Igor G Dubus

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There is worldwide interest in the application of probabilistic approaches to pesticide fate models to account for uncertainty in exposure assessments. The first steps in conducting a probabilistic analysis of any system are: (i) to identify where the uncertainties come from; and (ii) to pinpoint those uncertainties that are likely to affect most of the(More)
Monte Carlo techniques are increasingly used in pesticide exposure modeling to evaluate the uncertainty in predictions arising from uncertainty in input parameters and to estimate the confidence that should be assigned to the modeling results. The approach typically involves running a deterministic model repeatedly for a large number of input values sampled(More)
Sensitivity and uncertainty analyses based on Monte Carlo sampling were undertaken for various numbers of runs of the pesticide leaching model (PELMO). Analyses were repeated 10 times with different seed numbers. The ranking of PELMO input parameters according to their influence on predictions for leaching was stable for the most influential parameters. For(More)
The role of angiogenesis in acute leukaemia has been discussed since the cloning of the gene of vascular endothelial growth factor (VEGF) from the acute myelogenous leukemia cell line (HL60) and, thereafter, when the first studies reported increased bone marrow vascularity and elevation of angiogenic cytokines in acute lymphoblastic leukaemia (ALL). VEGF(More)
Several studies show that bone marrow (BM) microenvironment and hypoxia condition can promote the survival of leukemic cells and induce resistance to anti-leukemic drugs. However, the molecular mechanism for chemoresistance by hypoxia is not fully understood. In the present study, we investigated the effect of hypoxia on resistance to two therapies,(More)
An autoregressive approach for the prediction of water quality trends in systems subject to varying meteorological conditions and short observation periods is discussed. Under these conditions, the dynamics of the system can be reliably forecast, provided their internal processes are understood and characterized independently of the external inputs. A(More)
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