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Wheat is a major crop worldwide, mainly cultivated for human consumption and animal feed. Grain quality is paramount in determining its value and downstream use. While we know that climate change threatens global crop yields, a better understanding of impacts on wheat end-use quality is also critical. Combining quantitative genetics with climate model outputs, we investigated UK-wide trends in genotypic adaptation for wheat quality traits. In our approach, we augmented genomic prediction models with environmental characterisation of field trials to predict trait values and climate effects in historical field trial data between 2001 and 2020. Addition of environmental covariates, such as temperature and rainfall, successfully enabled prediction of genotype by environment interactions (G×E), and increased prediction accuracy of most traits for new genotypes in new year cross validation. We then extended predictions from these models to much larger numbers of simulated environments using climate scenarios projected under Representative Concentration Pathways (RCP) 8.5 for 2050-2069. We found geographically varying climate change impacts on wheat quality due to contrasting associations between specific weather covariables and quality traits across the UK. Notably, negative impacts on quality traits were predicted in the East of the UK due to increased summer temperatures while the climate in the North and South-west may become more favourable with increased summer temperatures. Furthermore, by projecting 167,040 simulated future genotype - environment combinations, we found only limited potential for breeding to exploit predictable G×E to mitigate year-to-year environmental variability for most traits except Hagberg falling number. This suggests low adaptability of current UK wheat germplasm across future UK climates. More generally, approaches demonstrated here will be critical to enable adaptation of global crops to near-term climate change.

Transcriptomics is being increasingly applied to generate new insight into the interactions between plants and their pathogens. For the wheat yellow (stripe) rust pathogen (Puccinia striiformis f. sp. tritici, Pst) RNA-based sequencing (RNA-Seq) has proved particularly valuable, overcoming the barriers associated with its obligate biotrophic nature. This includes the application of RNA-Seq approaches to study Pst and wheat gene expression dynamics over time and the Pst population composition through the use of a novel RNA-Seq based surveillance approach called “field pathogenomics”. As a dual RNA-Seq approach, the field pathogenomics technique also provides gene expression data from the host, giving new insight into host responses. However, this has created a wealth of data for interrogation. Results: Here, we used the field pathogenomics approach to generate 538 new RNA-Seq datasets from Pst-infected field wheat samples, doubling the amount of transcriptomics data available for this important pathosystem. We then analysed these datasets alongside 66 RNA-Seq datasets from four Pst infection time-courses and 420 Pst-infected plant field and laboratory samples that were publicly available. A database of gene expression values for Pst and wheat was generated for each of these 1024 RNA-Seq datasets and incorporated into the development of the rust expression browser (http://www.rust-expression.com). This enables for the first time simultaneous ‘point-and-click’ access to gene expression profiles for Pst and its wheat host and represents the largest database of processed RNA-Seq datasets available for any of the three Puccinia wheat rust pathogens. We also demonstrated the utility of the browser through investigation of expression of putative Pst virulence genes over time and examined the host plants response to Pst infection. Conclusions: The rust expression browser offers immense value to the wider community, facilitating data sharing and transparency and the underlying database can be continually expanded as more datasets become publicly available. EEA Bordenave Fil: Adams, Thomas M.. John Innes Centre. Norwich Research Park; Reino Unido Fil: Olsson, Tjelvar S.G. John Innes Centre. Norwich Research Park; Reino Unido Fil: Ramírez-González, Ricardo H. John Innes Centre. Norwich Research Park; Reino Unido Fil: Bryant, Ruth. RAGT Seeds Ltd.; Reino Unido Fil: Bryson, Rosie. BASF SE. Agricultural Centre; Alemania Fil: Campos, Pablo Eduardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; Argentina Fil: Fenwick, Paul. Limagrain UK Ltd.; Reino Unido Fil: Feuerhelm, David. Syngenta Seeds Ltd.; Reino Unido Fil: Hayes, Charlotte. Elsoms Wheat Ltd.; Reino Unido Fil: Henriksson, Tina. Lantmännen Lantbruk; Suecia Fil: Hubbard, Amelia. NIAB; Reino Unido Fil: Jevtić, Radivoje. Institute of Field and Vegetable Crops; Serbia Fil: Judge, Christopher. NIAB; Reino Unido Fil: Kerton, Matthew. DSV United Kingdom Ltd.; Reino Unido Fil: Lage, Jacob. KWS UK Limited; Reino Unido Fil: Lewis, Clare M. John Innes Centre. Norwich Research Park; Reino Unido Fil: Lilly, Christine. Frontier Agriculture; Reino Unido Fil: Meidan, Udi. Hazera Seeds Ltd.; Israel Fil: Novoselović, Dario. Agricultural Institute Osijek; Croacia Fil: Patrick, Colin. Masstock Arable (UK) Ltd.; Reino Unido Fil: Wanyera, Ruth. Kenya Agricultural and Livestock Research Organization; Kenia Fil: Saunders, Diane G.O. John Innes Centre. Norwich Research Park; Reino Unido

Arrhythmias are a common problem in the critically ill and they can have significant effects on patient outcome. They often require immediate and swift action and it is, therefore, essential that clinicians have a structured approach to the recognition and management of arrhythmias. Here, we provide a framework for the appropriate management of the more frequently encountered cardiac arrhythmias in critical care. We include the algorithms from the 2015 Resuscitation Council Guidelines for reference.