ASU Scholarship Showcase

Background: Glioblastoma is the most aggressive primary central nervous tumor and carries a very poor prognosis. Invasion precludes effective treatment and virtually assures tumor recurrence. In the current study, we applied analytical and bioinformatics approaches to identify a set of microRNAs (miRs) from several different human glioblastoma cell lines that exhibit significant differential expression between migratory (edge) and migration-restricted (core) cell populations. The hypothesis of the study is that differential expression of miRs provides an epigenetic mechanism to drive cell migration and invasion.

Results: Our research data comprise gene expression values for a set of 805 human miRs collected from matched pairs of migratory and migration-restricted cell populations from seven different glioblastoma cell lines. We identified 62 down-regulated and 2 up-regulated miRs that exhibit significant differential expression in the migratory (edge) cell population compared to matched migration-restricted (core) cells. We then conducted target prediction and pathway enrichment analysis with these miRs to investigate potential associated gene and pathway targets. Several miRs in the list appear to directly target apoptosis related genes. The analysis identifies a set of genes that are predicted by 3 different algorithms, further emphasizing the potential validity of these miRs to promote glioblastoma.

Conclusions: The results of this study identify a set of miRs with potential for decreased expression in invasive glioblastoma cells. The verification of these miRs and their associated targeted proteins provides new insights for further investigation into therapeutic interventions. The methodological approaches employed here could be applied to the study of other diseases to provide biomedical researchers and clinicians with increased opportunities for therapeutic interventions.

Background: Little research has explored who responds better to an automated vs. human advisor for health behaviors in general, and for physical activity (PA) promotion in particular. The purpose of this study was to explore baseline factors (i.e., demographics, motivation, interpersonal style, and external resources) that moderate intervention efficacy delivered by either a human or automated advisor.

Methods: Data were from the CHAT Trial, a 12-month randomized controlled trial to increase PA among underactive older adults (full trial N = 218) via a human advisor or automated interactive voice response advisor. Trial results indicated significant increases in PA in both interventions by 12 months that were maintained at 18-months. Regression was used to explore moderation of the two interventions.

Results: Results indicated amotivation (i.e., lack of intent in PA) moderated 12-month PA (d = 0.55, p < 0.01) and private self-consciousness (i.e., tendency to attune to one’s own inner thoughts and emotions) moderated 18-month PA (d = 0.34, p < 0.05) but a variety of other factors (e.g., demographics) did not (p > 0.12).

Conclusions: Results provide preliminary evidence for generating hypotheses about pathways for supporting later clinical decision-making with regard to the use of either human- vs. computer-delivered interventions for PA promotion.

Background: The discovery of genetic associations is an important factor in the understanding of human illness to derive disease pathways. Identifying multiple interacting genetic mutations associated with disease remains challenging in studying the etiology of complex diseases. And although recently new single nucleotide polymorphisms (SNPs) at genes implicated in immune response, cholesterol/lipid metabolism, and cell membrane processes have been confirmed by genome-wide association studies (GWAS) to be associated with late-onset Alzheimer's disease (LOAD), a percentage of AD heritability continues to be unexplained. We try to find other genetic variants that may influence LOAD risk utilizing data mining methods.

Methods: Two different approaches were devised to select SNPs associated with LOAD in a publicly available GWAS data set consisting of three cohorts. In both approaches, single-locus analysis (logistic regression) was conducted to filter the data with a less conservative p-value than the Bonferroni threshold; this resulted in a subset of SNPs used next in multi-locus analysis (random forest (RF)). In the second approach, we took into account prior biological knowledge, and performed sample stratification and linkage disequilibrium (LD) in addition to logistic regression analysis to preselect loci to input into the RF classifier construction step.

Results: The first approach gave 199 SNPs mostly associated with genes in calcium signaling, cell adhesion, endocytosis, immune response, and synaptic function. These SNPs together with APOE and GAB2 SNPs formed a predictive subset for LOAD status with an average error of 9.8% using 10-fold cross validation (CV) in RF modeling. Nineteen variants in LD with ST5, TRPC1, ATG10, ANO3, NDUFA12, and NISCH respectively, genes linked directly or indirectly with neurobiology, were identified with the second approach. These variants were part of a model that included APOE and GAB2 SNPs to predict LOAD risk which produced a 10-fold CV average error of 17.5% in the classification modeling.

Conclusions: With the two proposed approaches, we identified a large subset of SNPs in genes mostly clustered around specific pathways/functions and a smaller set of SNPs, within or in proximity to five genes not previously reported, that may be relevant for the prediction/understanding of AD.

Background: Many studies used the older ActiGraph (7164) for physical activity measurement, but this model has been replaced with newer ones (e.g., GT3X+). The assumption that new generation models are more accurate has been questioned, especially for measuring lower intensity levels. The low-frequency extension (LFE) increases the low-intensity sensitivity of newer models, but its comparability with older models is unknown. This study compared step counts and physical activity collected with the 7164 and GT3X + using the Normal Filter and the LFE (GT3X+N and GT3X+LFE, respectively).

Findings: Twenty-five adults wore 2 accelerometer models simultaneously for 3Âdays and were instructed to engage in typical behaviors. Average daily step counts and minutes per day in nonwear, sedentary, light, moderate, and vigorous activity were calculated. Repeated measures ANOVAs with post-hoc pairwise comparisons were used to compare mean values. Means for the GT3X+N and 7164 were significantly different in 4 of the 6 categories (p < .05). The GT3X+N showed 2041 fewer steps per day and more sedentary, less light, and less moderate than the 7164 (+25.6, -31.2, -2.9 mins/day, respectively). The GT3X+LFE showed non-significant differences in 5 of 6 categories but recorded significantly more steps (+3597 steps/day; p < .001) than the 7164.

Conclusion: Studies using the newer ActiGraphs should employ the LFE for greater sensitivity to lower intensity activity and more comparable activity results with studies using the older models. Newer generation ActiGraphs do not produce comparable step counts to the older generation devices with the Normal filter or the LFE.

Background: Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power.

Results: We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt.

Conclusions: Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avian influenza (H5N1) in Egypt.

Background: Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B) and InfoUSA commercial business lists.

Methods: We performed a full census in 274 randomly selected census tracts in the Chicago metropolitan area and collected detailed store attributes inside stores for classification. Store attributes were compared by classification match status and store type. Systematic classification bias by census tract characteristics was assessed in multivariate regression.

Results: D&B had a higher classification match rate than InfoUSA for supermarkets and grocery stores, while InfoUSA was higher for convenience stores. Both lists were more likely to correctly classify large supermarkets, grocery stores, and convenience stores with more cash registers and different types of service counters (supermarkets and grocery stores only). The likelihood of a correct classification match for supermarkets and grocery stores did not vary systemically by tract characteristics whereas convenience stores were more likely to be misclassified in predominately Black tracts.

Conclusion: Researches can rely on classification of food stores in commercial datasets for supermarkets and grocery stores whereas classifications for convenience and specialty food stores are subject to some systematic bias by neighborhood racial/ethnic composition.

Background: Public parks can be an important setting for physical activity promotion, but to increase park use and the activity levels of park users, the crucial attributes related to active park use need to be defined. Not only user characteristics and structural park attributes, but also characteristics of the surrounding neighborhood are important to examine. Furthermore, internationally comparable studies are needed, to find out if similar intervention strategies might be effective worldwide. The main aim of this study was to examine whether the overall number of park visitors and their activity levels depend on study site, neighborhood walkability and neighborhood income.

Methods: Data were collected in 20 parks in Ghent, Belgium and San Diego, USA. Two trained observers systematically coded park characteristics using the Environmental Assessment of Public Recreation Spaces (EAPRS) tool, and park user characteristics using the System for Observing Play and recreation in Communities (SOPARC) tool. Multilevel multiple regression models were conducted in MLwiN 2.25.

Results: In San Diego parks, activity levels of park visitors and number of vigorously active visitors were higher than in Ghent, while the number of visitors walking and the overall number of park visitors were lower. Neighborhood walkability was positively associated with the overall number of visitors, the number of visitors walking, number of sedentary visitors and mean activity levels of visitors. Neighborhood income was positively associated with the overall number of visitors, but negatively with the number of visitors being vigorously active.

Conclusions: Neighborhood characteristics are important to explain park use. Neighborhood walkability-related attributes should be taken into account when promoting the use of existing parks or creating new parks. Because no strong differences were found between parks in high- and low-income neighborhoods, it seems that promoting park use might be a promising strategy to increase physical activity in low-income populations, known to be at higher risk for overweight and obesity.

The aim of this article was to study sound source localization by cochlear implant (CI) listeners with low-frequency (LF) acoustic hearing in both the operated ear and in the contralateral ear. Eight CI listeners had symmetrical LF acoustic hearing and 4 had asymmetrical LF acoustic hearing. The effects of two variables were assessed: (i) the symmetry of the LF thresholds in the two ears and (ii) the presence/absence of bilateral acoustic amplification. Stimuli consisted of low-pass, high-pass, and wideband noise bursts presented in the frontal horizontal plane. Localization accuracy was 23° of error for the symmetrical listeners and 76° of error for the asymmetrical listeners. The presence of a unilateral CI used in conjunction with bilateral LF acoustic hearing does not impair sound source localization accuracy, but amplification for acoustic hearing can be detrimental to sound source localization accuracy.

The broad recognition specificity exhibited by integrin α_Mβ₂ (Mac-1, CD11b/CD18) has allowed this adhesion receptor to play innumerable roles in leukocyte biology, yet we know little about how and why α_Mβ₂ binds its multiple ligands. Within α_Mβ₂, the α_MI-domain is responsible for integrin’s multiligand binding properties. To identify its recognition motif, we screened peptide libraries spanning sequences of many known protein ligands for α_MI-domain binding and also selected the α_M I-domain recognition sequences by phage display. Analyses of >1400 binding and nonbinding peptides derived from peptide libraries showed that a key feature of the α_MI-domain recognition motif is a small core consisting of basic amino acids flanked by hydrophobic residues. Furthermore, the peptides selected by phage display conformed to a similar pattern. Identification of the recognition motif allowed the construction of an algorithm that reliably predicts the α_MI-domain binding sites in the α_Mβ₂ ligands. The recognition specificity of the α_MI-domain resembles that of some chaperones, which allows it to bind segments exposed in unfolded proteins. The disclosure of the α_Mβ₂ binding preferences allowed the prediction that cationic host defense peptides, which are strikingly enriched in the α_MI-domain recognition motifs, represent a new class of α_Mβ₂ ligands. This prediction has been tested by examining the interaction of α_Mβ₂ with the human cathelicidin peptide LL-37. LL-37 induced a potent α_Mβ₂-dependent cell migratory response and caused activation of α_Mβ₂ on neutrophils. The newly revealed recognition specificity of α_Mβ₂ toward unfolded protein segments and cationic proteins and peptides suggests that α_Mβ₂ may serve as a previously proposed “alarmin” receptor with important roles in innate host defense.

Background: Summer day camps (SDCs) serve 14 million children yearly in the U.S. and aim to provide participating children with 60 minutes of moderate-to-vigorous physical activity (MVPA). This study evaluated an intervention designed to increase the percent of children meeting this MVPA guideline.

Design: Two-group, pre-post quasi-experimental.

Setting/Participants: Twenty SDCs serving 1,830 children aged 5–12 years were assigned to MVPA intervention (n = 10) or healthy eating attention control (n = 10).

Intervention: The STEPs (Strategies to Enhance Practice) intervention is a capacity-building approach grounded in the Theory of Expanded, Extended and Enhanced Opportunities. Camp leaders and staff receive training to expand (e.g., introduction of activity breaks/active field trips), extend (e.g., schedule minimum of 3 hours/day for PA opportunities), and enhance (e.g., maximize MVPA children accumulate during schedule activity) activity opportunities. Camps in the comparison condition received support for improving the types of foods/beverages served.

Main Outcome Measures: Percent of children accumulating the 60min/d MVPA guideline at baseline (summer 2015) and post-test (summer 2016) measured via wrist-accelerometry.

Results: Multilevel logistic regression conducted fall 2016 indicated boys and girls attending intervention SDCs were 2.04 (95CI = 1.10,3.78) and 3.84 (95CI = 2.02,7.33) times more likely to meet the 60min/d guideline compared to boys and girls attending control SDCs, respectively. This corresponded to increases of +10.6% (78–89%) and +12.6% (69–82%) in the percentage of boys and girls meeting the guideline in intervention SDCs, respectively. Boys in comparison SDCs increased by +1.6% (81–83%) and girls decreased by -5.5% (76–71%). Process data indicated intervention SDCs successfully extended and enhanced PA opportunities, but were unable to expand PA opportunities, compared to control SDCs.

Conclusions: Although substantial proportions of children met the MVPA guideline at baseline, no SDCs ensured all children met the guideline. This intervention demonstrated that, with support, SDCs can help all children in attendance to accumulate their daily recommended 60min MVPA.