Deep water methane hydrates in the Arctic Ocean: Reassessing the significance of a shallow BSR on the Lomonosov Ridge

Recently published multichannel seismic data from the Lomonosov Ridge image a reversed polarity bottom-simulating reflector (BSR) tentatively attributed to the presence of deepwater marine hydrates and recognized throughout a survey area exceeding 100,000 km2. In addition to the importance of these findings for estimating Arctic hydrate reserves, if shown to correspond to the base of the hydrate stability zone, this seismic marker could provide a means for expanding spatial cover of heat flow data in deepwater settings of the Amerasian Basin, where little is known about the tectonic origin and nature of plate boundaries. As an initial test on the validity of this assumption, we develop a petrophysical model using sediments collected from circumpolar regions of the Lomonosov Ridge to derive an estimate of surface heat flow patterns from the BSR. The results show that the BSR inferred geothermal gradient and surface heat flow are exceedingly high when compared to published regional measurements. Although potential errors in the analysis may explain some of this discrepancy, the observation that the BSR remains at a constant subbottom depth despite large variations in water depths (>2400 m) and relative sedimentation rates provides additional evidence that it cannot mark the base of the hydrate stability zone. A further understanding of its origin requires a more detailed investigation of the existing seismic data and highlights the need for renewed collection of heat flow data from the Arctic Ocean

High-throughput determination of Hubbard U and Hund J values for transition metal oxides via linear response formalism

DFT+U provides a convenient, cost-effective correction for the self-interaction error (SIE) that arises when describing correlated electronic states using conventional approximate density functional theory (DFT). The success of a DFT+U(+J) calculation hinges on the accurate determination of its Hubbard U and Hund's J parameters, and the linear response (LR) methodology has proven to be computationally effective and accurate for calculating these parameters. This study provides a high-throughput computational analysis of the U and J values for transition metal d-electron states in a representative set of over 2000 magnetic transition metal oxides (TMOs), providing a frame of reference for researchers who use DFT+U to study transition metal oxides. In order to perform this high-throughput study, an atomate workflow is developed for calculating U and J values automatically on massively parallel supercomputing architectures. To demonstrate an application of this workflow, the spin-canting magnetic structure and unit cell parameters of the multiferroic olivine LiNiPO4 are calculated using the computed Hubbard U and Hund J values for Ni-d and O-p states, and are compared with experiment. Both the Ni-d U and J corrections have a strong effect on the Ni-moment canting angle. Additionally, including a O-p U value results in a significantly improved agreement between the computed lattice parameters and experiment.Comment: 18 pages, 6 figure

Quaternary paleoceanography of the central arctic based on Integrated Ocean Drilling Program Arctic Coring Expedition 302 foraminiferal assemblages

The Integrated Ocean Drilling Program (IODP) Arctic Coring Expedition (ACEX) Hole 4C from the Lomonosov Ridge in the central Arctic Ocean recovered a continuous 18 m record of Quaternary foraminifera yielding evidence for seasonally ice-free interglacials during the Matuyama, progressive development of large glacials during the mid-Pleistocene transition (MPT) �1.2–0.9 Ma, and the onset of high-amplitude 100-ka orbital cycles �500 ka. Foraminiferal preservation in sediments from the Arctic is influenced by primary (sea ice, organic input, and other environmental conditions) and secondary factors (syndepositional, long-term pore water dissolution). Taking these into account, the ACEX 4C record shows distinct maxima in agglutinated foraminiferal abundance corresponding to several interglacials and deglacials between marine isotope stages (MIS) 13–37, and although less precise dating is available for older sediments, these trends appear to continue through the Matuyama. The MPT is characterized by nearly barren intervals during major glacials (MIS 12, 16, and 22–24) and faunal turnover (MIS 12–24). Abundant calcareous planktonic (mainly Neogloboquadrina pachyderma sin.) and benthic foraminifers occur mainly in interglacial intervals during the Brunhes and very rarely in the Matuyama. A distinct faunal transition from calcareous to agglutinated foraminifers 200–300 ka in ACEX 4C is comparable to that found in Arctic sediments from the Lomonosov, Alpha, and Northwind ridges and the Morris Jesup Rise. Down-core disappearance of calcareous taxa is probably related to either reduced sea ice cover prior to the last few 100-ka cycles, pore water dissolution, or both

Beauty is in the AI of the beholder: Are we ready for the clinical integration of Artificial Intelligence in radiography? An exploratory analysis of perceived AI knowledge, skills, confidence, and education perspectives of UK radiographers

The use of artificial intelligence (AI) in medical imaging and radiotherapy has been met with both scepticism and excitement. However, clinical integration of AI is already well-underway. Many authors have recently reported on the AI knowledge and perceptions of radiologists/medical staff and students however there is a paucity of information regarding radiographers. Published literature agrees that AI is likely to have significant impact on radiology practice. As radiographers are at the forefront of radiology service delivery, an awareness of the current level of their perceived knowledge, skills, and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice. The aim of this survey was to determine the perceived knowledge, skills, and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem. A survey was created on Qualtrics® and promoted via social media (Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students and retired radiographers. Participants were recruited by convenience, snowball sampling. Demographic information was gathered as well as data on the perceived, self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what the participants understand by the term "AI" was gained by means of a free text response. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®. Four hundred and eleven responses were collected (80% from diagnostic radiography and 20% from a radiotherapy background), broadly representative of the workforce distribution in the UK. Although many respondents stated that they understood the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography respondents, respectively) there was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills, and confidence in the use of AI technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%, respectively, stated that there is not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education (77.4% of diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate training in AI), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and gender, age, and highest qualification were reported. Knowledge of AI terminology, principles, and applications by healthcare practitioners is necessary for adoption and integration of AI applications. The results of this survey highlight the perceived lack of knowledge, skills, and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare, to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender, and highest qualification to ensure optimal integration. [Abstract copyright: Copyright © 2021 Rainey, O'Regan, Matthew, Skelton, Woznitza, Chu, Goodman, McConnell, Hughes, Bond, McFadden and Malamateniou.

Export of nutrient rich Northern Component Water preceded early Oligocene Antarctic glaciation

The onset of the North Atlantic Deep Water formation is thought to have coincided with Antarctic ice-sheet growth about 34 million years ago (Ma). However, this timing is debated, in part due to questions over the geochemical signature of the ancient Northern Component Water (NCW) formed in the deep North Atlantic. Here we present detailed geochemical records from North Atlantic sediment cores located close to sites of deep-water formation. We find that prior to 36 Ma, the northwestern Atlantic was stratified, with nutrient-rich, low-salinity bottom waters. This restricted basin transitioned into a conduit for NCW that began flowing southwards approximately one million years before the initial Antarctic glaciation. The probable trigger was tectonic adjustments in subarctic seas that enabled an increased exchange across the Greenland–Scotland Ridge. The increasing surface salinity and density strengthened the production of NCW. The late Eocene deep-water mass differed in its carbon isotopic signature from modern values as a result of the leakage of fossil carbon from the Arctic Ocean. Export of this nutrient-laden water provided a transient pulse of CO2 to the Earth system, which perhaps caused short-term warming, whereas the long-term effect of enhanced NCW formation was a greater northward heat transport that cooled Antarctica

Plio-Pleistocene trends in ice rafted debris on the Lomonosov Ridge

Although more than 700 sediment cores exist from the Arctic Ocean, the Plio-Pleistocene evolution of the basin and its marginal seas remains virtually unknown. This is largely due the shallow penetration of most of these records, and difficulties associated with deriving chronologies for the recovered material. The Integrated Ocean Drilling Program’s (IODP) Expedition 302 (Arctic Coring Expedition, ACEX) recovered 197 m of Neogene/Quaternary sediment from the circumpolar regions of the Lomonosov Ridge. As detailed analyses of this material emerge, research is beginning to formulate a long-term picture of paleoceanographic changes in the central Arctic Ocean. This paper reviews the ACEX Plio-Pleistocene age model, identifies uncertainties, and addresses ways in which these may be eliminated. Within the established stratigraphic framework, a notable reduction in the abundance of ice rafted debris (IRD) occurs in the early part of the Pleistocene and persists until Marine Isotope Stage 6 (MIS 6). Therefore, while global oceanographic proxies indicate the gradual growth of terrestrial ice-sheets during this time, IRD delivery to the central Arctic Ocean remained comparatively low and stable. Within the resolution of existing data, the Pleistocene reduction in IRD is synchronous with predicted changes in both the inflow of North Atlantic and Pacific waters, which in modern times are known to exert a strong influence on sea ice stability

A 26 million year gap in the central Arctic record at the greenhouse-icehouse transition: Looking for clues

The Cenozoic record of the Lomonosov Ridge (central Arctic Ocean) recovered during Integrated Ocean Drilling Program (IODP) Expedition 302 revealed an unexpected 26 Ma hiatus, separating middle Eocene (�44.4 Ma) from lower Miocene sediments (�18.2 Ma). To elucidate the nature of this unconformity, we performed a multiproxy palynological (dinoflagellate cysts, pollen, and spores), micropaleontological (siliceous microfossils), inorganic, and organic (Tetra Ether Index of lipids with 86 carbon atoms (TEX86) and Branched and Isoprenoid Tetraether (BIT)) geochemical analysis of the sediments from �5 m below to �7 m above the hiatus. Four main paleoenvironmental phases (A–D) are recognized in the sediments encompassing the unconformity, two below (A–B) and two above (C–D): (A) Below the hiatus, proxies show relatively warm temperatures, with Sea Surface Temperatures (TEX86-derived SSTs) of about 8�C and high fresh to brackish water influence. (B) Approaching the hiatus, proxies indicate a cooling trend (TEX86-derived SSTs of �5�C), increased freshwater influence, and progressive shoaling of the Lomonosov Ridge drilling site, located close to or at sea level. (C) The interval directly above the unconformity contains sparse reworked Cretaceous to Oligocene dinoflagellate cysts. Sediments were deposited in a relatively shallow, restricted marine environment. Proxies show the simultaneous influence of both fresh and marine waters, with alternating oxic and anoxic conditions. Pollen indicates a relatively cold climate. Intriguingly, TEX86-derived SSTs are unexpectedly high, �15–19�C. Such warm surface waters may be partially explained by the ingression of warmer North Atlantic waters after the opening of the Fram Strait during the early Miocene. (D) Sediments of the uppermost interval indicate a phase of extreme oxic conditions, and a well-ventilated environment, which occurred after the complete opening of the Fram Strait. Importantly, and in contrast with classical postrifting thermal subsidence models for passive margins, our data suggest that sediment erosion and/or nondeposition that generated the hiatus was likely due to a progressive shoaling of the Lomonosov Ridge. A shallow water setting both before and after the hiatus suggests that the Lomonosov Ridge remained at or near sea level for the duration of the gap in the sedimentary record. Interacting sea level changes and/or tectonic activity (possibly uplift) must be invoked as possible causes for such a long hiatus

An insight into the current perceptions of UK radiographers on the future impact of AI on the profession: A cross-sectional survey

INTRODUCTION: As a profession, radiographers have always been keen on adapting and integrating new technologies. The increasing integration of artificial intelligence (AI) into clinical practice in the last five years has been met with scepticism by some, who predict the demise of the profession, whilst others suggest a bright future with AI, full of opportunities and synergies. Post COVID-19 pandemic need for economic recovery and a backlog of medical imaging and reporting may accelerate the adoption of AI. It is therefore timely to appreciate practitioners' perceptions of AI used in clinical practice and their perception of the short-term impact on the profession. AIM: This study aims to explore the perceptions of AI in the UK radiography workforce and to investigate its current AI applications and future technological expectations of radiographers. METHODS: An online survey (QualtricsⓇ) was created by a team of radiography AI experts. The survey was disseminated via social media and professional networks in the UK. Demographic information and perceptions of the impact of AI on several aspects of the radiography profession were gathered, including the current use of AI in practice, future expectations and the perceived impact of AI on the profession. RESULTS: 411 responses were collected (80% diagnostic radiographers (DR); 20% therapeutic radiographers (TR)). Awareness of AI used in clinical practice is low, with DR respondents suggesting AI will have the most value/potential in cross sectional imaging and image reporting. TR responses linked AI as having most value in treatment planning, contouring, and image acquisition/matching. Respondents felt that AI will impact radiographers' daily work (DR, 79.6%; TR, 88.9%) by standardising some aspects of patient care and technical factors of radiography practice. A mixed response about impact on careers was reported. CONCLUSIONS: Respondents were unsure about the ways in which AI is currently used in practice and how AI will impact on careers in the future. It was felt that AI integration will lead to increased job opportunities to contribute to decision making as an end user. Job security was not identified as a cause for concern