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As part of the design of future coupled forecasting systems, operational centres such as the Met Office are starting to include interactions between the atmosphere and the ocean within the data assimilation system. This requires an improved understanding and representation of the correlations between short-range forecast errors in different variables. To understand the potential benefit of further coupling in the data assimilation scheme it is important to understand the significance of any cross-correlations between atmosphere and ocean short-range forecast errors as well as their temporal and spatial variability. In this work we examine atmosphere-ocean cross-covariances from an ensemble of the Met Office coupled NWP system for December 2019, with particular focus on short-range forecast errors that evolve at lead times up to 6 hours.We find that significant correlations exist between atmosphere and ocean forecast errors on these timescales, and that these vary diurnally, from day to day, spatially and synoptically. Negative correlations between errors in sea-surface temperature (SST) and 10m wind correlations strengthen as the solar radiation varies from zero at night (local time) to a maximum insolation around midday (local time). In addition, there are significant variations in correlation intensities and structures in response to synoptic-timescale forcing. Significant positive correlations between SST and 10m wind errors appear in the western North Atlantic in early December and are associated with variations in low surface pressures and their associated high wind speeds, that advect cold, dry continental air eastward over the warmer Atlantic ocean. Negative correlations across the Indo-Pacific Warm Pool are instead associated with light wind conditions on these short timescales.When we consider the spatial extent of cross-correlations, we find that in the Gulf Stream region positive correlations between wind speed and sub-surface ocean temperatures are generally vertically coherent down to a depth of about 100m, consistent with the mixing depth; however, in the tropical Indian and West Pacific oceans, negative correlations break down just below the surface layer. This is likely due to the presence of surface freshwater layers that form from heavy precipitation on the tropical oceans, manifested by the presence of salinity-stratified barrier layers within deeper isothermal layers that can effectively limit turbulent mixing of heat between the ocean surface and the deeper thermocline.

BACKGROUND COVID-19 data have been generated across the UK as a by-product of clinical care and public health provision, and numerous bespoke and repurposed research endeavours. Analysis of these data has underpinned the UK’s response to the pandemic and informed public health policies and clinical guidelines. However, these data are held by different organisations and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find, navigate permissions to access and interrogate the data they need to inform the pandemic response at pace. OBJECTIVE To transform UK COVID-19 diagnostic datasets to be Findable, Accessible, Interoperable and Reusable (FAIR). METHODS A federated infrastructure model was rapidly built to enable the automated and reproducible mapping of health Data Partners’ pseudonymised data to the OMOP common data model without the need for any data to leave the data controllers’ secure environments and to support federated cohort discovery queries and meta-analysis. RESULTS 56 datasets from 19 organisations are being connected to the federated network. The data includes research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal healthcare records and demographics. The infrastructure is live, supporting aggregate level querying of data across the UK. CONCLUSIONS CO-CONNECT was developed by a multidisciplinary team enabling rapid COVID-19 data discovery, instantaneous meta-analysis across data sources, and is researching streamlined data extraction for egress into a Trusted Research Environment (TRE) for research and public health analysis. CO-CONNECT has the potential to make UK health data more interconnected and better able to answer national-level research questions whilst maintaining patient confidentiality and local governance procedures. CLINICALTRIAL

Hypertrophic cardiomyopathy (HCM) is characterised by increased left ventricular wall thickness that can lead to devastating conditions such as heart failure and sudden cardiac death. Despite extensive study, the mechanisms mediating many of the associated clinical manifestations remain unknown and human models are required. To address this, human induced pluripotent stem cell (hiPSC) lines were generated from patients with a HCM associated mutation (c.ACTC1G301A) and isogenic controls created by correcting the mutation using CRISPR/Cas9 gene editing technology. Cardiomyocytes (hiPSC-CMs) were differentiated from these hiPSCs and analysed at baseline, and at increased contractile workload (2 Hz electrical stimulation). Released extracellular vesicles (EVs) were isolated and characterised following a 24 hour culture period and transcriptomic analysis performed on both hiPSC-CMs and released EVs. Transcriptomic analysis of cellular mRNA showed the HCM mutation caused differential splicing within known HCM pathways, and disrupted metabolic pathways. Analysis at increasing contraction frequency showed further disruption of metabolic gene expression, with an additive effect in the HCM background. Intriguingly, we observed differences in snoRNA cargo within HCM released EVs, that specifically altered when HCM hiPSC-CMs were subjected to increased workload. These snoRNAs were predicted to have roles in post translational modifications and alternative splicing, processes differentially regulated in HCM. As such, the snoRNAs identified in this study may unveil mechanistic insight into unexplained HCM phenotypes and offer potential future use as HCM biomarkers or as targets in future RNA-targeting therapies.

Ein zentrales Ergebnis der Studie ���100 Radical innovation breakthroughs for the future��� (RIBRI) (2019) ist die Annahme, dass zahlreiche Innovationen im Bereich ���K��nstlicher Intelligenz��� starke Auswirkungen auf die Weltwirtschaft und Gesellschaft haben werden. Diese Arbeit umfasst eine qualitative Untersuchung, bei der einer der Datens��tze der RIBRI-Studie auf denk- und handlungsleitende, geteilte Orientierungsmuster hinsichtlich einer Zukunft mit ���Ku��nstlicher Intelligenz��� untersucht wird. Der f��r die Analyse genutzte Leitbildansatz der Forschungsgruppe Umweltbildung fand aufgrund zahlreicher Anforderungen an die ben��tigte Datengrundlage bisher nur bedingt Anwendung. In der Arbeit wird eine Modifizierung vorgeschlagen, die eine transparente, nachvollziehbare Erstellung von Leitbildern fu��r Datengrundlagen erm��glicht, die keine Faktoren- und Clusteranalysen hergeben. Diese Arbeit versteht sich daher nicht nur als Analyse von vorliegenden ���KI-Leitbildern��� im oben genannten Projekt ���Radical Innovation Breakthrough Inquirer���, sondern vor allem als Beitrag zur Weiterentwicklung der Methoden der Zukunftsforschung.

Streptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate that the combination of supervised machine learning and matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) mass spectrometry can discriminate strains of S. uberis causing clinical mastitis that are likely to be responsive or unresponsive to treatment. Diagnostics prediction systems trained on 90 individuals from 26 different farms achieved up to 86.2% and 71.5% in terms of accuracy and Cohen’s kappa. The performance was further increased by adding metadata (parity, somatic cell count of previous lactation and count of positive mastitis cases) to encoded MALDI-TOF spectra, which increased accuracy and Cohen’s kappa to 92.2% and 84.1% respectively. A computational framework integrating protein–protein networks and structural protein information to the machine learning results unveiled the molecular determinants underlying the responsive and unresponsive phenotypes.

Iris neural stem/progenitor cells from mature porcine eyes were investigated using a new protocol for tissue culture, which consists of dispase treatment and Matrigel embedding. We used a number of culture conditions and found an intense differentiation of neuronal cells from both the iris pigmented epithelial (IPE) cells and the stroma tissue cells. Rod photoreceptor-like cells were also observed but mostly in a later stage of culture. Neuronal differentiation does not require any additives such as fetal bovine serum or FGF2, although FGF2 and IGF2 appeared to promote neural differentiation in the IPE cultures. Furthermore, the stroma-derived cells were able to be maintained in vitro indefinitely. The evolutionary similarity between humans and domestic pigs highlight the potential for this methodology in the modeling of human diseases and characterizing human ocular stem cells.