Bioexploration of wild Alaskan berries: From field screening to functional food

Wild berries are fundamental components of traditional diet and medicine for Native American and Alaska Native tribes and contain a diverse array of phytochemicals, including anthocyanins and proanthocyanidins, with known efficacy against metabolic disorders. Bioexploration represents a new paradigm under which bioactive preparations are screened in coordination with indigenous communities, to prepare for subsequent in-depth chemical and biological analysis. The inclusive, participatory philosophical approach utilized in bioexploration has additional benefits that could be realized in seemingly disparate areas, such as education and economics. Five species of wild Alaskan berries (Vaccinium uliginosum, V. ovalifolium, Empetrum nigrum, Rubus chamaemorus, and R. spectabilis) were tested using “Screens-to-Nature” (STN), a community-participatory approach to screen for potential bioactivity, in partnership with tribal members from three geographically distinct Alaskan villages: Akutan, Seldovia, and Point Hope. Berries were subsequently evaluated via HPLC and LC-MS2, yielding significant species and location-based variation in anthocyanins (0.9-438.6 mg eq /100g fw) and proanthocyanins (73.7-625.2 mg eq /100g fw). A-type proanthocyanidin dimers through tetramers were identified in all species tested. Berries were analyzed for in vitro and in vivo activity related to diabetes and obesity. R. spectabilis samples increased preadipocyte-factor-1 levels by 82% over control, and proanthocyanidin-rich fractions from multiple species reduced lipid accumulation in 3T3-L1 adipocytes. Furthermore, extracts of V. uliginosum and E. nigrum (Point Hope) reduced serum glucose levels in C57bl/6j mice up to 45%. The same precepts of bioexploration, especially the inclusion of indigenous community perspectives and knowledge, have relevance in other areas of study, such as education and economics. Studies have established the apathetic, low-motivational environment characteristic of many introductory science laboratory classes is detrimental to student interest, learning, and continuation in scientific education. A primary means of arresting this decline and stimulating the students’ attention and excitement is via engagement in hands-on experimentation and research. Using field workshops, the STN system is investigated as to its potential as a novel participatory educational tool, using assays centered around bioexploration and bioactive plant compounds that hold the potential to offset human health conditions. This evaluation of the STN system provided ample evidence as to its ability to augment and improve science education. Furthermore, Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis was employed as a theoretical framework to review the potential benefits and hurdles associated with developing a wild Alaskan berry commodity. Synthesizing various sources of information – including logistics and harvest costs, sources of initial capital, opportunities in the current superfruit industry, and socioeconomic factors – the development of a berry commodity proves to be a complex amalgam of competing factors which would require a delicate balance before proceeding.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.

Improving the freshman electrical and computer engineering lab

This thesis covers the challenges of creating and maintaining an introductory engineering laboratory. The history of the University of Illinois Electrical and Computer Engineering department’s introductory course, ECE 110, is recounted. The current state of the course, as of Fall 2008, is discussed along with current challenges arising from the use of a hand-wired prototyping board with logic gates. A plan for overcoming these issues using a new microcontroller-based board with a pseudo hardware description language is discussed. The new microcontroller based system implementation is extensively detailed along with its new accompanying description language. This new system was tried in several sections of the Fall 2008 semester alongside the old system; the students’ final performances with the two different approaches are compared in terms of design, performance, complexity, and enjoyment. The system in its first run shows great promise, increasing the students’ enjoyment, and improving the performance of their designs.