Mohamed Hijri

The paper develops a machine learning framework to predict plant responsiveness to arbuscular mycorrhizal fungi using functional traits and environmental variables, finding that specific leaf and root traits are key predictors.

This study characterizes the bacterial communities in different compartments of Citrullus colocynthis in a semi-arid ecosystem, revealing compartment-specific diversity and functional traits related to stress tolerance and plant colonization.

This paper designs a synthetic bacterial consortium from arid plant endophytes to improve crop growth in controlled and natural soil conditions.

This paper isolates and characterizes a novel Rhizobium species, Rhizobium moroccans, from Peganum harmala roots in Moroccan drylands, revealing its plant-growth-promoting traits and adaptation to arid environments.

This study characterizes bacterial communities in sabkha ecosystems, showing that abiotic factors drive microbial structure and that halophilic core microbiomes are resilient and culturable.

The paper presents a chromosome-level phased genome assembly of the Argan tree using PacBio HiFi and Hi-C data, achieving high completeness and resolving telomere-to-telomere chromosomes.

SeSaMe classifies short metagenomic sequences from AMF-associated microorganisms, achieving genus-level accuracy for bacteria, fungi, and AMF.

SeSaMe PS Function analyzes short sequences from arbuscular mycorrhizal fungi metagenomes using codon usage and structural properties to identify functional sites.