Multi-diagnostic imaging of evaporating fuel wall-films in combustion as a source of PAH and soot

In direct-injection gasoline engines, evaporating fuel wall-films and the resulting inhomogeneities of the air-fuel mixture near those films make the formation of PAH and soot in subsequent combustion likely. Different optical techniques are needed to visualize the links of this process chain, such as the spray, film formation, evaporation, combustion, and soot formation. In our model experiment, a mixture of isooctane (surrogate fuel) and toluene (fluorescent tracer) is injected by a multi-hole injector into an optically accessible flow channel. Air flows continuously through the channel at room pressure. Combustion is initiated by a spark plug within the fuel/air-mixture cloud. Some of the liquid fuel impinges on the quartz-glass wall on the opposite side and forms wall films. The turbulent flame front propagates along the chamber and ignites pool fires above the wall films, leading to locally sooting combustion. Laser-induced fluorescence (LIF) of the toluene using 266 nm excitation images the fuel-film thickness and visualizes the fuel vapor above the liquid films. Laser-induced incandescence (LII) using 1064 nm excitation visualizes soot. As a complementary visualization of soot, the natural flame luminosity, mainly from soot incandescence, is captured with a high-speed camera. Schlieren imaging combines the visualization of the evaporating liquid and the sooting flame. The LIF images show that indeed the fuel wall-films remain on the surface long after the flame front has passed, leading to subsequent soot formation. In a next step, we will simultaneously visualize the formation of soot precursors (PAH) and soot with LIF and LII, respectively.</p

A generalized motif bicluster algorithm

In many application domains different clusters in data may be defined by different sets of variables. E.g., in maketing one group of consumers could mainly be concerned about price and technical features of a product, while others care most about design and how \cool" the product is (almost regardless of the price). Standard clustering algorithms use all variables for all clusters and hence may fail to detect such structures in the data. Biclustering is the simultaneous clustering of columns and rows in a data set: each cluster is defined by a different subset of variables, these subsets can of course be overlapping. R package biclust (Kaiser & Leisch 2008, Kaiser et al 2008) contains a comprehensive collection of bicluster algorithms, preprocessing methods, and validation and visualization techniques for bicluster results. The main focus of this presentation will be on recent additions to the package: There are new functions for bicluster validation and comparison. A new generalization of the well-known motif bicluster algorithm has been developed which is particularly suited for biclustering of marketing survey data. While the standard motif algorithm only searches for constant entries in the data matrix, our generalization is better suited for ordinal and metric data. The user can specify \neighborhood patterns" like intervals or density kernels of pre-specified size for metric data. In addition to finding more general patterns than constant groups only this also allows to calculate a posterior probabilities of cluster membership and can be seen as a first step towards fully model-based biclustering. All new methods will be demonstrated using real data from marketing applications

Mean inter-replicate variance was minimized by applying normalization methods to TLDA cards.

<p> <b>TLDA - Intra-platform.</b></p><p><b>Standard deviation</b> Legend information as specified for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038946#pone-0038946-t001" target="_blank">Table 1</a>.</p

Effect of normalization methods on the number of differentially expressed miRNAs detected by TLDA cards.

<p> <b>TLDA - Intra-platform.</b></p><p><b>Significant miRNAs</b> Legend information as specified for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038946#pone-0038946-t005" target="_blank">Table 5</a>.</p

Impact of normalization strategies on the similarity of differential miRNA expression of AGL array data.

<p><b>Agilent - Intra-platform Jaccard index</b> The mean Jaccard indices of significantly regulated miRNA overlap across normalized datasets were depicted for myoblast differentiation and cytokine treated samples analyzed of human and mouse. The Jaccard index ranges between zero and one per definition. The closer the Jaccard index is to one the higher the relative similarity and reproducibility of differential expression across platforms.</p

Platforms and normalization methods applied.

<p>Overview of intra- and inter-platform comparisons using miRNA microarrays from Agilent Technologies (AGL Array) and RT-qPCR arrays from Applied Biosystems (TLDA) for human and mouse miRNAs as well as singleplex TaqMan miRNA assays. Different normalization methods were applied to the platforms. Three distinct biological stages of mouse and primary human skeletal cells were analyzed.</p

Signal distribution of human microarray and qPCR profiling.

<p>Box-whisker plot with 5th and 95th percentiles (black dots) of log2-transformed human AGL array signals or Cq values of human TLDA platform were shown for nine samples each across different normalization techniques.</p

Mean area under the ROC curve of the inter-platform miRNA subsets for TLDA cards.

<p> <b>TLDA - Inter-platform.</b></p><p><b>ROC curves</b> Legend information as stated for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038946#pone-0038946-t009" target="_blank">Table 9</a>.</p

Mean inter-replicate variance was minimized by applying normalization methods to the AGL array.

<p> <b>Agilent - Intra-platform.</b></p><p> <b>Standard deviation.</b></p><p>The average of intra-replicate standard deviations in human and mouse myoblasts, myotubes, and cytokine treated myotubes based on the platform-specific miRNA datasets were depicted. The mean intra-platform standard deviations depended on the normalization method.</p

Impact of normalization strategies on the similarity of differential miRNA expression of TLDA card data.

<p><b>TLDA - Intra-platform Jaccard index</b> Legend information as stated for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038946#pone-0038946-t007" target="_blank">Table 7</a>.</p