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Estuary English
No full textThe term “Estuary English” first appeared in 1984 labelling a host of developments observed in the speech of the South East of England, expected to be a major source of impact on the pronunciation of Southern British English for years to come. While linguists are sceptical as to the existence of a uniform, well-definable variety in the Home Counties, the term, which passed into wide use, appears to be readily understood by the general public four decades since its inception, conjuring a stereotype of a fashionable younger speaker. The present entry looks at the early definitions of the term and the publicity surrounding it. It presents data from its supposed heartland, the Home Counties, in the light of developments in standardised and non-standardised accents (dialect levelling) and concludes that Estuary English is a social construct rather than a definable linguistic phenomenon
A covariance formula for the number of excursion set components of Gaussian fields and applications
Get PDFWe derive a covariance formula for the number of excursion or level set components of a smooth stationary Gaussian field on R d contained in compact domains. We also present two applications of this formula: (1) for fields whose correlations are integrable we prove that the variance of the component count in large domains is of volume order and give an expression for the leading constant, and (2) for fields with slower decay of correlation we give an upper bound on the variance which is of optimal order if correlations are regularly varying, and improves on best-known bounds if correlations are oscillating (e.g. monochromatic random waves)
On Minimizing Risk and Harm in the Use of Psychedelics
Get PDFObjective: This article outlines recommendations from 30 psychedelic researchers on how to create a better psychedelic safety net. Methods: A survey of 30 psychedelic researchers asked them to identify key critical research gaps around psychedelic harm and safety. Results: The critical research gaps identified by the authors included defining the main types of psychedelic harm, the predictors of those harms, and the most effective way to treat those harms. They also call for better support for those experiencing post‐psychedelic difficulties, including better online information, peer support groups, affordable therapy, and psychiatric consultation and medication. Finally, the authors call for better funding to create a psychedelic safety net, and suggest psychedelic philanthropists, investors and companies could commit 1% of their investment in psychedelics into supporting safety measures such as research and support services. Conclusions: The authors identify several practical steps to create a better psychedelic safety net and call for more funding to psychedelic safety measures such as research and support services. Relevance to clinical practice: The authors outline important gaps in our knowledge around the safety and risk profile of psychedelic medicines and identify practical steps forward for researchers and clinical practitioners to make this promising field safer
Biosurfactant/surfactant mixing properties at the air–water interface: comparing rhamnolipids and sophorolipids mixed with the anionic surfactant sodium dodecyl benzene sulfonate †
Get PDFThere is an increasing interest in the use of biosurfactants in the development of more biocompatible and biosustainable surfactant-based products. To optimise performance and mitigate production costs, biosurfactants are commonly mixed with different synthetic surfactants. Understanding in detail their mixing properties at interfaces and in solution is key to the development of optimal formulations. Reported here is a detailed thermodynamic analysis, using the latest developments in the pseudo phase approximation, PPA, of the mixing behaviour at the air–water interface of two glycolipid biosurfactants, rhamnolipids, RL, containing the mono and di-rhamnose isomers R1 and R2, and the sophorolipids, SL, containing the lactonic and acidic isomers LS and AS, with the anionic surfactant sodium dodecyl benzene sulfonate, LAS. The analysis uses the previously reported adsorption data, from neutron reflectivity measurements, NR, for the associated binary and ternary mixtures. The different rhamnolipid and sophorolipid biosurfactant structures and their relative surface activities have a profound effect on their mixing properties at the air–water interface with the anionic surfactant LAS, due predominantly to the steric constraints of the different molecular structures. This results in different synergistic excess free energies of mixing and different optimal compositions
High-throughput screen of 100 000 small molecules in C9ORF72 ALS neurons identifies spliceosome modulators that mobilize G4C2 repeat RNA into nuclear export and repeat associated non-canonical translation
Get PDFAn intronic G4C2 repeat expansion in the C9ORF72 gene is the major known cause for Amyotrophic Lateral Sclerosis (ALS), with current evidence for both, loss of function and pathological gain of function disease mechanisms. We screened 96 200 small molecules in C9ORF72 patient iPS neurons for modulation of nuclear G4C2 RNA foci and identified 82 validated hits, including the Brd4 inhibitor JQ1 as well as novel analogs of Spliceostatin-A, a known modulator of SF3B1, the branch point binding protein of the U2-snRNP. Spliceosome modulation by these SF3B1 targeted compounds recruits SRSF1 to nuclear G4C2 RNA, mobilizing it from RNA foci into nucleocytoplasmic export. This leads to increased repeat-associated non-canonical (RAN) translation and ultimately, enhanced cell toxicity. Our data (i) provide a new pharmacological entry point with novel as well as known, publicly available tool compounds for dissection of C9ORF72 pathobiology in C9ORF72 ALS models, (ii) allowing to differentially modulate RNA foci versus RAN translation, and (iii) suggest that therapeutic RNA foci elimination strategies warrant caution due to a potential storage function, counteracting translation into toxic dipeptide repeat polyproteins. Instead, our data support modulation of nuclear export via SRSF1 or SR protein kinases as possible targets for future pharmacological drug discovery
The overlapping global distribution of dengue, chikungunya, Zika and yellow fever
Get PDFArboviruses transmitted mainly by Aedes (Stegomyia) aegypti and Ae. albopictus, including dengue, chikungunya, and Zika viruses, and yellow fever virus in urban settings, pose an escalating global threat. Existing risk maps, often hampered by surveillance biases, may underestimate or misrepresent the true distribution of these diseases and do not incorporate epidemiological similarities despite shared vector species. We address this by generating new global environmental suitability maps for Aedes-borne arboviruses using a multi-disease ecological niche model with a nested surveillance model fit to a dataset of over 21,000 occurrence points. This reveals a convergence in suitability around a common global distribution with recent spread of chikungunya and Zika closely aligning with areas suitable for dengue. We estimate that 5.66 (95% confidence interval 5.64-5.68) billion people live in areas suitable for dengue, chikungunya and Zika and 1.54 (1.53-1.54) billion people for yellow fever. We find large national and subnational differences in surveillance capabilities with higher income more accessible areas more likely to detect, diagnose and report viral diseases, which may have led to overestimation of risk in the United States and Europe. When combined with estimates of uncertainty, these suitability maps can be used by ministries of health to target limited surveillance and intervention resources in new strategies against these emerging threats
Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND Global km‐Scale Simulations
Get PDFGlobal kilometer‐scale models represent the future of Earth system modeling, enabling explicit simulation of organized convective storms and their associated extreme weather. Here, we comprehensively evaluate tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) simulations for both summer and winter phases. Using 10 different feature trackers applied to simulations and satellite observations, we assess MCS frequency, precipitation, and other key characteristics. Substantial differences (a factor of 2–3) arise among trackers in observed MCS frequency and their precipitation contribution, but model‐observation differences in MCS statistics are more consistent across trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi‐model mean biases ranging from −2%–8% over land and −8%–8% over ocean (summer vs. winter). However, most DYAMOND models underestimate MCS precipitation amount (23%) and their contribution to total precipitation (17%). Biases in precipitation contributions are generally smaller over land (13%) than over ocean (21%), with moderate inter‐model variability. While models better simulate MCS diurnal cycles and cloud shield characteristics, they overestimate MCS precipitation intensity and underestimate stratiform rain contributions (up to a factor of 2), particularly over land, albeit observational uncertainties exist. Additionally, models exhibit a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, with many models showing exaggerated sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed here provide process‐oriented diagnostics to guide future model development
Investigating the static corrosion of T91 for lead-bismuth eutectic reactors at the atomic scale
Get PDFT91 steel is a candidate material for structural components in lead-bismuth-eutectic (LBE) cooled systems, for example, fast reactors and solar power plants. However, the corrosion mechanisms of T91 in LBE remain poorly understood. In this study, we have analysed the static corrosion of T91 in liquid LBE using a range of characterisation techniques at increasingly smaller scales. Both the corrosion in reducing and oxidising environments are considered. Several different corrosion mechanisms are identified and the effect of oxygen content on LBE corrosion is investigated.
Oxygen concentration strongly influences the corrosion pattern. In a reducing environment (low oxygen content), the physical dissolution of elements (e.g. Fe, Cr) in LBE is the main factor with slight oxidation happening at the interface of T91 and LBE. A unique pattern of liquid metal intrusion is observed that does not appear to correlate with the grain boundary network. Atomic scale elemental redistribution and 3D morphology of the corrosion interface are revealed using scanning transmission electron microscope (STEM) and atom probe tomography (APT). A thin surface oxide layer (presumably wüstite) is observed at the LBE-steel interface.
In an oxidising environment (high oxygen content), preferential oxidation at grain boundaries is the dominant process, with LBE penetration into materials happening more locally. Complex oxide structures are identified with the use of STEM and APT. Moreover, the quality of the oxide layer may directly influence the protection effect.
Upon closer inspection, electron backscatter diffraction (EBSD) reveals a change in the morphology of grains at the LBE-exposed surface for both reducing and oxidising environments, suggesting a local phase transition. Energy dispersive X-ray (EDX) maps show that Cr is depleted in the T91 material near the LBE interface. The dissociation of Cr carbides and the overall Cr depletion adjacent to the corrosion interface show a potential correlation to the phase change observed in this region. High-resolution electron backscatter diffraction (HR-EBSD) and micro-beam Laue X-ray diffraction measurements further support the results. Based on this detailed nano-scale information from STEM and APT, a potential mechanism of Cr depletion, Cr carbide dissociation, and phase change in T91 induced by LBE corrosion is proposed
Life histories and population dynamics in variable environments
Get PDFEnvironmental variability has broad impacts on the population dynamics of species across the tree of life. Importantly, global climate change is projected to increase environmental variability in regions hosting the highest biodiversity. Therefore, understanding the life history strategies by which populations can evolve to accommodate the often negative effects of environmental variability is critical to understand how global climate change may impact populations across taxa. In this dissertation, I explore one strategy by which populations can accommodate the impacts of environmental variability – i.e., demographic buffering. Specifically, I: (1) identify criteria for determining a buffering mechanism in ecological modelling, (2) demonstrate the utility of “new” perturbation approaches (i.e., the summation of stochastic elasticities of variance and self-second derivatives) when identifying demographic buffering, (3) analyse how environmental autocorrelation and variance impact demographic buffering (as measured by the summation of stochastic elasticities of variance) and the demographic mechanisms that underly these effects, (4) test for efficacy across four measures of demographic buffering (i.e., one correlational method, two methods using terms from Tuljapurkar’s approximation and the summation of stochastic elasticities of variance) and (5) broadly review modern perspectives and suggest new directions regarding where future life history research may lead. Overall, I suggest that the summation of stochastic elasticities of variance is an effective measure of demographic buffering, and that environmental autocorrelation and variance influence this measure through population structure and demographic rate variance, respectively. Furthermore, I outline multiple avenues for future research to better understand how life histories evolve in variable environments
Improving the understanding of cancer and cancer care by applying data science and machine learning methods to electronic patient records
No full textElectronic health records (EHR) hold great potential for improving the understanding of cancer care by containing high-resolution real-world data for large numbers of patients. This dissertation explores the application of data science and machine learning (ML) methods to EHRs for the purposes of translational colorectal cancer (CRC) research.
I first explore the challenges in using EHRs throughout the data life cycle. I present a lightweight information extraction pipeline that retrieves TNM staging scores---common descriptors of cancer severity---from free text clinical reports with high sensitivity and precision, and also retrieves information about the presence and recurrence of CRC. These data items are essential to CRC research, for identifying cases, studying treatment variation, and comparing treatment outcomes. The pipeline was developed using data from Oxford University Hospitals (OUH) and Royal Marsden (RMH) NHS Foundation Trusts (FT), and supported the establishment of the National Institute for Health Research (NIHR) Health Informatics Collaborative (HIC) CRC database.
I then focus on a specific application: combining the faecal immunochemical test (FIT) results with routinely collected data to predict CRC in symptomatic patients. The current practice is to refer patients with FIT above 10 μg/g for invasive endoscopic investigations, but only one in six investigated have CRC, motivating prediction model development. I demonstrate that an externally-derived model does not outperform FIT in the Oxford University Hospitals FIT dataset (OUH-FIT), and highlight the importance of clinically-relevant performance measures. I then show that employing more predictors, a spectrum of ML models, and novel training methods, was not sufficient to outperform FIT on OUH-FIT data. Finally, I build on and incorporate an existing sequence analysis method into an interactive app that allows to explore and cluster thousands of medical event sequences, such as visualising treatment patterns of CRC patients.
The principal contributions are: a holistic discussion of EHR data quality; a staging extraction algorithm that facilitates further research/audits; a comprehensive pipeline for developing/evaluating FIT-based CRC prediction models; and a fast medical sequence exploration app that can help check data quality and identify treatment variations. There is considerable potential to use these tools on larger datasets to understand if FIT-based models are bound to fail (or if they may work on subgroups with more severe disease); and to contrast different treatment patterns employed for subgroups of CRC patients with complex disease, such as those with liver metastases