley & Sons, Ltd.
Resumo:
Proteinuria was associated with cardiovascular events and mortality in community-based cohorts. The association of proteinuria with mortality and cardiovascular events in patients undergoing percutaneous coronary intervention (PCI) was unknown. The association of urinary dipstick proteinuria with mortality and cardiovascular events (composite of death, myocardial infarction, or nonhemorrhagic stroke) in 5,835 subjects of the EXCITE trial was evaluated. Dipstick urinalysis was performed before PCI, and proteinuria was defined as trace or greater. Subjects were followed up for 210 days/7 months after enrollment for the occurrence of events. Multivariate Cox regression analysis evaluated the independent association of proteinuria with each outcome. Mean age was 59 years, 21% were women, 18% had diabetes mellitus, and mean estimated glomerular filtration rate was 90 ml/min/1.73 m(2). Proteinuria was present in 750 patients (13%). During follow-up, 22 subjects (2.9%) with proteinuria and 54 subjects (1.1%) without proteinuria died (adjusted hazard ratio 2.83, 95% confidence interval [CI] 1.65 to 4.84, p <0.001). The severity of proteinuria attenuated the strength of the association with mortality after PCI (low-grade proteinuria, hazard ratio 2.67, 95% CI 1.50 to 4.75; high-grade proteinuria, hazard ratio 3.76, 95% CI 1.24 to 11.37). No significant association was present for cardiovascular events during the relatively short follow-up, but high-grade proteinuria tended toward increased risk of cardiovascular events (hazard ratio 1.45, 95% CI 0.81 to 2.61). In conclusion, proteinuria was strongly and independently associated with mortality in patients undergoing PCI. These data suggest that such a relatively simple and clinically easy to use tool as urinary dipstick may be useful to identify and treat patients at high risk of mortality at the time of PCI. (C) 2008 Elsevier Inc. All rights reserved. (Am J Cardiol 2008;102:1151-1155)
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Background-Randomized trials that studied clinical outcomes after percutaneous coronary intervention (PCI) with bare metal stenting versus coronary artery bypass grafting (CABG) are underpowered to properly assess safety end points like death, stroke, and myocardial infarction. Pooling data from randomized controlled trials increases the statistical power and allows better assessment of the treatment effect in high-risk subgroups. Methods and Results-We performed a pooled analysis of 3051 patients in 4 randomized trials evaluating the relative safety and efficacy of PCI with stenting and CABG at 5 years for the treatment of multivessel coronary artery disease. The primary end point was the composite end point of death, stroke, or myocardial infarction. The secondary end point was the occurrence of major adverse cardiac and cerebrovascular accidents, death, stroke, myocardial infarction, and repeat revascularization. We tested for heterogeneities in treatment effect in patient subgroups. At 5 years, the cumulative incidence of death, myocardial infarction, and stroke was similar in patients randomized to PCI with stenting versus CABG (16.7% versus 16.9%, respectively; hazard ratio, 1.04, 95% confidence interval, 0.86 to 1.27; P = 0.69). Repeat revascularization, however, occurred significantly more frequently after PCI than CABG (29.0% versus 7.9%, respectively; hazard ratio, 0.23; 95% confidence interval, 0.18 to 0.29; P<0.001). Major adverse cardiac and cerebrovascular events were significantly higher in the PCI than the CABG group (39.2% versus 23.0%, respectively; hazard ratio, 0.53; 95% confidence interval, 0.45 to 0.61; P<0.001). No heterogeneity of treatment effect was found in the subgroups, including diabetic patients and those presenting with 3-vessel disease. Conclusions-In this pooled analysis of 4 randomized trials, PCI with stenting was associated with a long-term safety profile similar to that of CABG. However, as a result of persistently lower repeat revascularization rates in the CABG patients, overall major adverse cardiac and cerebrovascular event rates were significantly lower in the CABG group at 5 years.
Resumo:
Dherte PM, Negrao MPG, Mori Neto S, Holzhacker R, Shimada V, Taberner P, Carmona MJC - Smart Alerts: Development of a Software to Optimize Data Monitoring. Background and objectives: Monitoring is useful for vital follow-ups and prevention, diagnosis, and treatment of several events in anesthesia. Although alarms can be useful in monitoring they can cause dangerous user`s desensitization. The objective of this study was to describe the development of specific software to integrate intraoperative monitoring parameters generating ""smart alerts"" that can help decision making, besides indicating possible diagnosis and treatment. Methods: A system that allowed flexibility in the definition of alerts, combining individual alarms of the parameters monitored to generate a more elaborated alert system was designed. After investigating a set of smart alerts, considered relevant in the surgical environment, a prototype was designed and evaluated, and additional suggestions were implemented in the final product. To verify the occurrence of smart alerts, the system underwent testing with data previously obtained during intraoperative monitoring of 64 patients. The system allows continuous analysis of monitored parameters, verifying the occurrence of smart alerts defined in the user interface. Results: With this system a potential 92% reduction in alarms was observed. We observed that in most situations that did not generate alerts individual alarms did not represent risk to the patient. Conclusions: Implementation of software can allow integration of the data monitored and generate information, such as possible diagnosis or interventions. An expressive potential reduction in the amount of alarms during surgery was observed. Information displayed by the system can be oftentimes more useful than analysis of isolated parameters.
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In this study, blood serum trace elements, biochemical and hematological parameters were obtained to assess the health status of an elderly population residing in So Paulo city, SP, Brazil. Results obtained showed that more than 93% of the studied individuals presented most of the serum trace element concentrations and of the hematological and biochemical data within the reference values used in clinical laboratories. However, the percentage of elderly presenting recommended low density lipoprotein (LDL) cholesterol concentrations was low (70%). The study indicated positive correlation between the concentrations of Zn and LDL-cholesterol (p < 0.06).
Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI
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Functional magnetic resonance imaging (fMRI) based on BOLD signal has been used to indirectly measure the local neural activity induced by cognitive tasks or stimulation. Most fMRI data analysis is carried out using the general linear model (GLM), a statistical approach which predicts the changes in the observed BOLD response based on an expected hemodynamic response function (HRF). In cases when the task is cognitively complex or in cases of diseases, variations in shape and/or delay may reduce the reliability of results. A novel exploratory method using fMRI data, which attempts to discriminate between neurophysiological signals induced by the stimulation protocol from artifacts or other confounding factors, is introduced in this paper. This new method is based on the fusion between correlation analysis and the discrete wavelet transform, to identify similarities in the time course of the BOLD signal in a group of volunteers. We illustrate the usefulness of this approach by analyzing fMRI data from normal subjects presented with standardized human face pictures expressing different degrees of sadness. The results show that the proposed wavelet correlation analysis has greater statistical power than conventional GLM or time domain intersubject correlation analysis. (C) 2010 Elsevier B.V. All rights reserved.
Resumo:
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.
Resumo:
Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.
Resumo:
Hepatitis C virus (HCV) transmission has decreased with the adoption of universal blood donor screening and social policies to reduce the risk of infection in intravenous drug users, but remains a worldwide health problem. The objective of this study was to evaluate the phylogenetic relationships among sequences from different HCV genomic regions from sexual partners of infected patients. Nine couples with a stable relationship and without other risk factors for HCV infection and 42 control patients were selected, and the NS3 and NS5B regions were analysed. Phylogenetic analysis showed that viruses from five of the couples had a common origin, clustering in the same monophyletic group, with bootstrap values greater than 70. For the other couples, monophyletic groups were observed, but without bootstrap support. Thus, using two different viral genome regions, a common source of infection was observed in both members of five couples. These data strongly support HCV transmission within couples.
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Mitochondrial DNA (mtDNA) population data for forensic purposes are still scarce for some populations, which may limit the evaluation of forensic evidence especially when the rarity of a haplotype needs to be determined in a database search. In order to improve the collection of mtDNA lineages from the Iberian and South American subcontinents, we here report the results of a collaborative study involving nine laboratories from the Spanish and Portuguese Speaking Working Group of the International Society for Forensic Genetics (GHEP-ISFG) and EMPOP. The individual laboratories contributed population data that were generated throughout the past 10 years, but in the majority of cases have not been made available to the scientific community. A total of 1019 haplotypes from Iberia (Basque Country, 2 general Spanish populations, 2 North and 1 Central Portugal populations), and Latin America (3 populations from Sao Paulo) were collected, reviewed and harmonized according to defined EMPOP criteria. The majority of data ambiguities that were found during the reviewing process (41 in total) were transcription errors confirming that the documentation process is still the most error-prone stage in reporting mtDNA population data, especially when performed manually. This GHEP-EMPOP collaboration has significantly improved the quality of the individual mtDNA datasets and adds mtDNA population data as valuable resource to the EMPOP database (www.empop.org). (C) 2010 Elsevier Ireland Ltd. All rights reserved.