Predictors and outcome impact of perioperative serum sodium changes in a high-risk population.

BACKGROUND: The perioperative period may be associated with a marked neurohumoral stress response, significant fluid losses, and varied fluid replacement regimes. Acute changes in serum sodium concentration are therefore common, but predictors and outcomes of these changes have not been investigated in a large surgical population. METHODS: We carried out a retrospective cohort analysis of 27 068 in-patient non-cardiac surgical procedures in a tertiary teaching hospital setting. Data on preoperative conditions, perioperative events, hospital length of stay, and mortality were collected, along with preoperative and postoperative serum sodium measurements up to 7 days after surgery. Logistic regression was used to investigate the association between sodium changes and mortality, and to identify clinical characteristics associated with a deviation from baseline sodium >5 mmol litre(-1). RESULTS: Changes in sodium concentration >5 mmol litre(-1) were associated with increased mortality risk (adjusted odds ratio 1.49 for a decrease, 3.02 for an increase). Factors independently associated with a perioperative decrease in serum sodium concentration >5 mmol litre(-1) included age >60, diabetes mellitus, and the use of patient-controlled opioid analgesia. Factors associated with a similar increase were preoperative oxygen dependency, mechanical ventilation, central nervous system depression, non-elective surgery, and major operative haemorrhage. CONCLUSIONS: Maximum deviation from preoperative serum sodium value is associated with increased hospital mortality in patients undergoing in-patient non-cardiac surgery. Specific preoperative and perioperative factors are associated with significant serum sodium changes.This work was supported by the Cambridge University Division of Anaesthesia.This is the author accepted manuscript. The final version is available from Oxford University Press via http://dx.doi.org/10.1093/bja/aeu40

Recurrence Quantification Analysis and Principal Components in the Detection of Short Complex Signals

Recurrence plots were introduced to help aid the detection of signals in complicated data series. This effort was furthered by the quantification of recurrence plot elements. We now demonstrate the utility of combining recurrence quantification analysis with principal components analysis to allow for a probabilistic evaluation for the presence of deterministic signals in relatively short data lengths.Comment: 10 pages, 3 figures; Elsevier preprint, elsart style; programs used for analysis available for download at http://homepages.luc.edu/~cwebbe

On the Schoenberg Transformations in Data Analysis: Theory and Illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A simple distance-based discriminant algorithm illustrates the theory, intimately connected to the Gaussian kernels of Machine Learning

Aprendendo com o insucesso: um estudo de caso de aplicação da resolução criativa de problemas ao projeto educativo

O Projeto Educativo, como instrumento fundamental para a autonomia das escolas, em Portugal, deve aglutinar as principais expectativas da comunidade escolar, implicando rigor na metodologia de investigação, utilizada para sua elaboração, e na implementação da mudança requerida. O presente artigo relata a forma como foi possível obter esses aspectos, numa escola secundária, ao longo de mais de um ano, por meio do uso do método de Resolução Criativa de Problemas, que envolveu toda a comunidade escolar. Conforme o planeamento definido, a elaboração do Projeto Educativo seguiu os passos de um trabalho de investigação e resultou num documento estratégico e operacional, sujeito, posteriormente, a várias tentativas de implementação que, no entanto, tiveram apenas sucesso relativo. A discussão das condições necessárias para que o documento final possa servir de base à implementação das políticas e ações definidas, o que simplificaria todo o esquema de funcionamento escolar, sob um prisma de gestão efetiva das organizações, é aqui iniciada e sugerida para futuras investigações

Importance of data structure in comparing two dimension reduction methods for classification of microarray gene expression data

BACKGROUND: With the advance of microarray technology, several methods for gene classification and prognosis have been already designed. However, under various denominations, some of these methods have similar approaches. This study evaluates the influence of gene expression variance structure on the performance of methods that describe the relationship between gene expression levels and a given phenotype through projection of data onto discriminant axes. RESULTS: We compared Between-Group Analysis and Discriminant Analysis (with prior dimension reduction through Partial Least Squares or Principal Components Analysis). A geometric approach showed that these two methods are strongly related, but differ in the way they handle data structure. Yet, data structure helps understanding the predictive efficiency of these methods. Three main structure situations may be identified. When the clusters of points are clearly split, both methods perform equally well. When the clusters superpose, both methods fail to give interesting predictions. In intermediate situations, the configuration of the clusters of points has to be handled by the projection to improve prediction. For this, we recommend Discriminant Analysis. Besides, an innovative way of simulation generated the three main structures by modelling different partitions of the whole variance into within-group and between-group variances. These simulated datasets were used in complement to some well-known public datasets to investigate the methods behaviour in a large diversity of structure situations. To examine the structure of a dataset before analysis and preselect an a priori appropriate method for its analysis, we proposed a two-graph preliminary visualization tool: plotting patients on the Between-Group Analysis discriminant axis (x-axis) and on the first and the second within-group Principal Components Analysis component (y-axis), respectively. CONCLUSION: Discriminant Analysis outperformed Between-Group Analysis because it allows for the dataset structure. An a priori knowledge of that structure may guide the choice of the analysis method. Simulated datasets with known properties are valuable to assess and compare the performance of analysis methods, then implementation on real datasets checks and validates the results. Thus, we warn against the use of unchallenging datasets for method comparison, such as the Golub dataset, because their structure is such that any method would be efficient

Stability of gene contributions and identification of outliers in multivariate analysis of microarray data

BACKGROUND: Multivariate ordination methods are powerful tools for the exploration of complex data structures present in microarray data. These methods have several advantages compared to common gene-by-gene approaches. However, due to their exploratory nature, multivariate ordination methods do not allow direct statistical testing of the stability of genes. RESULTS: In this study, we developed a computationally efficient algorithm for: i) the assessment of the significance of gene contributions and ii) the identification of sample outliers in multivariate analysis of microarray data. The approach is based on the use of resampling methods including bootstrapping and jackknifing. A statistical package of R functions was developed. This package includes tools for both inferring the statistical significance of gene contributions and identifying outliers among samples. CONCLUSION: The methodology was successfully applied to three published data sets with varying levels of signal intensities. Its relevance was compared with alternative methods. Overall, it proved to be particularly effective for the evaluation of the stability of microarray data

GM and KM immunoglobulin allotypes in the Galician population: new insights into the peopling of the Iberian Peninsula

<p>Abstract</p> <p>Background</p> <p>The current genetic structure of Iberian populations has presumably been affected by the complex orography of its territory, the different people and civilizations that settled there, its ancient and complex history, the diverse and persistent sociocultural patterns in its different regions, and also by the effects of the Iberian Peninsula representing a refugium area after the last glacial maximum. This paper presents the first data on <it>GM </it>and <it>KM </it>immunoglobulin allotypes in the Galician population and, thus, provides further insights into the extent of genetic diversity in populations settled in the geographic extremes of the Cantabrian region of northern Spain. Furthermore, the genetic relationships of Galicians with other European populations have been investigated.</p> <p>Results</p> <p>Galician population shows a genetic profile for <it>GM </it>haplotypes that is defined by the high presence of the European Mediterranean <it>GM</it>*<it>3 23 5* </it>haplotype, and the relatively high incidence of the African marker <it>GM*1,17 23' 5*</it>. Data based on comparisons between Galician and other Spanish populations (mainly from the north of the peninsula) reveal a poor correlation between geographic and genetic distances (<it>r </it>= 0.30, <it>P </it>= 0.105), a noticeable but variable genetic distances between Galician and Basque subpopulations, and a rather close genetic affinity between Galicia and Valencia, populations which are geographically separated by a long distance and have quite dissimilar cultures and histories. Interestingly, Galicia occupies a central position in the European genetic map, despite being geographically placed at one extreme of the European continent, while displaying a close genetic proximity to Portugal, a finding that is consistent with their shared histories over centuries.</p> <p>Conclusion</p> <p>These findings suggest that the population of Galicia is the result of a relatively balanced mixture of European populations or of the ancestral populations that gave rise to them. This would support the importance of the migratory movements that have taken place in Europe over the course of recent human history and their effects on the European genetic landscape.</p