Effects of in situ freezing on soil net nitrogen mineralization and net nitrification in fertilized grassland of northern China

Effects of soil freezing on nitrogen (N) mineralization have been the subject of increased attention in the ecological literature, though fewer studies have examined N mineralization responses to successive mild freezing, severe freezing and cyclic freeze–thaw events. Even less is known about relationships of responses to soil N status. This study measured soil N mineralization and nitrification in the field along an experimental N gradient in a grassland of northern China during the dormant season (October 2005–April 2006), a period in which freezing naturally occurs. Net N mineralization exhibited great temporal variability, with nitrification being the predominant N transformation process. Soil microbial biomass C and N and extractable NH4 + pools declined by 40, 52, and 56%, respectively, in April 2006, compared with their initial concentrations in October 2005; soil NO3– pools increased by 84%. Temporal patterns of N mineralization were correlated with soil microbial biomass C and N. N mineralization and nitrification increased linearly with added N. Microbial biomass C in treated soils increased by 10% relative to controls, whereas microbial N declined by 9%. Results further suggest that freezing events greatly alter soil N dynamics in the dormant season at this site, with considerable available N accumulating during this period.

MULTIPLE MODEL EVALUATION ABSENT THE GOLD STANDARD VIA MODEL COMBINATION

We describe a method for evaluating an ensemble of predictive models given a sample of observations comprising the model predictions and the outcome event measured with error. Our formulation allows us to simultaneously estimate measurement error parameters, true outcome — aka the gold standard — and a relative weighting of the predictive scores. We describe conditions necessary to estimate the gold standard and for these estimates to be calibrated and detail how our approach is related to, but distinct from, standard model combination techniques. We apply our approach to data from a study to evaluate a collection of BRCA1/BRCA2 gene mutation prediction scores. In this example, genotype is measured with error by one or more genetic assays. We estimate true genotype for each individual in the dataset, operating characteristics of the commonly used genotyping procedures and a relative weighting of the scores. Finally, we compare the scores against the gold standard genotype and find that Mendelian scores are, on average, the more refined and better calibrated of those considered and that the comparison is sensitive to measurement error in the gold standard.

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.