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  1. Article ; Online: Plant Responses to Changing Water Supply and Availability in High Elevation Ecosystems

    Emma Sumner / Susanna Venn

    Land, Vol 10, Iss 1150, p

    A Quantitative Systematic Review and Meta-Analysis

    2021  Volume 1150

    Abstract: Climate change is expected to lead to changes to the amount, frequency, intensity, and timing of precipitation and subsequent water supply and its availability to plants in mountain regions worldwide. This is likely to affect plant growth and ... ...

    Abstract Climate change is expected to lead to changes to the amount, frequency, intensity, and timing of precipitation and subsequent water supply and its availability to plants in mountain regions worldwide. This is likely to affect plant growth and physiological performance, with subsequent effects to the functioning of many important high-elevation ecosystems. We conducted a quantitative systematic review and meta-analysis of the effects of altered water supply on plants from high elevation ecosystems. We found a clear negative response of plants to decreases in water supply (mean Hedges’ g = −0.75, 95% confidence intervals: −1.09 to −0.41), and a neutral response to increases in water supply (mean Hedges’ g = 0.10, 95% confidence intervals: 0.43 to 0.62). Responses to decreases in water supply appear to be related to the magnitude of change in water supply, plant growth form, and to the measured response attribute. Changes to precipitation and water supply are likely to have important consequences for plant growth in high elevation ecosystems, with vegetation change more likely be triggered by reductions than increases in growing season precipitation. High elevation ecosystems that experience future reductions in growing-season precipitation are likely to exhibit plant responses such as reduced growth and higher allocation of carbohydrates to roots.
    Keywords alpine ; mountains ; climate change ; experimental manipulations ; PRISMA ; precipitation ; Agriculture ; S
    Subject code 580
    Language English
    Publishing date 2021-10-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Evaluation of the accuracy of the IDvet serological test for Mycoplasma bovis infection in cattle using latent class analysis of paired serum ELISA and quantitative real-time PCR on tonsillar swabs sampled at slaughter.

    Nelly Marquetoux / Matthieu Vignes / Amy Burroughs / Emma Sumner / Kate Sawford / Geoff Jones

    PLoS ONE, Vol 18, Iss 5, p e

    2023  Volume 0285598

    Abstract: Mycoplasma bovis (Mbovis) was first detected in cattle in New Zealand (NZ) in July 2017. To prevent further spread, NZ launched a world-first National Eradication Programme in May 2018. Existing diagnostic tests for Mbovis have been applied in countries ... ...

    Abstract Mycoplasma bovis (Mbovis) was first detected in cattle in New Zealand (NZ) in July 2017. To prevent further spread, NZ launched a world-first National Eradication Programme in May 2018. Existing diagnostic tests for Mbovis have been applied in countries where Mbovis is endemic, for detecting infection following outbreaks of clinical disease. Diagnostic test evaluation (DTE) under NZ conditions was thus required to inform the Programme. We used Bayesian Latent Class Analysis on paired serum ELISA (ID Screen Mycoplasma bovis Indirect from IDvet) and tonsillar swabs (qPCR) for DTE in the absence of a gold standard. Tested samples were collected at slaughter between June 2018 and November 2019, from infected herds depopulated by the Programme. A first set of models evaluated the detection of active infection, i.e. the presence of Mbovis in the host. At a modified serology positivity threshold of SP%> = 90, estimates of animal-level ELISA sensitivity was 72.8% (95% credible interval 68.5%-77.4%), respectively 97.7% (95% credible interval 97.3%-98.1%) for specificity, while the qPCR sensitivity was 45.2% (95% credible interval 41.0%-49.8%), respectively 99.6% (95% credible interval 99.4%-99.8%) for specificity. In a second set of models, prior information about ELISA specificity was obtained from the National Beef Cattle Surveillance Programme, a population theoretically free-or very low prevalence-of Mbovis. These analyses aimed to evaluate the accuracy of the ELISA test targeting prior exposure to Mbovis, rather than active infection. The specificity of the ELISA for detecting exposure to Mbovis was 99.9% (95% credible interval 99.7%-100.0%), hence near perfect at the threshold SP%=90. This specificity estimate, considerably higher than in the first set of models, was equivalent to the manufacturer's estimate. The corresponding ELISA sensitivity estimate was 66.0% (95% credible interval 62.7%-70.7%). These results confirm that the IDvet ELISA test is an appropriate tool for determining exposure and infection ...
    Keywords Medicine ; R ; Science ; Q
    Subject code 630
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Towards stratified treatment of JIA

    Stephanie J.W. Shoop-Worrall / Saskia Lawson-Tovey / Lucy R. Wedderburn / Kimme L. Hyrich / Nophar Geifman / Aline Kimonyo / Alyssia McNeece / Andrew Dick / Andrew Morris / Annie Yarwood / Athimalaipet Ramanan / Bethany R. Jebson / Chris Wallace / Daniela Dastros-Pitei / Damian Tarasek / Elizabeth Ralph / Emil Carlsson / Emily Robinson / Emma Sumner /
    Fatema Merali / Fatjon Dekaj / Helen Neale / Hussein Al-Mossawi / Jacqui Roberts / Jenna F. Gritzfeld / Joanna Fairlie / John Bowes / John Ioannou / Melissa Kartawinata / Melissa Tordoff / Michael Barnes / Michael W. Beresford / Michael Stadler / Paul Martin / Rami Kallala / Sandra Ng / Samantha Smith / Sarah Clarke / Soumya Raychaudhuri / Stephen Eyre / Sumanta Mukherjee / Teresa Duerr / Thierry Sornasse / Vasiliki Alexiou / Victoria J. Burton / Wei-Yu Lin / Wendy Thomson / Zoe Wanstall

    EBioMedicine, Vol 100, Iss , Pp 104946- (2024)

    machine learning identifies subtypes in response to methotrexate from four UK cohortsResearch in context

    2024  

    Abstract: Summary: Background: Methotrexate (MTX) is the gold-standard first-line disease-modifying anti-rheumatic drug for juvenile idiopathic arthritis (JIA), despite only being either effective or tolerated in half of children and young people (CYP). To ... ...

    Abstract Summary: Background: Methotrexate (MTX) is the gold-standard first-line disease-modifying anti-rheumatic drug for juvenile idiopathic arthritis (JIA), despite only being either effective or tolerated in half of children and young people (CYP). To facilitate stratified treatment of early JIA, novel methods in machine learning were used to i) identify clusters with distinct disease patterns following MTX initiation; ii) predict cluster membership; and iii) compare clusters to existing treatment response measures. Methods: Discovery and verification cohorts included CYP who first initiated MTX before January 2018 in one of four UK multicentre prospective cohorts of JIA within the CLUSTER consortium. JADAS components (active joint count, physician (PGA) and parental (PGE) global assessments, ESR) were recorded at MTX start and over the following year.Clusters of MTX ‘response’ were uncovered using multivariate group-based trajectory modelling separately in discovery and verification cohorts. Clusters were compared descriptively to ACR Pedi 30/90 scores, and multivariate logistic regression models predicted cluster-group assignment. Findings: The discovery cohorts included 657 CYP and verification cohorts 1241 CYP. Six clusters were identified: Fast improvers (11%), Slow Improvers (16%), Improve-Relapse (7%), Persistent Disease (44%), Persistent PGA (8%) and Persistent PGE (13%), the latter two characterised by improvement in all features except one. Factors associated with clusters included ethnicity, ILAR category, age, PGE, and ESR scores at MTX start, with predictive model area under the curve values of 0.65–0.71. Singular ACR Pedi 30/90 scores at 6 and 12 months could not capture speeds of improvement, relapsing courses or diverging disease patterns. Interpretation: Six distinct patterns following initiation of MTX have been identified using methods in artificial intelligence. These clusters demonstrate the limitations in traditional yes/no treatment response assessment (e.g., ACRPedi30) and can form the basis ...
    Keywords Juvenile idiopathic arthritis ; Machine learning ; Treatment outcome ; Epidemiology ; Methotrexate ; Medicine ; R ; Medicine (General) ; R5-920
    Subject code 310
    Language English
    Publishing date 2024-02-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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