Optimizing taxon addition order and branch lengths in the construction of phylogenetic trees using maximum likelihood

The potential of novel bacterial isolates from healthy ginseng for the control of ginseng root rot disease (Fusarium oxysporum)

Ginseng root rot caused by Fusarium oxysporum is serious disease that impacts ginseng production. In the present study, 145 strains of bacteria were isolated from the rhizosphere soil of healthy ginseng plants. Three strains with inhibitory activity against Fusarium oxysporum (accession number AF077393) were identified using the dual culture tests and designated as YN-42(L), YN-43(L), and YN-59(L). Morphological, physiological, biochemical, 16S rRNA gene sequencing and phylogenetic analyses were used to identify the strains as Bacillus subtilis [YN-42(L)] (accession number ON545980), Delftia acidovorans [YN-43(L)] (accession number ON545981), and Bacillus polymyxae [YN-59(L)] (accession number ON545982). All three isolates effectively inhibited the growth of Fusarium oxysporum in vitro and the antagonistic mechanism used by the three strains involved the secretion of multiple bioactive metabolites responsible for the hydrolysis of the fungal cell wall. All three biocontrol bacteria produce indoleacetic acid, which has a beneficial effect on plant growth. From our findings, all three antagonistic strains can be excellent candidates for ginseng root rot caused by the pathogenic fungus Fusarium oxysporum. These bacteria have laid the foundation for the biological control of ginseng root rot and for further research on the field control of ginseng pathogens.

Genetic Mean Reversion Strategy for Online Portfolio Selection with Transaction Costs

Online portfolio selection (OLPS) is a procedure for allocating portfolio assets using only past information to maximize an expected return. There have been successful mean reversion strategies that have achieved large excess returns on the traditional OLPS benchmark datasets. We propose a genetic mean reversion strategy that evolves a population of portfolio vectors using a hybrid genetic algorithm. Each vector represents the proportion of the portfolio assets, and our strategy chooses the best vector in terms of the expected returns on every trading day. To test our strategy, we used the price information of the S&P 500 constituents from 2000 to 2017 and compared various strategies for online portfolio selection. Our hybrid genetic framework successfully evolved the portfolio vectors; therefore, our strategy outperformed the other strategies when explicit or implicit transaction costs were incurred.