Robust Estimators in Generalized Pareto Models

This paper deals with optimally-robust parameter estimation in generalized Pareto distributions (GPDs). These arise naturally in many situations where one is interested in the behavior of extreme events as motivated by the Pickands-Balkema-de Haan extreme value theorem (PBHT). The application we have in mind is calculation of the regulatory capital required by Basel II for a bank to cover operational risk. In this context the tail behavior of the underlying distribution is crucial. This is where extreme value theory enters, suggesting to estimate these high quantiles parameterically using, e.g. GPDs. Robust statistics in this context offers procedures bounding the influence of single observations, so provides reliable inference in the presence of moderate deviations from the distributional model assumptions, respectively from the mechanisms underlying the PBHT.Comment: 26pages, 6 figure

Making the most of survey data: Incorporating age uncertainty when fitting growth parameters

Individual growth is an important parameter and is linked to a number of other biological processes. It is commonly modeled using the von Bertalanffy growth function (VBGF), which is regularly fitted to age data where the ages of the animals are not known exactly but are binned into yearly age groups, such as fish survey data. Current methods of fitting the VBGF to these data treat all the binned ages as the actual ages. We present a new VBGF model that combines data from multiple surveys and allows the actual age of an animal to be inferred. By fitting to survey data for Atlantic herring (Clupea harengus) and Atlantic cod (Gadus morhua), we compare our model with two other ways of combining data from multiple surveys but where the ages are as reported in the survey data. We use the fitted parameters as inputs into a yield-per-recruit model to see what would happen to advice given to management. We found that each of the ways of combining the data leads to different parameter estimates for the VBGF and advice for policymakers. Our model fitted to the data better than either of the other models and also reduced the uncertainty in the parameter estimates and models used to inform management. Our model is a robust way of fitting the VBGF and can be used to combine data from multiple sources. The model is general enough to fit other growth curves for any taxon when the age of individuals is binned into groups

Plant diversity and root traits benefit physical properties key to soil function in grasslands

Plant diversity loss impairs ecosystem functioning, including important effects on soil. Most studies that have explored plant diversity effects belowground, however, have largely focused on biological processes. As such, our understanding of how plant diversity impacts the soil physical environment remains limited, despite the fundamental role soil physical structure plays in ensuring soil function and ecosystem service provision. Here, in both a glasshouse and a long-term field study, we show that high plant diversity in grassland systems increases soil aggregate stability, a vital structural property of soil, and that root traits play a major role in determining diversity effects. We also reveal that the presence of particular plant species within mixed communities affects an even wider range of soil physical processes, including hydrology and soil strength regimes. Our results indicate that alongside well-documented effects on ecosystem functioning, plant diversity and root traits also benefit essential soil physical properties

Mathematical modeling of the West Africa Ebola epidemic

As of November 2015, the Ebola virus disease (EVD) epidemic that began in West Africa in late 2013 is waning. The human toll includes more than 28,000 EVD cases and 11,000 deaths in Guinea, Liberia, and Sierra Leone, the most heavily-affected countries. We reviewed 66 mathematical modeling studies of the EVD epidemic published in the peer-reviewed literature to assess the key uncertainties models addressed, data used for modeling, public sharing of data and results, and model performance. Based on the review, we suggest steps to improve the use of modeling in future public health emergencies. DOI: http://dx.doi.org/10.7554/eLife.09186.00

Body size but not warning signal luminance influences predation risk in recently metamorphosed poison frogs.

During early development, many aposematic species have bright and conspicuous warning appearance, but have yet to acquire chemical defenses, a phenotypic state which presumably makes them vulnerable to predation. Body size and signal luminance in particular are known to be sensitive to variation in early nutrition. However, the relative importance of these traits as determinants of predation risk in juveniles is not known. To address this question, we utilized computer-assisted design (CAD) and information on putative predator visual sensitivities to produce artificial models of postmetamorphic froglets that varied in terms of body size and signal luminance. We then deployed the artificial models in the field and measured rates of attack by birds and unknown predators. Our results indicate that body size was a significant predictor of artificial prey survival. Rates of attack by bird predators were significantly higher on smaller models. However, predation by birds did not differ between artificial models of varying signal luminance. This suggests that at the completion of metamorphosis, smaller froglets may be at a selective disadvantage, potentially because predators can discern they have relatively low levels of chemical defense compared to larger froglets. There is likely to be a premium on efficient foraging, giving rise to rapid growth and the acquisition of toxins from dietary sources in juvenile poison frogs.This study was conducted in compliance with the scientific permit SE/A-19-11 provided by the Panamanian National Authority for the Environment (ANAM). This study was supported by a PhD scholarship (IFARHU-SENACYT program) and a research grant No. APY-NI-010-006B/SENACYT both awarded to EEF by the Government of Panama, and by a Royal Society University Research Fellowship to JDB. MS was supported by a Biotechnology and Biological Sciences Research Council David Phillips Research Fellowship (BB/G022887/1). HMR was supported by a Junior Research Fellowship from Churchill College, Cambridge. Special thanks to Rachel Page at STRI for supporting EEF with the grant application, Sistema Nacional de Investigacion de Panama (SNI), and the People of Santa Fe for their collaboration during the study. We are grateful to Leesther Vásquez, Joelbin De La Cruz, Georgia Croxford and field assistants from AMIPARQUE for assistance with the production of frog models.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/ece3.173

Non-linear analysis of GeneChip arrays

The application of microarray hybridization theory to Affymetrix GeneChip data has been a recent focus for data analysts. It has been shown that the hyperbolic Langmuir isotherm captures the shape of the signal response to concentration of Affymetrix GeneChips. We demonstrate that existing linear fit methods for extracting gene expression measures are not well adapted for the effect of saturation resulting from surface adsorption processes. In contrast to the most popular methods, we fit background and concentration parameters within a single global fitting routine instead of estimating the background before obtaining gene expression measures. We describe a non-linear multi-chip model of the perfect match signal that effectively allows for the separation of specific and non-specific components of the microarray signal and avoids saturation bias in the high-intensity range. Multimodel inference, incorporated within the fitting routine, allows a quantitative selection of the model that best describes the observed data. The performance of this method is evaluated on publicly available datasets, and comparisons to popular algorithms are presented

Multiple House Occupancy is Associated with Mortality in Hospitalised Patients with Covid-19

Acknowledgement We acknowledge the dedication, commitment, and sacrifice of the staff from participating centres across UK and Italy, two amongst the most severely affected countries in Europe. We gratefully acknowledge the contribution of our collaborators, National Institute of Health Research (NIHR) Health Research Authority (HRA) in the UK and Ethics Committee of Policlinico Hospital Modena, which provided rapid approval of COPE study and respective Institutions’ Research and Development Offices and Caldicott Guardians for their assistance and guidance. We also thank COPE Study Sponsor, Cardiff University, Wales, UK.Peer reviewedPublisher PD

The changing environment of conservation conflict: geese and farming in Scotland

Conflict between conservation objectives and human livelihoods is ubiquitous and can be highly damaging, but the processes generating it are poorly understood. Ecological elements are central to conservation conflict, and changes in their dynamics — for instance due to anthropogenic environmental change — are likely to influence the emergence of serious human–wildlife impacts and, consequently, social conflict.  We used mixed-effects models to examine the drivers of historic spatio-temporal dynamics in numbers of Greenland barnacle geese (Branta leucopsis) on the Scottish island of Islay to identify the ecological processes that have shaped the environment in which conflict between goose conservation and agriculture has been triggered.  Barnacle goose numbers on Islay increased from 20,000 to 43,000 between 1987 and 2016. Over the same period, the area of improved grassland increased, the number of sheep decreased and the climate warmed.  Goose population growth was strongly linked to the increasing area of improved grassland, which provided geese with more high quality forage. Changing climatic conditions, particularly warming temperatures on Islay and breeding grounds in Greenland, have also boosted goose numbers.  As the goose population has grown, farms have supported geese more frequently and in larger numbers, with subsequent damaging effects on grassland. The creation of high-quality grassland appears to have largely driven the problem of serious economic damage by geese. Our analysis also reveals the drivers of spatial variation in goose impacts: geese were more likely to occur on farms closer to roosts and those with more improved grassland. However, as geese numbers have increased they have spread to previously less favoured farms.  Synthesis and applications. Our study demonstrates the primary role of habitat modification in the emergence of conflict between goose conservation and agriculture, alongside a secondary role of climate change. Our research illustrates the value of exploring socio-ecological history to understand the processes leading to conservation conflict. In doing so, we identify those elements that are more controllable, such as local habitat management, and less controllable, such as climate change, but which both need to be taken into account when managing conservation conflict

Pre-processing Agilent microarray data

<p>Abstract</p> <p>Background</p> <p>Pre-processing methods for two-sample long oligonucleotide arrays, specifically the Agilent technology, have not been extensively studied. The goal of this study is to quantify some of the sources of error that affect measurement of expression using Agilent arrays and to compare Agilent's Feature Extraction software with pre-processing methods that have become the standard for normalization of cDNA arrays. These include log transformation followed by loess normalization with or without background subtraction and often a between array scale normalization procedure. The larger goal is to define best study design and pre-processing practices for Agilent arrays, and we offer some suggestions.</p> <p>Results</p> <p>Simple loess normalization without background subtraction produced the lowest variability. However, without background subtraction, fold changes were biased towards zero, particularly at low intensities. ROC analysis of a spike-in experiment showed that differentially expressed genes are most reliably detected when background is not subtracted. Loess normalization and no background subtraction yielded an AUC of 99.7% compared with 88.8% for Agilent processed fold changes. All methods performed well when error was taken into account by t- or z-statistics, AUCs ≥ 99.8%. A substantial proportion of genes showed dye effects, 43% (99%<it>CI </it>: 39%, 47%). However, these effects were generally small regardless of the pre-processing method.</p> <p>Conclusion</p> <p>Simple loess normalization without background subtraction resulted in low variance fold changes that more reliably ranked gene expression than the other methods. While t-statistics and other measures that take variation into account, including Agilent's z-statistic, can also be used to reliably select differentially expressed genes, fold changes are a standard measure of differential expression for exploratory work, cross platform comparison, and biological interpretation and can not be entirely replaced. Although dye effects are small for most genes, many array features are affected. Therefore, an experimental design that incorporates dye swaps or a common reference could be valuable.</p

Evolution of Neuronal and Endothelial Transcriptomes in Primates

The study of gene expression evolution in vertebrates has hitherto focused on the analysis of transcriptomes in tissues of different species. However, because a tissue is made up of different cell types, and cell types differ with respect to their transcriptomes, the analysis of tissues offers a composite picture of transcriptome evolution. The isolation of individual cells from tissue sections opens up the opportunity to study gene expression evolution at the cell type level. We have stained neurons and endothelial cells in human brains by antibodies against cell type-specific marker proteins, isolated the cells using laser capture microdissection, and identified genes preferentially expressed in the two cell types. We analyze these two classes of genes with respect to their expression in 62 different human tissues, with respect to their expression in 44 human “postmortem” brains from different developmental stages and with respect to between-species brain expression differences. We find that genes preferentially expressed in neurons differ less across tissues and developmental stages than genes preferentially expressed in endothelial cells. We also observe less expression differences within primate species for neuronal transcriptomes. In stark contrast, we see more gene expression differences between humans, chimpanzees, and rhesus macaques relative to within-species differences in genes expressed preferentially in neurons than in genes expressed in endothelial cells. This suggests that neuronal and endothelial transcriptomes evolve at different rates within brain tissue