To engineer proteins with useful functions, researchers usually begin with a natural protein that has a desirable function, such as emitting fluorescent light, and put it through many rounds of random mutation that eventually generate an optimized version of the protein.

On the surface, the movement disorder amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease, and the cognitive disorder frontotemporal lobar degeneration (FTLD), which underlies frontotemporal dementia, manifest in very different ways. In addition, they are known to primarily affect very different regions of the brain.

Using machine learning, the computational method can provide details of how materials work as catalysts, semiconductors, or battery components.

The molecules, known as acenes, could be useful as organic light-emitting diodes or solar cells, among other possible applications.

Connectomics, the ambitious field of study that seeks to map the intricate network of animal brains, is undergoing a growth spurt. Within the span of a decade, it has journeyed from its nascent stages to a discipline that is poised to (hopefully) unlock the enigmas of cognition and the physical underpinning of neuropathologies such as in Alzheimer’s disease. 

While any two human genomes are about 99.9 percent identical, genetic variation in the remaining 0.1 percent plays an important role in shaping human diversity, including a person’s risk for developing certain diseases.