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Evaluators and research staff need to be trained in these strategies in order to maximize the success of interactions, and to ensure consistency of testing procedures and intervention implementation.
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For further improving the success of interaction fingerprint scoring in the future, it might be useful to expand the PADIF approach to the scoring functions of other docking programs and also to transfer the underlying concept of frequency weighted, multiple reference scoring to other interaction fingerprints.
The success of competitive interactions between plants determines the chance of survival of individuals and eventually of whole plant species.
Yet, it is well known that nonverbal motor behaviors play a fundamental role in the success of our interactions [1].
Yet, we know little about how individual differences variables such as spatial ability and prior knowledge of the navigational space contribute to the success of human robot interactions.
Ability, motivation, and opportunity are important factors that explain the success of this interaction process.
These ties depend on the success of the interaction in the public good game.
For kinases and for the other enzymes, scoring success of the interaction fingerprint methods strongly depends on the dataset.
Interestingly, the success of this interaction depends on the DNA methylation status of the PU.1 regulatory elements.
In order to test predictions from the mechanical model, we setup an experiment to focus on the effect of F/M TTL ratio, and the correlated MCG/FTG ratio, on the success of males in interactions during mating attempts.
These attributes make them an appealing subject for the study of adaptation and evolutionary success in parthenogens, and of interactions between parthenogens and their parental taxa.
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