Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe

Forest management requires prediction of forest growth, but there is no general agreement about which models best predict growth, how to quantify model parameters, and how to assess the uncertainty of model predictions. In this paper, we show how Bayesian calibration (BC), Bayesian model comparison (BMC) and Bayesian model averaging (BMA) can help address these issues. We used six models, ranging from simple parameter-sparse models to complex process-based models: 3PG, 4C, ANAFORE, BASFOR, BRIDGING and FORMIND. For each model, the initial degree of uncertainty about parameter values was expressed in a prior probability distribution. Inventory data for Scots pine on tree height and diameter, with estimates of measurement uncertainty, were assembled for twelve sites, from four countries: Austria, Belgium, Estonia and Finland. From each country, we used data from two sites of the National Forest Inventories (NFIs), and one Permanent Sample Plot (PSP). The models were calibrated using the NFI-data and tested against the PSP-data. Calibration was done both per country and for all countries simultaneously, thus yielding country-specific and generic parameter distributions. We assessed model performance by sampling from prior and posterior distributions and comparing the growth predictions of these samples to the observations at the PSPs. We found that BC reduced uncertainties strongly in all but the most complex model. Surprisingly, country-specific BC did not lead to clearly better within-country predictions than generic BC. BMC identified the BRIDGING model, which is of intermediate complexity, as the most plausible model before calibration, with 4C taking its place after calibration. In this BMC, model plausibility was quantified as the relative probability of a model being correct given the information in the PSP-data. We discuss how the method of model initialisation affects model performance. Finally, we show how BMA affords a robust way of predicting forest growth that accounts for both parametric and model structural uncertainty

Potency enhancement of the κ-opioid receptor antagonist probe ML140 through sulfonamide constraint utilizing a tetrahydroisoquinoline motif

Optimization of the sulfonamide-based kappa opioid receptor (KOR) antagonist probe molecule ML140 through constraint of the sulfonamide nitrogen within a tetrahydroisoquinoline moiety afforded a marked increase in potency. This strategy, when combined with additional structure-activity relationship exploration, has led to a compound only six-fold less potent than norBNI, a widely utilized KOR antagonist tool compound, but significantly more synthetically accessible. The new optimized probe is suitably potent for use as an in vivo tool to investigate the therapeutic potential of KOR antagonists

Studying organic matter molecular assemblage within a whole organic soil by nuclear magnetic resonance

This work shows the applicability of two-dimensional (2D) 1H-13C heteronudear correlation (HETCOR) nuclear magnetic resonance (NMR) spectroscopy to the characterization of whole soils. A combination of different mixing times and cross polarization (CP) methods, namely Lee-Goldberg (LG)-CP and Ramp-CP are shown to afford, for the first time, intra- and intermolecular connectivities, allowing for molecular assemblage information to be obtained on a whole soil. Our results show that, for the brackish marsh histosol under study, two isolated domains could be detected. The first domain consists of O-alkyl and aromatic moieties (lignocellulose material), while the second domain is comprised of alkyl type moieties (cuticular material). The role of these domains is discussed in terms of hydrophobic organic compound sorption within soil organic matter (SOM), including the possible effects of wetting and drying cycles. Copyright © 2008 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved