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SUMMARY Agrobacterium tumefaciens‐mediated transformation has been for decades the preferred tool to generate transgenic plants. During this process, a T‐DNA carrying transgenes is transferred from the bacterium to plant cells, where it randomly integrates into the genome via polymerase theta (Polθ)‐mediated end joining (TMEJ). Targeting of the T‐DNA to a specific genomic locus via homologous recombination (HR) is also possible, but such gene targeting (GT) events occur at low frequency and are almost invariably accompanied by random integration events. An additional complexity is that the product of recombination between T‐DNA and target locus may not only map to the target locus (true GT), but also to random positions in the genome (ectopic GT). In this study, we have investigated how TMEJ functionality affects the biology of GT in plants, by using Arabidopsis thaliana mutated for the TEBICHI gene, which encodes for Polθ. Whereas in TMEJ‐proficient plants we predominantly found GT events accompanied by random T‐DNA integrations, GT events obtained in the teb mutant background lacked additional T‐DNA copies, corroborating the essential role of Polθ in T‐DNA integration. Polθ deficiency also prevented ectopic GT events, suggesting that the sequence of events leading up to this outcome requires TMEJ. Our findings provide insights that can be used for the development of strategies to obtain high‐quality GT events in crop plants.

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Summary:We attempted to model attention allocation of experienced drivers using the SEEV model. Unlike previous attempts, the present work looked at attention to entities (vehicles, signs, traffic control devices) in the outside world rather than considering the outside world as a unitary construct. Model parameters were generated from rankings of entities by experienced drivers. Experienced drivers drove a scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Eye movements were monitored during the driving session. The results of fitting the observed eye movement data to our SEEV model were poor, and were no better than fitting the data to a randomized SEEV model. A number of explanations for this are discussed.

Summary:We compared the eye movements of novice drivers and experienced drivers while they drove a simulated driving scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Cassavaugh, Bos, McDonald, Gunaratne, & Backs (2013) attempted to model attention allocation of experienced drivers using the SEEV model. Here we compared two SEEV model fits between those experienced drivers and a sample of novice drivers. The first was a simplified model and the second was a more complex intersection model. The observed eye movement data was found to be a good fit to the simplified model for both experienced (R 2 = 0.88) and novice drivers (R 2 = 0.30). Like the previous results of the intersection model for the experienced drivers, the fit of the observed eye movement data to the intersection model for novice drivers was poor, and was no better than fitting the data to a randomized SEEV model. We concluded based on the simplified SEEV model, fixation count and fixation variance that experienced drivers were found to be more efficient at distributing their visual search compared to novice drivers.