3,014 research outputs found

    The effect of a cardiovascular risk factor education program on health behaviors of selected school age children

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    This study, using a quasi-experimental design, was conducted to explore the effect of a cardiovascular risk factor education program on the health behaviors of a group of fifth grade children. The following hypothesis was tested: There will be a statistically significant improvement in self-reported health behaviors of school age children who receive a cardiovascular risk factor education program as compared to the self-reported health behaviors of those school age children who do not receive a cardiovascular risk factor education program. One hundred and nineteen subjects, 63 in the experimental group and 56 in the control group, were tested using the researcher\u27s designed health behavior questionnaire, My Health Behaviors , before and after participation in the health education program. The program provided for the experimental group consisted of eight 45 minute sessions. The introductory and summary sessions were primarily concerned with administration of the pretest and post-test and sessions two through seven were informative sessions about high fat, high cholesterol diet, smoking and sedentary lifestyle. The program provided for the control group consisted of four 45 minute sessions; session one was concerned with introductory material and administration of the pre-test, sessions two and three were informative sessions related to general nutrition and foods high in salt and sugar, and session four was devoted to review of content as well as administration of the post—test. Data were statistically analyzed using the paired-sample student\u27s t-test. Results of the analysis revealed a significant difference between the two sample groups at p\u3c0.01 level. The hypothesis was accepted

    Small scale structure and mixing at the edge of the Antarctic vortex

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    Small scale correlations and patterns in the chemical tracers measured from the NASA ER-2 aircraft in the 1987 AAOE campaign can be used to investigate the structure of the edge of the polar vortex and the chemically perturbed region within it. Examples of several types of transport processes can be found in the data. Since ClO and O3 have similar vertical gradients and opposite horizontal gradients near the chemically perturbed region, the correlation between ClO and O3 can be used to study the extent of horizontal transport at the edge of the chemically perturbed region. Horizontal transport dominates the correlation for a latitude band up to 4 degrees on each side of the boundary. This implies a transition zone containing a substantial fraction of the mass of the total polar vortex. Similar horizontal transport can be seen in other tracers as well. It has not been possible to distinguish reversible transport from irreversible mixing. One manifestation of the horizontal transport is that the edge of the chemically perturbed region is often layered rather than a vertical curtain. This can be seen from the frequent reversed vertical gradients of NO2, caused by air with high NO2 overlapping layers with lower mixing ratios. Water and NO2 are positively correlated within the chemically perturbed region. This is the opposite sign to the correlation in the unperturbed stratosphere. The extent of the positive correlation is too great to be attributed solely to horizontal mixing. Instead, it is hypothesized that dehydration and descent are closely connected on a small scale, possibly due to radiative cooling of the clouds that also cause ice to fall to lower altitudes

    Characterizing disease states from topological properties of transcriptional regulatory networks

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    BACKGROUND: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome. RESULTS: We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity. CONCLUSION: Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states

    Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data

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    In this paper we introduce fuzzy forests, a novel machine learning algorithm for ranking the importance of features in high-dimensional classification and regression problems. Fuzzy forests is specifically designed to provide relatively unbiased rankings of variable importance in the presence of highly correlated features, especially when the number of features, p, is much larger than the sample size, n (p n). We introduce our implementation of fuzzy forests in the R package, fuzzyforest. Fuzzy forests works by taking advantage of the network structure between features. First, the features are partitioned into separate modules such that the correlation within modules is high and the correlation between modules is low. The package fuzzyforest allows for easy use of the package WGCNA (weighted gene coexpression network analysis, alternatively known as weighted correlation network analysis) to form modules of features such that the modules are roughly uncorrelated. Then recursive feature elimination random forests (RFE-RFs) are used on each module, separately. From the surviving features, a final group is selected and ranked using one last round of RFE-RFs. This procedure results in a ranked variable importance list whose size is pre-specified by the user. The selected features can then be used to construct a predictive model

    Reducing porosity in AlSi10Mg parts processed by selective laser melting

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    Selective laser melting (SLM) is widely gaining popularity as an alternative manufacturing technique for complex and customized parts. SLM is a near net shape process with minimal post processing machining required dependent upon final application. The fact that SLM produces little waste and enables more optimal designs also raises opportunities for environmental advantages. The use of aluminium (Al) alloys in SLM is still quite limited due to difficulties in processing that result in parts with high degrees of porosity. However, Al alloys are favoured in many high-end applications for their exceptional strength and stiffness to weight ratio meaning that they are extensively used in the automotive and aerospace industries. This study investigates the windows of parameters required to produce high density parts from AlSi10Mg alloy using selective laser melting. A compromise between the different parameters and scan strategies was achieved and used to produce parts achieving a density of 99.8%
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