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Further, the EFSA Panel assumed a factor of 2.85 based on data from Hungary [38] to infer to Urtica leaf pollen density.
To account for inhomogeneous deposition of pollen on leaves, including that of aggregation processes, Perry et al. [7] assumed a factor of 12 for a 99%% confidence interval (CI) around mean pollen densities; for worst-case field scenario assumptions, they estimated a mean pollen density of approximately 599 n/cm2 on nettle leaves and a daily mean density of 85 n/(cm2 day) [8, Appendix I].
We suspect that it is considerably more resistant than the boundary between grey and white matter (where we have assumed a factor of 5 difference in motility).
We assumed a factor of 1.7 (Stern and Smith 2003) in estimating the corresponding adult blood mercury level of 12 µg/L.
We assumed a factor of 1.0 for converting the absorption coefficient to EC mass [ 25, 26], which was then divided by the sampled air volume to calculate average EC exposure concentration [ 24].
We assumed a factor of 1 based on calibration factors reported in other studies examining air pollution in urban settings (Cyrys et al. 2003; Kinney et al. 2000) and on our own results of TOR EC measurements on 30, co-located quartz filters [see Supplemental Material (available online at http://www.ehponline.org/members/2009/0800335/suppl.pdf)].
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Considering the wind loads on a typical house, estimates for failure wind speeds were obtained, assuming a factor of safety, and compared to ASCE 7-05 wind regions.
Typical results are around 90 days per 10 μg/m3 of PM10 (assuming a factor 0.6 for the conversion from PM2.5 to PM10).
a) taking RR = 1.06 for 10 μg/m3 PM2.5 from Table 2 of Pope et al. [ 2] and assuming a factor 0.6 for the conversion from PM2.5 to PM10 b) rough estimate, reading RR from Fig.5 of Pope et al. [ 2] The LE loss for cohort studies has been calculated by Rabl [ 26] and Miller and Hurley [ 8], based on Pope et al. [ 2].
Although a naive analysis would treat the error matrix as having independent rows, an alternative model is to assume a factor-analytic structure on or, specifically (3) where is an matrix of CpG-specific factor loadings, is an matrix of latent effects and is an matrix of 'uniqueness' errors.
Our first goal was to perform a multi-group confirmatory factor analysis that assumed a one-factor model of the traditional MetS components while allowing for differences across sex and race/ethnicity [ 5].
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