Distributed Computing Grid Experiences in CMS

The CMS experiment is currently developing a computing system capable of serving, processing and archiving the large number of events that will be generated when the CMS detector starts taking data. During 2004 CMS undertook a large scale data challenge to demonstrate the ability of the CMS computing system to cope with a sustained data-taking rate equivalent to 25% of startup rate. Its goals were: to run CMS event reconstruction at CERN for a sustained period at 25 Hz input rate; to distribute the data to several regional centers; and enable data access at those centers for analysis. Grid middleware was utilized to help complete all aspects of the challenge. To continue to provide scalable access from anywhere in the world to the data, CMS is developing a layer of software that uses Grid tools to gain access to data and resources, and that aims to provide physicists with a user friendly interface for submitting their analysis jobs. This paper describes the data challenge experience with Grid infrastructure and the current development of the CMS analysis system

Revealing natural relationships among arbuscular mycorrhizal fungi: culture line BEG47 represents Diversispora epigaea, not Glomus versiforme

Background: Understanding the mechanisms underlying biological phenomena, such as evolutionarily conservative trait inheritance, is predicated on knowledge of the natural relationships among organisms. However, despite their enormous ecological significance, many of the ubiquitous soil inhabiting and plant symbiotic arbuscular mycorrhizal fungi (AMF, phylum Glomeromycota) are incorrectly classified. Methodology/Principal Findings: Here, we focused on a frequently used model AMF registered as culture BEG47. This fungus is a descendent of the ex-type culture-lineage of Glomus epigaeum, which in 1983 was synonymised with Glomus versiforme. It has since then been used as ‘G. versiforme BEG47’. We show by morphological comparisons, based on type material, collected 1860–61, of G. versiforme and on type material and living ex-type cultures of G. epigaeum, that these two AMF species cannot be conspecific, and by molecular phylogenetics that BEG47 is a member of the genus Diversispora. Conclusions: This study highlights that experimental works published during the last >25 years on an AMF named ‘G. versiforme’ or ‘BEG47’ refer to D. epigaea, a species that is actually evolutionarily separated by hundreds of millions of years from all members of the genera in the Glomerales and thus from most other commonly used AMF ‘laboratory strains’. Detailed redescriptions substantiate the renaming of G. epigaeum (BEG47) as D. epigaea, positioning it systematically in the order Diversisporales, thus enabling an evolutionary understanding of genetical, physiological, and ecological traits, relative to those of other AMF. Diversispora epigaea is widely cultured as a laboratory strain of AMF, whereas G. versiforme appears not to have been cultured nor found in the field since its original description

Fifteen years SIB Swiss Institute of Bioinformatics: life science databases, tools and support.

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) was created in 1998 as an institution to foster excellence in bioinformatics. It is renowned worldwide for its databases and software tools, such as UniProtKB/Swiss-Prot, PROSITE, SWISS-MODEL, STRING, etc, that are all accessible on ExPASy.org, SIB's Bioinformatics Resource Portal. This article provides an overview of the scientific and training resources SIB has consistently been offering to the life science community for more than 15 years

Data science

Even though it has only entered public perception relatively recently, the term "data science" already means many things to many people. This chapter explores both top-down and bottom-up views on the field, on the basis of which we define data science as "a unique blend of principles and methods from analytics, engineering, entrepreneurship and communication that aim at generating value from the data itself". The chapter then discusses the disciplines that contribute to this "blend", briefly outlining their contributions and giving pointers for readers interested in exploring their backgrounds further

Riding out of the storm: How to deal with the complexity of grid and cloud management

Over the last decade, Grid computing paved the way for a new level of large scale distributed systems. This infrastructure made it possible to securely and reliably take advantage of widely separated computational resources that are part of several different organizations. Resources can be incorporated to the Grid, building a theoretical virtual supercomputer. In time, cloud computing emerged as a new type of large scale distributed system, inheriting and expanding the expertise and knowledge that have been obtained so far. Some of the main characteristics of Grids naturally evolved into clouds, others were modified and adapted and others were simply discarded or postponed. Regardless of these technical specifics, both Grids and clouds together can be considered as one of the most important advances in large scale distributed computing of the past ten years; however, this step in distributed computing has came along with a completely new level of complexity. Grid and cloud management mechanisms play a key role, and correct analysis and understanding of the system behavior are needed. Large scale distributed systems must be able to self-manage, incorporating autonomic features capable of controlling and optimizing all resources and services. Traditional distributed computing management mechanisms analyze each resource separately and adjust specific parameters of each one of them. When trying to adapt the same procedures to Grid and cloud computing, the vast complexity of these systems can make this task extremely complicated. But large scale distributed systems complexity could only be a matter of perspective. It could be possible to understand the Grid or cloud behavior as a single entity, instead of a set of resources. This abstraction could provide a different understanding of the system, describing large scale behavior and global events that probably would not be detected analyzing each resource separately. In this work we define a theoretical framework that combines both ideas, multiple resources and single entity, to develop large scale distributed systems management techniques aimed at system performance optimization, increased dependability and Quality of Service (QoS). The resulting synergy could be the key 350 J. Montes et al. to address the most important difficulties of Grid and cloud management

Hv-CBF2A overexpression in barley accelerates COR gene transcript accumulation and acquisition of freezing tolerance during cold acclimation

Abstract C-Repeat Binding Factors (CBFs) are DNAbinding transcriptional activators of gene pathways imparting freezing tolerance. Poaceae contain three CBF subfamilies, two of which, HvCBF3/CBFIII and HvCBF4/CBFIV, are unique to this taxon. To gain mechanistic insight into HvCBF4/CBFIV CBFs we overexpressed Hv-CBF2A in spring barley (Hordeum vulgare) cultivar ‘Golden Promise’. The Hv-CBF2A overexpressing lines exhibited stunted growth, poor yield, and greater freezing tolerance compared to non-transformed ‘Golden Promise’. Differences in freezing tolerance were apparent only upon cold acclimation. During cold acclimation freezing tolerance of the Hv-CBF2A overexpressing lines increased more rapidly than that of ‘Golden Promise’ and paralleled the freezing tolerance of the winter hardy barley ‘Dicktoo’. Transcript levels of candidate CBF target genes, COR14B and DHN5 were increased in the overexpressor lines at warm temperatures, and at cold temperatures they accumulated to much higher levels in the Hv-CBF2A overexpressors than in ‘Golden Promise’. Hv-CBF2A overexpression also increased transcript levels of other CBF genes at FROST RESISTANCE-H2-H2 (FR-H2) possessing CRT/DRE sites in their upstream regions, the most notable of which was CBF12. CBF12 transcript levels exhibited a relatively constant incremental increase above levels in ‘Golden Promise’ both at warm and cold. These data indicate that Hv-CBF2A activates target genes at warm temperatures and that transcript accumulation for some of these targets is greatly enhanced by cold temperatures

Exposure to Candida albicans Polarizes a T-Cell Driven Arthritis Model towards Th17 Responses, Resulting in a More Destructive Arthritis

BACKGROUND: Fungal components have been shown very effective in generating Th17 responses. We investigated whether exposure to a minute amount of C. albicans in the arthritic joint altered the local cytokine environment, leading to enhanced Th17 expansion and resulting in a more destructive arthritis. METHODOLOGY: Chronic SCW arthritis was induced by repeated injection with Streptococcus pyogenes (SCW) cell wall fragments into the knee joint of C57Bl/6 mice, alone or in combination with the yeast of C. albicans or Zymosan A. During the chronic phase of the arthritis, the cytokine levels, mRNA expression and histopathological analysis of the joints were performed. To investigate the phenotype of the IL-17 producing T-cells, synovial cells were isolated and analyzed by flowcytometry. PRINCIPAL FINDINGS: Intra-articular injection of either Zymosan A or C. albicans on top of the SCW injection both resulted in enhanced joint swelling and inflammation compared to the normal SCW group. However, only the addition of C. albicans during SCW arthritis resulted in severe chondrocyte death and enhanced destruction of cartilage and bone. Additionally, exposure to C. albicans led to increased IL-17 in the arthritic joint, which was accompanied by an increased synovial mRNA expression of T-bet and RORgammaT. Moreover, the C. albicans-injected mice had significantly more Th17 cells in the synovium, of which a large population also produced IFN-gamma. CONCLUSION: This study clearly shows that minute amounts of fungal components, like C. albicans, are very potent in interfering with the local cytokine environment in an arthritic joint, thereby polarizing arthritis towards a more destructive phenotype

Mapping Neural Circuits with Activity-Dependent Nuclear Import of a Transcription Factor

Nuclear factor of activated T cells (NFAT) is a calcium-responsive transcription factor. We describe here an NFAT-based neural tracing method—CaLexA (calcium-dependent nuclear import of Lex A)—for labeling active neurons in behaving animals. In this system, sustained neural activity induces nuclear import of the chimeric transcription factor LexA-VP16-NFAT, which in turn drives green fluorescent protein (GFP) reporter expression only in active neurons. We tested this system in Drosophila and found that volatile sex pheromones excite specific neurons in the olfactory circuit. Furthermore, complex courtship behavior associated with multi-modal sensory inputs activated neurons in the ventral nerve cord. This method harnessing the mechanism of activity-dependent nuclear import of a transcription factor can be used to identify active neurons in specific neuronal population in behaving animals

Systematic identification of functional modules and cis-regulatory elements in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Several large-scale gene co-expression networks have been constructed successfully for predicting gene functional modules and cis-regulatory elements in Arabidopsis (<it>Arabidopsis thaliana</it>)<it>.</it> However, these networks are usually constructed and analyzed in an <it>ad hoc</it> manner. In this study, we propose a completely parameter-free and systematic method for constructing gene co-expression networks and predicting functional modules as well as cis-regulatory elements.</p> <p>Results</p> <p>Our novel method consists of an automated network construction algorithm, a parameter-free procedure to predict functional modules, and a strategy for finding known cis-regulatory elements that is suitable for consensus scanning without prior knowledge of the allowed extent of degeneracy of the motif. We apply the method to study a large collection of gene expression microarray data in Arabidopsis. We estimate that our co-expression network has ~94% of accuracy, and has topological properties similar to other biological networks, such as being scale-free and having a high clustering coefficient. Remarkably, among the ~300 predicted modules whose sizes are at least 20, 88% have at least one significantly enriched functions, including a few extremely significant ones (ribosome, <it>p</it> < 1E-300, photosynthetic membrane, <it>p</it> < 1.3E-137, proteasome complex, <it>p</it> < 5.9E-126). In addition, we are able to predict cis-regulatory elements for 66.7% of the modules, and the association between the enriched cis-regulatory elements and the enriched functional terms can often be confirmed by the literature. Overall, our results are much more significant than those reported by several previous studies on similar data sets. Finally, we utilize the co-expression network to dissect the promoters of 19 Arabidopsis genes involved in the metabolism and signaling of the important plant hormone gibberellin, and achieved promising results that reveal interesting insight into the biosynthesis and signaling of gibberellin.</p> <p>Conclusions</p> <p>The results show that our method is highly effective in finding functional modules from real microarray data. Our application on Arabidopsis leads to the discovery of the largest number of annotated Arabidopsis functional modules in the literature. Given the high statistical significance of functional enrichment and the agreement between cis-regulatory and functional annotations, we believe our Arabidopsis gene modules can be used to predict the functions of unknown genes in Arabidopsis, and to understand the regulatory mechanisms of many genes.</p