ASU Scholarship Showcase

A novel portable wireless volatile organic compound (VOC) monitoring device with disposable sensors is presented. The device is miniaturized, light, easy-to-use, and cost-effective. Different field tests have been carried out to identify the operational, analytical, and functional performance of the device and its sensors. The device was compared to a commercial photo-ionization detector, gas chromatography-mass spectrometry, and carbon monoxide detector. In addition, environmental operational conditions, such as barometric change, temperature change and wind conditions were also tested to evaluate the device performance. The multiple comparisons and tests indicate that the proposed VOC device is adequate to characterize personal exposure in many real-world scenarios and is applicable for personal daily use.

The multi-focus image fusion method is used in image processing to generate all-focus images that have large depth of field (DOF) based on original multi-focus images. Different approaches have been used in the spatial and transform domain to fuse multi-focus images. As one of the most popular image processing methods, dictionary-learning-based spare representation achieves great performance in multi-focus image fusion. Most of the existing dictionary-learning-based multi-focus image fusion methods directly use the whole source images for dictionary learning. However, it incurs a high error rate and high computation cost in dictionary learning process by using the whole source images. This paper proposes a novel stochastic coordinate coding-based image fusion framework integrated with local density peaks. The proposed multi-focus image fusion method consists of three steps. First, source images are split into small image patches, then the split image patches are classified into a few groups by local density peaks clustering. Next, the grouped image patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP) algorithm is used to carry out sparse representation. After the three steps, the obtained sparse coefficients are fused following the max L1-norm rule. The fused coefficients are inversely transformed to an image by using the learned dictionary. The results and analyses of comparison experiments demonstrate that fused images of the proposed method have higher qualities than existing state-of-the-art methods.

Glutamate plays a pivotal role in drug addiction, and the N-methyl-D-aspartate (NMDA) glutamate receptor subtype serves as a molecular target for several drugs of abuse. In this review, we will provide an overview of NMDA receptor structure and function, followed by a review of the mechanism of action, clinical efficacy, and side effect profile of NMDA receptor ligands that are currently in use or being explored for the treatment of drug addiction. These ligands include the NMDA receptor modulators memantine and acamprosate, as well as the partial NMDA agonist D-cycloserine. Data collected to date suggest that direct NMDA receptor modulators have relatively limited efficacy in the treatment of drug addiction, and that partial agonism of NMDA receptors may have some efficacy with regards to extinction learning during cue exposure therapy. However, the lack of consistency in results to date clearly indicates that additional studies are needed, as are studies examining novel ligands with indirect mechanisms for altering NMDA receptor function.

Myxoma virus (MYXV) is Leporipoxvirus that possesses a specific rabbit‐restricted host tropism but exhibits a much broader cellular host range in cultured cells. MYXV is able to efficiently block all aspects of the type I interferon (IFN)‐induced antiviral state in rabbit cells, partially in human cells and very poorly in mouse cells. The mechanism(s) of this species‐specific inhibition of type I IFN‐induced antiviral state is not well understood. Here we demonstrate that MYXV encoded protein M029, a truncated relative of the vaccinia virus (VACV) E3 double‐stranded RNA (dsRNA) binding protein that inhibits protein kinase R (PKR), can also antagonize the type I IFN‐induced antiviral state in a highly species‐specific manner. In cells pre‐treated with type I IFN prior to infection, MYXV exploits M029 to overcome the induced antiviral state completely in rabbit cells, partially in human cells, but not at all in mouse cells. However, in cells pre‐infected with MYXV, IFN‐induced signaling is fully inhibited even in the absence of M029 in cells from all three species, suggesting that other MYXV protein(s) apart from M029 block IFN signaling in a speciesindependent manner. We also show that the antiviral state induced in rabbit, human or mouse cells by type I IFN can inhibit M029‐knockout MYXV even when PKR is genetically knocked‐out, suggesting that M029 targets other host proteins for this antiviral state inhibition. Thus, the MYXV dsRNA binding protein M029 not only antagonizes PKR from multiple species but also blocks the type I IFN antiviral state independently of PKR in a highly species‐specific fashion.

Carbonaceous Chondrite (CC) meteorites are fragments of asteroids, solar planetesimals that never became large enough to separate matter by their density, like terrestrial planets. CC contains various amounts of organic carbon and carry a record of chemical evolution as it came to be in the Solar System, at the time the Earth was formed and before the origins of life. We review this record as it pertains to the chiral asymmetry determined for several organic compounds in CC, which reaches a broad molecular distribution and enantiomeric excesses of up to 50%–60%. Because homochirality is an indispensable attribute of extant polymers and these meteoritic enantiomeric excesses are still, to date, the only case of chiral asymmetry in organic molecules measured outside the biosphere, the possibility of an exogenous delivery of primed prebiotic compounds to early Earth from meteorites is often proposed. Whether this exogenous delivery held a chiral advantage in molecular evolution remains an open question, as many others regarding the origins of life are.

Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and other coarse- to medium-resolution remotely sensed imagery to estimate surface temperature. These data are frequently correlated with vegetation, impervious surfaces, and temperature to quantify the drivers of the UHI effect. Because of the coarse- to medium-resolution of the thermal imagery, researchers are unable to correlate these temperature data with the more generally available high-resolution land cover classification, which are derived from high-resolution multispectral imagery. The development of advanced thermal sensors with very high-resolution thermal imagery such as the MODIS/ASTER airborne simulator (MASTER) has investigators quantifying the relationship between detailed land cover and land surface temperature. While this is an obvious next step, the published literature, i.e., the MASTER data, are often used to discriminate burned areas, assess fire severity, and classify urban land cover. Considerably less attention is given to use MASTER data in the UHI research. We demonstrate here that MASTER data in combination with high-resolution multispectral data has made it possible to monitor and model the relationship between temperature and detailed land cover such as building rooftops, residential street pavements, and parcel-based landscaping. Here, we report on data sources to conduct this type of UHI research and endeavor to intrigue researchers and scientists such that high-resolution airborne thermal imagery is used to further explore the UHI effect.

This study brings together the literature on social network approaches to social capital and health and on migration and HIV risks to examine how non-migrating wives of labor migrants use their personal networks to cope with perceived risks of HIV infection in rural southern Mozambique. Using data from a 2006 survey of 1,680 women and their dyadic interactions, we compare the composition of personal networks, HIV/AIDS communication, and preventive behavior of women married to migrants and those married to non-migrants. Results show that migrants’ wives were more likely than non-migrants’ wives to have other migrants’ wives as personal network members, to engage in HIV/AIDS communication, and to discuss HIV prevention. However, they were no more likely to talk about HIV/AIDS with migrants’ wives than with non-migrants’ wives. They were also no more likely to talk about AIDS and its prevention than non-migrants’ wives who express worry about HIV infection from their spouses. Finally, we detect that network members’ prevention behavior was similar to respondents’, although this did not depend on migration. We contextualize these findings within the literature and discuss their policy implications.

Unidirectional glass fiber reinforced polymer (GFRP) is tested at four initial strain rates (25, 50, 100 and 200 s^-1) and six temperatures (−25, 0, 25, 50, 75 and 100 °C) on a servo-hydraulic high-rate testing system to investigate any possible effects on their mechanical properties and failure patterns. Meanwhile, for the sake of illuminating strain rate and temperature effect mechanisms, glass yarn samples were complementally tested at four different strain rates (40, 80, 120 and 160 s^-1) and varying temperatures (25, 50, 75 and 100 °C) utilizing an Instron drop-weight impact system. In addition, quasi-static properties of GFRP and glass yarn are supplemented as references. The stress–strain responses at varying strain rates and elevated temperatures are discussed. A Weibull statistics model is used to quantify the degree of variability in tensile strength and to obtain Weibull parameters for engineering applications.

In the decade since Yamanaka and colleagues described methods to reprogram somatic cells into a pluripotent state, human induced pluripotent stem cells (hiPSCs) have demonstrated tremendous promise in numerous disease modeling, drug discovery, and regenerative medicine applications. More recently, the development and refinement of advanced gene transduction and editing technologies have further accelerated the potential of hiPSCs. In this review, we discuss the various gene editing technologies that are being implemented with hiPSCs. Specifically, we describe the emergence of technologies including zinc-finger nuclease (ZFN), transcription activator-like effector nuclease (TALEN), and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 that can be used to edit the genome at precise locations, and discuss the strengths and weaknesses of each of these technologies. In addition, we present the current applications of these technologies in elucidating the mechanisms of human development and disease, developing novel and effective therapeutic molecules, and engineering cell-based therapies. Finally, we discuss the emerging technological advances in targeted gene editing methods.

Predicting the timing of a castrate resistant prostate cancer is critical to lowering medical costs and improving the quality of life of advanced prostate cancer patients. We formulate, compare and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). We accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). While these models are simplifications of a previously published model, they fit data with similar accuracy and improve forecasting results. Both models describe the progression of androgen resistance. Although Model 1 is simpler than the more realistic Model 2, it can fit clinical data to a greater precision. However, we found that Model 2 can forecast future PSA levels more accurately. These findings suggest that including more realistic mechanisms of androgen dynamics in a two population model may help androgen resistance timing prediction.