University of Glasgow

About: University of Glasgow is a education organization based out in Glasgow, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 40355 authors who have published 98254 publications receiving 3815419 citations. The organization is also known as: Glasgow University & Glasgow Uni.

Quantifying the roles of immigration and chance in shaping prokaryote community structure.

Abstract: Summary Naturally occurring populations of bacteria and archaea are vital to life on the earth and are of enormous practical significance in medicine, engineering and agriculture. However, the rules governing the formation of such communities are still poorly understood, and there is a need for a usable mathematical description of this process. Typically, microbial community structure is thought to be shaped mainly by deterministic factors such as competition and niche differentiation. Here we show, for a wide range of prokaryotic communities, that the relative abundance and frequency with which different taxa are observed in samples can be explained by a neutral community model (NCM). The NCM, which is a stochastic, birth–death immigration process, does not explicitly represent the deterministic factors and therefore cannot be a complete or literal description of community assembly. However, its success suggests that chance and immigration are important forces in shaping the patterns seen in prokaryotic communities.

Maximum likelihood methods for direction-of-arrival estimation

Abstract: Five methods of direction-of-arrival (DOA) estimation which can be derived from the maximum-likelihood (ML) principle are considered. The ML method (MLM) results from the application of the ML principle to the statistics of the observed raw data. The standard multiple signal classification (MUSIC) procedure, called MUSIC-1, is obtained as a brute-force approximation of the MLM. An improved MUSIC procedure, named MUSIC-2, is obtained by applying the ML principle to the statistics of certain linear combinations of the sample noise space eigenvectors. A procedure which compromises between the good performance of the MLM and the computational simplicity of MUSIC is a method of direction estimation (MODE-1) which is derived as a large sample realization of the MLM. A fifth method, called MODE-2, is obtained by using the ML principle on the statistics of certain linear combinations of the sample eigenvectors. MODE-2 is computationally less demanding than the MLM (it is of the same complexity as MODE-1) and statistically more efficient. A numerical comparison of these five DOA estimation methods is presented. It confirms the analytic results on their theoretical performance levels. >

Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models.

Abstract: Nakagawa & Schielzeth extended the widely used goodness-of-fit statistic R2 to apply to generalized linear mixed models (GLMMs). However, their R2GLMM method is restricted to models with the simplest random effects structure, known as random intercepts models. It is not applicable to another common random effects structure, random slopes models. I show that R2GLMM can be extended to random slopes models using a simple formula that is straightforward to implement in statistical software. This extension substantially widens the potential application of R2GLMM. Keywords: coefficient of determination, generalized linear mixed model, random slopes model, random regression Introduction The coefficient of determination, R2, is a widely used statistic for assessing the goodness-of-fit, on a scale from 0 to 1, of a linear regression model (LM). It is defined as the proportion of variance in the response variable that is explained by the explanatory variables or, equivalently, the proportional reduction in unexplained variance. Unexplained variance can be viewed as variance in model prediction error, so R2 can also be defined in terms of reduction in prediction error variance. Insofar as it is justifiable to make the leap from ‘prediction’ to ‘understanding’, R2 can be intuitively interpreted as a measure of how much better we understand a system once we have measured and modelled some of its components. R2 has been extended to apply to generalized linear models (GLMs) (Maddala 1983) and linear mixed effects models (LMMs) (Snijders & Bosker 1994) [reviewed by (Nakagawa & Schielzeth 2013)]. Nakagawa & Schielzeth (2013) proposed a further generalization of R2 to generalized linear mixed effects models (GLMMs), a useful advance given the ubiquity of GLMMs for data analysis in ecology and evolution (Bolker et al. 2009). A function to estimate this R2GLMM statistic, r.squaredGLMM, has been included in the MuMIn package (Barton 2014) for the R statistical software (R Core Team 2014). However, Nakagawa and Schielzeth's R2GLMM formula is applicable to only a subset of GLMMs known as random intercepts models. Random intercepts models are used to model clustered observations, for example, where multiple observations are taken on each of a sample of individuals. Correlations between clustered observations within individuals are accounted for by allowing each subject to have a different intercept representing the deviation of that subject from the global intercept. Random intercepts are typically modelled as being sampled from a normal distribution with mean zero and a variance parameter that is estimated from the data. Although random intercepts are probably the most popular random effects models in ecology and evolution, other random effect specifications are also common, in particular random slopes models, where not only the intercept but also the slope of the regression line is allowed to vary between individuals. Random intercepts and slopes are typically modelled as normally distributed deviations from the global intercept and slope, respectively. For example, random slopes models, under the name of ‘random regression’ models, are used to investigate individual variation in response to different environments (Nussey, Wilson & Brommer 2007). The aim of this article is to show how Nakagawa and Schielzeth's R2GLMM can be further extended to encompass random slopes models.

Role of systemic inflammatory response in predicting survival in patients with primary operable cancer

Abstract: Disease progression in cancer is dependent on the complex interaction between the tumor and the host inflammatory response. There is substantial evidence in advanced cancer that host factors, such as weight loss, poor performance status and the host systemic inflammatory response, are linked, and the latter is an important tumor-stage-independent predictor of outcome. Indeed, the systemic inflammatory response, as evidenced by an elevated level of C-reactive protein, is now included in the definition of cancer cachexia. This review examines the role of the systemic inflammatory response in predicting survival in patients with primary operable cancer. Approximately 80 studies have evaluated the role of the systemic inflammatory response using biochemical or hematological markers, such as elevated C-reactive protein levels, hypoalbuminemia or increased white cell, neutrophil and platelet counts. Combinations of such factors have been used to derive simple inflammation-based prognostic scores, such as the Gl...