Measurement of the Longitudinal Spin Transfer to Lambda and Anti-Lambda Hyperons in Polarised Muon DIS

The longitudinal polarisation transfer from muons to lambda and anti-lambda hyperons, D_LL, has been studied in deep inelastic scattering off an unpolarised isoscalar target at the COMPASS experiment at CERN. The spin transfers to lambda and anti-lambda produced in the current fragmentation region exhibit different behaviours as a function of x and xF . The measured x and xF dependences of D^lambda_LL are compatible with zero, while D^anti-lambda_LL tends to increase with xF, reaching values of 0.4 - 0.5. The resulting average values are D^lambda_LL = -0.012 +- 0.047 +- 0.024 and D^anti-lambda_LL = 0.249 +- 0.056 +- 0.049. These results are discussed in the frame of recent model calculations.Comment: 13 pages, 7 figure

Using Ribosomal Protein Genes as Reference: A Tale of Caution

Background: Housekeeping genes are needed in every tissue as their expression is required for survival, integrity or duplication of every cell. Housekeeping genes commonly have been used as reference genes to normalize gene expression data, the underlying assumption being that they are expressed in every cell type at approximately the same level. Often, the terms "reference genes'' and "housekeeping genes'' are used interchangeably. In this paper, we would like to distinguish between these terms. Consensus is growing that housekeeping genes which have traditionally been used to normalize gene expression data are not good reference genes. Recently, ribosomal protein genes have been suggested as reference genes based on a meta-analysis of publicly available microarray data. Methodology/Principal Findings: We have applied several statistical tools on a dataset of 70 microarrays representing 22 different tissues, to assess and visualize expression stability of ribosomal protein genes. We confirmed the housekeeping status of these genes, but further estimated expression stability across tissues in order to assess their potential as reference genes. One- and two-way ANOVA revealed that all ribosomal protein genes have significant expression variation across tissues and exhibit tissue-dependent expression behavior as a group. Via multidimensional unfolding analysis, we visualized this tissue-dependency. In addition, we explored mechanisms that may cause tissue dependent effects of individual ribosomal protein genes. Conclusions/Significance: Here we provide statistical and biological evidence that ribosomal protein genes exhibit important tissue-dependent variation in mRNA expression. Though these genes are most stably expressed of all investigated genes in a meta-analysis they cannot be considered true reference genes

A structured overview of simultaneous component based data integration

<p>Abstract</p> <p>Background</p> <p>Data integration is currently one of the main challenges in the biomedical sciences. Often different pieces of information are gathered on the same set of entities (e.g., tissues, culture samples, biomolecules) with the different pieces stemming, for example, from different measurement techniques. This implies that more and more data appear that consist of two or more data arrays that have a shared mode. An integrative analysis of such coupled data should be based on a simultaneous analysis of all data arrays. In this respect, the family of simultaneous component methods (e.g., SUM-PCA, unrestricted PCovR, MFA, STATIS, and SCA-P) is a natural choice. Yet, different simultaneous component methods may lead to quite different results.</p> <p>Results</p> <p>We offer a structured overview of simultaneous component methods that frames them in a principal components setting such that both the common core of the methods and the specific elements with regard to which they differ are highlighted. An overview of principles is given that may guide the data analyst in choosing an appropriate simultaneous component method. Several theoretical and practical issues are illustrated with an empirical example on metabolomics data for <it>Escherichia coli </it>as obtained with different analytical chemical measurement methods.</p> <p>Conclusion</p> <p>Of the aspects in which the simultaneous component methods differ, pre-processing and weighting are consequential. Especially, the type of weighting of the different matrices is essential for simultaneous component analysis. These types are shown to be linked to different specifications of the idea of a fair integration of the different coupled arrays.</p

DISCO-SCA and Properly Applied GSVD as Swinging Methods to Find Common and Distinctive Processes

BACKGROUND: In systems biology it is common to obtain for the same set of biological entities information from multiple sources. Examples include expression data for the same set of orthologous genes screened in different organisms and data on the same set of culture samples obtained with different high-throughput techniques. A major challenge is to find the important biological processes underlying the data and to disentangle therein processes common to all data sources and processes distinctive for a specific source. Recently, two promising simultaneous data integration methods have been proposed to attain this goal, namely generalized singular value decomposition (GSVD) and simultaneous component analysis with rotation to common and distinctive components (DISCO-SCA). RESULTS: Both theoretical analyses and applications to biologically relevant data show that: (1) straightforward applications of GSVD yield unsatisfactory results, (2) DISCO-SCA performs well, (3) provided proper pre-processing and algorithmic adaptations, GSVD reaches a performance level similar to that of DISCO-SCA, and (4) DISCO-SCA is directly generalizable to more than two data sources. The biological relevance of DISCO-SCA is illustrated with two applications. First, in a setting of comparative genomics, it is shown that DISCO-SCA recovers a common theme of cell cycle progression and a yeast-specific response to pheromones. The biological annotation was obtained by applying Gene Set Enrichment Analysis in an appropriate way. Second, in an application of DISCO-SCA to metabolomics data for Escherichia coli obtained with two different chemical analysis platforms, it is illustrated that the metabolites involved in some of the biological processes underlying the data are detected by one of the two platforms only; therefore, platforms for microbial metabolomics should be tailored to the biological question. CONCLUSIONS: Both DISCO-SCA and properly applied GSVD are promising integrative methods for finding common and distinctive processes in multisource data. Open source code for both methods is provided

VIH2 Regulates the Synthesis of Inositol Pyrophosphate InsP₈ and Jasmonate-Dependent Defenses in Arabidopsis

Diphosphorylated inositol polyphosphates, also referred to as inositol pyrophosphates, are important signaling molecules that regulate critical cellular activities in many eukaryotic organisms, such as membrane trafficking, telomere maintenance, ribosome biogenesis, and apoptosis. In mammals and fungi, two distinct classes of inositol phosphate kinases mediate biosynthesis of inositol pyrophosphates: Kcs1/IP6K- and Vip1/PPIP5K-like proteins. Here, we report that PPIP5K homologs are widely distributed in plants and that Arabidopsis thaliana VIH1 and VIH2 are functional PPIP5K enzymes. We show a specific induction of inositol pyrophosphate InsP8 by jasmonate and demonstrate that steady state and jasmonate-induced pools of InsP8 in Arabidopsis seedlings depend on VIH2. We identify a role of VIH2 in regulating jasmonate perception and plant defenses against herbivorous insects and necrotrophic fungi. In silico docking experiments and radioligand binding-based reconstitution assays show high-affinity binding of inositol pyrophosphates to the F-box protein COI1-JAZ jasmonate coreceptor complex and suggest that coincidence detection of jasmonate and InsP8 by COI1-JAZ is a critical component in jasmonate-regulated defenses

Autonomic Management of Large Clusters and Their Integration into the Grid

We present a framework for the co-ordinated, autonomic management of multiple clusters in a compute center and their integration into a Grid environment. Site autonomy and the automation of administrative tasks are prime aspects in this framework. The system behavior is continuously monitored in a steering cycle and appropriate actions are taken to resolve any problems. All presented components have been implemented in the course of the EU project DataGrid: The Lemon monitoring components, the FT fault-tolerance mechanism, the quattor system for software installation and configuration, the RMS job and resource management system, and the Gridification scheme that integrates clusters into the Grid