Daniel Kunk

This study examines transcriptional rhythmicity in aphids under different light and host conditions, finding limited rhythmic transcripts and host-dependent regulation.

This study investigates how aphid infestation affects diurnal rhythms in wheat by analyzing transcriptomic and metabolomic changes.

This paper develops a machine learning approach to automate EPG signal segmentation and classification, achieving high accuracy in identifying insect feeding behaviors.

DiscoEPG is a Python package that automatically annotates insect EPG signals with improved accuracy using machine learning, offering tools for analysis and visualization.

This study identifies diurnal rhythms in aphid metabolism and salivary effector expression, showing their impact on feeding behavior and host colonization.