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Loïc Rajjou

Publications and source records attributed to Loïc Rajjou.

2 recordsLinked to original sources

Seed-applied biocontrol: towards a new generation of protective strategies

Seeds are fundamental to agricultural productivity but also act as potential vectors for pathogens, leading to substantial losses at seedling emergence. This study focuses on one major arable crop (i.e., Wheat) which is subject to significant biotic stress. The objective is to evaluate the efficacy of biocontrol agents as sustainable alternatives to conventional seed treatments, with the goal of enhancing seed protection and increasing tolerance to biotic threats. Multi-season trials were conducted, integrating physiological, molecular, and metabolic analyses to elucidate the responses of treated seeds across key developmental stages (development, maturation, and germination). The findings indicate that the effectiveness of biocontrol agents is influenced by both the genotype and physiological stage of the seeds. Biocontrol treatments were shown to induce specific defence mechanisms and reduce pathogen pressure. This research study will pave the way to develop robust assessment tools for evaluating the performance of biocontrol strategies and to optimise their deployment in seed protection frameworks.

q-bio.OT

Estimation of large block structured covariance matrices: Application to "multi-omic" approaches to study seed quality

Motivated by an application in high-throughput genomics and metabolomics, we propose a novel, efficient and fully data-driven approach for estimating large block structured sparse covariance matrices in the case where the number of variables is much larger than the number of samples without limiting ourselves to block diagonal matrices. Our approach consists in approximating such a covariance matrix by the sum of a low-rank sparse matrix and a diagonal matrix. Our methodology also can deal with matrices for which the block structure appears only if the columns and rows are permuted according to an unknown permutation. Our technique is implemented in the R package \texttt{BlockCov} which is available from the Comprehensive R Archive Network (CRAN) and from GitHub. In order to illustrate the statistical and numerical performance of our package some numerical experiments are provided as well as a thorough comparison with alternative methods. Finally, our approach is applied to the use of "multi-omic" approaches for studying seed quality.

stat.ME