Status and Multiple Growth Regimes

In order to explain multiple growth regimes, one of the working hypotheses is based on initial conditions. Using a standard optimal growth with the status effect represented by wealth a la Friedman (1953), this paper obtains multiple growth regimes based on initial conditions without reliance on other assumptions such as nonlinearities of production or consumption functions and heterogeneous agents/savings behavior. With the status effect, the resulting equilibrium distribution is characterized by a group with a lower level of income and another group with a higher level of income. Globally, a sufficiently strong monetary policy may be an instrument in order for an economy in poverty traps to take off and become wealthy in the long run. Locally, our model sheds light on the relationship between money/inflation and capital in the long run that, given general cash-in-advance constraints on investment relative to consumption, is determined by the curvature of the utilities of wealth and consumption.one-sector growth model, wealth effect, CIA constraint, takeoff

Inflation and Growth: Impatience and a Qualitative Equivalence

This paper studies the role of an endogenous time preference on the relationship between inflation and growth in the long run in both the money-in-utility-function (MIUF) and transaction costs (TC) models. We establish a qualitative equivalence between the two models in a setup without a labor-leisure tradeoff. When the time preference is decreasing (or increasing) in consumption and real balances, both the MIUF and TC models are qualitatively equivalent in terms of predicting a negative (or positive) relationship between inflation and growth in a steady state. Both a decreasing and an increasing time preference in consumption are consistent with the arguments in the literature. While a decreasing time preference in real balances corroborates with empirical evidence, there is no evidence in support of an increasing time preference in real balances.endogenous time preferences, superneutrality, qualitative equivalence

A Deterministic Equivalent for the Analysis of Non-Gaussian Correlated MIMO Multiple Access Channels

Large dimensional random matrix theory (RMT) has provided an efficient analytical tool to understand multiple-input multiple-output (MIMO) channels and to aid the design of MIMO wireless communication systems. However, previous studies based on large dimensional RMT rely on the assumption that the transmit correlation matrix is diagonal or the propagation channel matrix is Gaussian. There is an increasing interest in the channels where the transmit correlation matrices are generally nonnegative definite and the channel entries are non-Gaussian. This class of channel models appears in several applications in MIMO multiple access systems, such as small cell networks (SCNs). To address these problems, we use the generalized Lindeberg principle to show that the Stieltjes transforms of this class of random matrices with Gaussian or non-Gaussian independent entries coincide in the large dimensional regime. This result permits to derive the deterministic equivalents (e.g., the Stieltjes transform and the ergodic mutual information) for non-Gaussian MIMO channels from the known results developed for Gaussian MIMO channels, and is of great importance in characterizing the spectral efficiency of SCNs.Comment: This paper is the revision of the original manuscript titled "A Deterministic Equivalent for the Analysis of Small Cell Networks". We have revised the original manuscript and reworked on the organization to improve the presentation as well as readabilit

Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table

Interleaved EPI based fMRI improved by multiplexed sensitivity encoding (MUSE) and simultaneous multi-band imaging

© 2014 Chang et al. Functional magnetic resonance imaging (fMRI) is a non-invasive and powerful imaging tool for detecting brain activities. The majority of fMRI studies are performed with single-shot echo-planar imaging (EPI) due to its high temporal resolution. Recent studies have demonstrated that, by increasing the spatial-resolution of fMRI, previously unidentified neuronal networks can be measured. However, it is challenging to improve the spatial resolution of conventional single-shot EPI based fMRI. Although multi-shot interleaved EPI is superior to single-shot EPI in terms of the improved spatial-resolution, reduced geometric distortions, and sharper point spread function (PSF), interleaved EPI based fMRI has two main limitations: 1) the imaging throughput is lower in interleaved EPI; 2) the magnitude and phase signal variations among EPI segments (due to physiological noise, subject motion, and B0 drift) are translated to significant in-plane aliasing artifact across the field of view (FOV). Here we report a method that integrates multiple approaches to address the technical limitations of interleaved EPI-based fMRI. Firstly, the multiplexed sensitivity-encoding (MUSE) post-processing algorithm is used to suppress in-plane aliasing artifacts resulting from time-domain signal instabilities during dynamic scans. Secondly, a simultaneous multi-band interleaved EPI pulse sequence, with a controlled aliasing scheme incorporated, is implemented to increase the imaging throughput. Thirdly, the MUSE algorithm is then generalized to accommodate fMRI data obtained with our multi-band interleaved EPI pulse sequence, suppressing both in-plane and through-plane aliasing artifacts. The blood-oxygenation-level-dependent (BOLD) signal detectability and the scan throughput can be significantly improved for interleaved EPI-based fMRI. Our human fMRI data obtained from 3 Tesla systems demonstrate the effectiveness of the developed methods. It is expected that future fMRI studies requiring high spatial-resolvability and fidelity will largely benefit from the reported techniques.published_or_final_versio

Alendronate prevents angiotensin II-induced collagen I production through geranylgeranylation-dependent RhoA/Rho kinase activation in cardiac fibroblasts

AbstractCollagen I is the main component of extracellular matrix in cardiac fibrosis. Our previous studies have reported inhibition of farnesylpyrophosphate synthase prevents angiotensin II-induced cardiac fibrosis, while the exact molecular mechanism was still unclear. This paper was designed to investigate the effect of alendronate, a farnesylpyrophosphate synthase inhibitor, on regulating angiotensin II-induced collagen I expression in cultured cardiac fibroblasts and to explore the underlying mechanism. By measuring the mRNA and protein levels of collagen I, we found that alendronate prevented angiotensin II-induced collagen I production in a dose-dependent manner. The inhibitory effect on collagen I expression was reversed by geranylgeraniol, and mimicked by inhibitors of RhoA/Rho kinase pathway including C3 exoenzyme and GGTI-286. Thus we suggested geranylgeranylation-dependent RhoA/Rho kinase activation was involved in alendronate-mediated anti-collagen I synthetic effect. Furthermore, we accessed the activation status of RhoA in alendronate-, geranylgeraniol- and GGTI-286-treated cardiac fibroblasts and gave an indirect evidence for RhoA activation via geranylgeranylation. Then we came to the conclusion that in cardiac fibroblasts, alendronate could protect against angiotensin II-induced collagen I synthesis through inhibition of geranylgeranylation and inactivation of RhoA/Rho kinase signaling. Targeting geranylgeranylation and RhoA/Rho kinase signaling will hopefully serve as therapeutic strategies to reduce fibrosis in heart remodeling