ASU Scholarship Showcase

With the advent of high-dimensional stored big data and streaming data, suddenly machine learning on a very large scale has become a critical need. Such machine learning should be extremely fast, should scale up easily with volume and dimension, should be able to learn from streaming data, should automatically perform dimension reduction for high-dimensional data, and should be deployable on hardware. Neural networks are well positioned to address these challenges of large scale machine learning. In this paper, we present a method that can effectively handle large scale, high-dimensional data. It is an online method that can be used for both streaming and large volumes of stored big data. It primarily uses Kohonen nets, although only a few selected neurons (nodes) from multiple Kohonen nets are actually retained in the end; we discard all Kohonen nets after training. We use Kohonen nets both for dimensionality reduction through feature selection and for building an ensemble of classifiers using single Kohonen neurons. The method is meant to exploit massive parallelism and should be easily deployable on hardware that implements Kohonen nets. Some initial computational results are presented.

The elongases of very long chain fatty acid (ELOVL or ELO) are essential in the biosynthesis of fatty acids longer than C14. Here, two ELO full-length cDNAs (TmELO1, TmELO2) from the yellow mealworm (Tenebrio molitor L.) were isolated and the functions were characterized. The open reading frame (ORF) lengths of TmELO1 and TmELO2 were 1005 bp and 972 bp, respectively and the corresponding peptide sequences each contained several conserved motifs including the histidine-box motif HXXHH. Phylogenetic analysis demonstrated high similarity with the ELO of Tribolium castaneum and Drosophila melanogaster. Both TmELO genes were expressed at various levels in eggs, 1st and 2nd instar larvae, mature larvae, pupae, male and female adults. Injection of dsTmELO1 but not dsTmELO2 RNA into mature larvae significantly increased mortality although RNAi did not produce any obvious changes in the fatty acid composition in the survivors. Heterologous expression of TmELO genes in yeast revealed that TmELO1 and TmELO2 function to synthesize long chain and very long chain fatty acids.

Essential or enduring understandings are often defined as the underlying core concepts or “big ideas” we’d like our students to remember when much of the course content has been forgotten. The central dogma of molecular biology and how cellular information is stored, used, and conveyed is one of the essential understandings students should retain after a course or unit in molecular biology or genetics. An additional enduring understanding is the relationships between DNA sequence, RNA sequence, mRNA production and processing, and the resulting polypeptide/protein product. A final big idea in molecular biology is the relationship between DNA mutation and polypeptide change. To engage students in these essential understandings in a Genetics course, I have developed a hands-on activity to simulate VDJ recombination. Students use a foldable type activity to splice out regions of a mock kappa light chain gene to generate a DNA sequence for transcription and translation. Students fold the activity several different times in multiple ways to “recombine” and generate several different DNA sequences. They then are asked to construct the corresponding mRNA and polypeptide sequence of each “recombined” DNA sequence and reflect on the products in a write-to-learn activity.

This paper describes a novel method for displaying data obtained by three-dimensional medical imaging, by which the position and orientation of a freely movable screen are optically tracked and used in real time to select the current slice from the data set for presentation. With this method, which we call a “freely moving in-situ medical image”, the screen and imaged data are registered to a common coordinate system in space external to the user, at adjustable scale, and are available for free exploration. The three-dimensional image data occupy empty space, as if an invisible patient is being sliced by the moving screen. A behavioral study using real computed tomography lung vessel data established the superiority of the in situ display over a control condition with the same free exploration, but displaying data on a fixed screen (ex situ), with respect to accuracy in the task of tracing along a vessel and reporting spatial relations between vessel structures. A “freely moving in-situ medical image” display appears from these measures to promote spatial navigation and understanding of medical data.

Astaxanthin (3,3′-dihydroxy-β,β-carotene-4,4′-dione), a high-value ketocarotenoid with a broad range of applications in food, feed, nutraceutical, and pharmaceutical industries, has been gaining great attention from science and the public in recent years. The green microalgae Haematococcus pluvialis and Chlorella zofingiensis represent the most promising producers of natural astaxanthin. Although H. pluvialis possesses the highest intracellular astaxanthin content and is now believed to be a good producer of astaxanthin, it has intrinsic shortcomings such as slow growth rate, low biomass yield, and a high light requirement. In contrast, C. zofingiensis grows fast phototrophically, heterotrophically and mixtrophically, is easy to be cultured and scaled up both indoors and outdoors, and can achieve ultrahigh cell densities. These robust biotechnological traits provide C. zofingiensis with high potential to be a better organism than H. pluvialis for mass astaxanthin production. This review aims to provide an overview of the biology and industrial potential of C. zofingiensis as an alternative astaxanthin producer. The path forward for further expansion of the astaxanthin production from C. zofingiensis with respect to both challenges and opportunities is also discussed.

Based on considerable neurophysiological evidence, Roy (2012) proposed the theory that localist representation is widely used in the brain, starting from the lowest levels of processing. Grandmother cells are a special case of localist representation. In this article, I present the theory that grandmother cells are also widely used in the brain. To support the proposed theory, I present neurophysiological evidence and an analysis of the concept of grandmother cells. Konorski (1967) first predicted the existence of grandmother cells (he called them “gnostic” neurons) - single neurons that respond to complex stimuli such as faces, hands, expressions, objects, and so on. The term “grandmother cell” was introduced by Jerry Lettvin in 1969 (Barlow, 1995).

This study investigates the presence of dynamical patterns of interpersonal coordination in extended deceptive conversations across multimodal channels of behavior. Using a novel "devil’s advocate" paradigm, we experimentally elicited deception and truth across topics in which conversational partners either agreed or disagreed, and where one partner was surreptitiously asked to argue an opinion opposite of what he or she really believed. We focus on interpersonal coordination as an emergent behavioral signal that captures interdependencies between conversational partners, both as the coupling of head movements over the span of milliseconds, measured via a windowed lagged cross correlation (WLCC) technique, and more global temporal dependencies across speech rate, using cross recurrence quantification analysis (CRQA). Moreover, we considered how interpersonal coordination might be shaped by strategic, adaptive conversational goals associated with deception. We found that deceptive conversations displayed more structured speech rate and higher head movement coordination, the latter with a peak in deceptive disagreement conversations. Together the results allow us to posit an adaptive account, whereby interpersonal coordination is not beholden to any single functional explanation, but can strategically adapt to diverse conversational demands.

Modern biology and epidemiology have become more and more driven by the need of mathematical models and theory to elucidate general phenomena arising from the complexity of interactions on the numerous spatial, temporal, and hierarchical scales at which biological systems operate and diseases spread. Epidemic modeling and study of disease spread such as gonorrhea, HIV/AIDS, BSE, foot and mouth disease, measles, and rubella have had an impact on public health policy around the world which includes the United Kingdom, The Netherlands, Canada, and the United States. A wide variety of modeling approaches are involved in building up suitable models. Ordinary differential equation models, partial differential equation models, delay differential equation models, stochastic differential equation models, difference equation models, and nonautonomous models are examples of modeling approaches that are useful and capable of providing applicable strategies for the coexistence and conservation of endangered species, to prevent the overexploitation of natural resources, to control disease’s outbreak, and to make optimal dosing polices for the drug administration, and so forth.

This essay outlines a recent assignment I designed for an upper-division cross-listed women and gender studies/social justice and human rights course I teach called, “Trash, Freaks, and SCUM.” In the context of the students reading Edward Humes’ (2012) Garbology, the trash bag assignment asked that students carry around their trash for two 48-hour periods and that they present it to the class. While the first two day period assesses their actual trash output, students are asked to produce as little trash as possible for the second two day period. This assignment aims to make trash visible and to help students learn about climate change, sustainability, conspicuous consumption, and how their individual carbon footprint contributes to the “big picture” of environmental strain. I describe this assignment and its goals in this essay, followed by an assessment of its role in teaching about social justice, in order to underscore the importance of experiential learning with trash and to highlight how this assignment fits the mission of my courses on feminism and social justice.

This study is an attempt to use group information collected on climate change from farmers in eastern Uttar Pradesh, India to address a key question related to climate change policy: How to encourage farmers to adapt to climate change? First, we investigate farmers’ perception of and adaptation to climate change using content analysis and group information. The findings are then compared with climatic and agriculture information collected through secondary sources. Results suggest that though farmers are aware of long-term changes in climatic factors (temperature and rainfall, for example), they are unable to identify these changes as climate change. Farmers are also aware of risks generated by climate variability and extreme climatic events. However, farmers are not taking concrete steps in dealing with perceived climatic changes, although we find out that farmers are changing their agricultural and farming practices. These included changing sowing and harvesting timing, cultivation of crops of short duration varieties, inter-cropping, changing cropping pattern, investment in irrigation, and agroforestry. Note that these changes may be considered as passive response or adaptation strategies to climate change. Perhaps farmers are implicitly taking initiatives to adapt climate change. Finally, the paper suggests some policy interventions to scale up adaptation to climate change in Indian agriculture.