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Articles by A Le
Total Records ( 2 ) for A Le
  C. E Foulds , A Tsimelzon , W Long , A Le , S. Y Tsai , M. J Tsai and B. W. O'Malley
 

The human steroid receptor RNA activator (SRA) gene encodes both noncoding RNAs (ncRNAs) and protein-generating isoforms. In reporter assays, SRA ncRNA enhances nuclear receptor and myogenic differentiation 1 (MyoD)-mediated transcription but also participates in specific corepressor complexes, serving as a distinct scaffold. That SRA RNA levels might affect some biological functions, such as proliferation, apoptosis, steroidogenesis, and myogenesis, has been reported. However, the breadth of endogenous target genes that might be regulated by SRA RNAs remains largely unknown. To address this, we depleted SRA RNA in two human cancer cell lines with small interfering RNAs and then assayed for changes in gene expression by microarray analyses. The majority of significantly changed genes were reduced upon SRA knockdown, implicating SRA RNAs as endogenous coactivators. Unexpectedly, only a small subset of direct estrogen receptor- target genes was affected in estradiol-treated MCF-7 cells. Eight bona fide SRA downstream target genes were identified (SLC2A3, SLC2A12, CCL20, TGFB2, DIO2, TMEM65, TBL1X, and TMPRSS2), representing entirely novel SRA targets, except for TMPRSS2. These data suggest unanticipated roles for SRA in glucose uptake, cellular signaling, T3 hormone generation, and invasion/metastasis. SRA depletion in MDA-MB-231 cells reduced invasiveness and expression of some genes critical for this process. Consistent with the knockdown data, overexpressed SRA ncRNA coactivates certain target promoters and may enhance the activity of some coregulatory proteins. This study is a valuable resource because it represents the first genome-wide analysis of a mammalian RNA coregulator.

  A Le , K. J Singleton and Q. Dai
 

This article develops a rich class of discrete-time, nonlinear dynamic term structure models (DTSMs). Under the risk-neutral measure, the distribution of the state vector Xt resides within a family of discrete-time affine processes that nests the exact discrete-time counterparts of the entire class of continuous-time models in Duffie and Kan (1996) and Dai and Singleton (2000). Under the historical distribution, our approach accommodates nonlinear (nonaffine) processes while leading to closed-form expressions for the conditional likelihood functions for zero-coupon bond yields. As motivation for our framework, we show that it encompasses many of the equilibrium models with habit-based preferences or recursive preferences and long-run risks. We illustrate our methods by constructing maximum likelihood estimates of a nonlinear discrete-time DTSM with habit-based preferences in which bond prices are known in closed form. We conclude that habit-based models, as typically parameterized in the literature, do not match key features of the conditional distribution of bond yields.

 
 
 
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