Phylogenetic analyses of the four core virion proteins support a new evolutionary model for the origin of the main groups of eukaryotic viruses in the kingdom Bamfordvirae.

Machine learning models of coordinated hippocampal ensemble activity during sharp wave ripple activity encode structure that mirrors the place cell map expressed during exploration, and enable a new paradigm for analyzing and understanding this offline activity.

The HMM-Fisher kernel approach leverages individual signatures of brain dynamics for prediction, which can be used, for example, to search for brain dynamics-informed biomarkers of neuropsychiatric disease or predict treatment response.

A fly's locomotion can be decomposed into a small number of locomotor features which can be modulated by odors and other sensory information.

A stochastic model of locomotory control in C. elegans based on an extensive new set of tracking data can explain and predict effects of ablations and mutations on behavior.

A general machine learning scheme for integrating time-series data from single-molecule experiments and molecular dynamics simulations is proposed and successfully demonstrated for the folding dynamics of the WW domain.

Whole-night fMRI-based sleep classification uncovers distinct substates within N2 and REM sleep stages, along with a transition structure between them.

Hidden Markov Modeling to fMRI data reveals how language comprehension arises from the dynamic interplay of large-scale brain networks.

Over 7,000 novel translated sequences have been identified from human cells, including several hundred in annotated noncoding RNA, pseudogenes and de novo assembled transcripts.

Protein language deep learning models can quickly and accurately translate amino acid sequences into profile hidden Markov models or a structure alphabet, dramatically improving remote homology search sensitivity without compromising space or time efficiency.