RESUMO
Badlands are considered hotspots of sediment production, contributing to large fractions of the sediment budget of catchments and river basins. The erosion rates of these areas can exceed 100 t ha-1 y-1, leading to significant environmental and economic impacts. This research aims to assess badland susceptibility and the relevance of its governing factors at different spatial scales using the well-known machine learning approach random forest (RF). The Upper Llobregat River Basin (ULRB, approx. 500 km2) and Catalonia (approx. 32,000 km2) have been selected as study areas. Previous studies stated that the RF approach is successful at making predictions for the same area where it has been trained, but the results of testing it in a different area remains unexplored. This work aims to evaluate the feasibility of upscaling to the large region of Catalonia a RF model trained in the small ULRB area. Two badland datasets of both small and large regions and a total of eleven governing factors have been used to determine the areas susceptible to badlands. Models performance has been analyzed through three different evaluation metrics: overall accuracy, kappa coefficient and area under receiver operating characteristic curve (AUC). The outcomes of this work confirmed that RF is a powerful tool for badland susceptibility analysis, specially when predictions are made in the same scale and spatial context where the model has been trained. Upscaling a RF model defined in the ULRB to the large area of Catalonia has been possible, but improved results have been obtained when the training of the models has directly been performed in the large region. Our final RF modelling results have facilitated the development of a large scale (32,000 km2) Badland Susceptibility Map for the full extension of Catalonia with a predictive overall accuracy of 97%, which strongly emphasizes lithology and Normalized Difference Vegetation Index (NDVI) as the main conditioning factors of badland distribution.
RESUMO
Emerging data suggest that costimulation blockade with belatacept effectively controls humoral alloimmune responses. However, whether this effect may be deleterious for protective anti-infectious immunity remains poorly understood. We performed a mechanistic exploratory study in 23 kidney transplant recipients receiving either the calcineurin-inhibitor tacrolimus (Tac, n=14) or belatacept (n=9) evaluating different cellular immune responses after influenza vaccination such as activated T follicular Helper (Tfh), plasmablasts and H1N1 hemagglutinin (HA)-specific memory B cells (HA+mBC) by flow-cytometry, and anti-influenza antibodies by hemagglutination inhibition test (HI), at baseline and days 10, 30 and 90 post-vaccination. The proportion of CD4+CD54RA-CXCR5+ Tfh was lower in belatacept than Tac patients at baseline (1.86%[1.25-3.03] vs 4.88%[2.40-8.27], p=0.01) and remained stable post-vaccination. At M3, HA+mBc were significantly higher in Tac-treated patients (0.56%[0.32-1.49] vs 0.27%[0.13-0.44], p=0.04) and correlated with activated Tfh numbers. When stratifying patients according to baseline HA+mBc frequencies, belatacept patients with low HA+mBC displayed significantly lower HA+mBc increases after vaccination than Tac patients (1.28[0.94-2.4] vs 2.54[1.73-5.70], p=0.04). Also, belatacept patients displayed significantly lower seroprotection rates against H1N1 at baseline than Tac-treated patients (44.4% vs 84.6%) as well as lower seroconversion rates at days 10, 30 and 90 after vaccination (50% vs 0%, 63.6% vs 0%, and 63.6% vs 0%, respectively). We show the efficacy of belatacept inhibiting T-dependent antigen-specific humoral immune responses, active immunization should be highly encouraged before starting belatacept therapy.
