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https://www.selleckchem.com/products/tabersonine.html Shoot biomass significantly increased with both PSB2 and PSB5. Root and shoot physiology significantly improved with PSB1 (lowest PSC) and PSB4 (moderate PSC), notably shoot total P (78.38%) and root phosphatase activity (390%). Moreover, nutrients acquisition and chlorophyll content increased in inoculated plants and was stimulated (PSB2, PSB4) more than rhizosphere P-solubilization, which was also revealed by the significant above- and below-ground inter-correlations, mainly chlorophyll and both total (R = 0.75, p = 0.001**) and intracellular (R = 0.7, p = 0.000114*) P contents. These findings demonstrate the necessity to timely monitor the plant-rhizosphere continuum responses, which may be a relevant approach to accurately evaluate PSB through considering below- and above-ground relationships; thus enabling unbiased interpretations prior to field applications.Private and public breeding programs, as well as companies and universities, have developed different genomics technologies that have resulted in the generation of unprecedented amounts of sequence data, which bring new challenges in terms of data management, query, and analysis. The magnitude and complexity of these datasets bring new challenges but also an opportunity to use the data available as a whole. Detailed phenotype data, combined with increasing amounts of genomic data, have an enormous potential to accelerate the identification of key traits to improve our understanding of quantitative genetics. Data harmonization enables cross-national and international comparative research, facilitating the extraction of new scientific knowledge. In this paper, we address the complex issue of combining high dimensional and unbalanced omics data. More specifically, we propose a covariance-based method for combining partial datasets in the genotype to phenotype spectrum. This method can be used to combine partially overlapping relationship/covariance matrices

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