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.

Contextual text mining

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the author(s) of a biomedical publication, or implicit, such as the positive or negative sentiment that an author had when she wrote a product review; there may also be complex context such as the social network of the authors. Many applications require analysis of topic patterns over different contexts. For instance, analysis of search logs in the context of the user can reveal how we can improve the quality of a search engine by optimizing the search results according to particular users; analysis of customer reviews in the context of positive and negative sentiments can help the user summarize public opinions about a product; analysis of blogs or scientific publications in the context of a social network can facilitate discovery of more meaningful topical communities. Since context information significantly affects the choices of topics and language made by authors, in general, it is very important to incorporate it into analyzing and mining text data. In general, modeling the context in text, discovering contextual patterns of language units and topics from text, a general task which we refer to as Contextual Text Mining, has widespread applications in text mining. In this thesis, we provide a novel and systematic study of contextual text mining, which is a new paradigm of text mining treating context information as the ``first-class citizen.'' We formally define the problem of contextual text mining and its basic tasks, and propose a general framework for contextual text mining based on generative modeling of text. This conceptual framework provides general guidance on text mining problems with context information and can be instantiated into many real tasks, including the general problem of contextual topic analysis. We formally present a functional framework for contextual topic analysis, with a general contextual topic model and its various versions, which can effectively solve the text mining problems in a lot of real world applications. We further introduce general components of contextual topic analysis, by adding priors to contextual topic models to incorporate prior knowledge, regularizing contextual topic models with dependency structure of context, and postprocessing contextual patterns to extract refined patterns. The refinements on the general contextual topic model naturally lead to a variety of probabilistic models which incorporate different types of context and various assumptions and constraints. These special versions of the contextual topic model are proved effective in a variety of real applications involving topics and explicit contexts, implicit contexts, and complex contexts. We then introduce a postprocessing procedure for contextual patterns, by generating meaningful labels for multinomial context models. This method provides a general way to interpret text mining results for real users. By applying contextual text mining in the ``context'' of other text information management tasks, including ad hoc text retrieval and web search, we further prove the effectiveness of contextual text mining techniques in a quantitative way with large scale datasets. The framework of contextual text mining not only unifies many explorations of text analysis with context information, but also opens up many new possibilities for future research directions in text mining.