Incorporating within-host diversity in transmission, as identified by deep sequencing, can significantly change previously-held inferences, with major implications for genomic studies of transmission in tuberculosis and other pathogens as well.
The DNA-bridging efficiency of H-NS, a genome organising and transcription regulatory protein, is modulated by changes in environmental conditions of the cell, which drive a structural rearrangement of the protein.
DeepFly3D, a deep learning-based software, measures limb and appendage movements in tethered, behaving Drosophila and enables precise behavioral measurements during neural recordings, stimulation, and other biological experiments.
A deep learning-based pipeline was developed for extracting cellular signals flexibly from moving cells in 3D time lapse images, and it outperformed previous methods under different imaging conditions.
A multi-compartment spiking neural network model demonstrates that biologically feasible deep learning can be achieved if sensory inputs and higher-order feedback are received by different dendritic compartments.