A machine learning method for action detection is developed and applied toward mouse grooming behavior.

New measures of influenza virus fitness could improve vaccine strain selection through more accurate forecasts of the evolution of the virus.

The combination of phenotypic measures of antigenic drift and genotypic measures of functional constraint improves the accuracy of long-term seasonal influenza A/H3N2 forecasts.

Three complementary computational approaches reveal a set of putative causes of initiation of pan-USA waves of influenza.

A general method for inferring fitness from a sample of nucleotide sequences can predict the progenitor of next year's seasonal influenza.

Machine learning and experimental tests of receiver bias identify signal components critical to correct species classification in guenons, linking face pattern diversity to selection for species discrimination.

Swine populations worldwide are sporadically infected by influenza viruses from humans and birds leading to geographically heterogeneous swine influenza virus populations that pose epizootic and pandemic threats.

New reconstruction methods are used to create a publicly available dense reconstruction of the neurons and chemical synapses of central brain of Drosophila, with analysis of its graph properties.

Development of a fully automated pain scale using machine learning tools in computational neuroethology and creation of new software, reveals a robust circuit-dissection compatible platform for objective pain measurement.

A proposal for a complete cellular/network implementation of trial-and-error motor learning in the olivo-cerebellar system.