RESUMEN
Embryonic animals are especially susceptible to metal exposure. Manganese (Mn) is an essential element, but in excess it can induce toxicity. In this study we used Drosophila melanogaster as an embryonic model to investigate biochemical and behavioral alterations due to Mn exposure. Flies were treated with standard medium supplemented with MnCl2 at 0.1 mM, 0.5 mM or 1 mM from the egg to the adult stage. At 0.5 mM and 1 mM Mn, newly ecloded flies showed significantly enhanced locomotor activity when assessed by negative geotaxis behavior. In addition, a significant increase in Mn levels (p < 0.0001) was observed, while Ca, Fe, Cu, Zn and S levels were significantly decreased. A significant drop in cell viability occurred in flies exposed to 1 mM Mn. There was also an induction of reactive oxygen species at 0.5 mM and 1 mM Mn (p < 0.05). At 1 mM, Mn increased Catalase (p < 0.005), Superoxide Dismutase (p < 0.005) and Hsp83 (p < 0.0001) mRNA expression, without altering Catalase or Superoxide Dismutase activity; the activity of Thioredoxin reductase and Glutatione-S-transferase enzymes was increased. Mn treatment did not alter ERK or JNK1/2 phosphorylation, but at 1 mM caused an inhibition of p38(MAPK) phosphorylation. Together these data suggest mechanisms of adaptation in the fly response to Mn exposure in embryonic life.
RESUMEN
Total sulfur concentration was determined in atmospheric residue (AR) and vacuum residue (VR) samples obtained from petroleum distillation process by Fourier transform infrared spectroscopy with attenuated total reflectance (FT-IR/ATR) in association with chemometric methods. Calibration and prediction set consisted of 40 and 20 samples, respectively. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). Different treatments and pre-processing steps were also evaluated for the development of models. The pre-treatment based on multiplicative scatter correction (MSC) and the mean centered data were selected for models construction. The use of siPLS as variable selection method provided a model with root mean square error of prediction (RMSEP) values significantly better than those obtained by PLS model using all variables. The best model was obtained using siPLS algorithm with spectra divided in 20 intervals and combinations of 3 intervals (911-824, 823-736 and 737-650 cm(-1)). This model produced a RMSECV of 400 mg kg(-1) S and RMSEP of 420 mg kg(-1) S, showing a correlation coefficient of 0.990.