ORCID Profile
0000-0002-2414-352X
Current Organisations
CSIRO
,
Oxford University Hospital NHS Foundation Trust
,
University of Oxford
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 09-2021
Publisher: Oxford University Press (OUP)
Date: 25-09-2020
DOI: 10.1002/BJS.12050
Publisher: Wiley
Date: 09-08-2022
DOI: 10.1002/AGJ2.21086
Abstract: Key management recommendations for cotton ( Gossypium hirsutum L.) management require estimates of the timing of crop phenology. Most commonly growing day degree (DD) (thermal time) approaches are used. Currently, across many cotton production regions, there is no consistent approach to predicting first square and flower timing. Day degree approaches vary considerably, with base thresholds different (12.0–15.6 °C) with no consistency using an optimum temperature threshold (i.e., temperature where development ceases to increase). As cotton is grown in variable and changing climates, and cultivars change, there is a need to ensure the accuracy of this approach for predicting timing of flowering for assisting cotton management. In this study new functions to predict first square and first flower were developed and validated using data collected in multiple seasons and regions (Australia and the United States). Earlier controlled environment studies that monitored crop development were used to assess in more detail how temperatures were affecting early cotton development. New DD functions developed predicted first square and first flower better than the existing Australian and U.S. approaches. The best performing functions had base temperatures like those of existing U.S. functions (15.6 °C) and an optimum threshold temperature of 32.0 °C. New universal DD targets for first square (343 DD [°C]) and first flower (584 DD) were developed. Controlled environment studies supported this base temperature outcome however, it was less clear that 32.0 °C was the optimum threshold temperature from these data. Precise predictions of cotton development will facilitate accurate growth stage assessments and hence better cotton management decisions.
Publisher: American Society of Clinical Oncology (ASCO)
Date: 2021
DOI: 10.1200/JCO.20.01933
Abstract: As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19–free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19–free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19–free surgical pathways. Patients who underwent surgery within COVID-19–free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19–free surgical pathways (2.2% v 4.9% adjusted odds ratio [aOR], 0.62 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score–matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19–free surgical pathways (2.1% v 3.6% aOR, 0.53 95% CI, 0.36 to 0.76). Within available resources, dedicated COVID-19–free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks.
Publisher: Elsevier BV
Date: 11-2021
Publisher: MDPI AG
Date: 30-05-2023
Abstract: Chickpea is the second-most-cultivated legume globally, with India and Australia being the two largest producers. In both of these locations, the crop is sown on residual summer soil moisture and left to grow on progressively depleting water content, finally maturing under terminal drought conditions. The metabolic profile of plants is commonly, correlatively associated with performance or stress responses, e.g., the accumulation of osmoprotective metabolites during cold stress. In animals and humans, metabolites are also prognostically used to predict the likelihood of an event (usually a disease) before it occurs, e.g., blood cholesterol and heart disease. We sought to discover metabolic biomarkers in chickpea that could be used to predict grain yield traits under terminal drought, from the leaf tissue of young, watered, healthy plants. The metabolic profile (GC-MS and enzyme assays) of field-grown chickpea leaves was analysed over two growing seasons, and then predictive modelling was applied to associate the most strongly correlated metabolites with the final seed number plant−1. Pinitol (negatively), sucrose (negatively) and GABA (positively) were significantly correlated with seed number in both years of study. The feature selection algorithm of the model selected a larger range of metabolites including carbohydrates, sugar alcohols and GABA. The correlation between the predicted seed number and actual seed number was R2 adj = 0.62, demonstrating that the metabolic profile could be used to predict a complex trait with a high degree of accuracy. A previously unknown association between D-pinitol and hundred-kernel weight was also discovered and may provide a single metabolic marker with which to predict large seeded chickpea varieties from new crosses. The use of metabolic biomarkers could be used by breeders to identify superior-performing genotypes before maturity is reached.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Matthew Byrne.