In spite of the acknowledged importance of growth-promoting bacteria, only a reduced number of studies were conducted with these microorganisms on Theobroma cacao. The objectives of this work were to study the population densities and genetic diversity of actinomycetes associated with the rhizosphere of cacao as a first step in their application in plant growth promotion and biological control. The populations densities of actinomycetes in soil and cacao roots were similar, with mean values of 1,0 x 10(6) CFU/g and 9,6 x 10(5) CFU/g, respectively. All isolates selected and used in this study were identified through sequencing analyses of a fragment of the rpoB gene that encodes the β-subunit of the RNA polymerase as species of the genus Streptomyces. In vitro cellulolytic, xilanolytic and chitinolytic activity, indolacetic acid production and phosphate solubilization activities were observed in most of the isolates tested. The data obtained in this study demonstrate that actinomycetes account for a higher percentage of the total population of culturable bacteria in soil than on cacao roots. Additionally, actinomycetes from the cacao rhizosphere are genetically diverse and have potential applications as agents of growth promotion.
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http://dx.doi.org/10.1590/S1517-838220080003000010 | DOI Listing |
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Department of Pediatric Endocrinology, Necmettin Erbakan University Faculty of Medicine, Konya, Turkey.
Objective: Children with congenital adrenal hyperplasia are considered to be at an elevated risk for cardiovascular morbidity and mortality. The objective of this study was to evaluate the association between periaortic fat thickness and the cardiometabolic profile in children diagnosed with congenital adrenal hyperplasia.
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Plant Commun
January 2025
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Hubei, China. Electronic address:
In the face of climate change and the growing global population, there is an urgent need to accelerate the development of high-yielding crop varieties. To this end, vast amounts of genotype-to-phenotype data have been collected, and many machine learning (ML) models have been developed to predict phenotype from a given genotype. However, the requirement for high densities of single-nucleotide polymorphisms (SNPs) and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding.
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