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This finding was extended using new odorants and its constraints and mechanisms were explored.
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This was repeated with the second and third complex scent mixtures and their corresponding key odorant and non-key-odorant mixtures, using new groups of bees for each experiment.
Use new and expensive brands.
Ethyl acetate (Sigma-Aldrich Danmark A/S) and ethanol (VWR Bie & Berntsen A/S) were used as odorants and were of the highest purity available.
Both groups were first trained to distinguish between two new odorants: anisole (S+) and cineole (S-).
After matching or non-matching training with four odorants, rats were tested for transfer twice with four new odorants on each test.
We demonstrate that it is possible to apply this approach using large-scale annotation and classification by applying it to new odorant-binding proteins, which are indeed a diverse family of proteins and pose a lot of challenges for regular identification and classification algorithms.
Based on this analysis, six new scent mixtures (Table 2) were made using either the odorants to which the bees had responded well (key odorant mixtures), or the odorants to which bees had not responded well (non-key-odorant mixtures).
We used the models to predict activity for a new set of odorants and tested the predictions in a new set of measurements from Drosophila ORNs.
We then tested these models by recording in vivo receptor neuron responses to a new set of odorants and successfully predicted the responses of five out of seven receptor neurons.
We describe the fabrication and performance of a biosensor for odorants, using wildtype and engineered mutants of the Italian honeybee (Apis mellifera ligustica) odorant binding protein 14 (OBP14), immobilized onto a reduced graphene oxide field-effect transistor (rGO-FET).
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