Figure - available from: Archives of Microbiology
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Serratia marcescens forms different colony patterns under distinct conditions. One of them is the concentric fountain-shaped pattern with pigmented center followed by unpigmented ring and pigmented rim. In this work, we study this pattern formation by construction of the mathematical model able to display this pattern based on putative metabolical...
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... short-wave infrared region (1,400-2,400 nm) were relatively lower than those in the visible spectrum region (including the red edge region). The reflectance in the short-wave infrared region has been associated with different primary and secondary metabolites, such as starch, celluloses, lignin, and other carbohydrates (Türker-Kaya and Huck, 2017;Čepl et al., 2019). In this sense, Paaso et al. (2017) reported that lignin exhibits a relatively low level of genetic control in senescent leaves of Betula pendula. ...
Plants produce a wide diversity of specialized metabolites, which fulfill a wide range of biological functions, helping plants to interact with biotic and abiotic factors. In this study, an integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed to examine the extent of heritable variation of foliar spectral reflectance and to predict the leaf hydrogen cyanide content in a genetically structured population of a cyanogenic eucalyptus (Eucalyptus cladocalyx F. Muell). In addition, the heritable variation (based on pedigree and genomic data) of more of 100 common spectral reflectance indices was examined. The first profile of heritable variation along the spectral reflectance curve indicated the highest estimate of genomic heritability (hg2=0.41) within the visible region of the spectrum, suggesting that several physiological and biological responses of trees to environmental stimuli (ex., light) are under moderate genetic control. The spectral reflectance index with the highest genomic-based heritability was leaf rust disease severity index 1 (hg2=0.58), followed by the anthocyanin reflectance index and the Browning reflectance index (hg2=0.54). Among the Bayesian prediction models based on spectral reflectance data, Bayes B had a better goodness of fit than the Bayes-C and Bayesian ridge regression models (in terms of the deviance information criterion). All models that included spectral reflectance data outperformed conventional genomic prediction models in their predictive ability and goodness-of-fit measures. Finally, we confirmed the proposed hypothesis that high-throughput phenotyping indirectly capture endophenotypic variants related to specialized metabolites (defense chemistry), and therefore, generally more accurate predictions can be made integrating phenomics and genomics.
