PRL alumnus, Richard Vierstra, elected to National Academy of Sciences
A MSU-DOE Plant Research Laboratory (PRL) alumnus and leading plant scientist, Richard Vierstra, has been elected to the National Academy of Sciences.
Vierstra was a former PRL graduate student in the lab of Kenneth Poff. He is currently the George and Charmaine Mallinckrodt Professor of Biology at Washington University in St. Louis.
Vierstra’s current work focuses on intracellular protein degradation in plants and the roles of the ubiquitin-26S proteasome and autophagy in this breakdown. He also studies how the phytochrome family of photoreceptors help plants sense light and entrain their growth and development to the daily and annual cycles in their light environment.
“It was a great honor to be elected into the National Academy,” Vierstra says. “Much of my lab’s success over the years began with the excellent graduate training provided to me by the PRL; it was certainly a rich experience.”
The 2018 academy cadre includes 84 new members and 21 foreign associates in recognition of their distinguished and continuing achievements in original research.
The National Academy of Sciences is a private, nonprofit institution established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and – with the National Academy of Engineering, Institute of Medicine and National Research Council – provides science, technology and health policy advice to the federal government and other organizations.
Researchers are integrating their work into undergraduate cell and molecular biology laboratory courses at Michigan State University through the use of Arabidopsis mutant screenings.
MSU-DOE Plant Research Laboratory (PRL) scientists have published a new study that furthers our understanding of how plants make membranes in chloroplasts, the photosynthesis powerhouse
A new AI system, called DeepLearnMOR, can identify organelles and classify hundreds of microscopy images in a matter of seconds and with an accuracy rate of over 97%. The study illustrates the potential of AI to significantly increase the scope, speed, and accuracy of screening tools in plant biology.