FORAMINIFERAL CHARACTERISATION OF MID-UPPER JURASSIC SEQUENCES IN THE WESSEX BASIN (UNITED KINGDOM)

The use of foraminifera in the characterisation of sequences (systems tracts, maximum flooding surfaces, etc.) has developed over the last decade. Much of this work has been based in the Cenozoic successions of the Gulf of Mexico, although there is a growing application of such data in the Middle East and the North Sea Basin. The easiest surface to characterise has been the maximum flooding surface with its high diversity and high(er) abundance faunas; the characterisation of individual systems tracts has been less successful. Using the well-known mid-Upper Jurassic successions of the Dorset coastal sections, we have investigated a number of high resolution (para)sequences for their foraminiferal content. Using data of foraminiferal diversity and standing crops from a range of modern substrates we have investigated the potential faunas available after deposition, taphonomy, compaction, groundwater dissolution and modern weathering. By understanding the processes involved we have identified the key foraminiferal features of typical mid-Upper Jurassic sequences and indicated how this work may help in the correlation of successions in North Dorset and Normandy

Determining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systems

A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the "high hazardous" category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity.

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19

Effect of remote ischaemic conditioning on clinical outcomes in patients with acute myocardial infarction (CONDI-2/ERIC-PPCI): a single-blind randomised controlled trial.

BACKGROUND: Remote ischaemic conditioning with transient ischaemia and reperfusion applied to the arm has been shown to reduce myocardial infarct size in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). We investigated whether remote ischaemic conditioning could reduce the incidence of cardiac death and hospitalisation for heart failure at 12 months. METHODS: We did an international investigator-initiated, prospective, single-blind, randomised controlled trial (CONDI-2/ERIC-PPCI) at 33 centres across the UK, Denmark, Spain, and Serbia. Patients (age >18 years) with suspected STEMI and who were eligible for PPCI were randomly allocated (1:1, stratified by centre with a permuted block method) to receive standard treatment (including a sham simulated remote ischaemic conditioning intervention at UK sites only) or remote ischaemic conditioning treatment (intermittent ischaemia and reperfusion applied to the arm through four cycles of 5-min inflation and 5-min deflation of an automated cuff device) before PPCI. Investigators responsible for data collection and outcome assessment were masked to treatment allocation. The primary combined endpoint was cardiac death or hospitalisation for heart failure at 12 months in the intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT02342522) and is completed. FINDINGS: Between Nov 6, 2013, and March 31, 2018, 5401 patients were randomly allocated to either the control group (n=2701) or the remote ischaemic conditioning group (n=2700). After exclusion of patients upon hospital arrival or loss to follow-up, 2569 patients in the control group and 2546 in the intervention group were included in the intention-to-treat analysis. At 12 months post-PPCI, the Kaplan-Meier-estimated frequencies of cardiac death or hospitalisation for heart failure (the primary endpoint) were 220 (8·6%) patients in the control group and 239 (9·4%) in the remote ischaemic conditioning group (hazard ratio 1·10 [95% CI 0·91-1·32], p=0·32 for intervention versus control). No important unexpected adverse events or side effects of remote ischaemic conditioning were observed. INTERPRETATION: Remote ischaemic conditioning does not improve clinical outcomes (cardiac death or hospitalisation for heart failure) at 12 months in patients with STEMI undergoing PPCI. FUNDING: British Heart Foundation, University College London Hospitals/University College London Biomedical Research Centre, Danish Innovation Foundation, Novo Nordisk Foundation, TrygFonden

The Cretaceous-Palaeogene boundary succession at Stevns Klint, Denmark : foraminifers and stable isotope stratigraphy

The Cretaceous–Palaeogene boundary exposed at Stevns Klint (Denmark) provides an important location for the investigation of the end-Cretaceous event. In this work we have used a number of sections across the boundary, including the little described, expanded stratigraphic sequence at Kulstirenden which is located 7 km north of Højerup (where most published work is derived). Using stratigraphical, micropalaeontological and stable isotope (δ18O and δ13C) data we have shown that there is clear evidence of shallowing in the latest Maastrichtian. Prior to, and across, the Cretaceous–Palaeogene boundary there are significant changes in sea water temperature, including an important cooling event in the very latest Maastrichtian. The δ13C record in the expanded boundary clay (Fish Clay) sequence at Kulstirenden indicates a number of significant excursions of decreasing magnitude up-section. The evidence from the distribution of the foraminifers has been incorporated with previous work to develop a sequence stratigraphical interpretation for the succession that can be compared to other Cretaceous–Palaeogene boundary successions in Europe

Appendix A. Selecting Bayesian network structure: number of states and threshold values.

Selecting Bayesian network structure: number of states and threshold values