784 research outputs found

    The formation of homogentisate in the biosynthesis of tocopherol and plastoquinone in spinach chloroplasts

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    Homogentisate is the precursor in the biosynthesis of -tocopherol and plastoquinone-9 in chloroplasts. It is formed of 4-hydroxyphenylpyruvate of the shikimate pathway by the 4-hydroxyphenylpyruvate dioxygenase. In experiments with spinach the dioxygenase was shown to be localized predominatedly in the chloroplasts. Envelope membranes exhibit the highest specific activity, however, because of the high stromal portion of chloroplasts, 60–80% of the total activity is housed in the stroma. The incorporation of 4-hydroxyphenylpyruvate into 2-methyl-6-phytylquinol as the first intermediate in the tocopherol synthesis by the two-step-reaction: 4-Hydroxyphenylpyruvate Homogentisate 2-Methyl-6-phytylquinol was demonstrated by using envelope membranes. Homogentisate originates directly from 4-hydroxyphenylpyruvate of the shikimate pathway. Additionally, a bypass exists in chloroplasts which forms 4-hydroxyphenylpyruvate from tyrosine by an L-amino-acid oxidase of the thylakoids and in peroxisomes by a transaminase reaction. Former results about the dioxygenase in peroxisomes were verified

    The impact of measurement error in modelled ambient particles exposures on health effect estimates in multi-level analysis: a simulation study.

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    Background: Various spatiotemporal models have been proposed for predicting ambient particulate exposure for inclusion in epidemiological analyses. We investigated the effect of measurement error in the prediction of particulate matter with diameter <10 µm (PM10) and <2.5 µm (PM2.5) concentrations on the estimation of health effects. Methods: We sampled 1,000 small administrative areas in London, United Kingdom, and simulated the “true” underlying daily exposure surfaces for PM10 and PM2.5 for 2009–2013 incorporating temporal variation and spatial covariance informed by the extensive London monitoring network. We added measurement error assessed by comparing measurements at fixed sites and predictions from spatiotemporal land-use regression (LUR) models; dispersion models; models using satellite data and applying machine learning algorithms; and combinations of these methods through generalized additive models. Two health outcomes were simulated to assess whether the bias varies with the effect size. We applied multilevel Poisson regression to simultaneously model the effect of long- and short-term pollutant exposure. For each scenario, we ran 1,000 simulations to assess measurement error impact on health effect estimation. Results: For long-term exposure to particles, we observed bias toward the null, except for traffic PM2.5 for which only LUR underestimated the effect. For short-term exposure, results were variable between exposure models and bias ranged from −11% (underestimate) to 20% (overestimate) for PM10 and of −20% to 17% for PM2.5. Integration of models performed best in almost all cases. Conclusions: No single exposure model performed optimally across scenarios. In most cases, measurement error resulted in attenuation of the effect estimate

    Long-term exposure to traffic pollution and hospital admissions in London.

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    Evidence on the effects of long-term exposure to traffic pollution on health is inconsistent. In Greater London we examined associations between traffic pollution and emergency hospital admissions for cardio-respiratory diseases by applying linear and piecewise linear Poisson regression models in a small-area analysis. For both models the results for children and adults were close to unity. In the elderly, linear models found negative associations whereas piecewise models found non-linear associations characterized by positive risks in the lowest and negative risks in the highest exposure category. An increased risk was observed among those living in areas with the highest socioeconomic deprivation. Estimates were not affected by adjustment for traffic noise. The lack of convincing positive linear associations between primary traffic pollution and hospital admissions agrees with a number of other reports, but may reflect residual confounding. The relatively greater vulnerability of the most deprived populations has important implications for public health

    Effects of air pollution and the introduction of the London Low Emission Zone on the prevalence of respiratory and allergic symptoms in schoolchildren in East London: a sequential cross-sectional study

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    The adverse effects of traffic-related air pollution on children’s respiratory health have been widely reported, but few studies have evaluated the impact of traffic-control policies designed to reduce urban air pollution. We assessed associations between traffic-related air pollutants and respiratory/allergic symptoms amongst 8–9 year-old schoolchildren living within the London Low Emission Zone (LEZ). Information on respiratory/allergic symptoms was obtained using a parent-completed questionnaire and linked to modelled annual air pollutant concentrations based on the residential address of each child, using a multivariable mixed effects logistic regression analysis. Exposure to traffic-related air pollutants was associated with current rhinitis: NOx (OR 1.01, 95% CI 1.00–1.02), NO2 (1.03, 1.00–1.06), PM10 (1.16, 1.04–1.28) and PM2.5 (1.38, 1.08–1.78), all per μg/m3 of pollutant, but not with other respiratory/allergic symptoms. The LEZ did not reduce ambient air pollution levels, or affect the prevalence of respiratory/allergic symptoms over the period studied. These data confirm the previous association between traffic-related air pollutant exposures and symptoms of current rhinitis. Importantly, the London LEZ has not significantly improved air quality within the city, or the respiratory health of the resident population in its first three years of operation. This highlights the need for more robust measures to reduce traffic emissions

    London Hybrid Exposure Model: Improving Human Exposure Estimates to NO2 and PM2.5 in an Urban Setting.

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    Here we describe the development of the London Hybrid Exposure Model (LHEM), which calculates exposure of the Greater London population to outdoor air pollution sources, in-buildings, in-vehicles, and outdoors, using survey data of when and where people spend their time. For comparison and to estimate exposure misclassification we compared Londoners LHEM exposure with exposure at the residential address, a commonly used exposure metric in epidemiological research. In 2011, the mean annual LHEM exposure to outdoor sources was estimated to be 37% lower for PM2.5 and 63% lower for NO2 than at the residential address. These decreased estimates reflect the effects of reduced exposure indoors, the amount of time spent indoors (∼95%), and the mode and duration of travel in London. We find that an individual's exposure to PM2.5 and NO2 outside their residential address is highly correlated (Pearson's R of 0.9). In contrast, LHEM exposure estimates for PM2.5 and NO2 suggest that the degree of correlation is influenced by their exposure in different transport modes. Further development of the LHEM has the potential to increase the understanding of exposure error and bias in time-series and cohort studies and thus better distinguish the independent effects of NO2 and PM2.5
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