Pathological and ultrastructural changes in cultured cells induced by venom from the ectoparasitic wasp Nasonia vitripennis (Walker) (Hymenoptera: Pteromalidae).

The ectoparasitic wasp Nasonia vitripennis produces a proteinaceous venom that induces death in fly hosts by non-paralytic mechanisms. Previous in vitro assays have suggested that the primary cause of cell and tissue death is oncosis, a non-programmed cell death (PCD) pathway characterized by cellular swelling and lysis. However, ultrastructural analyses of BTI-TN-5B1 cells exposed to LC(99) doses of wasp venom revealed cellular changes more consistent with apoptosis and/or non-apoptotic PCD than oncosis or necrosis: By 3h after incubation with venom, susceptible cells displayed indentations in the nuclear membranes, large nucleoli, and extensive vacuolization throughout the cytoplasm. In the vast majority of venom treated cells, annexin V bound to the plasma membrane surface within 15 min after treatment, a characteristic consistent with translocation of phosphatidylserine to the cell surface during the early stages of apoptosis. Likewise, mitochondrial transmembrane potential was depressed in cells within 15 min in venom-treated cells, an event that occurred in the absence of mitochondrial swelling or rupturing of cristae. Active caspase 3 was detected by fluorescent labeling in nearly all venom treated cells 3h after exposure to venom, and in turn, the potent caspase 3 inhibitor Z-VAD-FMK attenuated the morphological changes elicited by wasp venom and afforded protection to BTI-TN-5B1-4 cells.

Application of artificial neural networks to assess pesticide contamination in shallow groundwater.

In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient (R=0.87) and pesticide detection efficiency (E=89%), as well as good match between the observed and predicted "class" groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R, E, mean error (ME), root mean square error (RMSE), and pesticide occurrence "class" groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R, E, ME, RMSE, and pesticide occurrence "class" groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination.

Quantification of in Vivo 3H-Estrogen Uptake by Individual Anterior Pituitary Cell Types of Male Rat:A Combined Autoradiographic-Immunocytochemical Technique 1 , 2.

A refined version of the combined dry-mount auto-radiographic-immunocytochemical technique (Keefer DA, Stumpf WE, Petrusz P: Cell Tissue Res 166:25, 1976) is described in detail. In vivo nuclear estrogen uptake is measured by silver grain counting in immunocytochemically stained gonadotropes (G), somatotropes (S), lactotropes (L), corticotropes (C), and thyrotropes (T) of male rats. In rats 1 day after orchidectomy and adrenalectomy, the order of nuclear estrogen uptake was S = L G = C T, with T concentrating less than half as much radioactivity as L or S. Fifteen percent of anterior pituitary cells neither concentrated estrogen nor stained immunocytochemically. Estrogen uptake was examined in gonadotropes 1, 14, and 50 days after orchidectomy and was found to be identical at all three times. Estrogen uptake in gonadotropes of rats 14 days following orchidectomy and treatment with progesterone was reduced significantly. Guidelines for reliable quantification of the autoradiographic data is discussed.