Perils and pitfalls of mixed-effects regression models in biology

This is the final version. Available on open access from PeerJ via the DOI in this recordData Availability: The following information was supplied regarding data availability: The R code used to conduct all simulations in the paper is available in the Supplemental Files.Biological systems, at all scales of organisation from nucleic acids to ecosystems, are inherently complex and variable. Biologists therefore use statistical analyses to detect signal among this systemic noise. Statistical models infer trends, find functional relationships and detect differences that exist among groups or are caused by experimental manipulations. They also use statistical relationships to help predict uncertain futures. All branches of the biological sciences now embrace the possibilities of mixed-effects modelling and its flexible toolkit for partitioning noise and signal. The mixed-effects model is not, however, a panacea for poor experimental design, and should be used with caution when inferring or deducing the importance of both fixed and random effects. Here we describe a selection of the perils and pitfalls that are widespread in the biological literature, but can be avoided by careful reflection, modelling and model-checking. We focus on situations where incautious modelling risks exposure to these pitfalls and the drawing of incorrect conclusions. Our stance is that statements of significance, information content or credibility all have their place in biological research, as long as these statements are cautious and well-informed by checks on the validity of assumptions. Our intention is to reveal potential perils and pitfalls in mixed model estimation so that researchers can use these powerful approaches with greater awareness and confidence. Our examples are ecological, but translate easily to all branches of biology.University of Exete

Determination of generator groupings for an islanding scheme in the Manitoba Hydro system using the method of normal forms

This paper deals with the application of the method of normal forms in the analysis of a specific aspect of system dynamic behavior in the Manitoba Hydro system. Following a major loss of transmission capacity on the Manitoba Hydro HVDC system (Nelson River system), and the subsequent operation of protection systems, there is a major deficit of generation in the remaining system, comprising Manitoba and Saskatchewan. The method of normal forms is applied to determine the natural groupings which are formed by the machines in Manitoba Hydro due to nonlinear interaction. This grouping then provides a basis for developing a systematic procedure to island the remaining system.published_or_final_versio

The application of statistical network models in disease research

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Host social structure is fundamental to how infections spread and persist, and so the statistical modelling of static and dynamic social networks provides an invaluable tool to parameterise realistic epidemiological models. We present a practical guide to the application of network modelling frameworks for hypothesis testing related to social interactions and epidemiology, illustrating some approaches with worked examples using data from a population of wild European badgers Meles meles naturally infected with bovine tuberculosis. Different empirical network datasets generate particular statistical issues related to non-independence and sampling constraints. We therefore discuss the strengths and weaknesses of modelling approaches for different types of network data and for answering different questions relating to disease transmission. We argue that statistical modelling frameworks designed specifically for network analysis offer great potential in directly relating network structure to infection. They have the potential to be powerful tools in analysing empirical contact data used in epidemiological studies, but remain untested for use in networks of spatio-temporal associations. As a result, we argue that developments in the statistical analysis of empirical contact data are critical given the ready availability of dynamic network data from bio-logging studies. Furthermore, we encourage improved integration of statistical network approaches into epidemiological research to facilitate the generation of novel modelling frameworks and help extend our understanding of disease transmission in natural populations.M.J.S. is funded by a NERC standard grant (NE/M004546/1) awarded to R.A.M., D.P.C., D.J.H. and M.B., with the APHA team at Woodchester Park, UK (lead scientist is R.J.D.) as project partners

Optimizing genomic medicine in epilepsy through a gene-customized approach to missense variant interpretation

Gene panel and exome sequencing have revealed a high rate of molecular diagnoses among diseases where the genetic architecture has proven suitable for sequencing approaches, with a large number of distinct and highly penetrant causal variants identified among a growing list of disease genes. The challenge is, given the DNA sequence of a new patient, to distinguish disease-causing from benign variants. Large samples of human standing variation data highlight regional variation in the tolerance to missense variation within the protein-coding sequence of genes. This information is not well captured by existing bioinformatic tools, but is effective in improving variant interpretation. To address this limitation in existing tools, we introduce the missense tolerance ratio (MTR), which summarizes available human standing variation data within genes to encapsulate population level genetic variation. We find that patient-ascertained pathogenic variants preferentially cluster in low MTR regions (P < 0.005) of well-informed genes. By evaluating 20 publicly available predictive tools across genes linked to epilepsy, we also highlight the importance of understanding the empirical null distribution of existing prediction tools, as these vary across genes. Subsequently integrating the MTR with the empirically selected bioinformatic tools in a gene-specific approach demonstrates a clear improvement in the ability to predict pathogenic missense variants from background missense variation in disease genes. Among an independent test sample of case and control missense variants, case variants (0.83 median score) consistently achieve higher pathogenicity prediction probabilities than control variants (0.02 median score; Mann-Whitney U test, P < 1 × 10(-16)). We focus on the application to epilepsy genes; however, the framework is applicable to disease genes beyond epilepsy

Seasonal variation in daily patterns of social contacts in the European badger Meles meles

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Social interactions among hosts influence the persistence and spread of infectious pathogens. Daily 20 and seasonal variation in the frequency and type of social interactions will play an important role in 21 disease epidemiology, and alongside other factors may have an influence on wider disease dynamics 22 by causing seasonal forcing of infection, especially if the seasonal variation experienced by a 23 population is considerable. We explored temporal variation in within-group contacts in a high-24 density population of European badgers Meles meles naturally-infected with bovine tuberculosis. 25 Summer contacts were more likely and of longer duration during the daytime, while the frequency 26 and duration of winter contacts did not differ between day and night. In spring and autumn within-27 group contacts peaked at dawn and dusk, corresponding with when they were of shortest duration 28 with reduced potential for aerosol transmission of pathogens. Summer and winter could be critical 29 for bovine tuberculosis transmission in badgers, due to the high frequency and duration of contacts 30 during resting periods, and we discuss the links between this result and empirical data. This study 31 reveals clear seasonality in daily patterns of contact frequency and duration in species living in stable 32 social groups, suggesting that changes in social contacts could drive seasonal forcing of infection in 33 wildlife populations even when the number of individuals interacting remains similar.MJS is funded by NERC grant NE/M004546/1 awarded to RAM, DPC, DJH and MB, with RJD and the 386 APHA team at Woodchester Park, UK as project partners. Data were collected for NW’s PhD, funded 387 by Defra. We thank Jared Wilson-Aggarwal and two anonymous reviewers for useful comments and 388 Keith Silk for providing the photograph for Figure 1

Social structure contains epidemics and regulates individual roles in disease transmission in a group-living mammal

This is the final version. Available from Wiley via the DOI in this record. Data accessibility: The original weighted adjacency matrix for the high‐density population of European badgers, as well as code used for simulating networks and disease simulations can be found online https://doi.org/10.5061/dryad.49n3878.Population structure is critical to infectious disease transmission. As a result, theoretical and empirical contact network models of infectious disease spread are increasingly providing valuable insights into wildlife epidemiology. Analyzing an exceptionally detailed dataset on contact structure within a high-density population of European badgers Meles meles, we show that a modular contact network produced by spatially structured stable social groups, lead to smaller epidemics, particularly for infections with intermediate transmissibility. The key advance is that we identify considerable variation among individuals in their role in disease spread, with these new insights made possible by the detail in the badger dataset. Furthermore, the important impacts on epidemiology are found even though the modularity of the Badger network is much lower than the threshold that previous work suggested was necessary. These findings reveal the importance of stable social group structure for disease dynamics with important management implications for socially structured populations.Natural Environment Research Council (NERC

CMRnet: An R package to derive networks of social interactions and movement from mark‐recapture data

This is the final version. Available on open access from Wiley via the DOI in this record1. Long‐term capture‐mark‐recapture data provide valuable information on the movements of individuals between locations, and the contemporary and/or co‐located captures of individuals can be used to approximate the social structure of populations. 2. We introduce an R package (CMRnet) that generates social and movement networks from spatially‐explicit capture‐mark‐recapture data. It also provides functions for network and datastream permutations for these networks. Here we describe the package and key considerations for its application, providing two example case studies. 3. The conversion of spatially explicit mark‐recapture data into social and movement networks will provide insights into the interplay between demography and behaviour in wild animal populations, with important applications in their management and conservation.Natural Environment Research Council (NERC)University of Exete

GLAST: Understanding the High Energy Gamma-Ray Sky

We discuss the ability of the GLAST Large Area Telescope (LAT) to identify, resolve, and study the high energy gamma-ray sky. Compared to previous instruments the telescope will have greatly improved sensitivity and ability to localize gamma-ray point sources. The ability to resolve the location and identity of EGRET unidentified sources is described. We summarize the current knowledge of the high energy gamma-ray sky and discuss the astrophysics of known and some prospective classes of gamma-ray emitters. In addition, we also describe the potential of GLAST to resolve old puzzles and to discover new classes of sources.Comment: To appear in Cosmic Gamma Ray Sources, Kluwer ASSL Series, Edited by K.S. Cheng and G.E. Romer

Extragalactic Radio Continuum Surveys and the Transformation of Radio Astronomy

Next-generation radio surveys are about to transform radio astronomy by discovering and studying tens of millions of previously unknown radio sources. These surveys will provide new insights to understand the evolution of galaxies, measuring the evolution of the cosmic star formation rate, and rivalling traditional techniques in the measurement of fundamental cosmological parameters. By observing a new volume of observational parameter space, they are also likely to discover unexpected new phenomena. This review traces the evolution of extragalactic radio continuum surveys from the earliest days of radio astronomy to the present, and identifies the challenges that must be overcome to achieve this transformational change.Comment: To be published in Nature Astronomy 18 Sept 201