Justin S. Dyer

Transcriptional profiling and network analysis of the murine angiotensin II-induced abdominal aortic aneurysm

We sought to characterize temporal gene expression changes in the murine angiotensin II (ANG II)-ApoE-/- model of abdominal aortic aneurysm (AAA). Aortic ultrasound measurements were obtained over the 28-day time-course. Harvested suprarenal aortic segments were evaluated with whole genome expression profiling at 7, 14, and 28 days using the Agilent Whole Mouse Genome microarray platform and Statistical Analysis of Microarrays at a false discovery rate of <1%. A group of angiotensin-treated mice experienced contained rupture (CR) within 7 days and were analyzed separately. Progressive aortic dilatation occurred throughout the treatment period. However, the numerous early expression differences between ANG II-treated and control were not sustained over time. Ontologic analysis revealed widespread upregulation of inflammatory, immune, and matrix remodeling genes with ANG II treatment, among other pathways such as apoptosis, cell cycling, angiogenesis, and p53 signaling. CR aneurysms displayed significant decreases in TGF-β/BMP-pathway signaling, MAPK signaling, and ErbB signaling genes vs. non-CR/ANG II-treated samples. We also performed literature-based network analysis, extracting numerous highly interconnected genes associated with aneurysm development such as Spp1, Myd88, Adam17 and Lox. 1) ANG II treatment induces extensive early differential expression changes involving abundant signaling pathways in the suprarenal abdominal aorta, particularly wide-ranging increases in inflammatory genes with aneurysm development. 2) These gene expression changes appear to dissipate with time despite continued growth, suggesting that early changes in gene expression influence disease progression in this AAA model, and that the aortic tissue adapts to prolonged ANG II infusion. 3) Network analysis identified nexus genes that may constitute aneurysm biomarkers or therapeutic targets.

Corrections to, and comments on, "an improved approximation for the Gaussian Q-Function"

In a recent letter, Karagiannidis and Lioumpas (see ibid., vol.11, no.8, p.644-6, Aug. 2007) proposed a new approximation for the Gaussian Q-function. We provide corrections to two equations, corrected entries to a table, and an alternative to a figure in the letter, as well as a few comments.

Approximations to Error Functions

This paper addresses approximations to error functions and points out three representative approximations, each with its own merits. Codys approximation is the most computationally intensive of the three, it is not overly so, and there is no arguing over its accuracy. The other two approximations are much simpler computationally, and they both yield accuracies that would be considered more than sufficient in most practical situations. Absolute relative error provides an effective measure of goodness, and, for approximations to the Q-function, it also places a loose bound on the absolute error in the approximation. Codys approximation is an effective surrogate for the true error function; the values provided by that approximation match the actual values of the error function to within roughly the precision of double-precision floating point arithmetic.

Designing DS-CDMA spreading sequences via a low-complexity deterministic approach

This paper presents a novel deterministic technique for the generation of DS-CDMA signature sequences with a wide range of correlation properties that is based on a trellis structure and a trellis-exploration algorithm. A low-complexity implementation of the algorithm is then derived by decomposing the calculation of the metric used to generate the codeset. This approach provides a fast method of generating binary or polyphase codes with no inherent constraints on code length or size of the codeset. In addition, the algorithm is iterative and provides increasingly better codes in terms of average mean-square aperiodic correlation properties as the number of iterations increases. The algorithm is shown to outperform existing complex-code design techniques and provides codes with similar correlation properties to well-known codesets while using significantly fewer unique phases

Empirical stationary correlations for semi-supervised learning on graphs

In semi-supervised learning on graphs, response variables observed at one node are used to estimate missing values at other nodes. The methods exploit correlations between nearby nodes in the graph. In this paper we prove that many such proposals are equivalent to kriging predictors based on a fixed covariance matrix driven by the link structure of the graph. We then propose a data-driven estimator of the correlation structure that exploits patterns among the observed response values. By incorporating even a small fraction of observed covariation into the predictions, we are able to obtain much improved prediction on two graph data sets.

Implementation Problems in Inertial Road-Profiling: An Overview

This paper provides a brief overview of profilometry based on inertial-frame-based profilers, and enumerates some of the problems and research areas addressable by the instrumentation-and-measurement community. Recommendations are directed toward efforts that will lead to (1) determining effective guidelines for collecting and processing road profiles; (2) determining, insofar as possible, the specific causes of the poor repeatability in the data obtained by currently available systems; and (3) development of effective, useful, and universally acceptable standards for certification of inertial profilers.

Peak-to-Average Power Ratio Reduction in MIMO-OFDM with Trellis Exploration Algorithm

In this paper, we propose a multi-layer precoding scheme to reduce PAPR in MIMO-OFDM systems. Specifically, an outer-layer polyphase precoding matrix P is custom designed using the trellis exploration algorithm that was previously applied to the design of spreading sequences for CDMA systems. The algorithm determines the P matrix that results in the minimum aperiodic autocorrelation (and therefore minimum PAPR) among transmitted OFDM data symbols. Simulation results illustrate that the proposed multi-layer precoding scheme can not only significantly lower the PAPR in a MIMO-OFDM, but also well preserve the performance gains that are obtained by inner-layer precoding. Index Terms—PAPR, multi-layer precoding, MIMO-OFDM, trellis exploration algorithm

Estimating International Roughness Index from noisy profilograph measurements

In this paper, we propose a method for estimating the international roughness index (IRI) of a pavement profile using data collected by a computerized profilograph making noisy measurements. We begin by modeling the pavement as a nonstationary stochastic process and show that, despite this nonstationarity, we can exploit the design of the profilograph to derive a Wiener-deconvolution filter for estimating the pavement profile. We demonstrate the performance of this approach via numerical simulation and analysis on actual datasets.

Correct Ordering in the Zipf–Poisson Ensemble

Rankings based on counts are often presented to identify popular items, such as baby names, English words, or Web sites. This article shows that, in some examples, the number of correctly identified items can be very small. We introduce a standard error versus rank plot to diagnose possible misrankings. Then to explain the slowly growing number of correct ranks, we model the entire set of count data via a Zipf–Poisson ensemble with independent Xi ∼ Poi(Ni − α) for α > 1 and N > 0 and integers i ⩾ 1. We show that as N → ∞, the first n′(N) random variables have their proper order relative to each other, with probability tending to 1 for n′ up to (AN/log (N))1/(α + 2) for A = α2(α + 2)/4. We also show that the rate N 1/(α + 2) cannot be achieved. The ordering of the first n′(N) entities does not preclude for some interloping m > n′. However, we show that the first n″ random variables are correctly ordered exclusive of any interlopers, with probability tending to 1 if n″ ⩽ (BN/log (N))1/(α + 2) for any B < A. We also show how to compute the cutoff for alternative models such as a Zipf–Mandelbrot–Poisson ensemble.

Visualizing bivariate long-tailed data

Variables in large data sets in biology or e-commerce often have a head, made up of very frequent values and a long tail of ever rarer values. Models such as the Zipf or Zipf–Mandelbrot provide a good description. The problem we address here is the visualization of two such long-tailed variables, as one might see in a bivariate Zipf context. We introduce a copula plot to display the joint behavior of such variables. The plot uses an empirical ordering of the data; we prove that this ordering is asymptotically accurate in a Zipf–Mandelbrot–Poisson model. We often see an association between entities at the head of one variable with those from the tail of the other. We present two generative models (saturation and bipartite preferential attachment) that show such qualitative behavior and we characterize the power law behavior of the marginal distributions in these models.