Detection and landing behavior of emerald ash borer, Agrilus planipennis, at low population density

The exotic emerald ash borer, Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), was first discovered in North America in southeastern Michigan, USA, and Windsor, Ontario, Canada in 2002. Significant ash (Fraxinus spp.) mortality has been caused in areas where this insect has become well established, and new infestations continue to be discovered in several states in the United States and in Canada. This beetle is difficult to detect when it invades new areas or occurs at low density. Girdled trap tree and ground surveys have been important tools for detecting emerald ash borer populations, and more recently, purple baited prism traps have been used in detection efforts. Girdled trap trees were found to be more effective than purple prism traps at detecting emerald ash borer as they acted as sinks for larvae in an area of known low density emerald ash borer infestation. The canopy condition of the trap trees was not predictive of whether they were infested or not, indicating that ground surveys may not be effective for detection in an area of low density emerald ash borer population. When landing rates of low density emerald ash borer populations were monitored on non-girdled ash trees, landing rates were higher on larger, open grown trees with canopies that contain a few dead branches. As a result of these studies, we suggest that the threshold for emerald ash borer detection using baited purple prism traps hung at the canopy base of trees is higher than for girdled trap trees. In addition, detection of developing populations of EAB may be possible by selectively placing sticky trapping surfaces on non-girdled trap trees that are the larger and more open grown trees at a site.

Virus purification, detection and removal

The biopharmaceutical industry has a growing demand and an increasing need to improve the current virus purification technologies, especially as more and more vaccines are produced from cell-culture derived virus particles. Downstream purification strategies can be expensive and account for 70% of the overall manufacturing costs. The economic pressure and purification processes can be particularly challenging when the virus to be purified is small, as in our model virus, porcine parvovirus (PPV). Our efforts are focused on designing an easy, economical, scalable and efficient system for virus purification, and we focused on aqueous two-phase systems. Industry acceptable standards for virus vaccine recovery can be as low as 30% due to demand of high final titer, virus transduction inhibitors and presence of empty or defective virus capsids as impurities. We have overcome these shortcomings by recovering a high 64% of infectious virus using an aqueous two-phase system. We used high molecular weight polymer and citrate salt to achieve a good yield and eliminated the major contaminant bovine serum albumin. Viruses are also studied for ensuring pure and safe drinking water. Low pressure microfiltrations are continuously being investigated for water filters as they allow high permeate flux and low fouling. Viruses such as PPV are small enough to pass through the microporous membranes. Control of viruses in water is crucial for public health and we have designed an affinity based membrane filter to capture virus. Nanofibers have a high surface to volume ratio providing a highly accessible surface area for virus adsorption. Chitosan an insoluble, biocompatible and biodegradable polymer was used for adsorbing trimer peptide WRW. About 0.2 μmoles of cysteine terminal WRW peptide was conjugated to amine terminal chitosan using maleimide conjugation chemistry. We achieved 90-99% virus removal from water adjusted to a neutral pH. The virus removal from affinity based chitosan was attributed to electrostatic and hydrophobic driven binding effect.

EXPLORATION OF THE MTSAT2 SATELLITE CAPABILITIES FOR REAL TIME DETECTION AND CHARACTERIZATION OF VOLCANIC EMISSIONS

In this report, we attempt to define the capabilities of the infrared satellite remote sensor, Multifunctional Transport Satellite-2 (MTSAT-2) (i.e. a geosynchronous instrument), in characterizing volcanic eruptive behavior in the highly active region of Indonesia. Sulfur dioxide data from NASA's Ozone Monitoring Instrument (OMI) (i.e. a polar orbiting instrument) are presented here for validation of the processes interpreted using the thermal infrared datasets. Data provided from two case studies are analyzed specifically for eruptive products producing large thermal anomalies (i.e. lava flows, lava domes, etc.), volcanic ash and SO2 clouds; three distinctly characteristic and abundant volcanic emissions. Two primary methods used for detection of heat signatures are used and compared in this report including, single-channel thermal radiance (4-µm) and the normalized thermal index (NTI) algorithm. For automated purposes, fixed thresholds must be determined for these methods. A base minimum detection limit (MDL) for single-channel thermal radiance of 2.30E+05 Wm- 2sr-1m-1 and -0.925 for NTI generate false alarm rates of 35.78% and 34.16%, respectively. A spatial comparison method, developed here specifically for use in Indonesia and used as a second parameter for detection, is implemented to address the high false alarm rate. For the single-channel thermal radiance method, the utilization of the spatial comparison method eliminated 100% of the false alarms while maintaining every true anomaly. The NTI algorithm showed similar results with only 2 false alarms remaining. No definitive difference is observed between the two thermal detection methods for automated use; however, the single-channel thermal radiance method coupled with the SO2 mass abundance data can be used to interpret volcanic processes including the identification of lava dome activity at Sinabung as well as the mechanism for the dome emplacement (i.e. endogenous or exogenous). Only one technique, the brightness temperature difference (BTD) method, is used for the detection of ash. Trends of ash area, water/ice area, and their respective concentrations yield interpretations of increased ice formation, aggregation, and sedimentation processes that only a high-temporal resolution instrument like the MTSAT-2 can analyze. A conceptual model of a secondary zone of aggregation occurring in the migrating Kelut ash cloud, which decreases the distal fine-ash component and hazards to flight paths, is presented in this report. Unfortunately, SO2 data was unable to definitively reinforce the concept of a secondary zone of aggregation due to the lack of a sufficient temporal resolution. However, a detailed study of the Kelut SO2 cloud is used to determine that there was no climatic impacts generated from this eruption due to the atmospheric residence times and e-folding rate of ~14 days for the SO2. This report applies the complementary assets offered by utilizing a high-temporal and a high-spatial resolution satellite, and it demonstrates that these two instruments can provide unparalleled observations of dynamic volcanic processes.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.