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Isolation and characterization of endophytic bacteria associated with roots of jojoba (Simmondsia chinensis (Link) Schneid)

RICARDO VAZQUEZ JUAREZ TANIA ZENTENO SAVIN ENRIQUE MORALES BOJORQUEZ Elvia Pérez Rosales Lilia Alcaráz Meléndez María Esther Puente Eduardo Quiroz Guzmán (2017, [Artículo])

"In this communication, the diversity and beneficial characteristics of endophytic bacteria have been studied in Simmondsia chinensis that has industrial importance because of the quality of its seed oil. Endophytes were isolated (N = 101) from roots of the jojoba plants collected, of which eight were identified by partial sequencing of the 16S rDNA gene. The isolated bacteria were Bacillus sp., Methylobacterium aminovorans, Oceanobacillus kimchi, Rhodococcus pyridinivorans and Streptomyces sp. All isolates had at least one positive feature, characterizing them as potential plant growth promoting bacteria. In this study, R. pyridinivorans and O. kimchi are reported as plant growth promoters."

Endophytic bacteria, plant growth promoters, Simmondsia chinensis, seed oil BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA MICROBIOLOGÍA BACTERIOLOGÍA BACTERIOLOGÍA

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding