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To manage the large spatial and temporal variability in N requires a combination of both predictive and intervening management strategies to achieve the best congruence between crop N demand and soil N supply at all growth stages. An integrated approach featuring predictive models combined with temporally dense, real-time information will be required to properly manage N site-specifically in order to maximize plant N use efficiency and minimize N losses into the environment. In this paper, we attempt to integrate the critical N concept by Greenwood et al. (1990) into an existing modelling framework for site-specific nutrient management in irrigated rice (Dobermann and White, 1999) that is based on the model QUEFTS (Janssen et al., 1990). QUEFTS allows the calculation of total fertilizer rates for targeted yields, but does not consider plant nutrient requirements during crop growth.
Sarah Marzi is an Edmond and Lily Safra Research Fellow and UK DRI Emerging Leader at Imperial College's Dementia Research Institute. She is interested in epigenetic regulation in neurodegenerative disease, focussing specifically on the molecular drivers of Alzheimer's and Parkinson's disease. The Marzi lab uses a combination of wet lab and computational genomics approaches to understand the regulatory consequences of environmental and genetic risk factors of neurodegenerative disease. Dr Marzi leads the Experimental Models Working Group of the DEMON network.
Dr Marzi graduated in mathematics and psychology from the University of Freiburg, Germany, before undertaking a PhD at King's College London with Jonathan Mill. Her work looked at epigenetic signatures in human brain associated with ageing and neurodegeneration, as well as DNA modifications associated with early-life stress and victimisation. She identified widespread differences in histone acetylation in Alzheimer's disease compared to neuropathology-free brain, linking these epigenetic marks with genetic risk burden and gene expression signatures.
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In summary, the evidence on the factors associated with the uptake of hospitals of quality improvement, the impact of quality improvement systems on patient-level outcomes, and the association of quality improvement systems with constructs such as professional involvement, patient empowerment and organizational culture, is limited. These links will be explored further in the DUQUE research project.
Descriptive statistics and graphical representations will be used to summarize the central tendencies, spread, frequencies or distributions of all relevant variables. Data reduction, classification techniques and psychometric methods will be employed to investigate the psychometric properties of questionnaires and other instruments used in measuring constructs relevant to the project such as organisational culture, professional involvement, and patient involvement. Before embarking on multivariate analyses, the project will conduct a series of bivariate analyses. As a prelude to the multivariate models, unadjusted regressions linking the relevant explanatory variables to their outcomes will also represent a type of bivariate analysis. The multivariate analyses will be conducted on two levels: hospital-level and multilevel. The hospital-level multivariate analysis specifies both explanatory variables and outcomes at the hospital-level. All analyses will adjust for relevant covariates as determined within the analytical framework, guided by background knowledge and theory [49]. The multilevel multivariate analysis refers to regression analysis of patient outcomes nested within hospitals, with adjustment for both patient-level and hospital-level covariates [50]. The multivariate regressions are by far the most important analyses needed for providing answers to the project's research objectives. The techniques of policy analysis [51, 52] will be applied to the fourth research objective--identifying external factors influencing uptake of quality improvement activities.
At the policy level, as summarised in the previous section, considerable advances have been made in assessing quality improvement systems in the last years. However, an important question remains: what is the effect of quality improvement strategies and which combination of strategies work best? The objective of this project follows this question and aims at expanding previous research, taking into account the contextual variables of hospitals and patients' pathways at departmental level. This research would then be able to give guidance on a comprehensive set of strategies demonstrated to be effective whilst being sensitive to the context/country of a hospital. Having the unique advantage of basing DUQUE on leading existing research and the most recent contributions to the field, the following advances to the existing research literature will be made. Firstly, several innovative research projects are underway internationally, in particular in the USA and in Australia. We will link to these leading initiatives and apply the latest methods to European hospitals. Secondly, we are collaborating with existing international efforts to develop and validate performance indicators on quality and safety in health care. Thirdly, the results of several previous EU-funded projects are at this stage conceptually and empirically outdated [14, 15, 11]. We will advance existing work both conceptually, by exploring links between external pressure, quality systems and patient-level outcomes, and by collecting up-to-date data from a large sample of European hospitals. Fourthly, the MARQuIS project contributed to the research agenda by exploring the impact of hospital quality improvement systems on the uptake of specific quality and patient safety initiatives at the departmental level. DUQuE will further enhance, refine and systematise the various components (quality strategies and quality improvement systems) and will establish the nature of the relationship with process and outcome measures at patient level. Fifthly, the project is of high relevance in the context of the recent proposal for an EU directive on the application of patients' rights to cross-border health care [67]. Given that it is impossible to predict which hospitals will provide cross-border care in the future, all hospitals need to make sure that the appropriate quality and safety mechanisms are in place [68, 69]. The DUQUE research project addresses this policy issue.
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Falloon, P., P. Smith, J.U. Smith, J. Szab, K. Coleman, and S. Marshall, 1998: Regional estimates of carbon sequestration potential: linking the Rothamsted carbon model to GIS databases. Biology and Fertility of Soils, 27, 236-241.
Fan, S., M. Gloor, J. Mahlman, S. Pacala, J. Sarmiento, T. Takahashi, and P. Tans, 1998: A large terrestrial carbon sink in North America implied by atmospheric and oceanic CO2 data and models. Science, 282, 442-446.
Heimann, M., G. Esser, J. Kaduk, D. Kicklighter, G. Kohlmaier, D. McGuire, B. Moore III, C. Prentice, W. Sauf, A. Schloss, U. Wittenberg, and G. Wurth, 1998: Evaluation of terrestrial carbon cycle models through simulations of the seasonal cycle of atmospheric CO2: First results of a model intercomaprison study. GlobalBiogeochemical Cycles, 12, 1-24. 2ff7e9595c
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