Generation of both cortical and Aire(+) medullary thymic epithelial compartments from CD205(+) progenitors

In the adult thymus, the development of self-tolerant thymocytes requires interactions with thymic epithelial cells (TECs). Although both cortical and medullary TECs (cTECs/mTECs) are known to arise from common bipotent TEC progenitors, the phenotype of these progenitors and the timing of the emergence of these distinct lineages remain unclear. Here, we have investigated the phenotype and developmental properties of bipotent TEC progenitors during cTEC/mTEC lineage development. We show that TEC progenitors can undergo a stepwise acquisition of first cTEC and then mTEC hallmarks, resulting in the emergence of a progenitor population simultaneously expressing the cTEC marker CD205 and the mTEC regulator Receptor Activator of NF-κB (RANK). In vivo analysis reveals the capacity of CD205(+) TECs to generate functionally competent cortical and medullary microenvironments containing both cTECs and Aire(+) mTECs. Thus, TEC development involves a stage in which bipotent progenitors can co-express hallmarks of the cTEC and mTEC lineages through sequential acquisition, arguing against a simple binary model in which both lineages diverge simultaneously from bipotent lineage negative TEC progenitors. Rather, our data reveal an unexpected overlap in the phenotypic properties of these bipotent TECs with their lineage-restricted counterparts

Global Integration in Primary Equity Markets: The Role of U.S. Banks and U.S. Investors

We examine the costs and benefits of the global integration of primary equity markets associated with the parallel diffusion of U.S. underwriting methods. We analyze both direct and indirect costs (associated with underpricing) using a unique dataset of 2,132 IPOs by non-U.S. issuers from 65 countries in 1992-1999. Bookbuilding typically costs twice as much as a fixed-price offer, but on its own, does not lead to lower underpricing. However, when conducted by U.S. banks and/or targeted at U.S. investors, bookbuilding can reduce underpricing significantly, relative to fixed-price offerings or bookbuilding efforts conducted by ‘local’ banks. These results are obtained after allowing for the endogeneity and interdependence of issuers’ choices. For the great majority of issuers, the gains associated with lower underpricing outweighed the additional costs associated with hiring U.S. banks or marketing in the U.S. This suggests a quality/price trade-off contrasting with the findings of Chen and Ritter [Journal of Finance 55, 2000], particularly since non-U.S. issuers raising US$20m-80m also typically pay a 7% spread when U.S. banks and investors are involved

METHODS FOR COMPUTATION AND ANALYSIS OF MARKOVIAN DYNAMICS ON COMPLEX NETWORKS

A problem central to many scientific and engineering disciplines is how to deal with noisy dynamic processes that take place on networks. Examples include the ebb and flow of biochemical concentrations within cells, the firing patterns of neurons in the brain, and the spread of disease on social networks. In this thesis, we present a general formalism capable of representing many such problems by means of a master equation. Our study begins by synthesizing the literature to provide a toolkit of known mathematical and computational analysis techniques for dealing with this equation. Subsequently a novel exact numerical solution technique is developed, which can be orders of magnitude faster than the state-of-the-art numerical solver. However, numerical solutions are only applicable to a small subset of processes on networks. Thus, many approximate solution techniques exist in the literature to deal with this problem. Unfortunately, no practical tools exist to quantitatively evaluate the quality of an approximate solution in a given system. Therefore, a statistical tool that is capable of evaluating any analytical or Monte Carlo based approximation to the master equation is developed herein. Finally, we note that larger networks with more complex dynamical phenomena suffer from the same curse of dimensionality as the classical mechanics of a gas. We therefore propose that thermodynamic analysis techniques, adapted from statistical mechanics, may provide a new way forward in analyzing such systems. The investigation focuses on a behavior known as avalanching—complex bursting patterns with fractal properties. By developing thermodynamic analysis techniques along with a potential energy landscape perspective, we are able to demonstrate that increasing intrinsic noise causes a phase transition that results in avalanching. This novel result is utilized to characterize avalanching in an epidemiological model for the first time and to explain avalanching in biological neural networks, in which the cause has been falsely attributed to specific neural architectures. This thesis contributes to the existing literature by providing a novel solution technique, enhances existing and future literature by providing a general method for statistical evaluation of approximative solution techniques, and paves the way towards a promising approach to the thermodynamic analysis of large complex processes on networks

Increasing the detectability of external influence on precipitation by correcting feature location in GCMs

Understanding how precipitation varies as the climate changes is essential to determining the true impact of global warming. This is a difficult task not only due to the large internal variability observed in precipitation but also because of a limited historical record and large biases in simulations of precipitation by general circulation models (GCMs). Here we make use of a technique that spatially and seasonally transforms GCM fields to reduce location biases and investigate the potential of this bias correction to study historical changes. We use two versions of this bias correction—one that conserves intensities and another that conserves integrated precipitation over transformed areas. Focussing on multimodel ensemble means, we find that both versions reduce RMS error in the historical trend by approximately 11% relative to the Global Precipitation Climatology Project (GPCP) data set. By regressing GCMs' historical simulations of precipitation onto radiative forcings, we decompose these simulations into anthropogenic and natural time series. We then perform a simple detection and attribution study to investigate the impact of reducing location biases on detectability. A multiple ordinary least squares regression of GPCP onto the anthropogenic and natural time series, with the assumptions made, finds anthropogenic detectability only when spatial corrections are applied. The result is the same regardless of which form of conservation is used and without reducing the dimensionality of the fields beyond taking zonal means. While “detectability” is dependent both on the exact methodology and the confidence required, this nevertheless demonstrates the potential benefits of correcting location biases in GCMs when studying historical precipitation, especially in cases where a signal was previously undetectable