Web Derive 01 - an online Art VR work by the Transeuntis Mundi Project

The Transeuntis Mundi(TM) Project explores the complexity of the millennial human journey and the resulting cultural diversity. It investigates how it can be approached through artistic research and be converted into a nomadic creative practice in transmedia arts, using emerging technologies to generate an archive of human legacy. Compositions are based on field recordings in public spaces of multiple cities around the world, using 3D technology for sound and image, following Careri\u27s (2002) methodology of Walkscapes. The power of interactivity and immersion through 360o image and ambisonic sounds are crucial to insert the visitor in a global cultural experience. This transmedia work is designed to happen as a non-fictional VR application. Here we present the two main compositions of the project: Derive 01 and Web Derive 01

A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

SNPInterForest: A new method for detecting epistatic interactions

<p>Abstract</p> <p>Background</p> <p>Multiple genetic factors and their interactive effects are speculated to contribute to complex diseases. Detecting such genetic interactive effects, i.e., epistatic interactions, however, remains a significant challenge in large-scale association studies.</p> <p>Results</p> <p>We have developed a new method, named SNPInterForest, for identifying epistatic interactions by extending an ensemble learning technique called random forest. Random forest is a predictive method that has been proposed for use in discovering single-nucleotide polymorphisms (SNPs), which are most predictive of the disease status in association studies. However, it is less sensitive to SNPs with little marginal effect. Furthermore, it does not natively exhibit information on interaction patterns of susceptibility SNPs. We extended the random forest framework to overcome the above limitations by means of (i) modifying the construction of the random forest and (ii) implementing a procedure for extracting interaction patterns from the constructed random forest. The performance of the proposed method was evaluated by simulated data under a wide spectrum of disease models. SNPInterForest performed very well in successfully identifying pure epistatic interactions with high precision and was still more than capable of concurrently identifying multiple interactions under the existence of genetic heterogeneity. It was also performed on real GWAS data of rheumatoid arthritis from the Wellcome Trust Case Control Consortium (WTCCC), and novel potential interactions were reported.</p> <p>Conclusions</p> <p>SNPInterForest, offering an efficient means to detect epistatic interactions without statistical analyses, is promising for practical use as a way to reveal the epistatic interactions involved in common complex diseases.</p

Candidate gene analysis of spontaneous preterm delivery: New insights from re-analysis of a case-control study using case-parent triads and control-mother dyads

<p>Abstract</p> <p>Background</p> <p>Spontaneous preterm delivery (PTD) has a multifactorial etiology with evidence of a genetic contribution to its pathogenesis. A number of candidate gene case-control studies have been performed on spontaneous PTD, but the results have been inconsistent, and do not fully assess the role of how two genotypes can impact outcome. To elucidate this latter point we re-analyzed data from a previously published case-control candidate gene study, using a case-parent triad design and a hybrid design combining case-parent triads and control-mother dyads. These methods offer a robust approach to genetic association studies for PTD compared to traditional case-control designs.</p> <p>Methods</p> <p>The study participants were obtained from the Norwegian Mother and Child Cohort Study (MoBa). A total of 196 case triads and 211 control dyads were selected for the analysis. A case-parent triad design as well as a hybrid design was used to analyze 1,326 SNPs from 159 candidate genes. We compared our results to those from a previous case-control study on the same samples. Haplotypes were analyzed using a sliding window of three SNPs and a pathway analysis was performed to gain biological insight into the pathophysiology of preterm delivery.</p> <p>Results</p> <p>The most consistent significant fetal gene across all analyses was COL5A2. The functionally similar COL5A1 was significant when combining fetal and maternal genotypes. PON1 was significant with analytical approaches for single locus association of fetal genes alone, but was possibly confounded by maternal effects. Focal adhesion (hsa04510), Cell Communication (hsa01430) and ECM receptor interaction (hsa04512) were the most constant significant pathways.</p> <p>Conclusion</p> <p>This study suggests a fetal association of COL5A2 and a combined fetal-maternal association of COL5A1 with spontaneous PTD. In addition, the pathway analysis implied interactions of genes affecting cell communication and extracellular matrix.</p

Sample size requirements to detect the effect of a group of genetic variants in case-control studies

<p>Abstract</p> <p>Background</p> <p>Because common diseases are caused by complex interactions among many genetic variants along with environmental risk factors, very large sample sizes are usually needed to detect such effects in case-control studies. Nevertheless, many genetic variants act in well defined biologic systems or metabolic pathways. Therefore, a reasonable first step may be to detect the effect of a group of genetic variants before assessing specific variants.</p> <p>Methods</p> <p>We present a simple method for determining approximate sample sizes required to detect the average joint effect of a group of genetic variants in a case-control study for multiplicative models.</p> <p>Results</p> <p>For a range of reasonable numbers of genetic variants, the sample size requirements for the test statistic proposed here are generally not larger than those needed for assessing marginal effects of individual variants and actually decline with increasing number of genetic variants in many situations considered in the group.</p> <p>Conclusion</p> <p>When a significant effect of the group of genetic variants is detected, subsequent multiple tests could be conducted to detect which individual genetic variants and their combinations are associated with disease risk. When testing for an effect size in a group of genetic variants, one can use our global test described in this paper, because the sample size required to detect an effect size in the group is comparatively small. Our method could be viewed as a screening tool for assessing groups of genetic variants involved in pathogenesis and etiology of common complex human diseases.</p

Genetic Epidemiology of Tuberculosis Susceptibility: Impact of Study Design

Several candidate gene studies have provided evidence for a role of host genetics in susceptibility to tuberculosis (TB). However, the results of these studies have been very inconsistent, even within a study population. Here, we review the design of these studies from a genetic epidemiological perspective, illustrating important differences in phenotype definition in both cases and controls, consideration of latent M. tuberculosis infection versus active TB disease, population genetic factors such as population substructure and linkage disequilibrium, polymorphism selection, and potential global differences in M. tuberculosis strain. These considerable differences between studies should be accounted for when examining the current literature. Recommendations are made for future studies to further clarify the host genetics of TB

The search for transient astrophysical neutrino emission with IceCube-DeepCore

We present the results of a search for astrophysical sources of brief transient neutrino emission using IceCube and DeepCore data acquired between 2012 May 15 and 2013 April 30. While the search methods employed in this analysis are similar to those used in previous IceCube point source searches, the data set being examined consists of a sample of predominantly sub-TeV muon-neutrinos from the Northern Sky (-5 degrees < delta < 90 degrees) obtained through a novel event selection method. This search represents a first attempt by IceCube to identify astrophysical neutrino sources in this relatively unexplored energy range. The reconstructed direction and time of arrival of neutrino events are used to search for any significant self-correlation in the data set. The data revealed no significant source of transient neutrino emission. This result has been used to construct limits at timescales ranging from roughly 1 s to 10 days for generic soft-spectra transients. We also present limits on a specific model of neutrino emission from soft jets in core-collapse supernovae

Alcohol, binge drinking and associated mental health problems in young urban Chileans

OBJECTIVE: To explore the link between alcohol use, binge drinking and mental health problems in a representative sample of adolescent and young adult Chileans. METHODS: Age and sex-adjusted Odds Ratios (OR) for four mental wellbeing measures were estimated with separate conditional logistic regression models for adolescents aged 15-20 years, and young adults aged 21-25 years, using population-based estimates of alcohol use prevalence rates from the Chilean National Health Survey 2010. RESULTS: Sixty five per cent of adolescents and 85% of young adults reported drinking alcohol in the last year and of those 83% per cent of adolescents and 86% of young adults reported binge drinking in the previous month. Adolescents who reported binging alcohol were also more likely, compared to young adults, to report being always or almost always depressed (OR 12.97 [95% CI, 1.86-19.54]) or to feel very anxious in the last month (OR 9.37 [1.77-19.54]). Adolescent females were more likely to report poor life satisfaction in the previous year than adolescent males (OR 8.50 [1.61-15.78]), feel always or almost always depressed (OR 3.41 [1.25-9.58]). Being female was also associated with a self-reported diagnosis of depression for both age groups (adolescents, OR 4.74 [1.49-15.08] and young adults, OR 4.08 [1.65-10.05]). CONCLUSION: Young people in Chile self-report a high prevalence of alcohol use, binge drinking and associated mental health problems. The harms associated with alcohol consumption need to be highlighted through evidence-based prevention programs. Health and education systems need to be strengthened to screen and support young people. Focussing on policy initiatives to limit beverage companies targeting alcohol to young people will also be needed

The effect of resistance training interventions on weight status in youth:a meta-analysis

Abstract Background There has been a rise in research into obesity prevention and treatment programmes in youth, including the effectiveness of resistance-based exercise. The purpose of this meta-analysis was to examine the effect of resistance training interventions on weight status in youth. Methods Meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was registered on PROSPERO (registration number CRD42016038365). Eligible studies were from English language peer-reviewed published articles. Searches were conducted in seven databases between May 2016 and June 2017. Studies were included that examined the effect of resistance training on weight status in youth, with participants of school age (5–18 years). Results There were 24 complete sets of data from 18 controlled trials (CTs) which explored 8 outcomes related to weight status. Significant, small effect sizes were identified for body fat% (Hedges’ g = 0.215, 95% CI 0.059 to 0.371, P = 0.007) and skinfolds (Hedges’ g = 0.274, 95% CI 0.066 to 0.483, P = 0.01). Effect sizes were not significant for: body mass (Hedges’ g = 0.043, 95% CI − 0.103 to 0.189, P = 0.564), body mass index (Hedges’ g = 0.024, 95% CI − 0.205 to 0.253, P = 0.838), fat-free mass (Hedges’ g = 0.073, 95% CI − 0.169 to 0.316, P = 0.554), fat mass (Hedges’ g = 0.180, 95% CI − 0.090 to 0.451, P = 0.192), lean mass (Hedges’ g = 0.089, 95% CI − 0.122 to 0.301, P = 0.408) or waist circumference (Hedges’ g = 0.209, 95% CI − 0.075 to 0.494, P = 0.149). Conclusions The results of this meta-analysis suggest that an isolated resistance training intervention may have an effect on weight status in youth. Overall, more quality research should be undertaken to investigate the impact of resistance training in youth as it could have a role to play in the treatment and prevention of obesity

Grammatical evolution decision trees for detecting gene-gene interactions

<p>Abstract</p> <p>Background</p> <p>A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such epistatic models present an important analytical challenge, requiring that methods perform not only statistical modeling, but also variable selection to generate testable genetic model hypotheses. This challenge is amplified by recent advances in genotyping technology, as the number of potential predictor variables is rapidly increasing.</p> <p>Methods</p> <p>Decision trees are a highly successful, easily interpretable data-mining method that are typically optimized with a hierarchical model building approach, which limits their potential to identify interacting effects. To overcome this limitation, we utilize evolutionary computation, specifically grammatical evolution, to build decision trees to detect and model gene-gene interactions. In the current study, we introduce the Grammatical Evolution Decision Trees (GEDT) method and software and evaluate this approach on simulated data representing gene-gene interaction models of a range of effect sizes. We compare the performance of the method to a traditional decision tree algorithm and a random search approach and demonstrate the improved performance of the method to detect purely epistatic interactions.</p> <p>Results</p> <p>The results of our simulations demonstrate that GEDT has high power to detect even very moderate genetic risk models. GEDT has high power to detect interactions with and without main effects.</p> <p>Conclusions</p> <p>GEDT, while still in its initial stages of development, is a promising new approach for identifying gene-gene interactions in genetic association studies.</p