Nicholette Zeliadt manages The Transmitters’s staff reporters and interns, and she commissions and edits news articles. She joined The Transmitter as news writer in 2014. Before that, she was a freelance writer and editor. Her work has appeared in The New York Times, Scientific American, Nature Medicine, Science and The Scientist.
Nicholette Zeliadt
Managing editor
The Transmitter
From this contributor
Leaving lasting marks with Tessa Montague
Spotted around the web: INSAR; cerebellar gene expression; pangenome
Spotted around the web: Mapping histones; COVID-19 births; acetaminophen lawsuits
New diagnostic code for PTEN syndrome may spur research
Education
- Ph.D. in environmental health, University of Minnesota
- B.A. in biochemistry, University of Iowa
Fellowships
- AAAS Mass Media Fellowship
Explore more from The Transmitter
The term ‘model organism’ should be reserved for biological systems
By referring to artificial neural networks as model organisms, we run the risk of distorting funding priorities and disregard the explanatory power of a shared evolutionary history across biological organisms.
The term ‘model organism’ should be reserved for biological systems
By referring to artificial neural networks as model organisms, we run the risk of distorting funding priorities and disregard the explanatory power of a shared evolutionary history across biological organisms.
New phase of BRAIN Initiative spawns basal ganglia ‘parts list’
With a standardized cell naming system and infrastructure for data collection and analysis, the basal ganglia project sets the stage for a whole-brain primate atlas.
New phase of BRAIN Initiative spawns basal ganglia ‘parts list’
With a standardized cell naming system and infrastructure for data collection and analysis, the basal ganglia project sets the stage for a whole-brain primate atlas.
Machine-learning model spots autism-linked variants that scramble splicing
The model provides a way to investigate genetic variation in regions of the genome whose functions have been difficult to interpret.
Machine-learning model spots autism-linked variants that scramble splicing
The model provides a way to investigate genetic variation in regions of the genome whose functions have been difficult to interpret.