bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Use of Drugs and Nutritional Supplements in Top-Level Track and Field Athletes
Authors: Tscholl Philippe , Alonso Juan M. , Dollé Gabriel , Junge Astrid , Dvorak Jiri 
Published: October 2009

Background High use of medication and nutritional supplements has been reported in several sports. Purpose To document the use of prescribed medication and nutritional supplements in female and male junior, youth, and adult track and field athletes depending on their sports discipline. Study Design Descriptive epidemiology study. Methods Analysis of 3 887 doping control forms undertaken during 12 International Association of Athletics Federations World Championships and 1 out-of-competitions season in track and field. Results There were 6 523 nutritional supplements (1.7 per athlete) and 3 237 medications (0.8 per athlete) reported. Nonsteroidal anti-inflammatory drugs (NSAIDs; 0.27 per athlete, n = 884), respiratory drugs (0.21 per athlete, n = 682), and alternative analgesics (0.13, n = 423) were used most frequently. Medication use increased with age (0.33 to 0.87 per athlete) and decreased with increasing duration of the event (from sprints to endurance events; 1.0 to 0.63 per athlete). African and Asian track and field athletes reported using significantly fewer supplements (0.85 vs 1.93 per athlete) and medications (0.41 vs 0.96 per athlete) than athletes from other continents. The final ranking in the championships was unrelated to the quantity of reported medications or supplements taken. Compared with middle-distance and long-distance runners, athletes in power and sprint disciplines reported using more NSAIDs, creatine, and amino acids, and fewer antimicrobial agents. Conclusion The use of NSAIDs in track and field is less than that reported for team-sport events. However, nutritional supplements are used more than twice as often as they are in soccer and other multisport events; this inadvertently increases the risk of positive results of doping tests. Clinical Relevance It is essential that an evidence-based approach to the prescribing of medication and nutritional supplements is adopted to protect the athletes’ health and prevent them from testing positive in doping controls.
Mesenchymal gaucho homing on the range
Authors: Weinreb Neal J. 
Published: October 2009

Abstract
GD1 is a disorder whose pathophysiology is manifested primarily in tissue macrophages.1 In this issue of Blood, Campeau and colleagues demonstrate that lysosomal glucocerebrosidase deficiency in GD1 bone marrow MSCs is associated with increased cellular glucosylceramide and up-regulation of inflammatory mediators that may promote bone mineral loss and hematologic malignancy.2
Genetic variation in CXCR4 and risk of chronic lymphocytic leukemia
Authors: Crowther-Swanepoel Dalemari , Qureshi Mobshra , Dyer Martin J. S. , Matutes Estella , Dearden Claire , Catovsky Daniel , Houlston Richard S. 
Published: October 2009

Abstract
A genome-wide linkage scan has provided evidence for a chronic lymphocytic leukemia (CLL) susceptibility locus at 2q21 to which the chemokine receptor CXCR4 gene maps. Recent data provide some evidence for common variation in CXCR4 according to the polymorphic variant rs2228014 defining CLL risk. To examine the role of genetic variation in CXCR4 on CLL risk, we screened 188 familial CLL cases and 213 controls for germline mutations in the coding regions of CXCR4 and genotyped rs2228014 in 1058 CLL cases and 1807 controls. No association between rs2228014 and risk of CLL was seen (P = .83). One truncating (W195X) and 2 missense mutations with possible functional consequences (V139I and G335S) were identified among 186 familial cases and 0 in 213 controls sequenced. Our analysis provides no evidence that common variation in CXCR4 defined by rs228014 influences the risk of CLL, but that functional coding mutations in CXCR4 may contribute to familial CLL.
KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database
Authors: Moutselos Konstantinos , Kanaris Ioannis , Chatziioannou Aristotelis , Maglogiannis Ilias , Kolisis Fragiskos N 
Published: October 2009

Abstract

Background
The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes.

Results
A case study of the integration of six human metabolic pathways from KEGG depicts the ability of KEGGconverter to automatically produce merged and converted to SBML fully functional pathway models, enhanced with default kinetics. The suitability of the developed tool is demonstrated through a comparison with other state-of-the art relevant software tools for the same data fusion and conversion tasks, thus illustrating the problems and the relevant workflows. Moreover, KEGGconverter permits the inclusion of additional reactions in the resulting model which represent flux cross-talk with neighbouring pathways, providing in this way improved simulative accuracy. These additional reactions are introduced by exploiting relevant semantic information for the elements of the KEGG Pathways database. The architecture and functionalities of the web-based application are presented.

Conclusion
KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files. The web application acts as a user friendly shell which transparently enables the automated biochemically correct pathway merging, conversion to SBML format, proper renaming of the species, and insertion of default kinetic properties for the pertaining reactions. The tool is available at: http://www.grissom.gr/keggconverter
Analysis and modeling of time-course gene-expression profiles from nanomaterial-exposed primary human epidermal keratinocytes
Authors: Zollanvari Amin , Cunningham Mary Jane , Braga-Neto Ulisses , Dougherty Edward R 
Published: October 2009

AbstractBackgroundNanomaterials are being manufactured on a commercial scale for use in medical, diagnostic, energy, component and communications industries. However, concerns over the safety of engineered nanomaterials have surfaced. Humans can be exposed to nanomaterials in different ways such as inhalation or exposure through the integumentary system.ResultsThe interactions of engineered nanomaterials with primary human cells was investigated, using a systems biology approach combining gene expression microarray profiling with dynamic experimental parameters. In this experiment, primary human epidermal keratinocytes cells were exposed to several low-micron to nano-scale materials, and gene expression was profiled over both time and dose to compile a comprehensive picture of nanomaterial-cellular interactions. Very few gene-expression studies so far have dealt with both time and dose response simultaneously. Here, we propose different approaches to this kind of analysis. First, we used heat maps and multi-dimensional scaling (MDS) plots to visualize the dose response of nanomaterials over time. Then, in order to find out the most common patterns in gene-expression profiles, we used self-organizing maps (SOM) combined with two different criteria to determine the number of clusters. The consistency of SOM results is discussed in context of the information derived from the MDS plots. Finally, in order to identify the genes that have significantly different responses among different levels of dose of each treatment while accounting for the effect of time at the same time, we used a two-way ANOVA model, in connection with Tukey's additivity test and the Box-Cox transformation. The results are discussed in the context of the cellular responses of engineered nanomaterials.ConclusionThe analysis presented here lead to interesting and complementary conclusions about the response across time of human epidermal keratinocytes after exposure to nanomaterials. For example, we observed that gene expression for most treatments become closer to the expression of the baseline cultures as time proceeds. The genes found to be differentially-expressed are involved in a number of cellular processes, including regulation of transcription and translation, protein localization, transport, cell cycle progression, cell migration, cytoskeletal reorganization, signal transduction, and development.
Site-specific impacts on gene expression and behavior in fathead minnows (Pimephales promelas) exposed in situ to streams adjacent to sewage treatment plants
Authors: Garcia-Reyero Natàlia , Adelman Ira R , Martinović Dalma , Liu Li , Denslow Nancy D 
Published: October 2009

Abstract

Background
Environmental monitoring for pharmaceuticals and endocrine disruptors in the aquatic environment traditionally employs a variety of methods including analytical chemistry, as well as a variety of histological and biochemical endpoints that correlate with the fish fitness. It is now clear that analytical chemistry alone is insufficient to identify aquatic environments that are compromised because these measurements do not identify the biologically available dose. The biological endpoints that are measured are important because they relate to known impairments; however, they are not specific to the contaminants and often focus on only a few known endpoints. These studies can be enhanced by looking more broadly at changes in gene expression, especially if the analysis focuses on biochemical pathways. The present study was designed to obtain additional information for well-characterized sites adjacent to sewage treatment plants in MN that are thought to be impacted by endocrine disruptors.

Results
Here we examine five sites that have been previously characterized and examine changes in gene expression in fathead minnows (Pimephales promelas) that have been caged for 48 h in each of the aquatic environments. We find that the gene expression changes are characteristic and unique at each of the five sites. Also, fish exposed to two of the sites, 7 and 12, present a more aggressive behavior compared to control fish.

Conclusion
Our results show that a short-term exposure to sewage treatment plant effluents was able to induce a site-specific gene expression pattern in the fathead minnow gonad and liver. The short-term exposure was also enough to affect fish sexual behavior. Our results also show that microarray analysis can be very useful at determining potential exposure to chemicals, and could be used routinely as a tool for environmental monitoring.
NATbox: a network analysis toolbox in R
Authors: Chavan Shweta S , Bauer Michael A , Scutari Marco , Nagarajan Radhakrishnan 
Published: October 2009

Abstract

Background
There has been recent interest in capturing the functional relationships (FRs) from high-throughput assays using suitable computational techniques. FRs elucidate the working of genes in concert as a system as opposed to independent entities hence may provide preliminary insights into biological pathways and signalling mechanisms. Bayesian structure learning (BSL) techniques and its extensions have been used successfully for modelling FRs from expression profiles. Such techniques are especially useful in discovering undocumented FRs, investigating non-canonical signalling mechanisms and cross-talk between pathways. The objective of the present study is to develop a graphical user interface (GUI), NATbox: Network Analysis Toolbox in the language R that houses a battery of BSL algorithms in conjunction with suitable statistical tools for modelling FRs in the form of acyclic networks from gene expression profiles and their subsequent analysis.

Results
NATbox is a menu-driven open-source GUI implemented in the R statistical language for modelling and analysis of FRs from gene expression profiles. It provides options to (i) impute missing observations in the given data (ii) model FRs and network structure from gene expression profiles using a battery of BSL algorithms and identify robust dependencies using a bootstrap procedure, (iii) present the FRs in the form of acyclic graphs for visualization and investigate its topological properties using network analysis metrics, (iv) retrieve FRs of interest from published literature. Subsequently, use these FRs as structural priors in BSL (v) enhance scalability of BSL across high-dimensional data by parallelizing the bootstrap routines.

Conclusion
NATbox provides a menu-driven GUI for modelling and analysis of FRs from gene expression profiles. By incorporating readily available functions from existing R-packages, it minimizes redundancy and improves reproducibility, transparency and sustainability, characteristic of open-source environments. NATbox is especially suited for interdisciplinary researchers and biologists with minimal programming experience and would like to use systems biology approaches without delving into the algorithmic aspects. The GUI provides appropriate parameter recommendations for the various menu options including default parameter choices for the user. NATbox can also prove to be a useful demonstration and teaching tool in graduate and undergraduate course in systems biology. It has been tested successfully under Windows and Linux operating systems. The source code along with installation instructions and accompanying tutorial can be found at http://bioinformatics.ualr.edu/natboxWiki/index.php/Main_Page.
Novel software package for cross-platform transcriptome analysis (CPTRA)
Authors: Zhou Xin , Su Zhen , Sammons R Douglas , Peng Yanhui , Tranel Patrick J , Stewart C Neal , Yuan Joshua S 
Published: October 2009

Abstract

Background
Next-generation sequencing techniques enable several novel transcriptome profiling approaches. Recent studies indicated that digital gene expression profiling based on short sequence tags has superior performance as compared to other transcriptome analysis platforms including microarrays. However, the transcriptomic analysis with tag-based methods often depends on available genome sequence. The use of tag-based methods in species without genome sequence should be complemented by other methods such as cDNA library sequencing. The combination of different next generation sequencing techniques like 454 pyrosequencing and Illumina Genome Analyzer (Solexa) will enable high-throughput and accurate global gene expression profiling in species with limited genome information. The combination of transcriptome data acquisition methods requires cross-platform transcriptome data analysis platforms, including a new software package for data processing.

Results
Here we presented a software package, CPTRA: C ross-P latform TR anscriptome A nalysis, to analyze transcriptome profiling data from separate methods. The software package is available at http://people.tamu.edu/~syuan/cptra/cptra.html. It was applied to the case study of non-target site glyphosate resistance in horseweed; and the data was mined to discover resistance target gene(s). For the software, the input data included a long-read sequence dataset with proper annotation, and a short-read sequence tag dataset for the quantification of transcripts. By combining the two datasets, the software carries out the unique sequence tag identification, tag counting for transcript quantification, and cross-platform sequence matching functions, whereby the short sequence tags can be annotated with a function, level of expression, and Gene Ontology (GO) classification. Multiple sequence search algorithms were implemented and compared. The analysis highlighted the importance of transport genes in glyphosate resistance and identified several candidate genes for down-stream analysis.

Conclusion
CPTRA is a powerful software package for next generation sequencing-based transcriptome profiling in species with limited genome information. According to our case study, the strategy can greatly broaden the application of the next generation sequencing for transcriptome analysis in species without reference genome sequence.
An automated proteomic data analysis workflow for mass spectrometry
Authors: Pendarvis Ken , Kumar Ranjit , Burgess Shane C , Nanduri Bindu 
Published: October 2009

AbstractBackgroundMass spectrometry-based protein identification methods are fundamental to proteomics. Biological experiments are usually performed in replicates and proteomic analyses generate huge datasets which need to be integrated and quantitatively analyzed. The Sequest™ search algorithm is a commonly used algorithm for identifying peptides and proteins from two dimensional liquid chromatography electrospray ionization tandem mass spectrometry (2-D LC ESI MS2) data. A number of proteomic pipelines that facilitate high throughput 'post data acquisition analysis' are described in the literature. However, these pipelines need to be updated to accommodate the rapidly evolving data analysis methods. Here, we describe a proteomic data analysis pipeline that specifically addresses two main issues pertinent to protein identification and differential expression analysis: 1) estimation of the probability of peptide and protein identifications and 2) non-parametric statistics for protein differential expression analysis. Our proteomic analysis workflow analyzes replicate datasets from a single experimental paradigm to generate a list of identified proteins with their probabilities and significant changes in protein expression using parametric and non-parametric statistics.ResultsThe input for our workflow is Bioworks™ 3.2 Sequest (or a later version, including cluster) output in XML format. We use a decoy database approach to assign probability to peptide identifications. The user has the option to select "quality thresholds" on peptide identifications based on the P value. We also estimate probability for protein identification. Proteins identified with peptides at a user-specified threshold value from biological experiments are grouped as either control or treatment for further analysis in ProtQuant. ProtQuant utilizes a parametric (ANOVA) method, for calculating differences in protein expression based on the quantitative measure ΣXcorr. Alternatively ProtQuant output can be further processed using non-parametric Monte-Carlo resampling statistics to calculate P values for differential expression. Correction for multiple testing of ANOVA and resampling P values is done using Benjamini and Hochberg's method. The results of these statistical analyses are then combined into a single output file containing a comprehensive protein list with probabilities and differential expression analysis, associated P values, and resampling statistics.ConclusionFor biologists carrying out proteomics by mass spectrometry, our workflow facilitates automated, easy to use analyses of Bioworks (3.2 or later versions) data. All the methods used in the workflow are peer-reviewed and as such the results of our workflow are compliant with proteomic data submission guidelines to public proteomic data repositories including PRIDE. Our workflow is a necessary intermediate step that is required to link proteomics data to biological knowledge for generating testable hypotheses.
Graph ranking for exploratory gene data analysis
Authors: Gao Cuilan , Dang Xin , Chen Yixin , Wilkins Dawn 
Published: October 2009

AbstractBackgroundMicroarray technology has made it possible to simultaneously monitor the expression levels of thousands of genes in a single experiment. However, the large number of genes greatly increases the challenges of analyzing, comprehending and interpreting the resulting mass of data. Selecting a subset of important genes is inevitable to address the challenge. Gene selection has been investigated extensively over the last decade. Most selection procedures, however, are not sufficient for accurate inference of underlying biology, because biological significance does not necessarily have to be statistically significant. Additional biological knowledge needs to be integrated into the gene selection procedure.ResultsWe propose a general framework for gene ranking. We construct a bipartite graph from the Gene Ontology (GO) and gene expression data. The graph describes the relationship between genes and their associated molecular functions. Under a species condition, edge weights of the graph are assigned to be gene expression level. Such a graph provides a mathematical means to represent both species-independent and species-dependent biological information. We also develop a new ranking algorithm to analyze the weighted graph via a kernelized spatial depth (KSD) approach. Consequently, the importance of gene and molecular function can be simultaneously ranked by a real-valued measure, KSD, which incorporates the global and local structure of the graph. Over-expressed and under-regulated genes also can be separately ranked.ConclusionThe gene-function bigraph integrates molecular function annotations into gene expression data. The relevance of genes is described in the graph (through a common function). The proposed method provides an exploratory framework for gene data analysis.