Plant communities of field boundaries in Finnish farmland

Appendix: List of species found in boundaries

Ag Decision Maker, May 2014, Vol. 18, no. 7

A Business Newsletter for Agriculture

Ag Decision Maker, June 2014, Vol. 18, no. 8

A Business Newsletter for Agriculture

Ag Decision Maker, July 2014, Vol. 18, no. 9

A Business Newsletter for Agriculture

Ag Decision Maker, August 2014, Vol. 18, no. 10

A Business Newsletter for Agriculture

Ag Decision Maker, September 2014, Vol. 18, no. 11

A Business Newsletter for Agriculture

You are not alone...making an informed decision about your pregnancy, January 1997

When you are faced with a difficult decision, it’s not unusual to feel confused, frustrated, and perhaps a little frightened because you are not sure what to expect or where to turn for answers. The purpose of this handbook is to assist you in making an informed choice about your pregnancy.

Improvement of therapeutic drug monitoring of imatinib by Bayesian prediction of trough levels. Personalized drug dosage

Aims: Plasma concentrations of imatinib differ largely between patients despite same dosage, owing to large inter-individual variability in pharmacokinetic (PK) parameters. As the drug concentration at the end of the dosage interval (Cmin) correlates with treatment response and tolerability, monitoring of Cmin is suggested for therapeutic drug monitoring (TDM) of imatinib. Due to logistic difficulties, random sampling during the dosage interval is however often performed in clinical practice, thus rendering the respective results not informative regarding Cmin values.Objectives: (I) To extrapolate randomly measured imatinib concentrations to more informative Cmin using classical Bayesian forecasting. (II) To extend the classical Bayesian method to account for correlation between PK parameters. (III) To evaluate the predictive performance of both methods.Methods: 31 paired blood samples (random and trough levels) were obtained from 19 cancer patients under imatinib. Two Bayesian maximum a posteriori (MAP) methods were implemented: (A) a classical method ignoring correlation between PK parameters, and (B) an extended one accounting for correlation. Both methods were applied to estimate individual PK parameters, conditional on random observations and covariate-adjusted priors from a population PK model. The PK parameter estimates were used to calculate trough levels. Relative prediction errors (PE) were analyzed to evaluate accuracy (one-sample t-test) and to compare precision between the methods (F-test to compare variances).Results: Both Bayesian MAP methods allowed non-biased predictions of individual Cmin compared to observations: (A) - 7% mean PE (CI95% - 18 to 4 %, p = 0.15) and (B) - 4% mean PE (CI95% - 18 to 10 %, p = 0.69). Relative standard deviations of actual observations from predictions were 22% (A) and 30% (B), i.e. comparable to the intraindividual variability reported. Precision was not improved by taking into account correlation between PK parameters (p = 0.22).Conclusion: Clinical interpretation of randomly measured imatinib concentrations can be assisted by Bayesian extrapolation to maximum likelihood Cmin. Classical Bayesian estimation can be applied for TDM without the need to include correlation between PK parameters. Both methods could be adapted in the future to evaluate other individual pharmacokinetic measures correlated to clinical outcomes, such as area under the curve(AUC).

Ag Decision Maker, Novmeber 2014, Vol. 19, no. 1

A Business Newsletter for Agriculture

Ag Decision Maker, October 2014, Vol. 18, no. 12

A Business Newsletter for Agriculture

Bayesian-MCMC inversion of time-lapse geophysical data to characterize vadose zone hydraulic behaviour

Geophysical methods have the potential to provide valuable information on hydrological properties in the unsaturated zone. In particular, time-lapse geophysical data, when coupled with a hydrological model and inverted stochastically, may allow for the effective estimation of subsurface hydraulic parameters and their corresponding uncertainties. In this study, we use a Bayesian Markov-chain-Monte-Carlo (MCMC) inversion approach to investigate how much information regarding vadose zone hydraulic properties can be retrieved from time-lapse crosshole GPR data collected at the Arrenaes field site in Denmark during a forced infiltration experiment.

Ag Decision Maker, December 2014, Vol. 19, no. 2

A Business Newsletter for Agriculture

Ag Decision Maker, January 2015, Vol. 19, no. 3

A Business Newsletter for Agriculture