Determining the optimal design of a watershed is a highly subjective process which involves the consideration of many distinct factors by several different stakeholder groups. We describe additional functionality for our watershed planning system, called WRESTORE (Watershed REstoration Using Spatio-Temporal Optimization of REsources) (http://wrestore.iupui.edu), where stakeholders can collaboratively optimize best management practices on to the watershed. WRESTORE utilizes the USDA's public domain Soil and Water Assessment Tool hydrologic model for watershed simulations. Reinforcement learning and interactive genetic algorithms are applied for the search process. The new functionality described is a user modeling component that develops a computational model of a user's decision-making, based on real-time user-provided ratings for a subset of possible designs. The user modeling task utilizes neural network approaches, such as deep learning. We believe the originality of our approach centers on integrating user models in to the hydrological decision support process. This paper thus has three objectives: (i) outline current work in user modeling and watershed design, (ii) describe our system for interactive optimization of watershed design, and (iii) describe our work on implementing accurate and stable user predictive models to boost optimization performance.

SUMMARY Osteoporosis is characterized by reduced bone mass and debilitating fractures and is likely to reach epidemic proportions. Because of the vigorous research taking place in fields related to osteoporosis, bone biologists are overwhelmed by the amount of literature being generated on a regular basis. This problem can be alleviated by inferring and extracting novel relationships among biological entities appearing in the biological literature. With the development of large online publicly available databases of biological literature, such an approach becomes even more appealing. The novel relationships between biological terms thus discovered constitute new hypotheses that can be verified using experiments. This paper presents a novel method called multilevel text mining for the extraction of potentially meaningful biological relationships. Multilevel mining uses transitive maximum flow graph analysis coupled with set combination operations of union and intersection. Set operators are applied along and across the paths of a transitive flow graph to combine the data. In the first level of the multilevel mining process, protein domain names are used. Novel relationships between domains are extracted by the transitive text mining analysis. In the second level, these newly discovered relationships are used to extract relevant protein names. Set operators are used in various combinations to obtain different sets of results. Copyright © 2011 John Wiley & Sons, Ltd.

Osteoporosis, which is characterized by reduced bone mass and debilitating fractures, may reach epidemic proportions with the aging of the US population. The intensity of research in this field of study is reflected by the facts that The American Society of Bone and Mineral Research has a membership of nearly 4,000 physicians, clinical investigators, and basic research scientists from over fifty countries and that NIH is expected to spend over 200 million dollars on osteoporosis research alone in 2010. Bone biologists may be overwhelmed by the amount of literature constantly being generated, thus the identification and extraction of existing and novel relationships among biological entities or terms appearing in the biological literature is an ongoing problem. The problem has become more pressing with the development of large online publicly available databases of biological literature. Extraction and visualization of relationships between biological entities appearing in these databases offers the opportunity of keeping researchers up-to-date in their research domain. This may be achieved through helping them visualize possible biological pathways and by generating likely new hypotheses concerning novel interactions through methods such as transitive closure network flow. All generated predictions can be verified against already existing data, and possible new relationships can be verified against experiment. This paper presents a method for the extraction and visualization of potentially meaningful relationships.