DEVELOPING COMPREHENSIVE ECONOMIC INFORMATION FOR REGIONAL PLANNING: A CASE STUDY

Community/Rural/Urban Development,

A simple and objective method for reproducible resting state network (RSN) detection in fMRI

Spatial Independent Component Analysis (ICA) decomposes the time by space functional MRI (fMRI) matrix into a set of 1-D basis time courses and their associated 3-D spatial maps that are optimized for mutual independence. When applied to resting state fMRI (rsfMRI), ICA produces several spatial independent components (ICs) that seem to have biological relevance - the so-called resting state networks (RSNs). The ICA problem is well posed when the true data generating process follows a linear mixture of ICs model in terms of the identifiability of the mixing matrix. However, the contrast function used for promoting mutual independence in ICA is dependent on the finite amount of observed data and is potentially non-convex with multiple local minima. Hence, each run of ICA could produce potentially different IC estimates even for the same data. One technique to deal with this run-to-run variability of ICA was proposed by Yang et al. (2008) in their algorithm RAICAR which allows for the selection of only those ICs that have a high run-to-run reproducibility. We propose an enhancement to the original RAICAR algorithm that enables us to assign reproducibility p-values to each IC and allows for an objective assessment of both within subject and across subjects reproducibility. We call the resulting algorithm RAICAR-N (N stands for null hypothesis test), and we have applied it to publicly available human rsfMRI data (http://www.nitrc.org). Our reproducibility analyses indicated that many of the published RSNs in rsfMRI literature are highly reproducible. However, we found several other RSNs that are highly reproducible but not frequently listed in the literature.Comment: 54 pages, 13 figure

INTERREGIONAL AND INTERSEASONAL COMPETITION IN THE U.S. BEEF INDUSTRY: AN APPLICATION OF REACTIVE PROGRAMMING

Livestock Production/Industries,

Bit Level Synchronized MAC Protocol for Multireader RFID Networks

© 2010 Vinod Namboodiri and Ravi Pendse. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.The operation of multiple RFID readers in close proximity results in interference between the readers. This issue is termed the reader collision problem and cannot always be solved by assigning them to different frequency channels due to technical and regulatory limitations. The typical solution is to separate the operation of such readers across time. This sequential operation, however, results in a long delay to identify all tags. We present a bit level synchronized (BLSync) MAC protocol for multi-reader RFID networks that allows multiple readers to operate simultaneously on the same frequency channel. The BLSync protocol solves the reader collision problem by allowing all readers to transmit the same query at the same time. We analyze the performance of using the BLSync protocol and demonstrate benefits of 40%–50% in terms of tag reading delay for most settings. The benefits of BLSync, first demonstrated through analysis, are then validated and quantified through simulations on realistic reader-tag layouts.Peer reviewed articl

SMART: A statistical framework for optimal design matrix generation with application to fMRI

The general linear model (GLM) is a well established tool for analyzing functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM proceed in a massively univariate fashion where the same design matrix is used for analyzing data from each voxel. A major limitation of this approach is the locally varying nature of signals of interest as well as associated confounds. This local variability results in a potentially large bias and uncontrolled increase in variance for the contrast of interest. The main contributions of this paper are two fold (1) We develop a statistical framework called SMART that enables estimation of an optimal design matrix while explicitly controlling the bias variance decomposition over a set of potential design matrices and (2) We develop and validate a numerical algorithm for computing optimal design matrices for general fMRI data sets. The implications of this framework include the ability to match optimally the magnitude of underlying signals to their true magnitudes while also matching the "null" signals to zero size thereby optimizing both the sensitivity and specificity of signal detection. By enabling the capture of multiple profiles of interest using a single contrast (as opposed to an F-test) in a way that optimizes for both bias and variance enables the passing of first level parameter estimates and their variances to the higher level for group analysis which is not possible using F-tests. We demonstrate the application of this approach to in vivo pharmacological fMRI data capturing the acute response to a drug infusion, to task-evoked, block design fMRI and to the estimation of a haemodynamic response function (HRF) response in event-related fMRI. Our framework is quite general and has potentially wide applicability to a variety of disciplines.Comment: 68 pages, 34 figure