ASU Scholarship Showcase

We develop a general framework to analyze the controllability of multiplex networks using multiple-relation networks and multiple-layer networks with interlayer couplings as two classes of prototypical systems. In the former, networks associated with different physical variables share the same set of nodes and in the latter, diffusion processes take place. We find that, for a multiple-relation network, a layer exists that dominantly determines the controllability of the whole network and, for a multiple-layer network, a small fraction of the interconnections can enhance the controllability remarkably. Our theory is generally applicable to other types of multiplex networks as well, leading to significant insights into the control of complex network systems with diverse structures and interacting patterns.

Sensory systems encode both the static quality of a stimulus (e.g., color or shape) and its kinetics (e.g., speed and direction). The limits with which stimulus kinetics can be resolved are well understood in vision, audition, and somatosensation. However, the maximum temporal resolution of olfactory systems has not been accurately determined. Here, we probe the limits of temporal resolution in insect olfaction by delivering high frequency odor pulses and measuring sensory responses in the antennae. We show that transduction times and pulse tracking capabilities of olfactory receptor neurons are faster than previously reported. Once an odorant arrives at the boundary layer of the antenna, odor transduction can occur within less than 2 ms and fluctuating odor stimuli can be resolved at frequencies more than 100 Hz. Thus, insect olfactory receptor neurons can track stimuli of very short duration, as occur when their antennae encounter narrow filaments in an odor plume. These results provide a new upper bound to the kinetics of odor tracking in insect olfactory receptor neurons and to the latency of initial transduction events in olfaction.

We know very little about how soil-borne pollutants such as selenium (Se) can impact pollinators, even though Se has contaminated soils and plants in areas where insect pollination can be critical to the functioning of both agricultural and natural ecosystems. Se can be biotransferred throughout the food web, but few studies have examined its effects on the insects that feed on Se-accumulating plants, particularly pollinators. In laboratory bioassays, we used proboscis extension reflex (PER) and taste perception to determine if the presence of Se affected the gustatory response of honey bee (Apis mellifera L., Hymenoptera: Apidae) foragers. Antennae and proboscises were stimulated with both organic (selenomethionine) and inorganic (selenate) forms of Se that commonly occur in Se-accumulating plants. Methionine was also tested. Each compound was dissolved in 1 M sucrose at 5 concentrations, with sucrose alone as a control. Antennal stimulation with selenomethionine and methionine reduced PER at higher concentrations. Selenate did not reduce gustatory behaviors. Two hours after being fed the treatments, bees were tested for sucrose response threshold. Bees fed selenate responded less to sucrose stimulation. Mortality was higher in bees chronically dosed with selenate compared with a single dose. Selenomethionine did not increase mortality except at the highest concentration. Methionine did not significantly impact survival. Our study has shown that bees fed selenate were less responsive to sucrose, which may lead to a reduction in incoming floral resources needed to support coworkers and larvae in the field. If honey bees forage on nectar containing Se (particularly selenate), reductions in population numbers may occur due to direct toxicity. Given that honey bees are willing to consume food resources containing Se and may not avoid Se compounds in the plant tissues on which they are foraging, they may suffer similar adverse effects as seen in other insect guilds.

Neural representations of odors are subject to computations that involve sequentially convergent and divergent anatomical connections across different areas of the brains in both mammals and insects. Furthermore, in both mammals and insects higher order brain areas are connected via feedback connections. In order to understand the transformations and interactions that this connectivity make possible, an ideal experiment would compare neural responses across different, sequential processing levels. Here we present results of recordings from a first order olfactory neuropile – the antennal lobe (AL) – and a higher order multimodal integration and learning center – the mushroom body (MB) – in the honey bee brain. We recorded projection neurons (PN) of the AL and extrinsic neurons (EN) of the MB, which provide the outputs from the two neuropils. Recordings at each level were made in different animals in some experiments and simultaneously in the same animal in others. We presented two odors and their mixture to compare odor response dynamics as well as classification speed and accuracy at each neural processing level. Surprisingly, the EN ensemble significantly starts separating odor stimuli rapidly and before the PN ensemble has reached significant separation. Furthermore the EN ensemble at the MB output reaches a maximum separation of odors between 84–120 ms after odor onset, which is 26 to 133 ms faster than the maximum separation at the AL output ensemble two synapses earlier in processing. It is likely that a subset of very fast PNs, which respond before the ENs, may initiate the rapid EN ensemble response. We suggest therefore that the timing of the EN ensemble activity would allow retroactive integration of its signal into the ongoing computation of the AL via centrifugal feedback.

Octopamine plays an important role in many behaviors in invertebrates. It acts via binding to G protein coupled receptors located on the plasma membrane of responsive cells. Several distinct subtypes of octopamine receptors have been found in invertebrates, yet little is known about the expression pattern of these different receptor subtypes and how each subtype may contribute to different behaviors. One honey bee (Apis mellifera) octopamine receptor, AmOA1, was recently cloned and characterized. Here we continue to characterize the AmOA1 receptor by investigating its distribution in the honey bee brain. We used two independent antibodies produced against two distinct peptides in the carboxyl-terminus to study the distribution of the AmOA1 receptor in the honey bee brain. We found that both anti-AmOA1 antibodies revealed labeling of cell body clusters throughout the brain and within the following brain neuropils: the antennal lobes; the calyces, pedunculus, vertical (alpha, gamma) and medial (beta) lobes of the mushroom body; the optic lobes; the subesophageal ganglion; and the central complex. Double immunofluorescence staining using anti-GABA and anti-AmOA1 receptor antibodies revealed that a population of inhibitory GABAergic local interneurons in the antennal lobes express the AmOA1 receptor in the cell bodies, axons and their endings in the glomeruli. In the mushroom bodies, AmOA1 receptors are expressed in a subpopulation of inhibitory GABAergic feedback neurons that ends in the visual (outer half of basal ring and collar regions) and olfactory (lip and inner basal ring region) calyx neuropils, as well as in the collar and lip zones of the vertical and medial lobes. The data suggest that one effect of octopamine via AmOA1 in the antennal lobe and mushroom body is to modulate inhibitory neurons.

Background: Blindness has evolved repeatedly in cave-dwelling organisms, and many hypotheses have been proposed to explain this observation, including both accumulation of neutral loss-of-function mutations and adaptation to darkness. Investigating the loss of sight in cave dwellers presents an opportunity to understand the operation of fundamental evolutionary processes, including drift, selection, mutation, and migration.

Results: Here we model the evolution of blindness in caves. This model captures the interaction of three forces: (1) selection favoring alleles causing blindness, (2) immigration of sightedness alleles from a surface population, and (3) mutations creating blindness alleles. We investigated the dynamics of this model and determined selection-strength thresholds that result in blindness evolving in caves despite immigration of sightedness alleles from the surface. We estimate that the selection coefficient for blindness would need to be at least 0.005 (and maybe as high as 0.5) for blindness to evolve in the model cave-organism, Astyanax mexicanus.

Conclusions: Our results indicate that strong selection is required for the evolution of blindness in cave-dwelling organisms, which is consistent with recent work suggesting a high metabolic cost of eye development.

Octopamine (OA) underlies reinforcement during appetitive conditioning in the honey bee and fruit fly, acting via different subtypes of receptors. Recently, antibodies raised against a peptide sequence of one honey bee OA receptor, AmOA1, were used to study the distribution of these receptors in the honey bee brain (Sinakevitch et al., 2011). These antibodies also recognize an isoform of the AmOA1 ortholog in the fruit fly (OAMB, mushroom body OA receptor). Here we describe in detail the distribution of AmOA1 receptors in different types of neurons in the honey bee and fruit fly antennal lobes. We integrate this information into a detailed anatomical analysis of olfactory receptor neurons (ORNs), uni- and multi-glomerular projection neurons (uPNs, and mPNs) and local interneurons (LNs) in glomeruli of the antennal lobe. These neurons were revealed by dye injection into the antennal nerve, antennal lobe, medial and lateral antenno-protocerbral tracts (m-APT and l-APT), and lateral protocerebral lobe (LPL) by use of labeled cell lines in the fruit fly or by staining with anti-GABA. We found that ORN receptor terminals and uPNs largely do not show immunostaining for AmOA1. About seventeen GABAergic mPNs leave the antennal lobe through the ml-APT and branch into the LPL. Many, but not all, mPNs show staining for AmOA1. AmOA1 receptors are also in glomeruli on GABAergic processes associated with LNs. The data suggest that in both species one important action of OA in the antennal lobe involves modulation of different types of inhibitory neurons via AmOA1 receptors. We integrated this new information into a model of circuitry within glomeruli of the antennal lobes of these species.

Dynamical processes occurring on the edges in complex networks are relevant to a variety of real-world situations. Despite recent advances, a framework for edge controllability is still required for complex networks of arbitrary structure and interaction strength. Generalizing a previously introduced class of processes for edge dynamics, the switchboard dynamics, and exploit- ing the exact controllability theory, we develop a universal framework in which the controllability of any node is exclusively determined by its local weighted structure. This framework enables us to identify a unique set of critical nodes for control, to derive analytic formulas and articulate efficient algorithms to determine the exact upper and lower controllability bounds, and to evaluate strongly structural controllability of any given network. Applying our framework to a large number of model and real-world networks, we find that the interaction strength plays a more significant role in edge controllability than the network structure does, due to a vast range between the bounds determined mainly by the interaction strength. Moreover, transcriptional regulatory networks and electronic circuits are much more strongly structurally controllable (SSC) than other types of real-world networks, directed networks are more SSC than undirected networks, and sparse networks are typically more SSC than dense networks.

Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks.

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time series/signals can be collected from a single location. A key challenge is that the signals collected are necessarily time delayed, due to the varying physical distances from the nodes to the data collection centre. To meet this challenge, we develop a compressive-sensing-based approach enabling reconstruction of the full topology of the underlying geospatial network and more importantly, accurate estimate of the time delays. A standard triangularization algorithm can then be employed to find the physical locations of the nodes in the network. We further demonstrate successful detection of a hidden node (or a hidden source or threat), from which no signal can be obtained, through accurate detection of all its neighbouring nodes. As a geospatial network has the feature that a node tends to connect with geophysically nearby nodes, the localized region that contains the hidden node can be identified.