Similar Predictive Power Research Articles

Contrast Sensitivity Predicts Target Detection Field Performance of Pilots

Our recent research has shown that contrast sensitivity, not visual acuity, can predict a pilot's ability to detect an air-to-ground target in a flight simulator. Here we report a further study that shows a similar predictive power under actual field conditions. Eighty-four Air Force pilots, seated at the end of a runway, typically in groups of ten per week, reported the detection of an approaching T-39 jet aircraft under visibility conditions varying from 0.5 to over 15 miles for 10 field trials. The pilots' detection ranges were correlated with their individual contrast sensitivities and standard visual acuities. Contrast sensitivity, not visual acuity, was found to be a good predictor of detection range. These results have strong implications for the creation of performance-related vision standards.

Action and Contingency Postulates in Organization-Environment Relations

Two models of organization-environment relations are examined. One postulates organizational structure or behavior as contingent on environments; organizations are viewed as adapting to environmental changes. Conversely, the second model postulates environments as dependent upon organizations; organizations are viewed as actively manipulating environments. The two alternative models are empirically assessed and compared on a heterogeneous sample of 27 business corporations. In the contingency model, environmental manipulation is used to predict different dimensions of organizational technology. In the action model, the causal order is reversed; technology is used to predict environmental manipulation. Organizational assets are controlled in both models to eliminate the possible bias of differential resources. We find similar predictive power for both models. Explained variance was about equal, and standardized regression coefficients were similar across the models as well. It is thus concluded that a full understanding of organization-environment interaction will require simultaneous consideration of their effects upon one another.

Positional Vertigo and Nystagmus

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment.In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms.In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure.