Objectives To identify new susceptibility loci for Behçet's disease (BD), we performed a genome-wide association study (GWAS) using DNA pooling. Methods Two replicate pools of 292 Iranian BD cases and of 294 age- and sex-matched controls were allelotyped in quadruplicate on the Affymetrix Genome-Wide Human SNP Array 6.0. Of the 51 top markers, 47 were technically validated through individually genotyping. Replication of validated single nucleotide polymorphisms (SNPs) was performed in an independent Iranian dataset (684 cases and 532 controls). Results In addition to the well-established HLA-B locus, rs7528842 in a gene desert on chromosome 1p21.2, and rs632111 at the 3′UTR of FUT2 were associated in both the discovery and replication datasets (individually and in combination). However, only the FUT2 SNP was associated in a previous GWAS for BD in Turkish people. Fine-mapping of FUT2 in the full Iranian dataset showed additional associations in five coding SNPs (2.97E-06<p_combined<1.34E-04), including the rs601338 nonsense (W143X) variant which, in Caucasians, determines the secretion of the H antigen (precursor of the ABO blood group antigens) in body fluids and on the intestinal mucosa. Meta-analysis with the published Turkish GWAS data strengthened the FUT2 associations (4.78E-09<p_meta<1.66E-07). Conclusions This study suggests for the first time a putative link between a specific gene and environment in the aetiopathogenesis of BD. The non-secretor phenotype affects mucosal glycosylation, which may explain its known association with dysbiosis and altered susceptibility to infections. A different antigenic stimulation in early life and consequent increased propensity for autoimmunity and inflammation may contribute to BD development.

The practice of multicriteria forest management planning is often complicated by the need to explicit a priori goals and preferences of the decisionmaker. This

Assessing impacts of management strategies may allow designing more resistant forests to wildfires. Planning-oriented models to predict the effect of stand structure and forest composition on mortality for supporting fire-smart management decisions, and allowing its inclusion in forest management optimization systems were developed. Post-fire mortality was modeled as a function of measurable forest inventory data and projections over time in 165 pure and 76 mixed forest stands in Portugal, collected by the 5th National Forest Inventory plots (NFI) plus other sample plots from ForFireS project, intercepted within 2006–2008 wildfire perimeters’ data. Presence and tree survival were obtained by examining 2450 trees from 16 species one year after the wildfire occurrence. A set of logistic regression models were developed under a three-stage modeling system: firstly multiple fixed-effects at stand-level that comprises a sub-model to predict mortality from wildfire; and another for the proportion of dead trees on stands killed by fire. At tree-level due to the nested structure of the data analyzed (trees within stands), a mixed-effect model was developed to estimate mortality among trees in a fire event. The results imply that the variation of tree mortality decreases when tree diameter at breast height increases. Moreover, the relative mortality increases with stand density, higher altitude and steeper slopes. In the same conditions, conifers are more prone to die than eucalyptus and broadleaves. Pure stands of broadleaves exhibit noticeably higher fire resistance than mixed stands of broadleaves and others species composition.

This paper presents a model to predict annual wildfire risk in pure and even-aged eucalypt stands in Portugal. Emphasis was in developing a management-oriented model, i.e., a model that might both: (a) help assess wildfire occurrence probability as a function of readily available forest inventory data; and (b) help predict the effects of management options (e.g., silvicultural treatments) on the risk of fire in eucalypt stands. Data from both the 1995/1998 and the 2005/2006 Portuguese National Forest Inventories as well as wildfire perimeters’ data were used for modeling purposes. Specifically, this research considered 1122 inventory plots with approximately 1.2 million trees and 85 wildfire perimeters. The model to predict the probability of wildfire occurrence is a logistic function of measurable and controllable biometric and environmental variables. Results showed that wildfire occurrence probability in a stand increases with the ratio basal area/quadratic mean diameter and with the shrubs biomass load, while it decreases with stand dominant height. They further showed that the probability of wildfire occurrence is higher in stands that are over 1 Km distant from roads. These results are instrumental for assessing the impact of forest management options on wildfire risk levels thus helping forest managers develop plans that may mitigate wildfire impacts.

Maritime pine (Pinus pinaster Ait.) is an important conifer from the western Mediterranean Basin extending over 22%of the forest area in Portugal. In the last three decades nearly 4% of Maritime pine area has been burned by wildfires. Yetno wildfire occurrence probability models are available and forest and fire management planning activities are thus carriedout mostly independently of each other. This paper presents research to address this gap. Specifically, it presents a modelto assess wildfire occurrence probability in regular and pure Maritime pine stands in Portugal. Emphasis was in developinga model based on easily available inventory data so that it might be useful to forest managers. For that purpose, data fromthe last two Portuguese National Forest Inventories (NFI) and data from wildfire perimeters in the years from 1998 to 2004and from 2006 to 2007 were used. A binary logistic regression model was build using biometric data from the NFI. Biometricdata included indicators that might be changed by operations prescribed in forest planning. Results showed that the probabilityof wildfire occurrence in a stand increases in stand located at steeper slopes and with high shrubs load while it decreaseswith precipitation and with stand basal area. These results are instrumental for assessing the impact of forestmanagement options on wildfire probability thus helping forest managers to reduce the risk of wildfires.