Andrographolide, isolated from Andrographis paniculata, induces apoptosis in monocytic leukemia and multiple myeloma cells via augmentation of reactive oxygen species production

藤田医科大学博士(医療科学)doctoral thesi

Administrative Alternative Dispute Resolution for Environment in Japan: Achievements and Challenges of 50 Years of Activities

departmental bulletin pape

drummondiana

Salix drummondiana Barratt ex HookerDrummond's willow;blue willowsaule de DrummondRuby Lakecreek bank near outlet of lake6800 feetStudies in Northern American Salix. Shrub, 5 ft

Conceptual metaphors

This chapter offers an overview of conceptual metaphor research with particular attention to specialised language. It surveys the growing body of research showing the role of metaphor both as a tool to generate and develop scientific thinking and as a resource for specialised knowledge popularisation. This chapter also addresses specialised language studies that explore not only the embodied but also the socio-cultural dimension of metaphor in terminology, thus crucially refining conceptual metaphor theories and typologies in all spheres of communication

Chasm of semantic despair

<p>The chasm of semantic despair. Data across the biological and medical spectrum is difficult to use across modalities and translational stages. The NCATS Data Translator project aims to bridge this divide for mechanistic discovery. It features key "TOADS" components (Tools, Ontologies, Algorithms, Data and Standards) from the Monarch Initiative.<br></p

Accuracy Ranges of the Three-Gene Predictive Model of IFNβ Response

<p>After the initial data split into training and test sets, using IBIS on the training set only, nine best-performing triplets were identified. The triplet of <i>Caspase 2, Caspase 10,</i> and <i>FLIP</i> resulted in an accuracy rate of 86% correct prediction on the blind test set resulting from the original split. To minimize the effect of fortuitous initial data division in the accuracy outcome, an extra 100 data splits were performed as a coarse approximation of the possible ranges of accuracies in which this gene triplet could result. A histogram of prediction accuracy over the 100 trials for the gene triplet composed of <i>Caspase 2, Caspase 10,</i> and <i>FLIP</i> is shown as an example of classification and prediction of response to IFNβ at <i>T</i> = 0. A red Gaussian curve encompasses the distribution, where the mean prediction accuracy was 87.9%, with a maximum of 100% (in 11 cases) and a minimum of 64.3% (in two cases). The broken blue line indicates the tenth percentile (78.6%). No major differences were found when we performed the same classification/prediction strategy in 500 random splits of the data.</p

IFNβ-Induced Changes in Gene Expression over Time

<div><p>(A) An unsupervised hierarchical clustering representation of the weighted difference in gene expression at each time point versus baseline. For each gene, the obtained differences were log normalized and multiplied by the <i>F</i>-statistic from an ANOVA (time effect) run previously (shown in [B]). The “heat” colored bar represents the absolute value of this difference. With the exception of <i>IFNAR1</i> (arrow), all genes showing a significantly different expression in at least one time point with respect to baseline were arranged in the same cluster (framed in blue).</p> <p>(B) List of all genes showing a significant time effect along with their <i>F</i>-statistic and <i>p</i>-values. Genes that were part of any triplet showing more than 80% prediction accuracy at <i>T</i> = 0 are in bold.</p> <p>(C) A continuous representation of the longitudinal average expression of two representative genes over all samples. <i>MX1</i> (̂) shows a marked departure from <i>T</i> = 0 and remains elevated for the rest of the observed period. This correlates well with the shading (#) displayed in the clustering shown in (A). In contrast, <i>IRF6</i> (•) displays an almost flat curve, consistent with its color in the clustering (*).</p></div

Characteristic Gene Expression Profiles of Good and Poor Responders to IFNβ over Time

<div><p>(A) An unsupervised hierarchical clustering representation of the weighted difference between the average expression of good and poor responders. For each gene, the obtained differences were log normalized and multiplied by the <i>F</i>-statistic from an ANOVA (responder effect) run previously (shown in [B]). The “heat” colored bar represents the absolute value of this difference. With the exception of <i>MX1</i> (indicated by an arrow), all genes showing a significant difference in expression between the two groups of patients were automatically arranged in only two clusters (framed in blue).</p> <p>(B) List of all genes showing a significant responder effect along with their <i>F</i>-statistic and <i>p</i>-values. Genes that were part of any triplet showing more than 80% prediction accuracy at <i>T</i> = 0 are shown in bold.</p> <p>(C) A continuous representation of the longitudinal average expression of two representative genes for good (̂) and poor (•) responders. <i>TRADD</i> shows two widely parallel curves, indicative of a significant difference in the expression averages, correlating with its profile (#) observed in the clustering shown in (A). In contrast, <i>GATA</i><i>3</i> displays two almost overlapping curves, consistent with its shading (*) in the clustering in (A). </p></div

Nonsupervised Two-Way Hierarchical Clustering of Samples at <i>T</i> = 0

<p>A clear aggregation of samples cannot be seen by this technique. The first column indicates the type of responder to which each sample belongs (red, good; blue, poor).</p