ASU Scholarship Showcase

Honeybee workers are essentially sterile female helpers that make up the majority of individuals in a colony. Workers display a marked change in physiology when they transition from in-nest tasks to foraging. Recent technological advances have made it possible to unravel the metabolic modifications associated with this transition. Previous studies have revealed extensive remodeling of brain, thorax, and hypopharyngeal gland biochemistry. However, data on changes in the abdomen is scarce. To narrow this gap we investigated the proteomic composition of abdominal tissue in the days typically preceding the onset of foraging in honeybee workers.

In order to get a broader representation of possible protein dynamics, we used workers of two genotypes with differences in the age at which they initiate foraging. This approach was combined with RNA interference-mediated downregulation of an insulin/insulin-like signaling component that is central to foraging behavior, the insulin receptor substrate (irs), and with measurements of glucose and lipid levels.
Our data provide new insight into the molecular underpinnings of phenotypic plasticity in the honeybee, invoke parallels with vertebrate metabolism, and support an integrated and irs-dependent association of carbohydrate and lipid metabolism with the transition from in-nest tasks to foraging.

Background: Gene bodies are the most evolutionarily conserved targets of DNA methylation in eukaryotes. However, the regulatory functions of gene body DNA methylation remain largely unknown. DNA methylation in insects appears to be primarily confined to exons. Two recent studies in Apis mellifera (honeybee) and Nasonia vitripennis (jewel wasp) analyzed transcription and DNA methylation data for one gene in each species to demonstrate that exon-specific DNA methylation may be associated with alternative splicing events. In this study we investigated the relationship between DNA methylation, alternative splicing, and cross-species gene conservation on a genome-wide scale using genome-wide transcription and DNA methylation data.

Results: We generated RNA deep sequencing data (RNA-seq) to measure genome-wide mRNA expression at the exon- and gene-level. We produced a de novo transcriptome from this RNA-seq data and computationally predicted splice variants for the honeybee genome. We found that exons that are included in transcription are higher methylated than exons that are skipped during transcription. We detected enrichment for alternative splicing among methylated genes compared to unmethylated genes using fisher’s exact test. We performed a statistical analysis to reveal that the presence of DNA methylation or alternative splicing are both factors associated with a longer gene length and a greater number of exons in genes. In concordance with this observation, a conservation analysis using BLAST revealed that each of these factors is also associated with higher cross-species gene conservation.

Conclusions: This study constitutes the first genome-wide analysis exhibiting a positive relationship between exon-level DNA methylation and mRNA expression in the honeybee. Our finding that methylated genes are enriched for alternative splicing suggests that, in invertebrates, exon-level DNA methylation may play a role in the construction of splice variants by positively influencing exon inclusion during transcription. The results from our cross-species homology analysis suggest that DNA methylation and alternative splicing are genetic mechanisms whose utilization could contribute to a longer gene length and a slower rate of gene evolution.

Insect immune systems can recognize specific pathogens and prime offspring immunity. High specificity of immune priming can be achieved when insect females transfer immune elicitors into developing oocytes. The molecular mechanism behind this transfer has been a mystery. Here, we establish that the egg-yolk protein vitellogenin is the carrier of immune elicitors. Using the honey bee, Apis mellifera, model system, we demonstrate with microscopy and western blotting that vitellogenin binds to bacteria, both Paenibacillus larvae – the gram-positive bacterium causing American foulbrood disease – and to Escherichia coli that represents gram-negative bacteria. Next, we verify that vitellogenin binds to pathogen-associated molecular patterns; lipopolysaccharide, peptidoglycan and zymosan, using surface plasmon resonance. We document that vitellogenin is required for transport of cell-wall pieces of E. coli into eggs by imaging tissue sections. These experiments identify vitellogenin, which is distributed widely in oviparous species, as the carrier of immune-priming signals. This work reveals a molecular explanation for trans-generational immunity in insects and a previously undescribed role for vitellogenin.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

Background: Phosphatase and TENsin (PTEN) homolog is a negative regulator that takes part in IIS (insulin/insulin-like signaling) and Egfr (epidermal growth factor receptor) activation in Drosophila melanogaster. IIS and Egfr signaling events are also involved in the developmental process of queen and worker differentiation in honey bees (Apis mellifera). Here, we characterized the bee PTEN gene homologue for the first time and begin to explore its potential function during bee development and adult life.

Results: Honey bee PTEN is alternatively spliced, resulting in three splice variants. Next, we show that the expression of PTEN can be down-regulated by RNA interference (RNAi) in the larval stage, when female caste fate is determined. Relative to controls, we observed that RNAi efficacy is dependent on the amount of PTEN dsRNA that is delivered to larvae. For larvae fed queen or worker diets containing a high amount of PTEN dsRNA, PTEN knockdown was significant at a whole-body level but lethal. A lower dosage did not result in a significant gene down-regulation. Finally, we compared same-aged adult workers with different behavior: nursing vs. foraging. We show that between nurses and foragers, PTEN isoforms were differentially expressed within brain, ovary and fat body tissues. All isoforms were expressed at higher levels in the brain and ovaries of the foragers. In fat body, isoform B was expressed at higher level in the nurse bees.

Conclusion: Our results suggest that PTEN plays a central role during growth and development in queen- and worker-destined honey bees. In adult workers, moreover, tissue-specific patterns of PTEN isoform expression are correlated with differences in complex division of labor between same-aged individuals. Therefore, we propose that knowledge on the roles of IIS and Egfr activity in developmental and behavioral control may increase through studies of how PTEN functions can impact bee social phenotypes.

Persistent currents (PCs), one of the most intriguing manifestations of the Aharonov-Bohm (AB) effect, are known to vanish for Schrödinger particles in the presence of random scatterings, e.g., due to classical chaos. But would this still be the case for Dirac fermions? Addressing this question is of significant value due to the tremendous recent interest in two-dimensional Dirac materials. We investigate relativistic quantum AB rings threaded by a magnetic flux and find that PCs are extremely robust. Even for highly asymmetric rings that host fully developed classical chaos, the amplitudes of PCs are of the same order of magnitude as those for integrable rings, henceforth the term superpersistent currents (SPCs). A striking finding is that the SPCs can be attributed to a robust type of relativistic quantum states, i.e., Dirac whispering gallery modes (WGMs) that carry large angular momenta and travel along the boundaries. We propose an experimental scheme using topological insulators to observe and characterize Dirac WGMs and SPCs, and speculate that these features can potentially be the base for a new class of relativistic qubit systems. Our discovery of WGMs in relativistic quantum systems is remarkable because, although WGMs are common in photonic systems, they are relatively rare in electronic systems.

The phenomenon of Fano resonance is ubiquitous in a large variety of wave scattering systems, where the resonance profile is typically asymmetric. Whether the parameter characterizing the asymmetry should be complex or real is an issue of great experimental interest. Using coherent quantum transport as a paradigm and taking into account of the collective contribution from all available scattering channels, we derive a universal formula for the Fano-resonance profile. We show that our formula bridges naturally the traditional Fano formulas with complex and real asymmetry parameters, indicating that the two types of formulas are fundamentally equivalent (except for an offset). The connection also reveals a clear footprint for the conductance resonance during a dephasing process. Therefore, the emergence of complex asymmetric parameter when fitting with experimental data needs to be properly interpreted. Furthermore, we have provided a theory for the width of the resonance, which relates explicitly the width to the degree of localization of the close-by eigenstates and the corresponding coupling matrices or the self-energies caused by the leads. Our work not only resolves the issue about the nature of the asymmetry parameter, but also provides deeper physical insights into the origin of Fano resonance. Since the only assumption in our treatment is that the transport can be described by the Green’s function formalism, our results are also valid for broad disciplines including scattering problems of electromagnetic waves, acoustics, and seismology.