ASU Scholarship Showcase

This article develops a welfare theoretic framework for interpreting evidence on the impacts of public programs on housing markets. We extend Rosen's hedonic model to explain how housing prices capitalize exogenous shocks to public goods and externalities. The model predicts that trading between heterogeneous buyers and sellers will drive a wedge between these “capitalization effects” and welfare changes. We test this hypothesis in the context of changes in measures of school quality in five metropolitan areas. Results from boundary discontinuity designs suggest that capitalization effects understate parents’ willingness to pay for public school improvements by as much as 75%.

Background: An accurate method that can diagnose and predict lupus and its neuropsychiatric manifestations is essential since currently there are no reliable methods. Autoantibodies to a varied panel of antigens in the body are characteristic of lupus. In this study we investigated whether serum autoantibody binding patterns on random-sequence peptide microarrays (immunosignaturing) can be used for diagnosing and predicting the onset of lupus and its central nervous system (CNS) manifestations. We also tested the techniques for identifying potentially pathogenic autoantibodies in CNS-Lupus. We used the well-characterized MRL/lpr lupus animal model in two studies as a first step to develop and evaluate future studies in humans.

Results: In study one we identified possible diagnostic peptides for both lupus and altered behavior in the forced swim test. When comparing the results of study one to that of study two (carried out in a similar manner), we further identified potential peptides that may be diagnostic and predictive of both lupus and altered behavior in the forced swim test. We also characterized five potentially pathogenic brain-reactive autoantibodies, as well as suggested possible brain targets.

Conclusions: These results indicate that immunosignaturing could predict and diagnose lupus and its CNS manifestations. It can also be used to characterize pathogenic autoantibodies, which may help to better understand the underlying mechanisms of CNS-Lupus.

Increasing levels of financial inequality prompt questions about the relationship between income and well-being. Using a twins sample from the Survey of Midlife Development in the U. S. and controlling for personality as core self-evaluations (CSE), we found that men, but not women, had higher subjective financial well-being (SFWB) when they had higher incomes. This relationship was due to ‘unshared environmental’ factors rather than genes, suggesting that the effect of income on SFWB is driven by unique experiences among men. Further, for women and men, we found that CSE influenced income and SFWB, and that both genetic and environmental factors explained this relationship. Given the relatively small and male-specific relationship between income and SFWB, and the determination of both income and SFWB by personality, we propose that policy makers focus on malleable factors beyond merely income in order to increase SFWB, including financial education and building self-regulatory capacity.

The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings – in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system.

Recent infectious outbreaks highlight the need for platform technologies that can be quickly deployed to develop therapeutics needed to contain the outbreak. We present a simple concept for rapid development of new antimicrobials. The goal was to produce in as little as one week thousands of doses of an intervention for a new pathogen. We tested the feasibility of a system based on antimicrobial synbodies. The system involves creating an array of 100 peptides that have been selected for broad capability to bind and/or kill viruses and bacteria. The peptides are pre-screened for low cell toxicity prior to large scale synthesis. Any pathogen is then assayed on the chip to find peptides that bind or kill it. Peptides are combined in pairs as synbodies and further screened for activity and toxicity. The lead synbody can be quickly produced in large scale, with completion of the entire process in one week.

Although perceptions of physically, socially, and morally stigmatized occupations – ‘dirty work’ – are socially constructed, very little attention has been paid to how the context shapes those constructions. We explore the impact of historical trends (when), macro and micro cultures (where), and demographic characteristics (who) on the social construction of dirty work. Historically, the rise of hygiene, along with economic and technological development, resulted in greater societal distancing from dirty work, while the rise of liberalism has resulted in greater social acceptance of some morally stigmatized occupations. Culturally, masculinity tends to be preferred over femininity as an ideological discourse for dirty work, unless the occupation is female-dominated; members of collectivist cultures are generally better able than members of individualist cultures to combat the collective-level threat that stigma inherently represents; and members of high power-distance cultures tend to view dirty work more negatively than members of low power-distance cultures. Demographically, marginalized work tends to devolve to marginalized socioeconomic, gender, and racioethnic categories, creating a pernicious and entrapping recursive loop between ‘dirty work’ and being labeled as ‘dirty people.’

One of the gravest dangers facing cancer patients is an extended symptom-free lull between tumor initiation and the first diagnosis. Detection of tumors is critical for effective intervention. Using the body’s immune system to detect and amplify tumor-specific signals may enable detection of cancer using an inexpensive immunoassay. Immunosignatures are one such assay: they provide a map of antibody interactions with random-sequence peptides. They enable detection of disease-specific patterns using classic train/test methods. However, to date, very little effort has gone into extracting information from the sequence of peptides that interact with disease-specific antibodies. Because it is difficult to represent all possible antigen peptides in a microarray format, we chose to synthesize only 330,000 peptides on a single immunosignature microarray. The 330,000 random-sequence peptides on the microarray represent 83% of all tetramers and 27% of all pentamers, creating an unbiased but substantial gap in the coverage of total sequence space. We therefore chose to examine many relatively short motifs from these random-sequence peptides. Time-variant analysis of recurrent subsequences provided a means to dissect amino acid sequences from the peptides while simultaneously retaining the antibody–peptide binding intensities. We first used a simple experiment in which monoclonal antibodies with known linear epitopes were exposed to these random-sequence peptides, and their binding intensities were used to create our algorithm. We then demonstrated the performance of the proposed algorithm by examining immunosignatures from patients with Glioblastoma multiformae (GBM), an aggressive form of brain cancer. Eight different frameshift targets were identified from the random-sequence peptides using this technique. If immune-reactive antigens can be identified using a relatively simple immune assay, it might enable a diagnostic test with sufficient sensitivity to detect tumors in a clinically useful way.

National and state organizations have developed policies calling upon afterschool programs (ASPs, 3–6 pm) to serve a fruit or vegetable (FV) each day for snack, while eliminating foods and beverages high in added-sugars, and to ensure children accumulate a minimum of 30 min/d of moderate-to-vigorous physical activity (MVPA). Few efficacious and cost-effective strategies exist to assist ASP providers in achieving these important public health goals. This paper reports on the design and conceptual framework of Making Healthy Eating and Physical Activity (HEPA) Policy Practice in ASPs, a 3-year group randomized controlled trial testing the effectiveness of strategies designed to improve snacks served and increase MVPA in children attending community-based ASPs. Twenty ASPs, serving over 1800 children (6–12 years) will be enrolled and match-paired based on enrollment size, average daily min/d MVPA, and days/week FV served, with ASPs randomized after baseline data collection to immediate intervention or a 1-year delayed group. The framework employed, STEPs (Strategies To Enhance Practice), focuses on intentional programming of HEPA in each ASPs' daily schedule, and includes a grocery store partnership to reduce price barriers to purchasing FV, professional development training to promote physical activity to develop core physical activity competencies, as well as ongoing technical support/assistance. Primary outcome measures include children's accelerometry-derived MVPA and time spend sedentary while attending an ASP, direct observation of staff HEPA promoting and inhibiting behaviors, types of snacks served, and child consumption of snacks, as well as, cost of snacks via receipts and detailed accounting of intervention delivery costs to estimate cost-effectiveness.

Background: High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data.

Results: We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found ‘Naïve Bayes’ far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy.

Conclusions: ‘Naïve Bayes’ algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.

Background: GoGirlGo! (GGG) is designed to increase girls’ physical activity (PA) using a health behavior and PA-based curriculum and is widely available for free to afterschool programs across the nation. However, GGG has not been formally evaluated. The purpose of this pilot study was to evaluate the effectiveness of the GGG curricula to improve PA, and self-efficacy for and enjoyment of PA in elementary aged girls (i.e., 5-13 years).

Methods: Nine afterschool programs were recruited to participate in the pilot (within subjects repeated measures design). GGG is a 12-week program, with a once a week, one-hour lesson with 30 minutes of education and 30 minutes of PA). Data collection occurred at baseline, mid (twice), post, and at follow-up (3-months after the intervention ended). PA was assessed via accelerometry at each time point. Self-efficacy for and enjoyment of PA was measured using the Self-Efficacy Scale and the Short-PA enjoyment scale and was assessed at baseline, post, and follow-up. Fidelity was assessed at midpoint.

Results: Across all age groups there was a statistically significant increase in PA. Overall, on days GGG was offered girls accumulated an average of 11 minutes of moderate-to-vigorous PA compared to 8 minutes during non-GGG days. There was a statistically significant difference in girls’ self-efficacy for PA reported between baseline and post, which was maintained at follow-up. An improvement in enjoyment of PA for girls was found between baseline and follow-up. According to fidelity assessment, 89% of the activities within the curriculum were completed each lesson. Girls appeared to respond well to the curriculum but girls 5-7 years had difficulties paying attention and understanding discussion questions.

Conclusions: Even though there were statistically significant differences in self-efficacy for PA and enjoyment of PA, minimal increases in girls’ PA were observed. GGG curricula improvements are warranted. Future GGG programming should explore offering GGG every day, modifying activities so that they are moderate-to-vigorous in intensity, and providing additional trainings that allow staff to better implement PA and improve behavior management techniques. With modifications, GGG could provide a promising no-cost curriculum that afterschool programs may implement to help girls achieve recommendations for PA.