6,806 research outputs found

    Abundances and variability of tropospheric volatile organic compounds at the South Pole and other Antarctic locations

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    Multiyear (2000-2006) seasonal measurements of carbon monoxide, hydrocarbons, halogenated species, dimethyl sulfide, carbonyl sulfide and C1-C4 alkyl nitrates at the South Pole are presented for the first time. At the South Pole, short-lived species (such as the alkenes) typically were not observed above their limits of detection because of long transit times from source regions. Peak mixing ratios of the longer lived species with anthropogenic sources were measured in late winter (August and September) with decreasing mixing ratios throughout the spring. In comparison, compounds with a strong oceanic source, such as bromoform and methyl iodide, had peak mixing ratios earlier in the winter (June and July) because of decreased oceanic production during the winter months. Dimethyl sulfide (DMS), which is also oceanically emitted but has a short lifetime, was rarely measured above 5 pptv. This is in contrast to high DMS mixing ratios at coastal locations and shows the importance of photochemical removal during transport to the pole. Alkyl nitrate mixing ratios peaked during April and then decreased throughout the winter. The dominant source of the alkyl nitrates in the region is believed to be oceanic emissions rather than photochemical production due to low alkane levels.Sampling of other tropospheric environments via a Twin Otter aircraft included the west coast of the Ross Sea and large stretches of the Antarctic Plateau. In the coastal atmosphere, a vertical gradient was found with the highest mixing ratios of marine emitted compounds at low altitudes. Conversely, for anthropogenically produced species the highest mixing ratios were measured at the highest altitudes, suggesting long-range transport to the continent. Flights flown through the plume of Mount Erebus, an active volcano, revealed that both carbon monoxide and carbonyl sulfide are emitted with an OCS/CO molar ratio of 3.3 × 10-3 consistent with direct observations by other investigators within the crater rim. © 2010

    Integrating remote sensing datasets into ecological modelling: a Bayesian approach

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    Process-based models have been used to simulate 3-dimensional complexities of forest ecosystems and their temporal changes, but their extensive data requirement and complex parameterisation have often limited their use for practical management applications. Increasingly, information retrieved using remote sensing techniques can help in model parameterisation and data collection by providing spatially and temporally resolved forest information. In this paper, we illustrate the potential of Bayesian calibration for integrating such data sources to simulate forest production. As an example, we use the 3-PG model combined with hyperspectral, LiDAR, SAR and field-based data to simulate the growth of UK Corsican pine stands. Hyperspectral, LiDAR and SAR data are used to estimate LAI dynamics, tree height and above ground biomass, respectively, while the Bayesian calibration provides estimates of uncertainties to model parameters and outputs. The Bayesian calibration contrasts with goodness-of-fit approaches, which do not provide uncertainties to parameters and model outputs. Parameters and the data used in the calibration process are presented in the form of probability distributions, reflecting our degree of certainty about them. After the calibration, the distributions are updated. To approximate posterior distributions (of outputs and parameters), a Markov Chain Monte Carlo sampling approach is used (25 000 steps). A sensitivity analysis is also conducted between parameters and outputs. Overall, the results illustrate the potential of a Bayesian framework for truly integrative work, both in the consideration of field-based and remotely sensed datasets available and in estimating parameter and model output uncertainties

    DC utilization of existing LVAC distribution cables

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    Association Between Sedentary Time and Quality of Life From the Osteoarthritis Initiative: Who Might Benefit Most From Treatment?

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    Objective To investigate the relationship between sedentary behavior and quality-adjusted life years (QALYs) among participants in the Osteoarthritis Initiative. Design Longitudinal, observational design. Setting Osteoarthritis Initiative cohort. Participants Individuals (N=1794) from a prospective, multicenter longitudinal cohort were classified into quantile groups based on average daily sedentary time (most sedentary, quartile 1 [Q1] ≥11.6h; 10.7h≤ Q2 Interventions Not applicable. Main Outcome Measures Individual QALYs were estimated over 2 years from the area under the curve of health-related utility scores derived from the Medical Outcomes Study 12-Item Short-Form Health Survey versus time. The relationship between baseline sedentary behavior and median 2-year QALYs was estimated using quantile regression adjusted for socioeconomic factors and body mass index. Results Lower QALYs over 2 years were more frequently found among the most sedentary (Q1, median 1.59), and QALYs increased as time spent in baseline sedentary behavior decreased (median QALYs for Q2, 1.64; Q3, 1.65; Q4, 1.65). The relationship of sedentary time and median QALY change was only significant for the most sedentary Q1 group, where an additional hour of sedentary behavior significantly reduced QALYs by −.072 (95% confidence interval, −.121 to −.020). Conclusions Our findings suggest that individuals with the most extreme sedentary profiles may be vulnerable to additional losses of quality of life if they become more sedentary. Targeting these individuals to decrease sedentary behavior has the potential to be cost-effective

    Diagnostic accuracy of PAT-POPS and ManChEWS for admissions of children from the emergency department

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    Background The Pennine Acute Trust (PAT) Paediatric Observation Priority Score (PAT-POPS) is a specific emergency department (ED) physiological and observational aggregate scoring system, with scores of 0–18. A higher score indicates greater likelihood of admission. The Manchester Children’s Early Warning System (ManChEWS) assesses six physiological observations to create a trigger score, classified as Green, Amber or Red. Methods Prospectively collected data were used to calculate PAT-POPS and ManChEWS on 2068 patients aged under 16 years (mean 5.6 years, SD 4.6) presenting over 1 month to a UK District General Hospital Paediatric ED. Receiver operating characteristics (ROC) comparison, using STATA V.13, was used to investigate the ability of ManChEWS and PAT-POPS to predict admission to hospital within 72 h of presentation to the ED. Results Comparison of the area under the ROC curve indicates that the ManChEWS ROC is 0.67 (95% CI 0.64 to 0.70) and the PAT-POPS ROC is 0.72 (95% CI 0.68 to 0.75). The difference is statistically significant. At a PAT-POPS cut-off of ≥2, 80% of patients had their admission risk correctly classified ( positive likelihood ratio 3.40, 95% CI 2.90 to 3.98) whereas for ManChEWS with a cut off of ≥Amber only 71% of patients were correctly classified ( positive likelihood ratio 2.18, 95% CI 1.94 to 2.45). Conclusions PAT-POPS is a more accurate predictor of admission risk than ManChEWS. Replacing ManChEWS with PAT-POPS would appear to be clinically appropriate in a paediatric ED. This needs validation in a multicentre study

    Preliminary evidence of increased striatal dopamine in a nonhuman primate model of maternal immune activation.

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    Women exposed to a variety of viral and bacterial infections during pregnancy have an increased risk of giving birth to a child with autism, schizophrenia or other neurodevelopmental disorders. Preclinical maternal immune activation (MIA) models are powerful translational tools to investigate mechanisms underlying epidemiological links between infection during pregnancy and offspring neurodevelopmental disorders. Our previous studies documenting the emergence of aberrant behavior in rhesus monkey offspring born to MIA-treated dams extends the rodent MIA model into a species more closely related to humans. Here we present novel neuroimaging data from these animals to further explore the translational potential of the nonhuman primate MIA model. Nine male MIA-treated offspring and 4 controls from our original cohort underwent in vivo positron emission tomography (PET) scanning at approximately 3.5-years of age using [18F] fluoro-l-m-tyrosine (FMT) to measure presynaptic dopamine levels in the striatum, which are consistently elevated in individuals with schizophrenia. Analysis of [18F]FMT signal in the striatum of these nonhuman primates showed that MIA animals had significantly higher [18F]FMT index of influx compared to control animals. In spite of the modest sample size, this group difference reflects a large effect size (Cohen's d = 0.998). Nonhuman primates born to MIA-treated dams exhibited increased striatal dopamine in late adolescence-a hallmark molecular biomarker of schizophrenia. These results validate the MIA model in a species more closely related to humans and open up new avenues for understanding the neurodevelopmental biology of schizophrenia and other neurodevelopmental disorders associated with prenatal immune challenge
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