Late Holocene seasonal temperature variability of the western Scottish shelf (St Kilda) recorded in fossil shells of the bivalve Glycymeris glycymeris

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordThe North Atlantic Ocean and adjacent shelf seas play a crucial role in global climate. To better constrain long-term natural variability and marine-terrestrial linkages in this region, a network of highly resolved marine archives from the open ocean and continental shelves is needed. In recent decades, bivalve sclerochronology has emerged as a field providing such records from the mid- to high latitudes. In May 2014, dead valves and young live specimens of the bivalve Glycymeris glycymeris were collected at St Kilda, Scotland. A floating chronology spanning 187 years was constructed with fossil shells and radiocarbon dated to 3910–3340 cal yr before present (BP), with a probability density cluster at ca. 3700–3500 cal yr BP. Sub-annual δ18O data were obtained from five fossil and three modern specimens and showed a strong seasonal signal in both time intervals. The growth season of G. glycymeris at this location today lasts from May to October, with most growth occurring before the temperature peak in August. Thus, the modern specimens and the fossil chronology represent late-spring and summer sea surface temperatures (SST). The annual temperature range was 4.4 °C in the fossil shells, which is similar to the range observed today (3.8 °C). Average SSTs reconstructed from the fossil shells were 1 °C cooler than in 2003–2013 CE and similar to the early 20th century CE. The radiocarbon age of the floating chronology coincides with a climatic shift to wetter conditions on the British Isles and with a cold interval observed in palaeoceanographic records from south of Iceland. However, our data do not provide evidence of a cold interval on the Scottish shelf. The similarity in growth season and temperature range between the fossil and modern specimens are attributed to similar boundary conditions in the fourth millennium BP compared to today

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Developing UAV monitoring of South Georgia and the South Sandwich Islands’ iconic land-based marine predators

Many remote islands present barriers to effective wildlife monitoring in terms of challenging terrain and frequency of visits. The sub-Antarctic islands of South Georgia and the South Sandwich Islands are home to globally significant populations of seabirds and marine mammals. South Georgia hosts the largest breeding populations of Antarctic fur seals, southern elephant seals and king penguins as well as significant populations of wandering, black-browed and grey-headed albatross. The island also holds important populations of macaroni and gentoo penguins. The South Sandwich Islands host the largest colony of chinstrap penguins in addition to major populations of Adélie and macaroni penguins. A marine protected area was created around the islands in 2012 but monitoring populations of marine predators remains a challenge, particularly as these species breed over large areas in remote and often inaccessible locations. During the 2019/20 austral summer, we trialled the use of an unoccupied aerial vehicle (UAV; drone) to monitor populations of seals, penguins and albatross and here we report on our initial findings, including considerations about the advantages and limitations of the methodology. Three extensive southern elephant seal breeding sites were surveyed with complete counts made around the peak pupping date, two of these sites were last surveyed 24 years ago. A total of nine islands historically recorded as breeding sites for wandering albatross were surveyed with 144 fledglings and 48 adults identified from the aerial imagery. The UAV was effective at surveying populations of penguins that nest on flat, open terrain, such as Adélie and chinstrap penguin colonies at the South Sandwich Islands, and an extensive king penguin colony on South Georgia, but proved ineffective for monitoring macaroni penguins nesting in tussock habitat on South Georgia as individuals were obscured or hidden by vegetation. Overall, we show that UAV surveys can allow regular and accurate monitoring of these important wildlife populations

Performance of the ATLAS Detector using First Collision Data

More than half a million minimum-bias events of LHC collision data were collected by the ATLAS experiment in December 2009 at centre-of-mass energies of 0.9 TeV and 2.36 TeV. This paper reports on studies of the initial performance of the ATLAS detector from these data. Comparisons between data and Monte Carlo predictions are shown for distributions of several track- and calorimeter-based quantities. The good performance of the ATLAS detector in these first data gives confidence for successful running at higher energies