Elevated protein synthesis is a fundamental mechanism contributing to a growing heart, during which both translation efficiency and capacity are enhanced (8-10). Translation efficiency focus on existing ribosomes in the cytosol, involving how signaling pathways regulate translation initiation, elongation, termination and ribosome recycling to affect translation in a cell(11-14). In the cardiomyocytes, mRNA of MHC loading to the polysomes(15), phosphorylation of ribosomal protein, S6, or of the peptide chain initiation factor, elF-4E by mTOR (16) are well-characterized mechanisms of translation efficiency regulation. While little of translation capacity, or ribosome biogenesis in the heart has been explored. � In the heart, ribosome biogenesis is vital in rapid growth conditions such as cardiac development, hypertrophy and pulmonary artery stenosis(9). The left ventricular weight increases 82% in 4 days following birth in the newborn pig(17) serves as a good example illustrating that regulation of translation efficiency only cannot meet the demand of elevated protein synthesis.

Biogenesis of ribosomes, the only and universal translation machinery in the cell, is a tremendous work. All three RNA polymerases, estimated 70% of mature ribosomes and more than 200 assembly factors are devoted in this tightly controlled process(7, 18, 19). It starts from rDNA transcription, the pre-ribosomal RNA 45S/47S is synthesized by Pol I, followed by ribosomal RNA processing in the nucleoli, carried out by ~150 exonucleases, processing factors and ribosomal subunit assembly factors. The rRNA stands as a scaffold to be decorated by ribosomal proteins, then exported into the cytosol as the 40S and 60S subunits. In the heart, regulation of this assembly chain is largely dark as only few studies examined the rDNA transcription level.

In the thesis, we identified a long non-coding RNA Miat is associated with cardiac hypertrophy from an unbiased approach and analyzed the transcriptional change in the pressure overload mouse model to discover that Miat regulates the ribosomal genes and ribosome assembly associated genes. To confirm its impact on translation, we found that Miat is required for increased protein synthesis in cardiomyocytes. More remarkably, lack of Miat showed protective effect to the heart in multiple stress-induced cardiac growth models. Mechanistically, we found that Miat binds to nucleolin (NCL) via conserved binding motifs, and such binding is vital for ribosome biogenesis. NCL is a protein required for rDNA transcription and functions as the early processor of 45S/47S rRNA (1, 20, 21), however, how NCL mediates 45S/47S rRNA processing in the nucleoli region remained unknown. We found that Miat-NCL binding is vital for rRNA processing, nucleoli formation and ribosome biogenesis in the cardiomyocytes. Therefore, our study provided one of the first detailed examples of how ribosome biogenesis is regulated in the heart and how alteration of translation capacity affects cardiac function in pathological growth.

This dissertation covers several topics in the second-order bias and mean squared error (MSE) of quantile and expectile estimators.

Chapter one presents the introduction of this dissertation. Thefinite sample theory using higher order asymptotics provides better approximations of the bias and MSE for a class of estimators. Rilstone, Srivastava and Ullah (1996) provided the second-order bias results of conditional mean regression. The goal of this dissertation is to develops analytical results on the second-order bias and MSE for quantile and expectile estimators.

Chapter two develops new analytical results on the second-order bias up to order O(N^-1) and MSE up to order O(N^-2) of the conditional quantile regression estimators. First, we provide the general results on the second-order bias and MSE of conditional quantile estimators. The second-order bias result enables an improved bias correction and thus to obtain improved quantile estimation. In particular, we show that the second-order bias are much larger towards the tails of the conditional density than near the median, and therefore the benet of the second order bias correction is greater when we are interested in the deeper tail quantiles, e.g., for the study of income distribution and financial risk management. The higher order MSE result for the quantile estimation also enables us to better understand the sources of estimation uncertainty. Next, we consider three special cases of the general results, for the unconditional quantile estimation, for the conditional quantile regression with a binary covariate, and for the instrumental variable quantile regression (IVQR). For each of these special cases, we provide the second-order bias and MSE to illustrate their behavior which depends on certain parameters and distributional characteristics. The Monte Carlo simulation indicates that the bias is larger at the extreme low and high tail quantiles, and the second-order bias corrected estimator has better behavior than the uncorrected ones in both conditional and unconditional quantile regression. The second-order bias corrected estimators are numerically much closer to the true estimators of data generating processes. As the higher order bias and MSE decrease as the sample size increases or as the regression error variance decreases, the benefits of the finite sample theory are more apparent when there are larger sampling errors in estimation.

Chapter three develops the second-order asymptotic properties (bias and mean squared error) of the asymmetric least squares (ALS) or expectile estimator, extending the second-order asymptotic results for the symmetric least squares (LS) estimators of Rilstone, Srivastava and Ullah (1996). The LS gives the mean regression function while the ALS gives the "expectile" regression function, a generalization of the usual regression function. The second-order bias result enables an improved bias correction and thus to obtain improved ALS estimation. In particular, we show that the second-order bias is much larger as the asymmetry is stronger, and therefore the benet of the second-order bias correction is greater when we are interested in extreme expectiles which are used as a risk measure in financial economics. The higher order MSE result for the ALS estimation also enables us to better understand the sources of estimation uncertainty. The Monte Carlo simulation confirms the benefits of the second-order asymptotic theory and indicates that the second-order bias is larger at the extreme low and high expectiles, and the second-order bias correction improves the ALS estimator in bias.

Chapter four introduces the predictive quantile regression and predictive expectile regression. Predictive regression is a fundamental econometric model and widely discussed in finance literature. This chapter focuses on the second-order bias reduction for both regression models, which enable us to obtain a better predictive estimates. An empirical application to stock return prediction using the dividend yield illustrates the benet of the proposed second-order bias reduction method. We show that the bias is larger at the tails of the stock return distribution.

Chapter five contains the conclusion.

Importance

Objective

Design, setting, and participants

Main outcomes and measures

Results

Conclusions and relevance