MSU students win at 2019 ASPB Midwest Meeting
Michigan State University student researchers won multiple awards presenting their research at the 2019 Annual Meeting – Midwestern Section of the American Society of Plant Biologists.
Chase Lindeboom, an undergraduate in the lab of Christoph Benning and Biochemistry and Molecular Biology department: 2nd place in the undergraduate oral presentation category. He discussed the genetic and molecular analysis of a protein invoved in cell cycle regulation in the microalga Chlamydomonas.
Briaunna Murray, an undergraduate in the lab of Susanne Hoffmann-Benning and the Neuroscience program: 2nd place in the undergraduate poster presentations category. Briaunna presented her work on the generation, genotyping and phenotyping of plants overexpressing a phloem lipid-binding protein in the plant Arabidopsis.
Amanda Koenig, a grad student in the Hoffmann-Benning lab and a double major in Genetics and Molecular Plant Sciences: 3rd place in the graduate student oral presentation category. Amanda discussed her findings on the function of the lipid phosphatidic acid in long-distance stress and developmental signaling in plants.
ASPB Midwest meeting, which took place at West Virginia University, “provides scientists at all career stages opportunities to discuss research efforts, teaching programs, funding scenarios, and career designs,” in the field of plant biology. It is also an opportunity for students to practice presentation skills.
Congratulations to all that were recognized at this meeting!
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.