32,214 research outputs found
Artificially created stimuli produced by a genetic algorithm using a saliency model as its fitness function show that Inattentional Blindness modulates performance in a pop-out visual search paradigm
Salient stimuli are more readily detected than less salient stimuli, and individual differences in such detection may be relevant to why some people fail to notice an unexpected stimulus that appears in their visual field whereas others do notice it. This failure to notice unexpected stimuli is termed 'Inattentional Blindness' and is more likely to occur when we are engaged in a resource-consuming task. A genetic algorithm is described in which artificial stimuli are created using a saliency model as its fitness function. These generated stimuli, which vary in their saliency level, are used in two studies that implement a pop-out visual search task to evaluate the power of the model to discriminate the performance of people who were and were not Inattentionally Blind (IB). In one study the number of orientational filters in the model was increased to check if discriminatory power and the saliency estimation for low-level images could be improved. Results show that the performance of the model does improve when additional filters are included, leading to the conclusion that low-level images may require a higher number of orientational filters for the model to better predict participants' performance. In both studies we found that given the same target patch image (i.e. same saliency value) IB individuals take longer to identify a target compared to non-IB individuals. This suggests that IB individuals require a higher level of saliency for low-level visual features in order to identify target patches
Effect of body composition methodology on heritability estimation of body fatness
Heritability estimates of human body fatness vary widely and the contribution of body composition methodology to this variability is unknown. The effect of body composition methodology on estimations of genetic and environmental contributions to body fatness variation was examined in 78 adult male and female monozygotic twin pairs reared apart or together. Body composition was assessed by six methods - body mass index (BMI), dual energy x-ray absorptiometry (DXA), underwater weighing (UWW), total body water (TBW), bioelectric impedance (BIA), and skinfold thickness. Body fatness was expressed as percent body fat, fat mass, and fat mass/height2 to assess the effect of body fatness expression on heritability estimates. Model-fitting multivariate analyses were used to assess the genetic and environmental components of variance. Mean BMI was 24.5 kg/m2 (range of 17.8-43.4 kg/m2). There was a significant effect of body composition methodology (p<0.001) on heritability estimates, with UWW giving the highest estimate (69%) and BIA giving the lowest estimate (47%) for fat mass/height2. Expression of body fatness as percent body fat resulted in significantly higher heritability estimates (on average 10.3% higher) compared to expression as fat mass/height2 (p=0.015). DXA and TBW methods expressing body fatness as fat mass/height2 gave the least biased heritability assessments, based on the small contribution of specific genetic factors to their genetic variance. A model combining DXA and TBW methods resulted in a relatively low FM/ht2 heritability estimate of 60%, and significant contributions of common and unique environmental factors (22% and 18%, respectively). The body fatness heritability estimate of 60% indicates a smaller contribution of genetic variance to total variance than many previous studies using less powerful research designs have indicated. The results also highlight the importance of environmental factors and possibly genotype by environmental interactions in the etiology of weight gain and the obesity epidemic.R01 AR046124 - NIAMS NIH HHS; R01 MH065322 - NIMH NIH HHS; T32 HL069772 - NHLBI NIH HHS; R21 DK078867 - NIDDK NIH HHS; R37 DA018673 - NIDA NIH HHS; R01 DK076092 - NIDDK NIH HHS; R01 DK079003 - NIDDK NIH HHS; F32 DK009747 - NIDDK NIH HHS; R01 DA018673 - NIDA NIH HH
Tracking moving optima using Kalman-based predictions
The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a timechanging fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison
Simulation based estimation of branching models for LTR retrotransposons
Motivation: LTR retrotransposons are mobile elements that are able, like
retroviruses, to copy and move inside eukaryotic genomes. In the present work,
we propose a branching model for studying the propagation of LTR
retrotransposons in these genomes. This model allows to take into account both
positions and degradations of LTR retrotransposons copies. In our model, the
duplication rate is also allowed to vary with the degradation level.
Results: Various functions have been implemented in order to simulate their
spread and visualization tools are proposed. Based on these simulation tools,
we show that an accurate estimation of the parameters of this propagation model
can be performed. We applied this method to the study of the spread of the
transposable elements ROO, GYPSY, and DM412 on a chromosome of
\textit{Drosophila melanogaster}.
Availability: Our proposal has been implemented using Python software. Source
code is freely available on the web at
https://github.com/SergeMOULIN/retrotransposons-spread.Comment: 7 pages, 3 figures, 7 tables. Submit to "Bioiformatics" on March 1,
201
Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps
Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within
biological pathways, the incorporation of prior pathways information into a
statistical model is expected to increase the power to detect true associations
in a genetic association study. Most existing pathways-based methods rely on
marginal SNP statistics and do not fully exploit the dependence patterns among
SNPs within pathways. We use a sparse regression model, with SNPs grouped into
pathways, to identify causal pathways associated with a quantitative trait.
Notable features of our "pathways group lasso with adaptive weights" (P-GLAW)
algorithm include the incorporation of all pathways in a single regression
model, an adaptive pathway weighting procedure that accounts for factors
biasing pathway selection, and the use of a bootstrap sampling procedure for
the ranking of important pathways. P-GLAW takes account of the presence of
overlapping pathways and uses a novel combination of techniques to optimise
model estimation, making it fast to run, even on whole genome datasets. In a
comparison study with an alternative pathways method based on univariate SNP
statistics, our method demonstrates high sensitivity and specificity for the
detection of important pathways, showing the greatest relative gains in
performance where marginal SNP effect sizes are small.Comment: 29 page
Integrating genealogical and dynamical modelling to infer escape and reversion rates in HIV epitopes
The rates of escape and reversion in response to selection pressure arising
from the host immune system, notably the cytotoxic T-lymphocyte (CTL) response,
are key factors determining the evolution of HIV. Existing methods for
estimating these parameters from cross-sectional population data using ordinary
differential equations (ODE) ignore information about the genealogy of sampled
HIV sequences, which has the potential to cause systematic bias and
over-estimate certainty. Here, we describe an integrated approach, validated
through extensive simulations, which combines genealogical inference and
epidemiological modelling, to estimate rates of CTL escape and reversion in HIV
epitopes. We show that there is substantial uncertainty about rates of viral
escape and reversion from cross-sectional data, which arises from the inherent
stochasticity in the evolutionary process. By application to empirical data, we
find that point estimates of rates from a previously published ODE model and
the integrated approach presented here are often similar, but can also differ
several-fold depending on the structure of the genealogy. The model-based
approach we apply provides a framework for the statistical analysis of escape
and reversion in population data and highlights the need for longitudinal and
denser cross-sectional sampling to enable accurate estimate of these key
parameters
Heritability of variation in glycaemic response to metformin:a genome-wide complex trait analysis
BACKGROUND: Metformin is a first-line oral agent used in the treatment of type 2 diabetes, but glycaemic response to this drug is highly variable. Understanding the genetic contribution to metformin response might increase the possibility of personalising metformin treatment. We aimed to establish the heritability of glycaemic response to metformin using the genome-wide complex trait analysis (GCTA) method. METHODS: In this GCTA study, we obtained data about HbA1c concentrations before and during metformin treatment from patients in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study, which includes a cohort of patients with type 2 diabetes and is linked to comprehensive clinical databases and genome-wide association study data. We applied the GCTA method to estimate heritability for four definitions of glycaemic response to metformin: absolute reduction in HbA1c; proportional reduction in HbA1c; adjusted reduction in HbA1c; and whether or not the target on-treatment HbA1c of less than 7% (53 mmol/mol) was achieved, with adjustment for baseline HbA1c and known clinical covariates. Chromosome-wise heritability estimation was used to obtain further information about the genetic architecture. FINDINGS: 5386 individuals were included in the final dataset, of whom 2085 had enough clinical data to define glycaemic response to metformin. The heritability of glycaemic response to metformin varied by response phenotype, with a heritability of 34% (95% CI 1-68; p=0·022) for the absolute reduction in HbA1c, adjusted for pretreatment HbA1c. Chromosome-wise heritability estimates suggest that the genetic contribution is probably from individual variants scattered across the genome, which each have a small to moderate effect, rather than from a few loci that each have a large effect. INTERPRETATION: Glycaemic response to metformin is heritable, thus glycaemic response to metformin is, in part, intrinsic to individual biological variation. Further genetic analysis might enable us to make better predictions for stratified medicine and to unravel new mechanisms of metformin action. FUNDING: Wellcome Trust
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