1,495 research outputs found
A Bayesian shared component model for genetic association studies
We present a novel approach to address genome association studies between single nucleotide polymorphisms (SNPs) and disease. We propose a Bayesian shared component model to tease out the genotype information that is common to cases and controls from the one that is specific to cases only. This allows to detect the SNPs that show the strongest association with the disease. The model can be applied to case-control studies with more than one disease. In fact, we illustrate the use of this model with a dataset of 23,418 SNPs from a case-control study by The Welcome Trust Case Control Consortium (2007) with 2,000 patients with diabetes type 1, 2,000 with diabetes type 2 and a control group with 3,000 individuals. We carry out a simulation study to assess the sensitivity and specificity of our model to detect SNPs with excess risk. Our results show that the method we propose here can be a very useful tool for this type of studies. The model has been implemented in the bayesGen library of the R statistical package
VLTI/AMBER spectro-interferometry of the late-type supergiants V766 Cen (=HR 5171 A), sigma Oph, BM Sco, and HD 206859
We add four warmer late-type supergiants to our previous
spectro-interferometric studies of red giants and supergiants.
V766 Cen (=HR 5171 A) is found to be a high-luminosity log(L/L_sun)=5.8+-0.4
source of Teff 4290+-760 K and radius 1490+-540 Rsun located close to both the
Hayashi and Eddington limits; this source is consistent with a 40 Msun
evolutionary track without rotation and current mass 27-36 Msun. It exhibits
NaI in emission arising from a shell of radius 1.5 Rphot and a photocenter
displacement of about 0.1 Rphot. V766 Cen shows strong extended molecular (CO)
layers and a dusty circumstellar background component. This suggest an
optically thick pseudo-photosphere at about 1.5 Rphot at the onset of the wind.
V766 Cen is a red supergiant located close to the Hayashi limit instead of a
yellow hypergiant already evolving back toward warmer Teff as previously
discussed.
The stars sigma Oph, BM Sco, and HD 206859 are found to have lower
luminosities of about log(L/Lsun)=3.4-3.5 and Teff of 3900-5300 K,
corresponding to 5-9 Msun tracks. They do not show extended molecular layers as
observed for higher luminosity red supergiants of our sample. BM Sco shows an
unusually strong contribution by an over-resolved circumstellar dust component.
These stars are more likely high-mass red giants instead of red supergiants.
This leaves us with an unsampled locus in the HR diagram corresponding to
luminosities log(L/Lsun)~3.8-4.8 or masses 10-13 Msun, possibly corresponding
to the mass region where stars explode as type II-P supernovae during the RSG
stage.
Our previously found relation of increasing strength of extended molecular
layers with increasing luminosities is now confirmed to extend to double our
previous luminosities and up to the Eddington limit. This might further point
to steadily increasing radiative winds with increasing luminosity.
[Abridged]Comment: 16 pages, 14 figures, accepted for publication in Astronomy and
Astrophysics (A&A
What causes the large extensions of red-supergiant atmospheres? Comparisons of interferometric observations with 1-D hydrostatic, 3-D convection, and 1-D pulsating model atmospheres
We present the atmospheric structure and the fundamental parameters of three
red supergiants, increasing the sample of RSGs observed by near-infrared
spectro-interferometry. Additionally, we test possible mechanisms that may
explain the large observed atmospheric extensions of RSGs.
We carried out spectro-interferometric observations of 3 RSGs in the
near-infrared K-band with the VLTI/AMBER instrument at medium spectral
resolution. To comprehend the extended atmospheres, we compared our
observational results to predictions by available hydrostatic PHOENIX,
available 3-D convection, and new 1-D self-excited pulsation models of RSGs.
Our near-infrared flux spectra are well reproduced by the PHOENIX model
atmospheres. The continuum visibility values are consistent with a
limb-darkened disk as predicted by the PHOENIX models, allowing us to determine
the angular diameter and the fundamental parameters of our sources.
Nonetheless, in the case of V602 Car and HD 95686, the PHOENIX model
visibilities do not predict the large observed extensions of molecular layers,
most remarkably in the CO bands. Likewise, the 3-D convection models and the
1-D pulsation models with typical parameters of RSGs lead to compact
atmospheric structures as well, which are similar to the structure of the
hydrostatic PHOENIX models. They can also not explain the observed decreases in
the visibilities and thus the large atmospheric molecular extensions. The full
sample of our RSGs indicates increasing observed atmospheric extensions with
increasing luminosity and decreasing surface gravity, and no correlation with
effective temperature or variability amplitude, which supports a scenario of
radiative acceleration on Doppler-shifted molecular lines.Comment: Accepted for publication in A&
How well do protected area networks support taxonomic and functional diversity in non-target taxa? The case of Iberian freshwaters
Estimation of treatment policy estimands for continuous outcomes using off treatment sequential multiple imputation
The estimands framework outlined in ICH E9 (R1) describes the components
needed to precisely define the effects to be estimated in clinical trials,
which includes how post-baseline "intercurrent" events (IEs) are to be handled.
In late-stage clinical trials, it is common to handle intercurrent events like
"treatment discontinuation" using the treatment policy strategy and target the
treatment effect on all outcomes regardless of treatment discontinuation. For
continuous repeated measures, this type of effect is often estimated using all
observed data before and after discontinuation using either a mixed model for
repeated measures (MMRM) or multiple imputation (MI) to handle any missing
data. In basic form, both of these estimation methods ignore treatment
discontinuation in the analysis and therefore may be biased if there are
differences in patient outcomes after treatment discontinuation compared to
patients still assigned to treatment, and missing data being more common for
patients who have discontinued treatment. We therefore propose and evaluate a
set of MI models that can accommodate differences between outcomes before and
after treatment discontinuation. The models are evaluated in the context of
planning a phase 3 trial for a respiratory disease. We show that analyses
ignoring treatment discontinuation can introduce substantial bias and can
sometimes underestimate variability. We also show that some of the MI models
proposed can successfully correct the bias but inevitably lead to increases in
variance. We conclude that some of the proposed MI models are preferable to the
traditional analysis ignoring treatment discontinuation, but the precise choice
of MI model will likely depend on the trial design, disease of interest and
amount of observed and missing data following treatment discontinuation
Fabrication and Characterisation of an Adaptable Plasmonic Nanorod Array for Solar Energy Conversion
The surface plasmonic modes of a side-by-side aligned gold nanorod array supported on a gold substrate has been characterised by electron energy loss spectroscopy (EELS). Plasmonic coupling within the array splits the nanorods' longitudinal mode into a bright mode (symmetrically aligned dipoles) and a dark mode (anti-symmetrically aligned dipoles). We support this observation by means of finite element modelling (FEM)
Feasibility and utility of mapping disease risk at the neighbourhood level within a Canadian public health unit: an ecological study
<p>Abstract</p> <p>Background</p> <p>We conducted spatial analyses to determine the geographic variation of cancer at the neighbourhood level (dissemination areas or DAs) within the area of a single Ontario public health unit, Wellington-Dufferin-Guelph, covering a population of 238,326 inhabitants. Cancer incidence data between 1999 and 2003 were obtained from the Ontario Cancer Registry and were geocoded down to the level of DA using the enhanced Postal Code Conversion File. The 2001 Census of Canada provided information on the size and age-sex structure of the population at the DA level, in addition to information about selected census covariates, such as average neighbourhood income.</p> <p>Results</p> <p>Age standardized incidence ratios for cancer and the prevalence of census covariates were calculated for each of 331 dissemination areas in Wellington-Dufferin-Guelph. The standardized incidence ratios (SIR) for cancer varied dramatically across the dissemination areas. However, application of the Moran's I statistic, a popular index of spatial autocorrelation, suggested significant spatial patterns for only two cancers, lung and prostate, both in males (p < 0.001 and p = 0.002, respectively). Employing Bayesian hierarchical models, areas in the urban core of the City of Guelph had significantly higher SIRs for male lung cancer than the remainder of Wellington-Dufferin-Guelph; and, neighbourhoods in the urban and surrounding rural areas of Orangeville exhibited significantly higher SIRs for prostate cancer. After adjustment for age and spatial dependence, average household income attenuated much of the spatial pattern of lung cancer, but not of prostate cancer.</p> <p>Conclusion</p> <p>This paper demonstrates the feasibility and utility of a systematic approach to identifying neighbourhoods, within the area served by a public health unit, that have significantly higher risks of cancer. This exploratory, ecologic study suggests several hypotheses for these spatial patterns that warrant further investigations. To the best of our knowledge, this is the first Canadian study published in the peer-reviewed literature estimating the risk of relatively rare public health outcomes at a very small areal level, namely dissemination areas.</p
Adaptive Control Optimization of Cutting Parameters for High Quality Machining Operations Based on Neural Networks and Search Algorithms
This book chapter presents an Adaptive Control with Optimization (ACO) system for optimising a multi-objective function based on material removal rate, quality loss function related to surface roughness, and cutting-tool life subjected to surface roughness specifications constraint
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