Doctor of Philosophy (Ph.D.), Bowling Green State University, 2017, Biological Sciences

Polychlorinated biphenyls (PCBs) are environmental pollutants and endocrine disruptors, harmfully affecting reproductive, endocrine, neurological and immunological systems. This has implications for processes such as wound healing, which is modulated by the immunological response of the body. Conversely, while PCBs can be linked to diminished wound healing, outside of PCB pollution systems, exercise has been shown to accelerate wound healing. However, the potential for moderate intensity exercise to modulate or offset the harmful effects of a toxin like PCB are yet unknown. Exploration of this possible moderation on local immune response was achieved by measuring wound size and analyzing the concentrations of proinflammatory cytokines, interleukin-1ß (IL-1ß), interleukin-6 (IL-6), keratinocyte chemoattractant (KC), monocyte chemoattractant protein-1 (MCP-1), and tumor necrosis factor-a (TNF-a) in wounds (inflicted by punch biopsy) in mice that were not exercised as compared with those previously exercised at moderate intensity by running on a treadmill for 30min/day and then injected intraperitoneally with Aroclor 1254 (industrial mixture of PCB congeners) in doses of either 0, 100, 500 and 1000 ppm (wt/wt of mice). Mice were euthanized at Day 3 or Day 5 (n = 3-6) and skin excised from the wound area was homogenized and analyzed for cytokine content. Systemic effects of exercise on immune function in PCB exposed animals were examined by lipopolysaccharide (LPS) challenge (intraperitoneal injections) and analyzed by measuring the average body temperatures using a thermal imaging camera. Wound healing data revealed that in animals not exercised only the greatest dose of PCB (1000 µg/g) showed a pattern for faster wound healing. Exercise produced a pattern of more rapid wound healing rates compared to the animals administerd similar doses, except for animals administered 100 µg/g PCB. Concentrations of pro-inflammatory cytokines revealed patterns t (open full item for complete abstract)

Committee: Lee Meserve Dr. (Advisor); Howard Cromwell Dr. (Other); Todd Keylock Dr. (Committee Member); Scott Rogers Dr. (Committee Member); Vipaporn Phuntumart Dr. (Committee Member) Subjects: Immunology; Kinesiology; Molecular Biology; Physiology; Toxicology

Doctor of Philosophy, University of Toledo, 2014, Engineering

Many birds and bats fatalities have been reported in the vicinity of wind farms. An acoustic, infrared camera, and marine radar based system is developed to monitor the nocturnal migration of birds and bats. The system is deployed and tested in an area of potential wind farm development. The area is also a stopover for migrating birds and bats. Multi-sensory data fusion is developed based on acoustics, infrared camera (IR), and radar. The diversity of the sensors technologies complicated its development. Different signal processing techniques were developed for processing of various types of data. Data fusion is then implemented from three diverse sensors in order to make inferences about the targets. This approach leads to reduction of uncertainties and provides a desired level of confidence and detail information about the patterns. This work is a unique, multifidelity, and multidisciplinary approach based on pattern recognition, machine learning, signal processing, bio-inspired computing, probabilistic methods, and fuzzy reasoning. Sensors were located in the western basin of Lake Erie in Ohio and were used to collect data over the migration period of 2011 and 2012. Acoustic data were collected using acoustic detectors (SM2 and SM2BAT). Data were preprocessed to convert the recorded files to standard wave format. Acoustic processing was performed in two steps: feature extraction, and classification. Acoustic features of bat echolocation calls were extracted based on three different techniques: Short Time Fourier Transform (STFT), Mel Frequency Cepstrum Coefficient (MFCC), and Discrete Wavelet Transform (DWT). These features were fed into an Evolutionary Neural Network (ENN) for their classification at the species level using acoustic features. Results from different feature extraction techniques were compared based on classification accuracy. The technique can identify bats and will contribute towards developing mitigation procedures for reducing bat fata (open full item for complete abstract)

Committee: Mohsin Jamali Dr. (Committee Chair); Jackson Carvalho Dr. (Committee Member); Mohammed Niamat Dr. (Committee Member); Richard Molyet Dr. (Committee Member); Mehdi Pourazady Dr. (Committee Member) Subjects: Biology; Computer Engineering; Computer Science; Ecology; Electrical Engineering; Energy; Engineering