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The precise pattern and timing of speciation events that gave rise to all living placental mammals remain controversial. We provide a comprehensive phylogenetic analysis of genetic variation across an alignment of 241 placental mammal genome assemblies, addressing prior concerns regarding limited genomic sampling across species. We compared neutral genome-wide phylogenomic signals using concatenation and coalescent-based approaches, interrogated phylogenetic variation across chromosomes, and analyzed extensive catalogs of structural variants. Interordinal relationships exhibit relatively low rates of phylogenomic conflict across diverse datasets and analytical methods. Conversely, X-chromosome versus autosome conflicts characterize multiple independent clades that radiated during the Cenozoic. Genomic time trees reveal an accumulation of cladogenic events before and immediately after the Cretaceous-Paleogene (K-Pg) boundary, implying important roles for Cretaceous continental vicariance and the K-Pg extinction in the placental radiation.

This chapter presents an overview of how migrant networks have been researched and theorised. In so doing, we highlight some persistent gaps in knowledge about how migrants, and their descendants, forge networks and generate particular kinds of resources, especially in accessing the labour market and developing careers, and we explain how the chapters of this book tackle these issues. By looking not only at migrants but also at the second generation, we reflect on opportunities, but also enduring inequalities, and the ways in which networks may be mobilised to support employment strategies across different sectors and in different European countries.The chapter discusses the importance of disentangling social capital and social networks. Relatedly, we discuss the need to look beyond the ethnic lens and simple binaries of ‘bonding’ versus ‘bridging’ capital, to explore how ties to different kinds of actors, in varied social positions, may facilitate or indeed hinder career development. Referring to new empirical data and theoretically informed analysis, in the various chapters of this book, we build upon but also complicate understanding of ‘weak’ and ‘strong’ ties, not as fixed ties, but rather as a continuum of dynamic relationships that may ebb and flow over time.In the concluding section, we highlight the contribution of this book and also consider the need for further cross-fertilisation of conceptual and empirical innovations beyond migration studies to avoid a silo-effect in social network research.

Psychiatric and social epidemiology often involves assessing the effects of environmental exposure on outcomes that are difficult to measure directly. To address this problem, it is common to measure outcomes using a comprehensive battery of different tests thought to be related to a common, underlying construct of interest. In the application that motivates our work, for example, researchers wanted to assess the impact of in utero alcohol exposure on child cognition and neuropsychological development, which were evaluated using a range of different tests. Statistical analysis of the resulting multiple outcomes data can be challenging, not only because of the need to account for the correlation between outcomes measured on the same individual, but because it is often unclear, a priori, which outcomes are impacted by the exposure under study. While researchers will generally have some hypotheses about which outcomes are important, a framework is needed to help identify outcomes that are sensitive to the exposure and to quantify the associated treatment or exposure effects of interest. We propose such a framework using a modification of stochastic search variable selection (SSVS), a popular Bayesian variable selection model and use it to quantify an overall effect of the exposure on the affected outcomes. We investigate the performance of the method via simulation and illustrate its application to data from a study involving the effects of prenatal alcohol exposure on child cognition.

While it is well known that high levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, the exact nature of the dose response is less well understood. In particular, there is a pressing need to identify the levels of PAE associated with an increased risk of clinically significant adverse effects. To address this issue, data have been combined from six longitudinal birth cohort studies in the United States that assessed the effects of PAE on cognitive outcomes measured from early school age through adolescence. Structural equation models (SEMs) are commonly used to capture the association among multiple observed outcomes in order to characterise the underlying variable of interest (in this case, cognition) and then relate it to PAE. However, it was not possible to apply classic SEM software in our context because different outcomes were measured in the six studies. In this paper we show how a Bayesian approach can be used to fit a multi-group multi-level structural model that maps cognition to a broad range of observed variables measured at multiple ages. These variables map to several different cognitive subdomains and are examined in relation to PAE after adjusting for confounding using propensity scores. The model also tests the possibility of a change point in the dose-response function.

Cell wall-modifying enzymes have been previously investigated in charophyte green algae (CGA) in cultures of uniform age, giving limited insight into their roles. Therefore, we investigated the in situ localisation and specificity of enzymes acting on hemicelluloses in CGA genera of different morphologies and developmental stages. In vivo transglycosylation between xyloglucan and an endogenous donor in filamentous Klebsormidium and Zygnema was observed in longitudinal cell walls of young (1 month) but not old cells (1 year), suggesting that it has a role in cell growth. By contrast, in parenchymatous Chara, transglycanase action occurred in all cell planes. In Klebsormidium and Zygnema, the location of enzyme action mainly occurred in regions where xyloglucans and mannans, and to a lesser extent mixed-linkage β-glucan (MLG), were present, indicating predominantly xyloglucan:xyloglucan endotransglucosylase (XET) activity. Novel transglycosylation activities between xyloglucan and xylan, and xyloglucan and galactomannan were identified in vitro in both genera. Our results show that several cell wall-modifying enzymes are present in CGA, and that differences in morphology and cell age are related to enzyme localisation and specificity. This indicates an evolutionary significance of cell wall modifications, as similar changes are known in their immediate descendants, the land plants. This article has an associated First Person interview with the first author of the paper., Summary: The distribution of major hemicelluloses and cell wall-modifying enzymes in three charophyte green algae (CGA), representing the ancestors of land plants, relate to algal morphology and developmental stage.

This article is concerned with the fitting of multinomial regression models using the so-called "Poisson Trick". The work is motivated by Chen & Kuo (2001) and Malchow-M��ller & Svarer (2003) which have been criticized for being computationally inefficient and sometimes producing nonsense results. We first discuss the case of independent data and offer a parsimonious fitting strategy when all covariates are categorical. We then propose a new approach for modelling correlated responses based on an extension of the Gamma-Poisson model, where the likelihood can be expressed in closed-form. The parameters are estimated via an Expectation/Conditional Maximization (ECM) algorithm, which can be implemented using functions for fitting generalized linear models readily available in standard statistical software packages. Compared to existing methods, our approach avoids the need to approximate the intractable integrals and thus the inference is exact with respect to the approximating Gamma-Poisson model. The proposed method is illustrated via a reanalysis of the yogurt data discussed by Chen & Kuo (2001).

This article concerns a class of generalized linear mixed models for clustered data, where the random effects are mapped uniquely onto the grouping structure and are independent between groups. We derive necessary and sufficient conditions that enable the marginal likelihood of such class of models to be expressed in closed-form. Illustrations are provided using the Gaussian, Poisson, binomial and gamma distributions. These models are unified under a single umbrella of conjugate generalized linear mixed models, where "conjugate" refers to the fact that the marginal likelihood can be expressed in closed-form, rather than implying inference via the Bayesian paradigm. Having an explicit marginal likelihood means that these models are more computationally convenient, which can be important in big data contexts. Except for the binomial distribution, these models are able to achieve simultaneous conjugacy, and thus able to accommodate both unit and group level covariates.

Provides a table of description of selected variables collected in the 45 and Up Study baseline survey. (DOCX 28Â kb)

© 2017 American Statistical Association. The big data era demands new statistical analysis paradigms, since traditional methods often break down when datasets are too large to fit on a single desktop computer. Divide and Recombine (D&R) is becoming a popular approach for big data analysis, where results are combined over subanalyses performed in separate data subsets. In this article, we consider situations where unit record data cannot be made available by data custodians due to privacy concerns, and explore the concept of statistical sufficiency and summary statistics for model fitting. The resulting approach represents a type of D&R strategy, which we refer to as summary statistics D&R; as opposed to the standard approach, which we refer to as horizontal D&R. We demonstrate the concept via an extended Gamma–Poisson model, where summary statistics are extracted from different databases and incorporated directly into the fitting algorithm without having to combine unit record data. By exploiting the natural hierarchy of data, our approach has major benefits in terms of privacy protection. Incorporating the proposed modelling framework into data extraction tools such as TableBuilder by the Australian Bureau of Statistics allows for potential analysis at a finer geographical level, which we illustrate with a multilevel analysis of the Australian unemployment data. Supplementary materials for this article are available online.

The study is an historical investigation exploring the impact of the Carnegie Corporation's philanthropic cultural and educational activities in North America and Australia during the 1940s. The author examines the Carnegie's formation of public values and perceptions using cultural and aesthetic material in order to transmit American ideological ideals with the goal of influencing Australian, Canadian and USA cultural norms. The principal case examined in the paper is the "Art of Australia 1788-1941" exhibition, which toured the USA and Canada during 1941-42. Scrutiny of the exhibition uncovers the role it played in alliance building and the promotion of a range of cultural and political agendas. The investigation deploys a theoretical framework derived from the writings of Tony Bennett. The framework takes the form of a matrix that uses concepts of institutionalized agencies/power and individual agencies/knowledge detailed in a nine-cell matrix composed of propositional statements under the intersecting categories of culture, technologies, ethics, zones, objects, and visualization. The "Art of Australia" Exhibition is a paradigmatic case of the instrumental, cultural application of exhibitions in the interest of the state, using government and non-government, public and private organizations as intermediaries. The analysis reveals the existence of diverse agendas and power/knowledge relationships between governments, corporations and the exhibition. This account highlights the museum as a significant arena for establishing and legitimating social norms and practices whilst steering cultural values. Such actions sponsored by government and entrepreneurial philanthropy are analyzed and interpreted as an early instance of building civic values and promoting the public belief in shared national identity. In this sense the investigation explores the educational mission of the museum and it's supporting agencies in the broadest public context.