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Summary Plant secondary growth drives stem thickening and biomass accumulation, but its regulation is not yet fully understood.We have identified a novel semi‐dominant mutant,rbe‐d, characterized by a significant reduction in cambium cells and a complete absence of secondary growth in interfascicular regions in the stem. Gene cloning experiments indicated that the activation of the C2H2 zinc finger transcription factor, AT5G06070/RABBIT EARS (RBE), is responsible for therbe‐dphenotype.Transgenic analysis confirmed that overexpression of RBE represses secondary growth, while therbe‐2mutant increased the width of the interfascicular cambium‐derived (ICD) region. TheRBEgene is expressed in the procambium and cambium regions. Transcriptomic analysis showed that genes of the tracheary element differentiation inhibitory factor‐phloem intercalated with xylem (TDIF‐PXY) central regulatory pathway are repressed in therbe‐dmutant plants. Biochemical analyses confirmed that RBE binds directly to the promoter of WUSCHEL‐related homeobox (WOX4), a TDIF‐PXY downstreamWOXgene that regulates cambium cell proliferation. Moreover, genetic analysis confirmed thatWOX4is epistatic toRBEin secondary growth.Our results indicate that RBE inhibits cambium proliferation and thereby impacts secondary growth by directly repressingWOX4. These findings offer valuable new insight into the regulation of secondary growth in the Arabidopsis stem. 

Summary This paper is concerned with empirical likelihood inference on the population mean when the dimension $$p$$ and the sample size $$n$$ satisfy $$p/n\rightarrow c\in [1,\infty)$$. As shown in Tsao (2004), the empirical likelihood method fails with high probability when $p/n>1/2$ because the convex hull of the $$n$$ observations in $$\mathbb{R}^p$$ becomes too small to cover the true mean value. Moreover, when $p> n$, the sample covariance matrix becomes singular, and this results in the breakdown of the first sandwich approximation for the log empirical likelihood ratio. To deal with these two challenges, we propose a new strategy of adding two artificial data points to the observed data. We establish the asymptotic normality of the proposed empirical likelihood ratio test. The proposed test statistic does not involve the inverse of the sample covariance matrix. Furthermore, its form is explicit, so the test can easily be carried out with low computational cost. Our numerical comparison shows that the proposed test outperforms some existing tests for high-dimensional mean vectors in terms of power. We also illustrate the proposed procedure with an empirical analysis of stock data.