RESUMEN
MOTIVATION: As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data. We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth. RESULTS: We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data. AVAILABILITY AND IMPLEMENTATION: All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl.
Asunto(s)
Conducta Animal , Drosophila , Larva , Programas Informáticos , Animales , Conducta Animal/fisiología , Grabación en Video/métodos , Redes Neurales de la ComputaciónRESUMEN
Chiral mesoporous materials have been prepared with purely organic monomers employing an enantiomerically pure biphenyl bis-siloxane as a dopant along with simple 4,4'-bis(triethoxysilyl)biphenyl as the bulk material. Analysis of these materials by CD spectroscopy indicates the formation of new chiral aggregates in the solid state consistent with charge delocalization from adjacent aryl rings in close contact. These aggregates are only observed when the biphenyl and enantiomerically pure biaryl are both present. The red-shift resulting of the CD signal resulting from these interactions is modeled computationally using time-dependent DFT calculations.