Automated Author Profile

Yuan, Chenyang

Toyota Research Institute

Current S-Index

1.8

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

1.8

Average Dataset Index per dataset

Total Datasets

1

Total datasets for this author

Average FAIR Score

76.9%

Average FAIR Score per dataset

Total Citations

2

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

A machine learning enabled approach to assess trade-offs between growth and stress tolerance in Pooideae grasses following domestication (Version: 6)

Plant domestication may create trade-offs between growth and stress tolerance, raising concerns about yield stability in future climates. Previous studies have found limited direct evidence for such trade-offs, often focusing on weakened defenses associated with higher growth rates. Trade-offs can also occur when traits optimized for favorable conditions perform less efficiently under stress. Deciphering these mechanisms is crucial for maintaining growth in changing environments. We examine one key aspect of vegetative growth, leaf elongation, in six species of grasses. We use a machine learning-enabled pipeline to extract cell dimensions and positions from leaf microscope images to study cell kinematics. We find that domesticated plants generally have longer leaves, larger division zones, and higher cell production rates. While no clear trade-off is observed between domestication and drought response in final leaf length, a trade-off occurs in development; wild species exhibit a smaller decrease in the elongation zone size under drought compared with domesticated species. This pattern points to compensatory mechanisms, such as extended elongation duration or increased cell production, mitigating drought effects in domesticated plants. These nuanced trade-offs associated with domestication highlight the importance of robustly phenotyping developmental and physiological traits, possibly informing breeding strategies to enhance crop resilience in future climates.

Authors

  • Yun, Jie ;
  • Yuan, Chenyang ;
  • Irelan, Katherine ;
  • Kabongo, Marie-Jeanne ;
  • Urkumbayev, Eldar ;
  • Des Marais, David
2 Citations0 Mentions77% FAIR1.8 Dataset Index
10.5061/dryad.47d7wm3ppAugust 2025