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This paper explores some 220,000 medieval objects recorded in the Portable Antiquities Scheme (PAS) online database of archaeological small finds through Geographic Information System analysis of their relationship with contemporary market sites. First, an overview of the contents of the PAS database is presented in terms of its spatial and ‘object type’ distribution. Second, the relationship of the medieval finds data against documentary evidence of commercial activity is investigated at a national level. Finally, PAS data is contextualised in its historical landscape context through case studies. It is argued that the distribution of PAS finds on the national scale can be linked with patterns of commercial activity, and that while rural and urban finds scatters have distinguishing trends, the countryside population enjoyed access to a range of sophisticated metalwork culture; also, that certain assemblages can be analysed statistically to yield new data and perspectives on local historical development.
Emergency Medical Services (EMS) systems have developed protocols for prehospital activation of the cardiac catheterization laboratory for patients with suspected ST-elevation myocardial infarction (STEMI) to decrease first-medical-contact-to-balloon time (FMC2B). The rate of “false positive” prehospital activations is high. In order to decrease this rate and expedite care for patients with true STEMI, the American Heart Association (AHA; Dallas, Texas USA) developed the Mission Lifeline PreAct STEMI algorithm, which was implemented in Los Angeles County (LAC; California USA) in 2015. The hypothesis of this study was that implementation of the PreAct algorithm would increase the positive predictive value (PPV) of prehospital activation.
This is an observational pre-/post-study of the effect of the implementation of the PreAct algorithm for patients with suspected STEMI transported to one of five STEMI Receiving Centers (SRCs) within the LAC Regional System. The primary outcome was the PPV of cardiac catheterization laboratory activation for percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG). The secondary outcome was FMC2B.
A total of 1,877 patients were analyzed for the primary outcome in the pre-intervention period and 405 patients in the post-intervention period. There was an overall decrease in cardiac catheterization laboratory activations, from 67% in the pre-intervention period to 49% in the post-intervention period (95% CI for the difference, -14% to -22%). The overall rate of cardiac catheterization declined in post-intervention period as compared the pre-intervention period, from 34% to 30% (95% CI, for the difference -7.6% to 0.4%), but actually increased for subjects who had activation (48% versus 58%; 95% CI, 4.6%-15.0%). Implementation of the PreAct algorithm was associated with an increase in the PPV of activation for PCI or CABG from 37.9% to 48.6%. The overall odds ratio (OR) associated with the intervention was 1.4 (95% CI, 1.1-1.8). The effect of the intervention was to decrease variability between medical centers. There was no associated change in average FMC2B.
The implementation of the PreAct algorithm in the LAC EMS system was associated with an overall increase in the PPV of cardiac catheterization laboratory activation.
First episode psychosis (FEP) patients who use cannabis experience more frequent psychotic and euphoric intoxication experiences compared to controls. It is not clear whether this is consequent to patients being more vulnerable to the effects of cannabis use or to their heavier pattern of use. We aimed to determine whether extent of use predicted psychotic-like and euphoric intoxication experiences in patients and controls and whether this differs between groups.
We analysed data on patients who had ever used cannabis (n = 655) and controls who had ever used cannabis (n = 654) across 15 sites from six countries in the EU-GEI study (2010–2015). We used multiple regression to model predictors of cannabis-induced experiences and to determine if there was an interaction between caseness and extent of use.
Caseness, frequency of cannabis use and money spent on cannabis predicted psychotic-like and euphoric experiences (p ⩽ 0.001). For psychotic-like experiences (PEs) there was a significant interaction for caseness × frequency of use (p < 0.001) and caseness × money spent on cannabis (p = 0.001) such that FEP patients had increased experiences at increased levels of use compared to controls. There was no significant interaction for euphoric experiences (p > 0.5).
FEP patients are particularly sensitive to increased psychotic-like, but not euphoric experiences, at higher levels of cannabis use compared to controls. This suggests a specific psychotomimetic response in FEP patients related to heavy cannabis use. Clinicians should enquire regarding cannabis related PEs and advise that lower levels of cannabis use are associated with less frequent PEs.
Daily use of high-potency cannabis has been reported to carry a high risk for developing a psychotic disorder. However, the evidence is mixed on whether any pattern of cannabis use is associated with a particular symptomatology in first-episode psychosis (FEP) patients.
We analysed data from 901 FEP patients and 1235 controls recruited across six countries, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) study. We used item response modelling to estimate two bifactor models, which included general and specific dimensions of psychotic symptoms in patients and psychotic experiences in controls. The associations between these dimensions and cannabis use were evaluated using linear mixed-effects models analyses.
In patients, there was a linear relationship between the positive symptom dimension and the extent of lifetime exposure to cannabis, with daily users of high-potency cannabis having the highest score (B = 0.35; 95% CI 0.14–0.56). Moreover, negative symptoms were more common among patients who never used cannabis compared with those with any pattern of use (B = −0.22; 95% CI −0.37 to −0.07). In controls, psychotic experiences were associated with current use of cannabis but not with the extent of lifetime use. Neither patients nor controls presented differences in depressive dimension related to cannabis use.
Our findings provide the first large-scale evidence that FEP patients with a history of daily use of high-potency cannabis present with more positive and less negative symptoms, compared with those who never used cannabis or used low-potency types.
A classic example of microbiome function is its role in nutrient assimilation in both plants and animals, but other less obvious roles are becoming more apparent, particularly in terms of driving infectious and non-infectious disease outcomes and influencing host behaviour. However, numerous biotic and abiotic factors influence the composition of these communities, and host microbiomes can be susceptible to environmental change. How microbial communities will be altered by, and mitigate, the rapid environmental change we can expect in the next few decades remain to be seen. That said, given the enormous range of functional diversity conferred by microbes, there is currently something of a revolution in microbial bioengineering and biotechnology in order to address real-world problems including human and wildlife disease and crop and biofuel production. All of these concepts are explored in further detail throughout the book.
Hobby metal detecting is a controversial subject. Legal and policy approaches differ widely across national and regional contexts, and the attitudes of archaeologists and heritage professionals towards detectorists are often polarized and based on ethical or emotive arguments. We, the European Public Finds Recording Network (EPFRN), have implemented collaborative approaches towards detectorist communities in our respective contexts (Denmark, England and Wales, Finland, Flanders, and the Netherlands). Although our motivations are affected by our national circumstances, we base our work on an agreed set of goals, practices, and visions. This article presents the EPFRN's vision statement and provides insight into its underlying thoughts. We hope to create a debate on how to develop best practice approaches that acknowledge the inherent challenges of hobby metal detecting while realizing its potential.
The surprising election of Donald Trump to the presidency calls for a comprehensive assessment of what motivated voters to opt for a controversial political novice rather than a provocative but experienced political veteran. Our study provides a novel exploration of the Trump victory through the prism of the defeated candidate—Hillary Rodham Clinton (HRC). Losing candidates’ perceptions are usually not subject to academic analyses. Nevertheless, these people often hold substantial sway in their parties and thus understanding their views on the loss is essential, especially as a party regroups after defeat. Using HRC’s memoir What Happened, we devise the Hillary Hypotheses, her rationale for her electoral defeat. Using the 2016 American National Election Study (ANES), we provide the first systematic test of a losing candidate’s rationale for their defeat. We show that more often than not, HRC’s assumptions are supported. However, we find little evidence to support HRC’s most crucial assertion, namely that the e-mail scandal and specifically James Comey’s intervention ten days before Election Day cost her the presidency. Our findings have implications for understanding why Donald Trump won, but more broadly the contribution explores an understudied aspect of elections—a defeated candidate’s impression of their loss.
Are ordinary citizens better at predicting election results than conventional voter intention polls? The authors address this question by comparing eight forecasting models for British general elections: one based on voters' expectations of who will win and seven based on who voters themselves intend to vote for (including ‘uniform national swing model’ and ‘cube rule’ models). The data come from ComRes and Gallup polls as well as the Essex Continuous Monitoring Surveys, 1950–2017, yielding 449 months with both expectation and intention polls. The large sample size permits comparisons of the models' prediction accuracy not just in the months prior to the election, but in the years leading up to it. Vote expectation models outperform vote intention models in predicting both the winning party and parties' seat shares.