A stand-level model derived from National Forest Inventory data to predict periodic annual volume increment of forests in Italy

<p>A model was developed for predicting the periodic annual volume increment (PAI) of forests using variables commonly recorded through field surveys or the remote sensing. The model was developed using the Italian National Forest Inventory (INFC2005) data, publicly available at <a href="http://www.inventarioforestale.org" target="_blank">www.inventarioforestale.org</a>. Data from 5707 plots were split into two groups. The first was used for fitting the model; the second was used for cross validation. Model reliability for applications at the local, in the Alpine and Mediterranean regions, and at the country level was tested. A sensitivity analysis was carried out to investigate the effects of entering inaccurate values of the number of trees per hectare, one of the predictors of the final model, that may occur in case of biased estimates from the remote sensing. During model calibration, the highest proportion of increment variation was captured using forest category (FC) as dummy variable and, in this respect, this study supports the classification of forests on ecological basis as a stratification criterion in environmental sampling. The model explained 72% of PAI and it predicted annual increment at plot level with no statistical difference to the observed value in any FC, at the country level.</p

Additional file 1: of Serum miR-22 as potential non-invasive predictor of poor clinical outcome in newly diagnosed, uniformly treated patients with diffuse large B-cell lymphoma: an explorative pilot study

Fig. S1. Kaplan-Meier Overall Survival curves. Correlation between the indicated miRNAs and the Overall Survival of 47 DLBCL patients from TCGA data analysis. (PDF 204 kb