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  • Authors: Borst, Anouk Margaretha; Finch, Adrian Anthony; Friis, Henrik; Horsburgh, Nicky; +12 Authors

    The purpose of this doi is to host raw data associated with our publications using X-ray Absorption Spectroscopy to probe the structural state of metals in mineral systems. It includes data from several publications, divided into subdirectories, as well as data collected which were not deemed suitable for publication.

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  • Authors: Blaszczyk, Oskar; Krishnan Jagadamma, Lethy; Ruseckas, Arvydas; Sajjad, Muhammad Tariq; +2 Authors
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Erovenko, Igor V.; Bauer, Johann; Broom, Mark; Pattni, Karan; +1 Authors

    We model a mobile population interacting over an underlying spatial structure using a Markov movement model. Interactions take the form of public goods games, and can feature an arbitrary group size. Individuals choose strategically to remain at their current location or to move to a neighbouring location, depending upon their exploration strategy and the current composition of their group. This builds upon previous work where the underlying structure was a complete graph (i.e. there was effectively no structure). Here, we consider alternative network structures and a wider variety of, mainly larger, populations. Previously, we had found when cooperation could evolve, depending upon the values of a range of population parameters. In our current work, we see that the complete graph considered before promotes stability, with populations of cooperators or defectors being relatively hard to replace. By contrast, the star graph promotes instability, and often neither type of population can resist replacement. We discuss potential reasons for this in terms of network topology.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Dataset . 2019
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Dataset . 2019
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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  • Authors: Sudkaemper, A;

    Study 8, raw data, anonymised, excel

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  • Authors: Santoro, Orlando; Elsegood, Mark R. J.; Bedwell, Elizabeth V.; Pryce, Jake A.; +1 Authors

    Related Article: Orlando Santoro, Mark R. J. Elsegood, Elizabeth V. Bedwell, Jake A. Pryce, Carl Redshaw|2020|Dalton Trans.|49|11978|doi:10.1039/D0DT02130J

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sampson, Dayle; Yager, Thomas D.; Fox, Brian; Shallcross, Laura; +13 Authors

    Abstract Background There is an urgent need to develop biomarkers that stratify risk of bacterial infection in order to support antimicrobial stewardship in emergency hospital admissions. Methods We used computational machine learning to derive a rule-out blood transcriptomic signature of bacterial infection (SeptiCyte™ TRIAGE) from eight published case-control studies. We then validated this signature by itself in independent case-control data from more than 1500 samples in total, and in combination with our previously published signature for viral infections (SeptiCyte™ VIRUS) using pooled data from a further 1088 samples. Finally, we tested the performance of these signatures in a prospective observational cohort of emergency department (ED) patients with fever, and we used the combined SeptiCyte™ signature in a mixture modelling approach to estimate the prevalence of bacterial and viral infections in febrile ED patients without microbiological diagnoses. Results The combination of SeptiCyte™ TRIAGE with our published signature for viral infections (SeptiCyte™ VIRUS) discriminated bacterial and viral infections in febrile ED patients, with a receiver operating characteristic area under the curve of 0.95 (95% confidence interval 0.90–1), compared to 0.79 (0.68–0.91) for WCC and 0.73 (0.61–0.86) for CRP. At pre-test probabilities 0.35 and 0.72, the combined SeptiCyte™ score achieved a negative predictive value for bacterial infection of 0.97 (0.90–0.99) and 0.86 (0.64–0.96), compared to 0.90 (0.80–0.94) and 0.66 (0.48–0.79) for WCC and 0.88 (0.69–0.95) and 0.60 (0.31–0.72) for CRP. In a mixture modelling approach, the combined SeptiCyte™ score estimated that 24% of febrile ED cases receiving antibacterials without a microbiological diagnosis were due to viral infections. Our analysis also suggested that a proportion of patients with bacterial infection recovered without antibacterials. Conclusions Blood transcriptional biomarkers offer exciting opportunities to support precision antibacterial prescribing in ED and improve diagnostic classification of patients without microbiologically confirmed infections.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
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    Collection . 2020
    License: CC BY
    Data sources: Datacite
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      Collection . 2020
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      Data sources: Datacite
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  • Authors: Arlegui, Leigh; Smallcombe, James W.; Fournet, Damien; Tolfrey, Keith; +1 Authors

    Supplementary Information Files for: Body mapping of sweating patterns of pre-pubertal children during intermittent exercise in a warm environmentPurposeTo determine sweating responses of pre-pubertal children during intermittent exercise in a warm environment and create whole-body maps of regional sweat rate (RSRs) distribution across the body.MethodsThirteen pre-pubertal children; six girls and seven boys (8.1 �� 0.8 years) took part. Sweat was collected using the technical absorbent method in the last 5 min of a 30-min intermittent exercise protocol performed at 30 ���, 40% relative humidity and 2 m��s���1 frontal wind.ResultsMean gross sweat loss (GSL) was 126 �� 47 g��m���2��h���1 and metabolic heat production was 278 �� 50 W��m2. The lower anterior torso area had the lowest RSR with a median (IQR) sweat rate (SR) of 40 (32) g��m���2��h���1. The highest was the forehead with a median SR of 255 (163) g��m���2��h���1. Normalised sweat maps (the ratio of each region���s SR to the mean SR for all measured pad regions) showed girls displayed lower ratio values at the anterior and posterior torso, and higher ratios at the hands, feet and forehead compared to boys. Absolute SRs were similar at hands and feet, but girls sweated less in most other areas, even after correction for metabolic rate.ConclusionPre-pubertal children have different RSRs across the body, also showing sex differences in sweat distribution. Distributions differ from adults. Hands and feet RSR remain stable, but SR across other body areas increase with maturation. These data can increase specificity of models of human thermoregulation, improve the measurement accuracy of child-sized thermal manikins, and aid companies during product design and communication

    Loughborough Data Re...arrow_drop_down
    Loughborough Data Repository
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
    Loughborough Data Repository
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      Loughborough Data Repository
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      Loughborough Data Repository
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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  • Authors: Michael Symonds;
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  • Authors: Gavin Kelsey;
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  • Authors: Manning, Janet R.; Lakshman Chelvarajan; Levitan, Bryana M.; Withers, Catherine N.; +8 Authors

    (A) Representative M-mode echocardiographic tracings of wild-type (WT) and Rad−/− mice 4 weeks after myocardial infarction (MI). (B,C) Ejection fraction and fractional shortening for sham and MI (circles and squares, respectively) WT (closed) and Rad−/− (open) mice. (D,E) Left ventricular interior dimension values for WT (closed) and Rad−/− (open) at diastole (LVIDd; D) and systole (LVIDs; E). (F) Left ventricular posterior wall thickness at diastole (LVPWd). (G) Left ventricular anterior wall thickness at diastole (LVAWd). N = 19 MI and 28 sham Rad−/− and 30 MI and 38 sham WT mice per group. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001 versus WT MI.

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  • Authors: Borst, Anouk Margaretha; Finch, Adrian Anthony; Friis, Henrik; Horsburgh, Nicky; +12 Authors

    The purpose of this doi is to host raw data associated with our publications using X-ray Absorption Spectroscopy to probe the structural state of metals in mineral systems. It includes data from several publications, divided into subdirectories, as well as data collected which were not deemed suitable for publication.

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  • Authors: Blaszczyk, Oskar; Krishnan Jagadamma, Lethy; Ruseckas, Arvydas; Sajjad, Muhammad Tariq; +2 Authors
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Erovenko, Igor V.; Bauer, Johann; Broom, Mark; Pattni, Karan; +1 Authors

    We model a mobile population interacting over an underlying spatial structure using a Markov movement model. Interactions take the form of public goods games, and can feature an arbitrary group size. Individuals choose strategically to remain at their current location or to move to a neighbouring location, depending upon their exploration strategy and the current composition of their group. This builds upon previous work where the underlying structure was a complete graph (i.e. there was effectively no structure). Here, we consider alternative network structures and a wider variety of, mainly larger, populations. Previously, we had found when cooperation could evolve, depending upon the values of a range of population parameters. In our current work, we see that the complete graph considered before promotes stability, with populations of cooperators or defectors being relatively hard to replace. By contrast, the star graph promotes instability, and often neither type of population can resist replacement. We discuss potential reasons for this in terms of network topology.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
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    Dataset . 2019
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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  • Authors: Sudkaemper, A;

    Study 8, raw data, anonymised, excel

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  • Authors: Santoro, Orlando; Elsegood, Mark R. J.; Bedwell, Elizabeth V.; Pryce, Jake A.; +1 Authors

    Related Article: Orlando Santoro, Mark R. J. Elsegood, Elizabeth V. Bedwell, Jake A. Pryce, Carl Redshaw|2020|Dalton Trans.|49|11978|doi:10.1039/D0DT02130J

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    Authors: Sampson, Dayle; Yager, Thomas D.; Fox, Brian; Shallcross, Laura; +13 Authors