Rna Virus Ecology In Bumble Bees (bombus Spp.) And Evidence For Disease Spillover

The inadvertent spread of exotic pests and pathogens has resulted in devastating losses for bees. The vast majority of bee disease research has focused on a single species of managed bee, the European honey bee (Apis mellifera). More recently, pathogen spillover from managed bees is implicated in the decline of several bumble bee species (Bombus spp.) demonstrating a need to better understand the mechanisms driving disease prevalence in bees, transmission routes, and spillover events. RNA viruses, once considered specific to honey bees, are suspected of spilling over from managed honey bees into wild bumble bee populations. To test this, I collected bees and flowers in the field from areas with and without honey bee apiaries nearby. Prevalence of deformed wing virus (DWV) and black queen cell virus (BQCV) as well as replicating DWV infections in Bombus vagans and B. bimaculatus were highest in bumble bees collected near honey bee apiaries (χ 12 \u3c 6.531, P \u3c 0.05). My results suggest that honey bees are significant contributors of viruses to bumble bees. Flowers have been suspected as bridges in virus transmission among bees. I detected bee viruses on 18% of the flowers collected within honey bee apiaries and detected no virus on flowers in areas without apiaries, thus providing evidence that viruses are transmitted at flowers from infected honey bees. In controlled experiments using captive colonies in flight cages, I found that honey bees leave viruses on flowers but not equally across plant species. My results suggest that there are differences in virus ecology mediated by floral morphology and/or pollinator behavior. No bumble bees became infected in controlled experiments, indicating that virus transmission through plants is a rare event that is likely to require repeated exposure. The few studies examining viruses in bumble bees are generally limited to virus detection, resulting in little understanding of the conditions affecting virus titers. In honeybees, infections may remain latent, capable of replicating under certain conditions, such as immunosuppression induced by pesticide exposure. I tested whether exposure to imidacloprid, a neonicotinoid pesticide, affects virus titers in bumble bees. In previous honey bee studies, imidacloprid exposure increased virus titers. In contrast, I found that bumble bee exposure to imidacloprid decreased BQCV and DWV titers (χ42 \u3c 20.873, p \u3c 0.02). My findings suggest that virus-pesticide interactions are species-specific and results from honey bee studies should not be generalized across other bee species. Having found that honey bees are significant contributors of viruses to wild bees and flowers, I investigated how honey bee management practices affect disease spread and developed recommendations and tools to lesson the risk of spillover events. Honey bee disease may be exacerbated by migratory beekeeping which increases stress and opportunities for disease transmission. I experimentally tested whether migratory conditions contribute to disease spread in honey bees and found negative yet varying effects on bees suggesting that the effects of migratory practices may be ameliorated with rest time between pollination events. State apiary inspection programs are critical to controlling disease spread and reducing the risk of spillover. However, these programs are often resource constrained. I developed and deployed a toolkit that enables state programs to prioritize inspections and provide a platform for beekeeper education. Using novel data collected in Vermont, I discovered several promising avenues for future research and provided realistic recommendations to improve bee health

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy