A Modern Ethernet Data Acquisition Architecture for Fermilab Beam Instrumentation

The Fermilab Accelerator Division, Instrumentation Department is adopting an open-source framework to replace our embedded VME-based data acquisition systems. Utilizing an iterative methodology, we first moved to embedded Linux, removing the need for VxWorks. Next, we adopted Ethernet on each data acquisition module eliminating the need for the VME backplane in addition to communicating with a rack mount server. Development of DDCP (Distributed Data Communications Protocol), allowed for an abstraction between the firmware and software layers. Each data acquisition module was adapted to read out using 1 GbE and aggregated at a switch which up linked to a 10 GbE network. Current development includes scaling the system to aggregate more modules, to increase bandwidth to support multiple systems and to adopt MicroTCA as a crate technology. The architecture was utilized on various beamlines around the Fermilab complex including PIP2IT, FAST/IOTA and the Muon Delivery Ring. In summary, we were able to develop a data acquisition framework which incrementally replaces VxWorks & VME hardware as well as increases our total bandwidth to 10 Gbit/s using off the shelf Ethernet technology

MetabR: an R script for linear model analysis of quantitative metabolomic data

Background Metabolomics is an emerging high-throughput approach to systems biology, but data analysis tools are lacking compared to other systems level disciplines such as transcriptomics and proteomics. Metabolomic data analysis requires a normalization step to remove systematic effects of confounding variables on metabolite measurements. Current tools may not correctly normalize every metabolite when the relationships between each metabolite quantity and fixed-effect confounding variables are different, or for the effects of random-effect confounding variables. Linear mixed models, an established methodology in the microarray literature, offer a standardized and flexible approach for removing the effects of fixed- and random-effect confounding variables from metabolomic data. Findings Here we present a simple menu-driven program, “MetabR”, designed to aid researchers with no programming background in statistical analysis of metabolomic data. Written in the open-source statistical programming language R, MetabR implements linear mixed models to normalize metabolomic data and analysis of variance (ANOVA) to test treatment differences. MetabR exports normalized data, checks statistical model assumptions, identifies differentially abundant metabolites, and produces output files to help with data interpretation. Example data are provided to illustrate normalization for common confounding variables and to demonstrate the utility of the MetabR program. Conclusions We developed MetabR as a simple and user-friendly tool for implementing linear mixed model-based normalization and statistical analysis of targeted metabolomic data, which helps to fill a lack of available data analysis tools in this field. The program, user guide, example data, and any future news or updates related to the program may be found at http://metabr.r-forge.r-project.org/ webcite

MetabR: an R script for linear model analysis of quantitative metabolomic data

Background Metabolomics is an emerging high-throughput approach to systems biology, but data analysis tools are lacking compared to other systems level disciplines such as transcriptomics and proteomics. Metabolomic data analysis requires a normalization step to remove systematic effects of confounding variables on metabolite measurements. Current tools may not correctly normalize every metabolite when the relationships between each metabolite quantity and fixed-effect confounding variables are different, or for the effects of random-effect confounding variables. Linear mixed models, an established methodology in the microarray literature, offer a standardized and flexible approach for removing the effects of fixed- and random-effect confounding variables from metabolomic data. Findings Here we present a simple menu-driven program, “MetabR”, designed to aid researchers with no programming background in statistical analysis of metabolomic data. Written in the open-source statistical programming language R, MetabR implements linear mixed models to normalize metabolomic data and analysis of variance (ANOVA) to test treatment differences. MetabR exports normalized data, checks statistical model assumptions, identifies differentially abundant metabolites, and produces output files to help with data interpretation. Example data are provided to illustrate normalization for common confounding variables and to demonstrate the utility of the MetabR program. Conclusions We developed MetabR as a simple and user-friendly tool for implementing linear mixed model-based normalization and statistical analysis of targeted metabolomic data, which helps to fill a lack of available data analysis tools in this field. The program, user guide, example data, and any future news or updates related to the program may be found at http://metabr.r-forge.r-project.org

High Resolution BPM Upgrade for the ATF Damping Ring at KEK

A beam position monitor (BPM) upgrade at the KEK Accelerator Test Facility (ATF) damping ring has been accomplished, carried out by a KEK/FNAL/SLAC collaboration under the umbrella of the global ILC R&D effort. The upgrade consists of a high resolution, high reproducibility read-out system, based on analog and processing, and also implements a new automatic gain error correction schema. The technical concept and realization as well as results of beam studies are presented.Comment: 3 pp. 10th European Workshop on Beam Diagnostics and Instrumentation for Particle Accelerators DIPAC 2011, 16-18 May 2011. Hamburg, German

Comparison of threshold selection methods for microarray gene co-expression matrices

<p>Abstract</p> <p>Background</p> <p>Network and clustering analyses of microarray co-expression correlation data often require application of a threshold to discard small correlations, thus reducing computational demands and decreasing the number of uninformative correlations. This study investigated threshold selection in the context of combinatorial network analysis of transcriptome data.</p> <p>Findings</p> <p>Six conceptually diverse methods - based on number of maximal cliques, correlation of control spots with expressed genes, top 1% of correlations, spectral graph clustering, Bonferroni correction of p-values, and statistical power - were used to estimate a correlation threshold for three time-series microarray datasets. The validity of thresholds was tested by comparison to thresholds derived from Gene Ontology information. Stability and reliability of the best methods were evaluated with block bootstrapping.</p> <p>Two threshold methods, number of maximal cliques and spectral graph, used information in the correlation matrix structure and performed well in terms of stability. Comparison to Gene Ontology found thresholds from number of maximal cliques extracted from a co-expression matrix were the most biologically valid. Approaches to improve both methods were suggested.</p> <p>Conclusion</p> <p>Threshold selection approaches based on network structure of gene relationships gave thresholds with greater relevance to curated biological relationships than approaches based on statistical pair-wise relationships.</p

Maternal consumption of fish oil programs reduced adiposity in broiler chicks

Maternal intake of eicosapentaenoic acid (EPA; 20:5 n-3) and docosahexaenoic acid (22:6 n-3) has been associated with reduced adiposity in children, suggesting the possibility to program adipose development through dietary fatty acids before birth. This study determined if enriching the maternal diet in fish oil, the primary source of EPA and DHA, affected adipose development in offspring. Broiler chickens were used because they are obesity-prone, and because fatty acids provided to the embryo can be manipulated through the hen diet. Hens were fed diets supplemented (2.8% wt:wt) with corn oil (CO; n-6) or fish oil (FO; n-3) for 28 d. Chicks from both maternal diet groups were fed the same diet after hatch. Maternal FO consumption enriched chick adipose tissue in EPA and DHA and reduced adiposity by promoting more, but smaller, adipocytes. This adipocyte profile was paralleled by upregulated expression of the adipogenic regulator PPARGand its co-activator PPARGC1B, and reduced expression of LPL. Proteomics identified 95 differentially abundant proteins between FO and CO adipose tissue, including components of glucose metabolism, lipid droplet trafficking, and cytoskeletal organization. These results demonstrate that the maternal dietary fatty acid profile programs offspring adipose development

Power tests of a string of magnets comprising a full cell of the Superconducting Super Collider

In this paper we describe the operation and testing of a string of magnets comprising a full cell of the Superconducting Super Collider (SSC). The full cell configuration composed of ten dipoles, two quadrupoles, and three spool pieces is the longest SSC magnet string ever tested. Although the tests of the full cell were undertaken after the SSC project was marked for termination, their completion was deemed necessary and useful to future efforts at other accelerator laboratories utilizing Superconducting magnets. The focus of this work is on the electrical and cryogenic performance of the string components and the quench protection system with an emphasis on solving some of the questions concerning electrical performance raised during the previous two experimental runs involving a half cell configuration

High resolution BPMS with integrated gain correction system

High resolution beam position monitors (BPM) are an essential tool to achieve and reproduce a low vertical beam emittance at the KEK Accelerator Test Facility (ATF) damping ring. The ATF damping ring (DR) BPMs are currently upgraded with new high resolution read-out electronics. Based on analog and digital down-conversion techniques, the upgrade includes an automatic gain calibration system to correct for slow drift effects and ensure high reproducible beam position readings. The concept and its technical realization, as well as preliminary results of beam studies are presented

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.&#xd;&#xa;&#xd;&#xa