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.

Scheduling of track inspection and maintenance activities in railroad networks

The U.S. railroad companies spend billions of dollars every year on railroad track maintenance in order to ensure safety and operational efficiency of their railroad networks. Besides maintenance costs, other costs such as train accident costs, train and shipment delay costs and rolling stock maintenance costs are also closely related to track maintenance activities. Optimizing the track maintenance process on the extensive railroad networks is a very complex problem with major cost implications. Currently, the decision making process for track maintenance planning is largely manual and primarily relies on the knowledge and judgment of experts. There is considerable potential to improve the process by using operations research techniques to develop solutions to the optimization problems on track maintenance. In this dissertation study, we propose a range of mathematical models and solution algorithms for three network-level scheduling problems on track maintenance: track inspection scheduling problem (TISP), production team scheduling problem (PTSP) and job-to-project clustering problem (JTPCP). TISP involves a set of inspection teams which travel over the railroad network to identify track defects. It is a large-scale routing and scheduling problem where thousands of tasks are to be scheduled subject to many difficult side constraints such as periodicity constraints and discrete working time constraints. A vehicle routing problem formulation was proposed for TISP, and a customized heuristic algorithm was developed to solve the model. The algorithm iteratively applies a constructive heuristic and a local search algorithm in an incremental scheduling horizon framework. The proposed model and algorithm have been adopted by a Class I railroad in its decision making process. Real-world case studies show the proposed approach outperforms the manual approach in short-term scheduling and can be used to conduct long-term what-if analyses to yield managerial insights. PTSP schedules capital track maintenance projects, which are the largest track maintenance activities and account for the majority of railroad capital spending. A time-space network model was proposed to formulate PTSP. More than ten types of side constraints were considered in the model, including very complex constraints such as mutual exclusion constraints and consecution constraints. A multiple neighborhood search algorithm, including a decomposition and restriction search and a block-interchange search, was developed to solve the model. Various performance enhancement techniques, such as data reduction, augmented cost function and subproblem prioritization, were developed to improve the algorithm. The proposed approach has been adopted by a Class I railroad for two years. Our numerical results show the model solutions are able to satisfy all hard constraints and most soft constraints. Compared with the existing manual procedure, the proposed approach is able to bring significant cost savings and operational efficiency improvement. JTPCP is an intermediate problem between TISP and PTSP. It focuses on clustering thousands of capital track maintenance jobs (based on the defects identified in track inspection) into projects so that the projects can be scheduled in PTSP. A vehicle routing problem based model and a multiple-step heuristic algorithm were developed to solve this problem. Various side constraints such as mutual exclusion constraints and rounding constraints were considered. The proposed approach has been applied in practice and has shown good performance in both solution quality and efficiency.