Studies on Cytochrome P450 genes in \u3cem\u3eDrosophila melanogaster\u3c/em\u3e: Relationship between over expression and DDT resistance, and xenobiotic induction

Cytochrome P450 monooxygenases (CYPs), are involved in the metabolism of a diverse group of endogenous and xenobiotic compounds. In insects, CYPs are involved in conferring resistance against insecticides. In Drosophila, the expression of Cyp6a2, Cyp6a8, Cyp6g1, Cyp6w1 and Cyp12d1 is higher in the resistant compared to susceptible strains. Recent study by Daborn et al (2002, Science, 297, 2253-2256) showed that Cyp6g1 alone can confer resistance phenotype. The aim of my first objective was to re- examine this claim and second to examine the effect of common xenobiotic compounds on the transcriptome of Drosophila. In first objective, six strains of Drosophila were examined for DDT resistance and Cyp6g1 expression. Results showed that some of the highly susceptible strains showed high level of Cyp6g1 expression and Accord element in the Cyp6g1 upstream DNA. When Cyp6g1 allele of the resistant 91-R strain was substituted with that of the susceptible 91-C strain via recombination, the resulting three recombinant lines retained high level of resistance like the 91-R strain, but showed very low Cyp6g1 expression. This suggests that there is a correlation between overexpression of Cyp6g1 and the presence of Accord transposable element but not DDT resistance. In the second part of the first objective, I directly examined the role of the Cyp6a2 and Cyp6g1 in DDT resistance. Germ line transformation in susceptible strain showed that there was a two-fold increase in DDT resistance (LD50) in transformed flies showing two-fold higher expression of GAL4/UAS driven CYP6A2 or CYP6G1 cDNA. A cumulative increase (4-fold) in DDT resistance was observed when both cDNAs were overexpressed in the same fly. Results suggest that the expression of multiple Cyp genes may be needed to confer a high level of DDT resistance. In the second objective, microarray was used to examine the transcripts induced by caffeine and phenobarbital. Results showed that genes involved in detoxification, carbohydrate metabolism, signal transduction and Cyp genes are induced by caffeine and phenobarbital. These are the same group of genes overexpressed in the resistant 91-R and recombinant strains. These studies shed light on the molecular basis of induction of Cyp genes and insecticide resistance

Development of mobile agent framework in wireless sensor networks for multi-sensor collaborative processing

Recent advances in processor, memory and radio technology have enabled production of tiny, low-power, low-cost sensor nodes capable of sensing, communication and computation. Although a single node is resource constrained with limited power, limited computation and limited communication bandwidth, these nodes deployed in large number form a new type of network called the wireless sensor network (WSN). One of the challenges brought by WSNs is an efficient computing paradigm to support the distributed nature of the applications built on these networks considering the resource limitations of the sensor nodes. Collaborative processing between multiple sensor nodes is essential to generate fault-tolerant, reliable information from the densely-spatial sensing phenomenon. The typical model used in distributed computing is the client/server model. However, this computing model is not appropriate in the context of sensor networks. This thesis develops an energy-efficient, scalable and real-time computing model for collaborative processing in sensor networks called the mobile agent computing paradigm. In this paradigm, instead of each sensor node sending data or result to a central server which is typical in the client/server model, the information processing code is moved to the nodes using mobile agents. These agents carry the execution code and migrate from one node to another integrating result at each node. This thesis develops the mobile agent framework on top of an energy-efficient routing protocol called directed diffusion. The mobile agent framework described has been mapped to collaborative target classification application. This application has been tested in three field demos conducted at Twentynine palms, CA; BAE Austin, TX; and BBN Waltham, MA

Techniques for Wireless Channel Modeling in Harsh Environments

With the rapid growth in the networked environments for different industrial, scientific and defense applications, there is a vital need to assure the user or application a certain level of Quality of Service (QoS). Environments like the industrial environment are particularly harsh with interference from metal structures (as found in the manufacturing sector), interference generated during wireless propagation, and multipath fading of the radio frequency (RF) signal all invite novel mitigation techniques. The challenge of achieving the benefits like improved energy efficiency using wireless is closely coupled with maintaining network QoS requirements. Assessment and management of QoS needs to occur, allowing the network to adapt to changes in the RF, information, and operational environments. The capacity to adapt is paramount to maintaining the required operational performance (throughput, latency, reliability and security). This thesis address the need for accurate radio channel modeling techniques to improve the performance of the wireless communication systems. Multiple different channel modeling techniques are considered including statistical models, ray tracing techniques, finite time-difference technique, transmission line matrix method (TLM), and stochastic differential equation-based (SDE) dynamic channel models. Measurement of ambient RF is performed at several harsh industrial environments to demonstrate the existence of uncertainty in channel behavior. Comparison of various techniques is performed with metrics including accuracy, applicability, and computational efficiency. SDE- and TLM-based methods are validated using indoor and outdoor measurements. Fast, accurate techniques for modeling multipath fading in harsh environments is explored. Application of dynamic channel models is explored for improving QoS of wireless communication system. The TLM-based models provide accurate site-specific path loss calculations taking into consideration materials and propagation characteristics of propagating environment. The validation studies confirm the technique is comparable with existing channel models. The TLM-based channel models is extended to compute the site-specific multipath characteristics of the radio channel eliminating the need for experimental measurement. The TLM-based simulator is also integrated with packet-level network simulator to perform end to end-to-end site specific calculation of wireless network performance. The SDE-channel models provide accurate online estimations of the channel performance along with accurate one-step prediction of the signal strength. The validation studies confirm the accuracy of the technique. Application of the SDE-based models for adaptive antenna control is formulated using online recursive estimation

Graphical user interface for improved laser eye surgery

This thesis develops a system for laser eye surgery that is a significant improvement over existing technology. The new system uses image matching and MEMS micromirrors to introduce feedback control, automation and record keeping to the laser surgery process, eliminating the unwanted effects of eye movement during surgery and introducing a wide range of other benefits. It is expected that this improvement can be implemented as an addition to existing systems as well as a foundation for new tools, perhaps even remote, internet enabled ones. The basic components of the new system are described and demonstrated, including image matching between reference and real-time images through well-known algorithms, application of matching information to generate control voltages to lock positioning and firing LASER onto selected locations on the eye using electrostatically controlled micromirrors, and characterization of the micromirrors for this application. This thesis focuses particularly on the development of a prototype graphical user interface that integrates image capture, image matching, mirror control, and user interaction into a single unit

An Implementation for Transforming a Home Energy Management System to a Multi-agent System

In the United States, 41% of produced energy is consumed by the building sector, i.e. residential and commercial buildings (Building Energy Data Book, Buildings Sector, US Department of Energy, Office of Energy Efficiency and Renewable Energy, Building Technologies Office, 2012. n.d.). The anticipation is that Home Energy Management Systems (HEMS) will support energy efficiency gains through control of the devices in an optimal fashion. New opportunities are offering the ability to integrate grid type controls and the most suitable way to perform these controls is through a multi-agent system (MAS). In this paper, approaches on supporting a HEMS and MAS integration are discussed

Integration of Photovoltaics into Building Energy Usage through Advanced Control of Rooftop Unit

As the United States sees the continued expansion of photovoltaic (PV) and other distributed solar generation technologies into the distribution grid, there is an increased need to find approaches to mitigate integration challenges associated with renewable resources. Depending on the renewable resource, the integration challenges will vary. Much of the challenge with integration is associated with the uncontrolled oscillations of output power, for example, from a PV array. Both solar and wind resources rely on environmental conditions to produce power. However compared to wind, solar generation resources such as PV typically produce more second to minute oscillations due to cloud patterns. With low levels of penetration, the impact is minimal. This paper focuses on developing advanced control strategies for building equipment like the rooftop units along with energy storage technologies to support seamless PV integration into buildings. A forecasting approach for PV is presented along with model-based control strategies for using load to support the integration of PV. The forecasting model takes as input solar irradiance and module temperature to estimate the output power of PV based on an interconnected voltage. The first step is to poll the cloud patterns for the day and utilize this information to project the cloud density each hour. The trained neural network defines relationship of this cloud cover to the amount of expected solar irradiance that is measured. Temperature data is collected from weather application and is inserted as an initial temperature to the PV model and thermal model. The model develops the corresponding PV curves based on the current module temperature reading and the solar irradiance data provided. The model predicts the average power output of the PV array over the next one-hour time window. A control algorithm for the rooftop unit is presented that utilizes this PV forecast to optimize the energy consumption to match the PV peak generation. The model is validated using irradiance, temperature, and PV output power measurements from Oak Ridge National Laboratory’s 50kW PV array