Probing cell surfaces of host and nonhost species of a mycoparasite, using FITC-labeled lectins

Cell surfaces of susceptible host species (Mortierella pusllla and Cboanepilora cucurbitarum ), resistant host (Pilascolomyces articulosus ), nonhost (Mortierella candelabrum ) and the mycoparasite (Piptocepilalis virginiana) were examined for sugar distribution patterns using light and fluorescent microscopy techniques. The susceptible host, resistant host and the mycoparasite species exhibited a similar sugar distribution profile; they all showed N-acetyl glucosamine and D-glucose on their cell wall surfaces. The nonhost cell wall surface showed a positive binding reaction to FITClectins specific for N-acetyl glucosamine and also for OI.-fucose, N-acetyl galactosamine and galactose. Treatment of these fungi with mild concentrations of proteinases (both commercial as well as the mycoparasiteproteinase) resulted in the revelation of additional sugars on the fungal cell walls. The susceptible host treated with proteinase expressed higher levels of N-acetyl glucosamine and D-glucose. The susceptible host also showed the presence of OI.-fucose, N-acetyl galactosamine and galactose. The proteinasetreated susceptible host cell walls also showed an increase in the levels of attachment with the mycoparasite. Treatment of the resistant host with proteinases revealed OI.-fucose in addition to N-acetyl glucosamine and D-glucose. Treatment of the nonhost cell wall with proteinase resulted in the exposure of low levels of D-glucose, in addition to sugars found on the untreated nonhost cell wall surface. The mycoparasite treated with proteinase revealed OI.-fucose, N-acetyl galactosamine and galactose on its cell surface in addition to the sugars N-acetyl glucosamine and D-glucose. Protoplasts were isolated from hosts and nonhost fungi and their surfaces were examined for sugar distribution patterns. The susceptible host and nonhost protoplast membranes showed all the sugars (N-acetyl glucosamine, D-glucose, (It.-fucose, N-acetyl galactosamine and galactose) tested for. The resistant host protoplast membrane however, had only N-acetyl glucosamine and D-glucose exposed. This sugar distribution profile resembles that exhibited by the untreated resistant host cell wall, as well as that shown by the untreated mycoparasite cell surface. Only susceptible host protoplasts were successful in attaching to the mycoparasite surface. Resistant host protoplasts did not show any interaction with the i mycoparasite cell surface. Both susceptible as well as resistant host protoplasts were incapable of attaching to agarose beads surface-coated with specific carbohydrates. The mycoparasite however, did attach to agarose beads surface-coated with either N-acetyl glucosamine, D-glucose/Dmannose or o:,- methyl-D-mannose. The relevance of the cell wall and the protoplast membrane in the light of the present results, in reacting appropriately to bring about either a susceptible, a resistant or a nonhost response has been discussed.

Using a Bayesian model for bankruptcy prediction : a comparative approach

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors' concerns with respect to assessing failure risk.