Darren M. Wells

Chromosome-scale genome assembly of diploid halophyte Thinopyrum bessarabicum excludes J genome from polyploid Thinopyrum ancestry

This paper presents a chromosome-scale genome of Thinopyrum bessarabicum, showing the J genome is not part of polyploid Thinopyrum ancestry and identifies genomic features linked to salt tolerance.

Persistent legacy effects on soil microbiota facilitate plant adaptive responses to drought

The paper shows that soil microbiota legacy effects from precipitation influence plant drought responses, benefiting native grasses but not maize.

The Rapid Anatomics Tool (RAT): A low-cost root anatomical phenotyping pipeline reveals changes in root anatomy along the root axis

The paper introduces RAT, a low-cost root anatomical imaging system, and shows reduced vascular complexity in wheat crown roots from basal to distal regions.

Elucidating tissue and sub-cellular specificity of the entire SUMO network reveals how stress responses are fine-tuned in a eukaryote

This study creates the first complete SUMO Cell Atlas in eukaryotes, revealing how SUMO components vary spatially and are regulated during stress responses in Arabidopsis roots.

Genetic control of the leaf ionome in pearl millet and correlation with root and agromorphological traits

This study identifies genetic factors influencing leaf ion concentrations in pearl millet and links them to root and agromorphological traits to improve nutrient efficiency.

Root metaxylem area influences drought tolerance and transpiration in pearl millet in a soil texture dependent manner

The paper examines how root metaxylem area affects drought tolerance and transpiration in pearl millet, showing soil texture-dependent water use strategies.

RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures

RootNav 2.0 uses deep learning to automatically extract root architectures from plant images, achieving comparable accuracy to semi-automatic methods with significantly increased speed.

Deep Learning for Multi-task Plant Phenotyping

The paper introduces a dataset and a deep learning model for accurate wheat spike and spikelet localization and counting in plant phenotyping.