A unified viscoplastic model for high temperature low cycle fatigue of service-aged P91 steel

The finite element (FE) implementation of a hyperbolic sine unified cyclic viscoplasticity model is presented. The hyperbolic sine flow rule facilitates the identification of strain-rate independent material parameters for high temperature applications. This is important for the thermo-mechanical fatigue of power plants where a significant stress range is experienced during operational cycles and at stress concentration features, such as welds and branched connections. The material model is successfully applied to the characterisation of the high temperature low cycle fatigue behavior of a service-aged P91 material, including isotropic (cyclic) softening and nonlinear kinematic hardening effects, across a range of temperatures and strain-rates

Pathway map of the alpha-linolenic acid metabolism (KEGG) with marked entries.

<p>Entries mapped to features from all data sets are marked in gray, selected entries from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089297#pone-0089297-t005" target="_blank">Tables 5</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089297#pone-0089297-t007" target="_blank">7</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089297#pone-0089297-t009" target="_blank">9</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089297#pone-0089297-t011" target="_blank">11</a> are marked in red.</p

Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets

<div><p>A major challenge in current systems biology is the combination and integrative analysis of large data sets obtained from different high-throughput omics platforms, such as mass spectrometry based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics. Especially in the case of non-targeted Metabolomics experiments, where it is often impossible to unambiguously map ion features from mass spectrometry analysis to metabolites, the integration of more reliable omics technologies is highly desirable. A popular method for the knowledge-based interpretation of single data sets is the (Gene) Set Enrichment Analysis. In order to combine the results from different analyses, we introduce a methodical framework for the meta-analysis of p-values obtained from Pathway Enrichment Analysis (Set Enrichment Analysis based on pathways) of multiple dependent or independent data sets from different omics platforms. For dependent data sets, e.g. obtained from the same biological samples, the framework utilizes a covariance estimation procedure based on the nonsignificant pathways in single data set enrichment analysis. The framework is evaluated and applied in the joint analysis of Metabolomics mass spectrometry and Transcriptomics DNA microarray data in the context of plant wounding. In extensive studies of simulated data set dependence, the introduced correlation could be fully reconstructed by means of the covariance estimation based on pathway enrichment. By restricting the range of p-values of pathways considered in the estimation, the overestimation of correlation, which is introduced by the significant pathways, could be reduced. When applying the proposed methods to the real data sets, the meta-analysis was shown not only to be a powerful tool to investigate the correlation between different data sets and summarize the results of multiple analyses but also to distinguish experiment-specific key pathways.</p></div

Expression profile of promoter:GUS-constructs for CYP94B1, CYP94B2, CYP94B3 and CYP94C1 in vegetative organs of <i>A</i>. <i>thaliana</i>.

<p>Transformed plants were grown on soil under long-day (16 h light / 8 h dark) conditions. Seedlings were grown on ½ MS plates. All plant lines were stained with 2 mM X-Gluc. Staining was performed with two independent plant lines per construct with comparable results. Staining was performed ≥3 times with each line with comparable results.</p

List of Primers used for semi-quantitative reverse transcriptase polymerase chain reaction.

<p>List of Primers used for semi-quantitative reverse transcriptase polymerase chain reaction.</p

Selected feature mappings from data set T1.

<p>The table contains selected feature mappings from data set T1 (25392 features) to the first three pathways in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089297#pone-0089297-t002" target="_blank">tables 2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089297#pone-0089297-t003" target="_blank">3</a>. Multiple mappings correspond to different spots on the microarray.</p

Big Data analysis to improve care for people living with serious illness: The potential to use new emerging technology in palliative care.

<p>The table contains the high-ranking pathways from enrichment analysis of data set M1 based on the Kolmogorov-Smirnov (KS) and rank-sum test. The pathways are sorted according to the restandardized p-values derived from the rank-sum test. The last two columns comprise the false discovery rates calculated from the restandardized p-values.</p

Pathway of JA activation and inactivation.

<p>Plant wounding induces the conversion of 18:3(n-3) to JA (for details see text). JA might be conjugated to Ile yielding JA-Ile, a reaction catalyzed by JAR1. JA-Ile is the bioactive phytohormone, which can be perceived by the SCF^COI-complex leading to the de-repression/induction of JA-responsive genes. Inactivation of JA-Ile signaling can be achieved via two possible routes: either by the enzymatic activity of the amido-hydrolases ILL6 and IAR3 that catalyze the hydrolytic cleavage of JA-Ile, or by enzymatic activity of distinct members of the cytochrome P450 subfamily CYP94 (<i>i</i>.<i>e</i>. CYP94B1, CYP94B3 and CYP94C1) that catalyze the sequential ω-oxidation of JA-Ile to 12-hydroxy-JA-Ile and 12-carboxy-JA-Ile. Although all three mentioned CYP94-enzymes have the capacity to catalyze the hydroxylation (mono-oxygenation) as well as the carboxylation (double oxygenation), they exhibit distinct catalytic specificities. Beside JA-Ile, oxidized JA-Ile derivatives may also serve as substrate for IAR3 (and Ill6) <i>in planta</i>. JAR, JASMONATE RESISTENT1; IAR3, IAA-ALA-RESISTENT3; ILL6, IAA-LEU RESISTENT-like6.</p

Selected feature mappings from data set T2.

<p>The table contains selected feature mappings from data set T2 (25392 features) to the first three pathways in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089297#pone-0089297-t002" target="_blank">tables 2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089297#pone-0089297-t003" target="_blank">3</a>. Multiple mappings correspond to different spots on the microarray.</p

Overview on data sets.

<p>The table gives an overview on the four data sets used for evaluation and application. The third column (Times) summarizes the different points in time when the wounded plants were harvested in the respective experiment. The T1 and T2 data sets can be obtained from the ArrayExpress <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089297#pone.0089297-Brazma1" target="_blank">[44]</a> website.</p