A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Disentangling the Relative Importance of Changes in Climate and Land-Use Intensity in Driving Recent Bird Population Trends

Threats to biodiversity resulting from habitat destruction and deterioration have been documented for many species, whilst climate change is regarded as increasingly impacting upon species' distribution and abundance. However, few studies have disentangled the relative importance of these two drivers in causing recent population declines. We quantify the relative importance of both processes by modelling annual variation in population growth of 18 farmland bird species in the UK as a function of measures of land-use intensity and weather. Modelled together, both had similar explanatory power in accounting for annual fluctuations in population growth. When these models were used to retrodict population trends for each species as a function of annual variation in land-use intensity and weather combined, and separately, retrodictions incorporating land-use intensity were more closely linked to observed population trends than retrodictions based only on weather, and closely matched the UK farmland bird index from 1970 onwards. Despite more stable land-use intensity in recent years, climate change (inferred from weather trends) has not overtaken land-use intensity as the dominant driver of bird populations

Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding

The CCP4 suite: integrative software for macromolecular crystallography

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world

National early warning score at Emergency Department triage may allow earlier identification of patients with severe sepsis and septic shock:a retrospective observational study

Introduction Severe sepsis and septic shock (SS) are time-critical medical emergencies that affect millions of people in the world. Earlier administration of antibiotics has been shown to reduce mortality from SS; however, the initiation of early resuscitation requires recognition that a patient may have sepsis. Early warning scores (EWS) are broadly used to detect patient deterioration, but to date have not been evaluated to detect patients at risk for SS. The purpose of our study was to look at the relationship between the initial national EWS (NEWS) in the emergency department (ED) and the diagnosis of SS.Methods We performed a retrospective, single-centre, observational study in the ED of an urban university hospital with an annual attendance of 140 000 patients. We aimed to include 500 consecutive non-trauma adult patients presenting to the ED with Manchester Triage System (MTS) category 1–3. The final diagnosis was taken from either the ED medical records or the hospital discharge summary. For all NEWS, the sensitivity and specificity to detect patients with SS was calculated.Results A total of 500 patients were included, 27 patients (5.4%) met the criteria for SS. The area under the curve (AUC) for NEWS to identify patient at risk for SS is 0.89 (95% CI 0.84 to 0.94). A NEWS of 3 or more at ED triage has a sensitivity of 92.6% (95% CI 74.2% to 98.7%) and a specificity of 77% (95% CI 72.8% to 80.6%) to detect patients at risk for SS at ED triage.Conclusions A NEWS of 3 or more at ED triage may be the trigger to systematically screen the patient for SS, which may ultimately lead to early recognition and treatment