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The fungal genus Aspergillusis of critical importance to humankind. Species include those with industrial applications, important pathogens of humans, animals and crops, a source of potent carcinogenic contaminants of food, and an important genetic model. The genome sequences of eight aspergilli have already been explored to investigate aspects of fungal biology, raising questions about evolution and specialization within this genus. We have generated genome sequences for ten novel, highly diverse Aspergillusspecies and compared these in detail to sister and more distant genera. Comparative studies of key aspects of fungal biology, including primary and secondary metabolism, stress response, biomass degradation, and signal transduction, revealed both conservation and diversity among the species. Observed genomic differences were validated with experimental studies. This revealed several highlights, such as the potential for sex in asexual species, organic acid production genes being a key feature of black aspergilli, alternative approaches for degrading plant biomass, and indications for the genetic basis of stress response. A genome-wide phylogenetic analysis demonstrated in detail the relationship of the newly genome sequenced species with other aspergilli. Many aspects of biological differences between fungal species cannot be explained by current knowledge obtained from genome sequences. The comparative genomics and experimental study, presented here, allows for the first time a genus-wide view of the biological diversity of the aspergilli and in many, but not all, cases linked genome differences to phenotype. Insights gained could be exploited for biotechnological and medical applications of fungi.

EEG data has been used to discriminate levels of mental workload when classifiers are created for each subject, but the reliability of classifiers trained on multiple subjects has yet to be investigated. Artificial neural network and naive Bayesian classifiers were trained with data from single and multiple subjects and their ability to discriminate among three difficulty conditions was tested. When trained on data from multiple subjects, both types of classifiers poorly discriminated between the three levels. However, a novel model, the naive Bayesian classifier with a hidden node, performed nearly as well as the models trained and tested on individuals.

This research investigated an observer's ability to track and maintain multiple uniquely identified objects in a dynamic environment similar to ATC. The experimental task consisted of tracking a set of moving objects for twenty seconds. The objects were 6-character strings; three letters followed by three numbers. After tracking the objects for twenty seconds, the participants were instructed to locate a target-object. The time required to find the target object was recorded. The number of objects and the magnitude of direction changes (entropy) were manipulated. We found (1) a highly significant effect of increasing response times as a function of number of objects, (2) a marginally significant effect of entropy, decreasing response times as a function of entropy, and (3) a significant effect of displacement, increasing response times as a function of objects' displacement.