118 research outputs found
Fortissat Science Alliance podcast: Layla Mathieson
No full textLayla Mathieson was an EPSRC/MRC OPTIMA CDT PhD student studying optical medical imaging alongside an integrated Masters in healthcare innovation and entrepreneurship at the University of Edinburgh. She took part in the Fortissat Science Alliance podcast recordings in September 2021.What is the Fortissat Science Alliance?The Fortissat Science Alliance was a Wellcome Trust & Children In Need "Curiosity" project. This scheme provided informal STEM learning opportunities for young people who attended the community centre Getting Better Together Shotts (GBT Shotts) between 2019 and 2023. Due to the COVID-19 pandemic, deliveries had to pivot online so the podcast was founded. These recordings were made via Zoom with warm-up STEM activities sent to every young person in advance, along with a profile page for each researcher, so that they were relaxed and able to ask excellent questions.Link to episode on Spotify.Depending on the broadcast date, podcast deliveries were co-sponsored by Glasgow Science Festival, EXPLORATHON 2021, or EXPLORATHON 2022/23.For the duration of the project, it was supported jointly by Children in Need and the Wellcome Trust. In 2021, EXPLORATHON episodes were supported by the European Commission [grant agreement ID 101036101]. In 2022-23, EXPLORATHON episodes were supported by the Engineering & Physical Sciences Research Council [grant number EP/X020894/1]. Layla was supported by the EPSRC/MRC Centre for Doctoral Training in Optical Medical Imaging (OPTIMA).Author contributions to contentLayla Mathieson was the guest featured on this episode. Rebecca Hay was the youth worker coordinating the young people who conducted the interviews as well as co-editing and broadcasting the recordings. Iain Hamilton co-edited the episodes. Kirsty Ross was the STEM consultant for the project and uploaded completed episodes to Figshare.</p
<i>FADS1</i>and the timing of human adaptation to agriculture
No full textAbstractVariation at theFADS1/FADS2gene cluster is functionally associated with differences in lipid metabolism and is often hypothesized to reflect adaptation to an agricultural diet. Here, we test the evidence for this relationship using both modern and ancient DNA data. We show that almost all the inhabitants of Europe carried the ancestral allele until the derived allele was introduced approximately 8,500 years ago by Early Neolithic farming populations. However, we also show that it was not under strong selection in these populations. We find that this allele, and other proposed agricultural adaptations atLCT/MCM6andSLC22A4, were not strongly selected until much later, perhaps as late as the Bronze Age. Similarly, increased copy number variation at the salivary amylase geneAMY1is not linked to the development of agriculture although, in this case, the putative adaptation precedes the agricultural transition. Our analysis shows that selection at theFADSlocus was not tightly linked to the initial introduction of agriculture and the Neolithic transition. Further, it suggests that the strongest signals of recent human adaptation in Europe did not coincide with the Neolithic transition but with more recent changes in environment, diet or efficiency of selection due to increases in effective population size.</jats:p
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Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus
No full textThis manuscript represents a large collaboration with many "middle" contributing authors, including the author requesting this waiver (Daniel Chasman). Iain Mathieson, Felix R. Day, Nicola Barban, Felix C. Tropf, and David M. Brazel are the starred (equal contribution) first authors; Melinda C. Mills, and John R.B. Perry are the starred last authors. Corresponding: Iain Mathieson, Melinda C. Mills, and John Perry are the corresponding authors.
Please note that while the uploaded version of the manuscript has been accepted, there may still be changes that would be incorporated in the final publication.Identifying genetic determinants of reproductive success may highlight mechanisms underlying fertility and identify alleles under present-day selection. Using data in 785,604 individuals of European ancestry, we identified 43 genomic loci associated with either number of children ever born (NEB) or childlessness. These loci span diverse aspects of reproductive biology, including puberty timing, age at first birth, sex hormone regulation, endometriosis and age at menopause. Missense variants in ARHGAP27 were associated with higher NEB but shorter reproductive lifespan, suggesting a trade-off at this locus between reproductive ageing and intensity. Other genes implicated by coding variants include PIK3IP1, ZFP82 and LRP4, and our results suggest a new role for the melanocortin 1 receptor (MC1R) in reproductive biology. As NEB is one component of evolutionary fitness, our identified associations indicate loci under present-day natural selection. Integration with data from historical selection scans highlighted an allele in the FADS1/2 gene locus that has been under selection for thousands of years and remains so today. Collectively, our findings demonstrate that a broad range of biological mechanisms contribute to reproductive success.Accepted Manuscrip
Lending booms, reserves, and the sustainability of short-term debt - inferences from the pricing of syndicated bank loans
No full textAcademics pay little attention to international bank lending, focusing instead on rapidly growing market segments such as the international bond market and derivative credit instruments. The authors argue for paying more attention to international bank lending. Why? Three reasons. First, the syndicated bank loan is one of the workhorses of international capital markets. Second, international bank lending is especially important for private-sector borrowers, whose participation in international capital markets will grow as capital markets are liberalized and state enterprises privatized. Sovereigns and other governmental borrowers rely more on the bond market, while private borrowers are disproportionately important to the market in international bank loans. Private-sector borrowers establish long-term relationships with banks to resolve information problems. The authors find that international banks provide more credit to smaller borrowers (about whom information is least complete) than bond markets do. Bank finance dominates that segment of international financial markets with the greatest information asymmetry. Third, spreads on syndicated bank loans show much less variation than spreads on international bonds. Are bank lenders properly pricing country and credit risk? Does spread compression on syndicated bank loans suggest excessive moral hazard in international bank lending? The authors warn against over-dependence on high levels of domestic debt. While growth in domestic debt reflects improved inter-mediation between savers and investors, rapid increases to high levels are viewed as unsustainable and raise the cost of international borrowing. They find evidence of growing bullishness among bank lenders to East Asia in the first half of the 1990s, which could reflect moral hazard, but the jury is still out on that issue. High external short-term debt can coexist with rapid growth for extended periods but is likely to unravel if perceptions of sustainability shift.Payment Systems&Infrastructure,Economic Theory&Research,Banks&Banking Reform,International Terrorism&Counterterrorism,Financial Intermediation,Housing Finance,Economic Adjustment and Lending,Banks&Banking Reform,Financial Intermediation,Economic Theory&Research
ImaGene: a convolutional neural network to quantify natural selection from genomic data
No full textBACKGROUND: The genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determining such genetic bases is an evolutionary framework. As sites targeted by natural selection are likely to harbor important functionalities for the carrier, the identification of selection signatures in the genome has the potential to unveil the genetic mechanisms underpinning human phenotypes. Popular methods of detecting such signals rely on compressing genomic information into summary statistics, resulting in the loss of information. Furthermore, few methods are able to quantify the strength of selection. Here we explored the use of deep learning in evolutionary biology and implemented a program, called ImaGene, to apply convolutional neural networks on population genomic data for the detection and quantification of natural selection. RESULTS: ImaGene enables genomic information from multiple individuals to be represented as abstract images. Each image is created by stacking aligned genomic data and encoding distinct alleles into separate colors. To detect and quantify signatures of positive selection, ImaGene implements a convolutional neural network which is trained using simulations. We show how the method implemented in ImaGene can be affected by data manipulation and learning strategies. In particular, we show how sorting images by row and column leads to accurate predictions. We also demonstrate how the misspecification of the correct demographic model for producing training data can influence the quantification of positive selection. We finally illustrate an approach to estimate the selection coefficient, a continuous variable, using multiclass classification techniques. CONCLUSIONS: While the use of deep learning in evolutionary genomics is in its infancy, here we demonstrated its potential to detect informative patterns from large-scale genomic data. We implemented methods to process genomic data for deep learning in a user-friendly program called ImaGene. The joint inference of the evolutionary history of mutations and their functional impact will facilitate mapping studies and provide novel insights into the molecular mechanisms associated with human phenotypes
Interpreting generative adversarial networks to infer natural selection from genetic data
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Charlemagne, Common Sense, and Chartism: how Robert Blakey wrote his History of Political Literature
No full textThis article examines the life and works of Robert Blakey, author of the first English-language history of political thought. Studies of Blakey have typically concentrated on one aspect of his life, whether as an authority on field sports or as an historian of philosophy. However, some of Blakey’s lesser-known ventures, particularly his early Radical politics, his hagiographies, and his attempts to write a biography of Charlemagne, heavily influenced his more famous works. Similarly, Blakey’s upbringing in a Calvinist tradition, rooted in the Scottish School of Common Sense philosophy helps makes sense of his philosophical and theological commitments, yet has been largely ignored. This article provides a sketch of Blakey’s life, tying these disparate strands together, and explaining their influence upon, and relevance to, the first history of political philosophy
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