Not in my back yard! Sports stadia location and the property market

In recent years sports stadia have been built in the UK, not only for their intended sporting purpose but with the twin aim of stimulating economic and physical regeneration. However, proposals to locate stadia in urban areas often prompt a negative reaction from local communities, fearing a decline in property prices. This paper will use a case study of the Millennium Stadium in Cardiff and the City of Manchester Stadium to illustrate that in contrast to this widely held belief, sports stadia can actually enhance the value of residential property. Furthermore, it will argue that stadia also contribute indirectly to property value through the creation of pride, confidence and enhanced image of an area.</p

Estimating and testing direct genetic effects in directed acyclic graphs using estimating equations

In genetic association studies, it is important to distinguish direct and indirect genetic effects in order to build truly functional models. For this purpose, we consider a directed acyclic graph setting with genetic variants, primary and intermediate phenotypes, and confounding factors. In order to make valid statistical inference on direct genetic effects on the primary phenotype, it is necessary to consider all potential effects in the graph, and we propose to use the estimating equations method with robust Huber-White sandwich standard errors. We evaluate the proposed causal inference based on estimating equations (CIEE) method and compare it with traditional multiple regression methods, the structural equation modeling method, and sequential G-estimation methods through a simulation study for the analysis of (completely observed) quantitative traits and time-to-event traits subject to censoring as primary phenotypes. The results show that CIEE provides valid estimators and inference by successfully removing the effect of intermediate phenotypes from the primary phenotype and is robust against measured and unmeasured confounding of the indirect effect through observed factors. All other methods except the sequential G-estimation method for quantitative traits fail in some scenarios where their test statistics yield inflated type I errors. In the analysis of the Genetic Analysis Workshop 19 dataset, we estimate and test genetic effects on blood pressure accounting for intermediate gene expression phenotypes. The results show that CIEE can identify genetic variants that would be missed by traditional regression analyses. CIEE is computationally fast, widely applicable to different fields, and available as an R package

Chromospheric Variability in SDSS M Dwarfs. II. Short-Timescale H-alpha Variability

[Abridged] We present the first comprehensive study of short-timescale chromospheric H-alpha variability in M dwarfs using the individual 15 min spectroscopic exposures for 52,392 objects from the Sloan Digital Sky Survey. Our sample contains about 10^3-10^4 objects per spectral type bin in the range M0-M9, with a total of about 206,000 spectra and a typical number of 3 exposures per object (ranging up to a maximum of 30 exposures). Using this extensive data set we find that about 16% of the sources exhibit H-alpha emission in at least one exposure, and of those about 45% exhibit H-alpha emission in all of the available exposures. Within the sample of objects with H-alpha emission, only 26% are consistent with non-variable emission, independent of spectral type. The H-alpha variability, quantified in terms of the ratio of maximum to minimum H-alpha equivalent width (R_EW), and the ratio of the standard deviation to the mean (sigma_EW/), exhibits a rapid rise from M0 to M5, followed by a plateau and a possible decline in M9 objects. In particular, R_EW increases from a median value of about 1.8 for M0-M3 to about 2.5 for M7-M9, and variability with R_EW>10 is only observed in objects later than M5. For the combined sample we find that the R_EW values follow an exponential distribution with N(R_EW) exp[-(R_EW-1)/2]; for M5-M9 objects the characteristic scale is R_EW-1\approx 2.7, indicative of stronger variability. In addition, we find that objects with persistent H-alpha emission exhibit smaller values of R_EW than those with intermittent H-alpha emission. Based on these results we conclude that H-alpha variability in M dwarfs on timescales of 15 min to 1 hr increases with later spectral type, and that the variability is larger for intermittent sources.Comment: Submitted to ApJ; 20 pages, 15 figure

Data assimilation with correlated observation errors: experiments with a 1-D shallow water model

Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on appropriate scales. Thus an alternative approach to dealing with observation error correlations is needed. In this article, we consider several approaches to approximating observation error correlation matrices: diagonal approximations, eigendecomposition approximations and Markov matrices. These approximations are applied in incremental variational assimilation experiments with a 1-D shallow water model using synthetic observations. Our experiments quantify analysis accuracy in comparison with a reference or ‘truth’ trajectory, as well as with analyses using the ‘true’ observation error covariance matrix. We show that it is often better to include an approximate correlation structure in the observation error covariance matrix than to incorrectly assume error independence. Furthermore, by choosing a suitable matrix approximation, it is feasible and computationally cheap to include error correlation structure in a variational data assimilation algorithm

A hazard model of the probability of medical school dropout in the United Kingdom

From individual level longitudinal data for two entire cohorts of medical students in UK universities, we use multilevel models to analyse the probability that an individual student will drop out of medical school. We find that academic preparedness—both in terms of previous subjects studied and levels of attainment therein—is the major influence on withdrawal by medical students. Additionally, males and more mature students are more likely to withdraw than females or younger students respectively. We find evidence that the factors influencing the decision to transfer course differ from those affecting the decision to drop out for other reasons

Collagen sequence analysis of the extinct giant ground sloths <i>Lestodon</i> and <i>Megatherium</i>

For over 200 years, fossils of bizarre extinct creatures have been described from the Americas that have ranged from giant ground sloths to the 'native' South American ungulates, groups of mammals that evolved in relative isolation on South America. Ground sloths belong to the South American xenarthrans, a group with modern although morphologically and ecologically very different representatives (anteaters, armadillos and sloths), which has been proposed to be one of the four main eutherian clades. Recently, proteomics analyses of bone collagen have recently been used to yield a molecular phylogeny for a range of mammals including the unusual 'Malagasy aardvark' shown to be most closely related to the afrotherian tenrecs, and the south American ungulates supporting their morphological association with condylarths. However, proteomics results generate partial sequence information that could impact upon the phylogenetic placement that has not been appropriately tested. For comparison, this paper examines the phylogenetic potential of proteomicsbased sequencing through the analysis of collagen extracted from two extinct giant ground sloths, Lestodon and Megatherium. The ground sloths were placed as sister taxa to extant sloths, but with a closer relationship between Lestodon and the extant sloths than the basal Megatherium. These results highlight that proteomics methods could yield plausible phylogenies that share similarities with other methods, but have the potential to be more useful in fossils beyond the limits of ancient DNA survival.Facultad de Ciencias Naturales y Muse

verification of a parkinson s disease protein signature in t lymphocytes by multiple reaction monitoring

Diagnosis of Parkinson's disease, the second most common neurodegenerative disease, is based on the appearance of motor symptoms. A panel of protein biomarkers in the T-lymphocyte proteome was previously proposed as a Parkinson's disease signature. Here, we designed an LC–MS based method to quantitatively evaluate this protein signature by multiple reaction monitoring (MRM) in T-lymphocytes and peripheral blood mononuclear cells from a new cohort of nine patients with Parkinson's disease and nine unaffected subjects. Patients were classified using the discriminant function obtained from two-dimensional electrophoresis and protein amounts measured by MRM, thus assigning seven controls out of nine as true negatives and nine patients out of nine as true positives. A good discriminant power was obtained by selecting a subset of peptides from the protein signature, with an area under the receiver operating characteristic curve of 0.877. A similar result is achieved by evaluating all peptides of a selected pane..

Differential expression analysis for sequence count data

*Motivation:* High-throughput nucleotide sequencing provides quantitative readouts in assays for RNA expression (RNA-Seq), protein-DNA binding (ChIP-Seq) or cell counting (barcode sequencing). Statistical inference of differential signal in such data requires estimation of their variability throughout the dynamic range. When the number of replicates is small, error modelling is needed to achieve statistical power.&#xd;&#xa;&#xd;&#xa;*Results:* We propose an error model that uses the negative binomial distribution, with variance and mean linked by local regression, to model the null distribution of the count data. The method controls type-I error and provides good detection power. &#xd;&#xa;&#xd;&#xa;*Availability:* A free open-source R software package, _DESeq_, is available from the Bioconductor project and from &#x22;http://www-huber.embl.de/users/anders/DESeq&#x22;:http://www-huber.embl.de/users/anders/DESeq