ASU Scholarship Showcase
This growing collection consists of scholarly works authored by ASU-affiliated faculty, staff, and community members, and it contains many open access articles. ASU-affiliated authors are encouraged to Share Your Work in KEEP.
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- Creators: Stafford, Phillip
- Creators: Wang, Liping
- Creators: Buman, Matthew
Background: Malignant brain tumors affect people of all ages and are the second leading cause of cancer deaths in children. While current treatments are effective and improve survival, there remains a substantial need for more efficacious therapeutic modalities. The ketogenic diet (KD) - a high-fat, low-carbohydrate treatment for medically refractory epilepsy - has been suggested as an alternative strategy to inhibit tumor growth by altering intrinsic metabolism, especially by inducing glycopenia.
Methods: Here, we examined the effects of an experimental KD on a mouse model of glioma, and compared patterns of gene expression in tumors vs. normal brain from animals fed either a KD or a standard diet.
Results: Animals received intracranial injections of bioluminescent GL261-luc cells and tumor growth was followed in vivo. KD treatment significantly reduced the rate of tumor growth and prolonged survival. Further, the KD reduced reactive oxygen species (ROS) production in tumor cells. Gene expression profiling demonstrated that the KD induces an overall reversion to expression patterns seen in non-tumor specimens. Notably, genes involved in modulating ROS levels and oxidative stress were altered, including those encoding cyclooxygenase 2, glutathione peroxidases 3 and 7, and periredoxin 4.
Conclusions: Our data demonstrate that the KD improves survivability in our mouse model of glioma, and suggests that the mechanisms accounting for this protective effect likely involve complex alterations in cellular metabolism beyond simply a reduction in glucose.
Background: Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (216 - 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.
Results: We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.
Conclusions: The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.
Background: Immunosignaturing is a new peptide microarray based technology for profiling of humoral immune responses. Despite new challenges, immunosignaturing gives us the opportunity to explore new and fundamentally different research questions. In addition to classifying samples based on disease status, the complex patterns and latent factors underlying immunosignatures, which we attempt to model, may have a diverse range of applications.
Methods: We investigate the utility of a number of statistical methods to determine model performance and address challenges inherent in analyzing immunosignatures. Some of these methods include exploratory and confirmatory factor analyses, classical significance testing, structural equation and mixture modeling.
Results: We demonstrate an ability to classify samples based on disease status and show that immunosignaturing is a very promising technology for screening and presymptomatic screening of disease. In addition, we are able to model complex patterns and latent factors underlying immunosignatures. These latent factors may serve as biomarkers for disease and may play a key role in a bioinformatic method for antibody discovery.
Conclusion: Based on this research, we lay out an analytic framework illustrating how immunosignatures may be useful as a general method for screening and presymptomatic screening of disease as well as antibody discovery.
Background: The Nike + Fuelband is a commercially available, wrist-worn accelerometer used to track physical activity energy expenditure (PAEE) during exercise. However, validation studies assessing the accuracy of this device for estimating PAEE are lacking. Therefore, this study examined the validity and reliability of the Nike + Fuelband for estimating PAEE during physical activity in young adults. Secondarily, we compared PAEE estimation of the Nike + Fuelband with the previously validated SenseWear Armband (SWA).
Methods: Twenty-four participants (n = 24) completed two, 60-min semi-structured routines consisting of sedentary/light-intensity, moderate-intensity, and vigorous-intensity physical activity. Participants wore a Nike + Fuelband and SWA, while oxygen uptake was measured continuously with an Oxycon Mobile (OM) metabolic measurement system (criterion).
Results: The Nike + Fuelband (ICC = 0.77) and SWA (ICC = 0.61) both demonstrated moderate to good validity. PAEE estimates provided by the Nike + Fuelband (246 ± 67 kcal) and SWA (238 ± 57 kcal) were not statistically different than OM (243 ± 67 kcal). Both devices also displayed similar mean absolute percent errors for PAEE estimates (Nike + Fuelband = 16 ± 13 %; SWA = 18 ± 18 %). Test-retest reliability for PAEE indicated good stability for Nike + Fuelband (ICC = 0.96) and SWA (ICC = 0.90).
Conclusion: The Nike + Fuelband provided valid and reliable estimates of PAEE, that are similar to the previously validated SWA, during a routine that included approximately equal amounts of sedentary/light-, moderate- and vigorous-intensity physical activity.
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.
Cubic (space group: Fmm) iridium phosphide, Ir2P, has been synthesized at high pressure and high temperature. Angle-dispersive synchrotron X-ray diffraction measurements on Ir2P powder using a diamond-anvil cell at room temperature and high pressures (up to 40.6 GPa) yielded a bulk modulus of B[subscript 0] = 306(6) GPa and its pressure derivative B0′ = 6.4(5). Such a high bulk modulus attributed to the short and strongly covalent Ir-P bonds as revealed by first – principles calculations and three-dimensionally distributed [IrP4] tetrahedron network. Indentation testing on a well–sintered polycrystalline sample yielded the hardness of 11.8(4) GPa. Relatively low shear modulus of ~64 GPa from theoretical calculations suggests a complicated overall bonding in Ir2P with metallic, ionic, and covalent characteristics. In addition, a spin glass behavior is indicated by magnetic susceptibility measurements.
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: 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.
We have previously shown that the diversity of antibodies in an individual can be displayed on chips on which 130,000 peptides chosen from random sequence space have been synthesized. This immunosignature technology is unbiased in displaying antibody diversity relative to natural sequence space, and has been shown to have diagnostic and prognostic potential for a wide variety of diseases and vaccines. Here we show that a global measure such as Shannon’s entropy can be calculated for each immunosignature. The immune entropy was measured across a diverse set of 800 people and in 5 individuals over 3 months. The immune entropy is affected by some population characteristics and varies widely across individuals. We find that people with infections or breast cancer, generally have higher entropy values than non-diseased individuals. We propose that the immune entropy as measured from immunosignatures may be a simple method to monitor health in individuals and populations.
Background: To identify social ecological correlates of objectively measured workplace sedentary behavior.
Methods: Participants from 24 worksites - across academic, industrial, and government sectors - wore an activPAL-micro accelerometer for 7-days (Jan-Nov 2016). Work time was segmented using daily logs. Sedentary behavior outcomes included time spent sitting, standing, in light intensity physical activity (LPA, stepping cadence <100 steps/min), and in prolonged sitting bouts (>30 min). Outcomes were standardized to an 8 h work day. Two electronic surveys were completed to derive individual (job type and work engagement), cultural (lunch away from the desk, walking at lunch and face-to-face interaction), physical (personal printer and office type) and organizational (sector) factors. Mixed-model analyses with worksite-level clustering were performed to examine multi-level associations. Secondary analyses examined job type and sector as moderators of these associations. All models were adjusted for age, race/ethnicity and gender.
Results: Participants (N = 478; 72% female; age: 45.0 ± 11.3 years; 77.8% non-Hispanic white) wore the activPAL-micro for 90.2 ± 15.5% of the reported workday. Walking at lunch was positively associated with LPA (5.0 ± 0.5 min/8 h, P < 0.001). Regular face-to-face interaction was negatively associated with prolonged sitting (−11.3 ± 4.8 min/8 h, P < 0.05). Individuals in private offices sat more (20.1 ± 9.1 min/8 h, P < 0.05), stood less (−21.5 ± 8.8 min/8 h, P < 0.05), and engaged in more prolonged sitting (40.9 ± 11.2 min/8 h, P < 0.001) than those in public office space. These associations were further modified by job type and sector.
Conclusions: Work-specific individual, cultural, physical and organizational factors are associated with workplace sedentary behavior. Associations vary by job type and sector and should be considered in the design of workplace interventions to reduce sedentary behavior.