Characteristics of children with the best and poorest first- and second-year growth during rhGH therapy: data from 25 years of the Genentech national cooperative growth study (NCGS)

Background Models assessing characteristics contributing to response to recombinant human growth hormone (rhGH) response rarely address growth extremes in both years 1 and 2 or examine how children track from year to year. Using National Cooperative Growth Study (NCGS) data, we determined characteristics contributing to responsiveness to rhGH and the pattern of change from years 1 to 2. Patients and methods Height velocity standard deviation score (HV SDS) for 2 years for prepubertal children with idiopathic GH deficiency (IGHD) (n = 1899) and idiopathic short stature (ISS) (n = 1186) treated with similar doses for two years were computed. Group 1 = HV SDS \u3c −1; 2 = HV SDS −1 to +1; 3 = HV SDS \u3e +1. Results For IGHD, mean age was 7.5 years and similar in all groups. Year 1 HV SDS was associated with greater body mass index (BMI) SDS, lower pre-treatment HV, baseline height SDS, greater target height SDS minus height SDS, and lower maximum stimulated GH (P \u3c0.0001). Year 2, 172/271 (73%) in group 1 moved to either group 2 (n = 156) or 3 (n = 16). Year 2 HV SDS was associated with greater year 1 HV SDS (r = 0.045, P \u3c0.0001), greater BMI SDS, taller parents and lower peak GH. For ISS, year 1 HV SDS was associated with greater BMI SDS and lower pre-treatment HV (P≤0.0001). 109/169 (64%) in group 1 moved to group 2 (n = 90) or group 3 (n = 19). Greater year 2 HV SDS was related to year 1 HV SDS (r = 0.27, P \u3c0.0001). Conclusion For IGHD, multiple characteristics contributed to best first-year response but for ISS, best first-year HV SDS was associated only with BMI SDS and inversely with pre-treatment HV. For both GHD and ISS, year 1 HV SDS was not a strong enough predictor of year 2 HV SDS to use first-year HV alone to determine GH continuation

Velocity–conductivity relationships for mantle mineral assemblages in Archean cratonic lithosphere based on a review of laboratory data and Hashin–Shtrikman extremal bounds

Author Posting. © Elsevier B.V., 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Lithos 109 (2009): 131-143, doi:10.1016/j.lithos.2008.10.014.Can mineral physics and mixing theories explain field observations of seismic velocity and electrical conductivity, and is there an advantage to combining seismological and electromagnetic techniques? These two questions are at the heart of this paper. Using phenomologically-derived state equations for individual minerals coupled with multi-phase, Hashin-Shtrikman extremal-bound theory we derive the likely shear and compressional velocities and electrical conductivity at three depths, 100 km, 150 km and 200 km, beneath the central part of the Slave craton and beneath the Kimberley region of the Kaapvaal craton based on known petrologically-observed mineral abundances and magnesium numbers, combined with estimates of temperatures and pressures. We demonstrate that there are measurable differences between the physical properties of the two lithospheres for the upper depths, primarily due to the different ambient temperature, but that differences in velocity are negligibly small at 200 km. We also show that there is an advantage to combining seismic and electromagnetic data, given that conductivity is exponentially dependent on temperature whereas the shear and bulk moduli have only a linear dependence in cratonic lithospheric rocks. Focussing on a known discontinuity between harzburgite-dominated and lherzolitic mantle in the Slave craton at a depth of about 160 km, we demonstrate that the amplitude of compressional (P) wave to shear (S) wave conversions would be very weak, and so explanations for the seismological (receiver function) observations must either appeal to effects we have not considered (perhaps anisotropy), or imply that the laboratory data require further refinement

Some simple procedures for handling missing data in multivariate analysis

bias, correlation, discriminant analysis, factor analysis, pivoting, regression, stepwise,