T. D. Pereira

This paper introduces a segmentation-free root phenotyping method using pose estimation, showing high accuracy and efficiency compared to segmentation-based approaches.

The paper tracks behavioral development in gerbils using machine learning to identify distinct trajectories for food/water acquisition, exploration, and social behaviors, correlating with neural maturation.

This study shows that social isolation in early life disrupts brain development and gene expression in bumblebees, while limited social experience preserves typical patterns.

SLEAP is a deep learning framework for multi-animal pose tracking that enables accurate and efficient tracking of social interactions across various species and experimental conditions.

The paper introduces LEAP, a deep learning method for fast animal pose estimation, validated on fruit flies and mice with high accuracy and speed.