Effects of beta-alanine supplementation on brain homocarnosine/carnosine signal and cognitive function: an exploratory study

Objectives: Two independent studies were conducted to examine the effects of 28 d of beta-alanine supplementation at 6.4 g d-1 on brain homocarnosine/carnosine signal in omnivores and vegetarians (Study 1) and on cognitive function before and after exercise in trained cyclists (Study 2). Methods: In Study 1, seven healthy vegetarians (3 women and 4 men) and seven age- and sex-matched omnivores undertook a brain 1H-MRS exam at baseline and after beta-alanine supplementation. In study 2, nineteen trained male cyclists completed four 20-Km cycling time trials (two pre supplementation and two post supplementation), with a battery of cognitive function tests (Stroop test, Sternberg paradigm, Rapid Visual Information Processing task) being performed before and after exercise on each occasion. Results: In Study 1, there were no within-group effects of beta-alanine supplementation on brain homocarnosine/carnosine signal in either vegetarians (p = 0.99) or omnivores (p = 0.27); nor was there any effect when data from both groups were pooled (p = 0.19). Similarly, there was no group by time interaction for brain homocarnosine/carnosine signal (p = 0.27). In study 2, exercise improved cognitive function across all tests (P0.05) of beta-alanine supplementation on response times or accuracy for the Stroop test, Sternberg paradigm or RVIP task at rest or after exercise. Conclusion: 28 d of beta-alanine supplementation at 6.4g d-1 appeared not to influence brain homocarnosine/ carnosine signal in either omnivores or vegetarians; nor did it influence cognitive function before or after exercise in trained cyclists

Chlamydophila pneumoniae induces a sustained airway hyperresponsiveness and inflammation in mice

Background: It has been reported that Chlamydophila (C.) pneumoniae is involved in the initiation and promotion of asthma and chronic obstructive pulmonary diseases (COPD). Surprisingly, the effect of C. pneumoniae on airway function has never been investigated.Methods: In this study, mice were inoculated intranasally with C. pneumoniae (strain AR39) on day 0 and experiments were performed on day 2, 7, 14 and 21.Results: We found that from day 7, C. pneumoniae infection causes both a sustained airway hyperresponsiveness and an inflammation. Interferon-γ (IFN-γ) and macrophage inflammatory chemokine-2 (MIP-2) levels in bronchoalveolar lavage (BAL)-fluid were increased on all experimental days with exception of day 7 where MIP-2 concentrations dropped to control levels. In contrast, tumor necrosis factor-α (TNF-α) levels were only increased on day 7. From day 7 to 21 epithelial damage and secretory cell hypertrophy was observed. It is suggested that, the inflammatory cells/mediators, the epithelial damage and secretory cell hypertrophy contribute to initiation of airway hyperresponsiveness.Conclusion: Our study demonstrates for the first time that C. pneumoniae infection can modify bronchial responsiveness. This has clinical implications, since additional changes in airway responsiveness and inflammation-status induced by this bacterium may worsen and/or provoke breathlessness in asthma and COPD

Methods for visual mining of genomic and proteomic data atlases

<p>Abstract</p> <p>Background</p> <p>As the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.</p> <p>Results</p> <p>This paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.</p> <p>Conclusions</p> <p>The mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.</p

A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

Bayesian models have advanced the idea that humans combine prior beliefs and sensory observations to optimize behavior. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent probability distributions. An alternative view is that the brain relies on simple algorithms that can implement Bayes-optimal behavior only when the computational demands are low. To distinguish between these alternatives, we devised a task in which Bayes-optimal performance could not be matched by simple algorithms. We asked subjects to estimate and reproduce a time interval by combining prior information with one or two sequential measurements. In the domain of time, measurement noise increases with duration. This property takes the integration of multiple measurements beyond the reach of simple algorithms. We found that subjects were able to update their estimates using the second measurement but their performance was suboptimal, suggesting that they were unable to update full probability distributions. Instead, subjects’ behavior was consistent with an algorithm that predicts upcoming sensory signals, and applies a nonlinear function to errors in prediction to update estimates. These results indicate that the inference strategies employed by humans may deviate from Bayes-optimal integration when the computational demands are high

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe