INTRODUCTION: Fanconi Anemia Type C (FAC) is an autosomal recessive disorder characterized by skeletal malformations, bone marrow failure, increased risk of malignancy, and severe aplastic anemia. Currently, carrier testing for FAC is recommended for the Ashkenazi Jewish (AJ) population. A joint ACOG/ACMG statement highlighted the increasingly multi-ethnic society as consideration for pan-ethnic carrier testing, and recent ACOG committee opinion states that pan-ethnic testing is a reasonable strategy. Our current study assesses how many non-AJ individuals screened positive for FAC when pan-ethnic carrier testing was implemented. METHODS: Retrospective database analysis of individuals that received expanded carrier testing utilizing a genotyping panel for three pathologic variants of FAC (c.322delG, IVS4 c.456(+4)A>T, p.R548X) was performed. Expected number of positive carriers in AJ and non-AJ groups were calculated and compared to observed rates. Chi-square analysis was performed to assess for statistical significance (p < 0.01). RESULTS: A total of 71,235 individuals were tested for FAC. A higher than expected number of non-AJ individuals tested positive for FAC (56 observed vs 8.89 expected, p<0.001). The number of AJ individuals that screened positive was consistent with expected values (10 observed vs 11.11 expected, p<0.73). CONCLUSION: Non-AJ carriers of FAC were more common than expected in this cohort. Pan-ethnic expanded carrier testing will increase the detection of carriers for FAC compared with current ethnicity based screening recommendations. Importantly, carriers of FAC who would be missed by ethnicity based testing convention, will be identified allowing for more complete genetic counseling and family planning options for those who choose testing.

Questions: Does the floristic composition of trees differ between bamboo forests and adjacent non-bamboo forests? Can the degree of compositional differences be predicted from differences in canopy reflectance as measured by Landsat satellites? Are the results sensitive to different taxonomical data cleaning strategies, or to which tree-size class is considered? Are some tree taxa associated with either bamboo or non-bamboo forests? Location: Peruvian Amazonia. Methods: We used national forestry inventory data to characterise floristic composition of trees at 25 sites. Bamboo and non-bamboo forests were identified with a pixel-based time series analysis using the entire Landsat TM/ETM+ archive. To visualise floristic similarity among the plots, we used non-metric multidimensional scaling. Floristic differences between bamboo and non-bamboo forests were tested using an analysis of similarity (ANOSIM). Mantel tests were used to assess correlation between floristic turnover and differences in canopy reflectance. We tested the impact of applying three different taxonomic data cleaning strategies and of analysing different tree-size classes. Finally, we ran an indicator species analysis to identify taxa that were associated with either bamboo or non-bamboo forests. Results: In floristic ordinations, bamboo-dominated forests appeared floristically distinct from non-bamboo forests regardless of the taxonomic cleaning strategy and tree-size class. This floristic separation was also confirmed through analysis of floristic similarity (ANOSIM). Turnover in floristic composition was strongly correlated with differences in the reflectance values of Landsat bands, especially when using genus-level rather than species-level data. Different palm taxa were associated with bamboo (Socratea) and non-bamboo forests (Iriartea). Conclusions: Floristic differences of trees between bamboo-dominated and nonbamboo forests are consistent enough to be observable with different tree-size classes and taxonomic cleaning strategies. Although coarsening the taxonomic 2 of 15 |

The increase in extreme weather events is a major consequence of climate change in tropical mountain rangeslike the Andes of Peru. The impact on farming households is of growing interest since adaptation and mitigation strategies are required to keep race with environmental conditions and to prevent people from increasing poverty. In this regard it becomes more and more obvious that a bottom-up approach incorporating the local socioeconomic processes and their interplay is needed. Socio-economic field laboratories are used to understand such processes on site. This integrates multi-disciplinary and participatory analyses of production and its relationship with biophysical and socio-economic determinants. Farmers react individually based on their experiences, financial situation, labor conditions, or attitude among others. In this regard socio-economic field laboratories also serve to develop and test scenarios about development paths, which involve the combination of both, local and scientific knowledge. For a comprehensive understanding of the multitude of interactions the agent-based modeling framework MPMAS (Mathematical Programming-based Multi-Agent System) is applied. In combination with continued ground-truthing, the model is used to gain insights into the functioning of the complex social system and to forecast its development in the near future. The assessment of the effect of humans’ behavior in changing environmental conditions including the comparison of different sites, transforms the model to a communication tool bridging the gap between adaptation policies and local realities.