Although more difficult to detect, interactions between genotype and environment are involved.
We use this novel spatiotemporal formulation to detect interactions between two individuals from video in scale, time, and space.
Third, we can detect interactions with varying durations, even those that differ significantly from those seen during training.
We first generate a feature pyramid of the types in D to detect interactions at various scales.
This chapter describes a method that can detect interactions of membrane proteins with other soluble or membrane proteins in vivo.
Especially, we detect interactions involving the hydrophobic core, residues K16 and E22/D23 of the Aβ sequence.
Y2H might not detect interactions that are dependent on post-translational modifications or that should be stabilized by the presence of another protein.
We confirmed the in vivo interaction using Bimolecular Fluorescence Complementation (BiFC) methods that detect interactions between two proteins in living cells.
We also detail the procedures for training such a model on a small set of video examples, and to detect interactions in unsegmented videos.
We detect interactions in both space and time and use the average intersection over union of the ground truth G and detected tube P as in [17].
(DOC 3 MB) 13568_2011_39_MOESM3_ESM.DOC Additional file 3: Results of EMSA performed to detect interactions of SimReg1 to part of the simD4 gene.
