Path-based systems to guide scientists in the maze of biological data sources

Fueled by novel technologies capable of producing massive amounts of data for a single experiment, scientists are faced with an explosion of information which must be rapidly analyzed and combined with other data to form hypotheses and create knowledge. Today, numerous biological questions can be answered without entering a wet lab. Scientific protocols designed to answer these questions can be run entirely on a computer. Biological resources are often complementary, focused on different objects and reflecting various experts\u27 points of view. Exploiting the richness and diversity of these resources is crucial for scientists. However, with the increase of resources, scientists have to face the problem of selecting sources and tools when interpreting their data. In this paper, we analyze the way in which biologists express and implement scientific protocols, and we identify the requirements for a system which can guide scientists in constructing protocols to answer new biological questions. We present two such systems, BioNavigation and BioGuide dedicated to help scientists select resources by following suitable paths within the growing network of interconnected biological resources

Microgravity: A Teacher's Guide With Activities in Science, Mathematics, and Technology

The purpose of this curriculum supplement guide is to define and explain microgravity and show how microgravity can help us learn about the phenomena of our world. The front section of the guide is designed to provide teachers of science, mathematics, and technology at many levels with a foundation in microgravity science and applications. It begins with background information for the teacher on what microgravity is and how it is created. This is followed with information on the domains of microgravity science research; biotechnology, combustion science, fluid physics, fundamental physics, materials science, and microgravity research geared toward exploration. The background section concludes with a history of microgravity research and the expectations microgravity scientists have for research on the International Space Station. Finally, the guide concludes with a suggested reading list, NASA educational resources including electronic resources, and an evaluation questionnaire

Brain in Space: A Teacher's Guide with Activities for Neuroscience

The lessons and activities in this guide will engage your students in the excitement of space life science investigations after the Neurolab Spacelab mission. It is the authors' goal that the information in this guide will inspire both you and your students to become interested and active participants in this space mission. Few experiences can compare with the excitement and thrill of watching a Shuttle launch. This guide provides an opportunity for you and your students to go one step further by conducting the experiments on Earth that are relevent to the research conducted in space

Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation

Artificial reaction networks.

In this paper we present a novel method of simulating cellular intelligence, the Artificial Reaction Network (ARN). The ARN can be described as a modular S-System, with some properties in common with other Systems Biology and AI techniques, including Random Boolean Networks, Petri Nets, Artificial Biochemical Networks and Artificial Neural Networks. We validate the ARN against standard biological data, and successfully apply it to simulate cellular intelligence associated with the well-characterized cell signaling network of Escherichia coli chemotaxis. Finally, we explore the adaptability of the ARN, as a means to develop novel AI techniques, by successfully applying the simulated E. coli chemotaxis to a general optimization problem