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.

To make reliable but metabolically efficient perceptual inferences in a changing world, neural systems should dynamically adapt based on surprise and uncertainty about the sensory environment.

A description of evolution that is based on birth-death processes, and in which fitness is at most a derived quantity, is advocated.

Mathematical modeling supports a scenario where cell-cell adhesion gradually evolves through natural selection, leading to the emergence of cohesive aggregates in microbial populations.

A model of purely competitive ecological dynamics is shown to be equivalent to adaptive evolution of a single individual, suggesting a new way to formalize the "superorganism" metaphor.

A new approach focusing on dynamic modularity helps overcome traditional challenges detecting modularity in complex adaptive systems.

Analysis of experiments on bacteria suggests that the dependence of cell size on growth rate is not an adaptation but a causal consequence of a regulatory mechanism that controls DNA replication.

Each stage of the scientific life cycle stands to benefit from the introduction of data science techniques.

The great pond snail is a multipurpose model organism and a contemporary choice for addressing a wide range of biological questions, problems and phenomena in the laboratory and the field.