Report from the Annual Conference of the British Society of Echocardiography, November 2016, Queen Elizabeth II Conference Centre, London.

Peer reviewedPublisher PD

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Loss of coral reef growth capacity to track future increases in sea level

Water-depths above coral reefs is predicted to increase due to global sea-level rise (SLR). As ecological degradation inhibits the vertical accretion of coral reefs, it is likely that coastal wave exposure will increase but there currently exists a lack of data in projections concerning local rates of reef growth and local SLR. In this study we have aggregated ecological data of more than 200 tropical western Atlantic and Indian Ocean reefs and calculated their vertical growth which we have then compared with recent and projected rates of SLR across different Representative Concentration Pathway (RCP) scenarios. While many reefs currently show vertical growth that would be sufficient to keep-up with recent historic SLR, future projections under scenario RCP4.5 reveal that without substantial ecological recovery many reefs will not have the capacity to track SLR. Under RCP8.5, we predict that mean water depth will increase by over half a metre by 2100 across the majority of reefs. We found that coral cover strongly predicted whether a reef could track SLR, but that the majority of reefs had coral cover significantly lower than that required to prevent reef submergence. To limit reef submergence, and thus the impacts of waves and storms on adjacent coasts, climate mitigation and local impacts that reduce coral cover (e.g., local pollution and physical damage through development land reclamation) will be necessary

Regional-scale dominance of non-framework building corals on Caribbean reefs affects carbonate production and future reef growth

publication-status: Publishedtypes: ArticleThis is the author's post-print version of an article published in Global Change Biology, Vol. 21, issue 3 pp. 1153 – 1164 Copyright © Wiley-Blackwell 2015. The definitive version is available at www3.interscience.wiley.comCoral cover on Caribbean reefs has declined rapidly since the early 1980's. Diseases have been a major driver, decimating communities of framework building Acropora and Orbicella coral species, and reportedly leading to the emergence of novel coral assemblages often dominated by domed and plating species of the genera Agaricia, Porites and Siderastrea. These corals were not historically important Caribbean framework builders, and typically have much smaller stature and lower calcification rates, fuelling concerns over reef carbonate production and growth potential. Using data from 75 reefs from across the Caribbean we quantify: (i) the magnitude of non-framework building coral dominance throughout the region and (ii) the contribution of these corals to contemporary carbonate production. Our data show that live coral cover averages 18.2% across our sites and coral carbonate production 4.1 kg CaCO3 m−2 yr−1. However, non-framework building coral species dominate and are major carbonate producers at a high proportion of sites; they are more abundant than Acropora and Orbicella at 73% of sites; contribute an average 68% of the carbonate produced; and produce more than half the carbonate at 79% of sites. Coral cover and carbonate production rate are strongly correlated but, as relative abundance of non-framework building corals increases, average carbonate production rates decline. Consequently, the use of coral cover as a predictor of carbonate budget status, without species level production rate data, needs to be treated with caution. Our findings provide compelling evidence for the Caribbean-wide dominance of non-framework building coral taxa, and that these species are now major regional carbonate producers. However, because these species typically have lower calcification rates, continued transitions to states dominated by non-framework building coral species will further reduce carbonate production rates below ‘predecline’ levels, resulting in shifts towards negative carbonate budget states and reducing reef growth potential.Trust International Research NetworkNational Geographic Society Researc

Citation: Epigenetic landscape correlates with genetic subtype but does not predict outcome in childhood acute lymphoblastic leukemia

Epigenetic landscape correlates with genetic subtype but does not predict outcome in childhood acute lymphoblastic leukemia. Epigenetics, 10 (8 Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Although children with acute lymphoblastic leukemia (ALL) generally have a good outcome, some patients do relapse and survival following relapse is poor. Altered DNA methylation is highly prevalent in ALL and raises the possibility that DNA methylation-based biomarkers could predict patient outcome. In this study, genome-wide methylation analysis, using the Illumina Infinium HumanMethylation450 BeadChip platform, was carried out on 52 diagnostic patient samples from 4 genetic subtypes [ETV6-RUNX1, high hyperdiploidy (HeH), TCF3-PBX1 and dic(9;20) (p11-13;q11)] in a 1:1 case-control design with patients who went on to relapse (as cases) and patients achieving longterm remission (as controls). Pyrosequencing assays for selected loci were used to confirm the array-generated data. Non-negative matrix factorization consensus clustering readily clustered samples according to genetic subgroups and gene enrichment pathway analysis suggested that this is in part driven by epigenetic disruption of subtype specific signaling pathways. Multiple bioinformatics approaches (including bump hunting and individual locus analysis) were used to identify CpG sites or regions associated with outcome. However, no associations with relapse were identified. Our data revealed that ETV6-RUNX1 and dic(9;20) subtypes were mostly associated with hypermethylation; conversely, TCF3-PBX1 and HeH were associated with hypomethylation. We observed significant enrichment of the neuroactive ligand-receptor interaction pathway in TCF3-PBX1 as well as an enrichment of genes involved in immunity and infection pathways in ETV6-RUNX1 subtype. Taken together, our results suggest that altered DNA methylation may have differential impacts in distinct ALL genetic subtypes

Epigenetic landscape correlates with genetic subtype but does not predict outcome in childhood acute lymphoblastic leukemia.

Although children with acute lymphoblastic leukemia (ALL) generally have a good outcome, some patients do relapse and survival following relapse is poor. Altered DNA methylation is highly prevalent in ALL and raises the possibility that DNA methylation-based biomarkers could predict patient outcome. In this study, genome-wide methylation analysis, using the Illumina Infinium HumanMethylation450 BeadChip platform, was carried out on 52 diagnostic patient samples from 4 genetic subtypes [ETV6-RUNX1, high hyperdiploidy (HeH), TCF3-PBX1 and dic(9;20)(p11–13;q11)] in a 1:1 case-control design with patients who went on to relapse (as cases) and patients achieving long-term remission (as controls). Pyrosequencing assays for selected loci were used to confirm the array-generated data. Non-negative matrix factorization consensus clustering readily clustered samples according to genetic subgroups and gene enrichment pathway analysis suggested that this is in part driven by epigenetic disruption of subtype specific signaling pathways. Multiple bioinformatics approaches (including bump hunting and individual locus analysis) were used to identify CpG sites or regions associated with outcome. However, no associations with relapse were identified. Our data revealed that ETV6-RUNX1 and dic(9;20) subtypes were mostly associated with hypermethylation; conversely, TCF3-PBX1 and HeH were associated with hypomethylation. We observed significant enrichment of the neuroactive ligand-receptor interaction pathway in TCF3-PBX1 as well as an enrichment of genes involved in immunity and infection pathways in ETV6-RUNX1 subtype. Taken together, our results suggest that altered DNA methylation may have differential impacts in distinct ALL genetic subtypes