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This study explored the relation between visual processing and word-decoding ability in a normal reading population. Forty participants were recruited at Arizona State University. Flicker fusion thresholds were assessed with an optical chopper using the method of limits by a 1-deg diameter green (543 nm) test field. Word decoding was

This study explored the relation between visual processing and word-decoding ability in a normal reading population. Forty participants were recruited at Arizona State University. Flicker fusion thresholds were assessed with an optical chopper using the method of limits by a 1-deg diameter green (543 nm) test field. Word decoding was measured using reading-word and nonsense-word decoding tests. A non-linguistic decoding measure was obtained using a computer program that consisted of Landolt C targets randomly presented in four cardinal orientations, at 3-radial distances from a focus point, for eight compass points, in a circular pattern. Participants responded by pressing the arrow key on the keyboard that matched the direction the target was facing. The results show a strong correlation between critical flicker fusion thresholds and scores on the reading-word, nonsense-word, and non-linguistic decoding measures. The data suggests that the functional elements of the visual system involved with temporal modulation and spatial processing may affect the ease with which people read.

ContributorsHolloway, Steven (Author) / Nanez, Jose (Author) / Seitz, Aaron R. (Author) / College of Liberal Arts and Sciences (Contributor)
Created2013-12-20
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Description

Visual perceptual learning (VPL) is defined as visual performance improvement after visual experiences. VPL is often highly specific for a visual feature presented during training. Such specificity is observed in behavioral tuning function changes with the highest improvement centered on the trained feature and was originally thought to be evidence

Visual perceptual learning (VPL) is defined as visual performance improvement after visual experiences. VPL is often highly specific for a visual feature presented during training. Such specificity is observed in behavioral tuning function changes with the highest improvement centered on the trained feature and was originally thought to be evidence for changes in the early visual system associated with VPL. However, results of neurophysiological studies have been highly controversial concerning whether the plasticity underlying VPL occurs within the visual cortex. The controversy may be partially due to the lack of observation of neural tuning function changes in multiple visual areas in association with VPL. Here using human subjects we systematically compared behavioral tuning function changes after global motion detection training with decoded tuning function changes for 8 visual areas using pattern classification analysis on functional magnetic resonance imaging (fMRI) signals. We found that the behavioral tuning function changes were extremely highly correlated to decoded tuning function changes only in V3A, which is known to be highly responsive to global motion with human subjects. We conclude that VPL of a global motion detection task involves plasticity in a specific visual cortical area.

ContributorsShibata, Kazuhisa (Author) / Chang, Li-Hung (Author) / Kim, Dongho (Author) / Nanez, Jose (Author) / Kamitani, Yukiyasu (Author) / Watanabe, Takeo (Author) / Sasaki, Yuka (Author) / New College of Interdisciplinary Arts and Sciences (Contributor)
Created2012-08-28
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Description

Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and

Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.

Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.

Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.

ContributorsKostelich, Eric (Author) / Kuang, Yang (Author) / McDaniel, Joshua (Author) / Moore, Nina Z. (Author) / Martirosyan, Nikolay L. (Author) / Preul, Mark C. (Author) / College of Liberal Arts and Sciences (Contributor)
Created2011-12-21
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Description
In June 2016, the Arizona Department of Health Services (ADHS) with researchers from Arizona State University (ASU) convened a one-day workshop of public health professionals and experts from Arizona’s county and state agencies to advance statewide preparedness for extreme weather events and climate change. The United States Centers for Disease

In June 2016, the Arizona Department of Health Services (ADHS) with researchers from Arizona State University (ASU) convened a one-day workshop of public health professionals and experts from Arizona’s county and state agencies to advance statewide preparedness for extreme weather events and climate change. The United States Centers for Disease Control and Prevention (CDC) sponsors the Climate-Ready Cities and States Initiative, which aims to help communities across the country prepare for and prevent projected disease burden associated with climate change. Arizona is one of 18 public health jurisdictions funded under this initiative. ADHS is deploying the CDC’s five-step Building Resilience Against Climate Effects (BRACE) framework to assist counties and local public health partners with becoming better prepared to face challenges associated with the impacts of climate-sensitive hazards. Workshop participants engaged in facilitated exercises designed to rigorously consider social vulnerability to hazards in Arizona and to prioritize intervention activities for extreme heat, wildfire, air pollution, and flooding.

This report summarizes the proceedings of the workshop focusing primarily on two sessions: the first related to social vulnerability mapping and the second related to the identification and prioritization of interventions necessary to address the impacts of climate-sensitive hazards.
ContributorsRoach, Matthew (Author) / Hondula, David M. (Author) / Putnam, Hana (Author) / Chhetri, Nalini (Author) / Chakalian, Paul (Author) / Watkins, Lance (Author) / Dufour, Brigette (Author)
Created2016-11-28