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.

The structural characteristics of a circuit and its hierarchical dimensions are shown to influence regional time-series dynamics after targeted cellular-level manipulations.

A generative-model-based, unsupervised learning toolbox for characterizing oscillatory bursting and brain network dynamics in univariate or multivariate time series.

Fish environmental DNA remaining in a cup of seawater provides information about interaction strengths among marine fish species in nature, highlighting the potential impact of global climate change on community dynamics and stability.

Fluctuation of biomarkers is a novel way of studying system stability during stable and unstable states of health and disease, revealing the systems' ability to cope with external perturbations.

A landmark-based cross-modality alignment method robust to variation in landmark sets is applied to annotate an EM time series of Caenorhabditis elegans embryonic development as a community resource.

Intrinsic dynamics are shaped by the intersection of molecular, microstructural, connectomic, and activation gradients.

Properties of various microbial communities time series, such as the noise color and neutrality, are captured by stochastic generalized Lotka-Volterra equations, even in the absence of interactions.

Hybrid brain network models predict neurophysiological processes that link structural and functional empirical data across scales and modalities in order to better understand neural information processing and its relation to brain function.

A simple model provides an accessible framework to infer macroscopic parameters of effective resource competition from longitudinal studies of microbial communities.