SummaryTrichothecenes are isoprenoid mycotoxins produced in wheat infected with the filamentous fungus Fusarium graminearum. Some fungal genes for trichothecene biosynthesis (Tri genes) are known to be under control of transcription factors encoded by Tri6 and Tri10. Tri6 and Tri10 deletion mutants were constructed in order to discover additional genes regulated by these factors in planta. Both mutants were greatly reduced in pathogenicity and toxin production and these phenotypes were largely restored by genetic complementation with the wild-type gene. Transcript levels for over 200 genes were altered Ն twofold for Dtri6 or Dtri10 mutants including nearly all known Tri genes. Also reduced were transcript levels for enzymes in the isoprenoid biosynthetic pathway leading to farnesyl pyrophosphate, the immediate molecular precursor of trichothecenes. DNA sequences 5Ј to isoprenoid biosynthetic genes were enriched for the Tri6p DNA binding motif, YNAGGCC, in F. graminearum but not in related species that do not produce trichothecenes. To determine the effect of trichothecene metabolites on gene expression, cultures were treated with trichodiene, the first metabolic intermediate specific to the trichothecene biosynthetic pathway. A total of 153 genes were upregulated by added trichodiene and were significantly enriched for genes likely involved in cellular transport. Differentially regulated genes will be targeted for functional analysis to discover additional factors involved in toxin biosynthesis, toxin resistance and pathogenesis.

No abstract

a b s t r a c tThis paper studies bias correction methods for Random Forest in regression. Random Forest is a special bagging trees that can be used in regression and classification. It is a popular method because of its high prediction accuracy. However, we find that Random Forest can have significant bias in regression at times. We propose a method to reduce the bias of Random Forest in regression using residual rotation. The real data applications show that our method can reduce the bias of Random Forest significantly.

No abstract

No abstract