Charles Anderson, Ph.D. - US grants

Renal allografts undergoing rejection metabolize arachidonic acid (AA) to a variety of cyclooxygenase and lipoxygenase products and these eicosanoids possess powerful effects on the immune system and renal function. This proposal investigates the role of altered eicosanoid production on renal allograft function and immunologic reactions occurring in the allograft. Also, the metabolism of AA by kidneys subjected to ischemia-reperfusion injury and toxic doses of cyclosporine A (CYS) will be determined and correlated with renal function. A standard dog renal allograft model will be used to study renal allograft function during pharmacologic intervention of AA metabolism. Renal allograft function will be assessed by daily measurements of total renal blood flow (implantable flow probes), cortical blood flow (H2 Washout), GFR (creatinine clearance), and electrolyte excretion (flame photometry). Concomitant immunologic monitoring of T- lymphocyte function in the renal allograft, spleen, and blood will be performed by one way mixed lymphocyte reactions, cell mediated cytotoxicity assays, and interleukin-2 production and utilization by helper and cytotoxic T-lymphocytes. The strategy will be to inhibit "pro-rejection" eicosanoids (TXA2 and lipoxygenase products) and enhance production of anti-rejection eicosanoids (PGI2 and PGE2) by administering compounds that alter AA metabolism either alone or in combination. It is hypothesized that rational alterations in AA metabolism will result in improved graft function and survival. Correlation of alterations in T-cell function with changes in both renal allograft function and renal eicosanoid production during manipulation of AA metabolism should contribute to an understanding of the mechanisms involved in renal allograft rejection. In addition, renal damage occurring from ischemia-reperfusion and CYS nephrotoxicity may be associated with altered AA metabolism and maybe attenuated with agents directed at various arms of the AA cascade.

Successful adaptation to organ allografting occurs under a variety of poorly understood conditions. The use of donor-specific antigen, administered intravenously under immunosuppressive coverage, to induce enhancement or tolerance in a large variety of allograft models is well established. More recently, there is an awareness of the beneficial effects of random blood tranfusion prior to human renal transplantation. The purpose of this project is to confirm previous pilot studies which indicate that administration of donor-specific blood products under immunosuppressive coverage prior to human renal allografting led to no evidence of sensitization and no immunologic graft loss in the postoperate period. One haplotype living-donor renal transplants, will be performed after a six to eight week course of oral azathioprine during which time the recipient will have received 600 ml of donor-specific blood in three aliquots. Immunologic monitoring will include: T- and B-cell crossmatches with temperature variants, ADCC crossmatch, direct CML, one-way MLC (cellular responsiveness), one-way MLC (indirect assay for serum blocking factors), li titer, heterophile titer, T- and B-cell autoantibody with temperature variants and monoclonal antibody characterization of T-cells. Testing will be performed before, and after transfusion and in the postrenal tranplantation period. Posternal transplantation immunosuppression will include azathioprine and prednisone. Randomized control groups will include: (1) neither intentional preoperative blood nor azathioprine treatment, and (2) azathioprine and non-donar-specific blood. Sensitization in the form of donor-specific warm T-cell lymphocytoxic antibody development will preclude transplantation. Results in the experimental and control groups will be analyzed with regard to: (1) allograft survival (absence of immunologic graft failure) at six months as a prediction of long-term function; (2) immunologic monitoring changes and the development of sensitization (warm donor-specific T-cell antibody) during the pretransplantation phase; and (3) during the postransplantation phase. A statistically significant improvement in allograft survival from donor-specific as opposed to non-donor-specific or no blood transfusions may eventually permit the use of related kidney donors with less than one haplotype match thus enlarging the potential pool of donors.

The purpose of this project is to design an approach to science instruction that takes into account some findings from cognitive research. It has been widely demonstrated that students come into the classroom with invalid perceptions about physical phenomena which are not altered by current forms of instruction. Through interviews, this project will document the general misconceptions held by students, identify these for teachers, and develop methods which directly confront the misconceptions. The student materials will consist of a unit, "Models of Matter", which will be modified to also confront the commonly held misconceptions. The final product will be an educational system that integrates learning activities and teaching strategies to cause conceptual change. The modified "Models of Matter" unit will be randomly assigned to 12 of 24 Lansing, Michigan Public School grade six classes to test its effectiveness in increasing student achievement and promoting recommended teaching strategies. The remaining 12 sixth grade classes will use the unmodified "Models of Matter" unit. The project expects to demonstrate how authors can use existing research findings to modify commercial textbook units and teacher guides to increase student learning. Since most teachers structure their teaching around a commercial textbook, the project has national significance for improving student achievement in science.

The rapid development and intensification of winter cyclones in coastal regions presents a major weather forecasting problem. Along the East Coast of the United States, the development of cyclonic storms and frontal systems often occurs along the coastal region of the Carolinas. These weather systems may lead to severe weather in the heavily populated Northeast Corridor in the form of freezing rain, heavy snowfall, blizzards and extensive coastal erosion. A complex interaction of cold continental air, warm ocean currents, traveling atmospheric disturbances and local geography all appear to contribute to the rapid development of these systems; however, the processes are not well understood and the phenomena are often poorly forecast. In January-March 1986 a cooperative research project to study winter storms in this region was held by a consortium of universities and Federal agencies. Entitled GALE (Genesis of Atlantic Lows Experiment), the project was designed to provide detailed information on the role of air-sea interaction, planetary boundary-layer processes and mesoscale meteorological processes. As one of the investigators of the GALE, the Principal Investigator was instrumental in designing and executing the experiment. This research will extend initial analyses of data obtained during the GALE. Preliminary work reveals that the mesoscale structure is prevalent within the synoptic field and can better explain the observed precipitation and local weather than can the synoptic field. There is evidence, however, that mesoscale features occur in predictable patterns governed by the topography and synoptic-scale flow. The objectives of the research are to continue to identify and analyze mesoscale features in the atmospheric boundary layer over the near-shore waters and adjacent coastal plain and to explore the time- evolution of these features and their interactions with synoptic-scale systems especially as such interactions affect the precipitation process.

This proposal extends the research with donor specific transfusions and concomitant azathioprine immunosuppression (DST+A) in haplotype mismatched living donor kidney transplants to the cadaver situation. It is intended to demonstrate that pretreatment of potential renal allograft recipients with tissue typed and HLA-public antigen mismatched buffy coat transfusions (BCT) and concomitant azathioprine immunosuppression will result in improved cadaver renal allograft survival. Possible mechanisms of immunomodulation that suppress the allograft rejection response will be investigated in the blood (standard serologic and cellular immunologic tests) and in the allograft (fine needle aspiration) of the cadaver transplant recipient. The poteintial cadaver kidney transplant recipient will undergo three to six buffy coat transfusions over a six to ten week period from a single tissue typed volunteer donor (plasmapheresis program). Blood donors will be selected on the basis of the maximum number of public antigens mismatched for the recipient. A properly selected single donor can cover most of the public antigens not present in the recipient because of the limited number of different public anigens and their high frequency in the population. All recipients will receive continuous azathioprine immunosuppression starting prior to the transfusions and continuing into the posttransplant period. Cadaver kidney transplant will be performed two weeks to four weeks after the last transfusion using a donor kidney with public antigens the same as those present in the buffy coat transfusion. Recipients will undergo immunologic monitoring by: T- and B-cell crossmatch, mixed leukocyte reaction (MLR), antiidiotypic antibody analysis, interleukin 2 production during MLR, effects of recipient serum on the response to interleukin 2 (qualitative analysis), quantitative assay for interleukin 2 response inhibitory activity, and fine needle aspiration biopsy of the allograft analyzed for histocytology and cellular immunity. A major objective is to evaluate the contribution of IL-2 response inhibition to successful transplantation and its potential use as a test in assessing the induction of the salutary effects of DST+A/BCT+A. The ability to immunomodulate potential cadaver renal allograft recepients by down regulating the immune response to specific alloantigen would constitute an important advance in improving renal allograft survival over general nonspecific immunosuppressive measures.

The project is a three year investigation of collaborative problem solving in science for the purpose of developing accounts of learning and problem solving by middle school students and developing instructional strategies and materials that improve both students' conceptual understanding of science and their capacities for self- regulation in complex problem solving situations. Year 1 is an intervention study investigating the comparative effects of three instructional conditions: a) mediated independent problem solving, b) group problem solving, and c) mediated group problem solving. This investigation will be conducted in eight classrooms as the students study a unit on the nature and properties of matter. Analyses of Year 1 data will occur in Year 2 for the purpose of: a) evaluating the success with which the desired outcomes were achieved across the various conditions in terms of conceptual understanding, self-regulatory abilities, and internalization of the social norms practiced across the three conditions, b) evaluating the reasons for the outcomes, principally through microanalysis of classroom interactions, and c) revising the instructional procedures. In Year 3, we will investigate the implementation of the revised instructional program in eight classrooms. The proposed study will contribute both to research on problem solving and conceptual understanding in science and to the improvement of educational practice, focusing on the opportunities for teachers and students to work together in "learning communities."

Recent discoveries of the theoretical relationships between reinforcement learning and dynamic programming suggest exciting possibilities for developing automatic controllers that learn with experience to follow optimal control strategies. Combining reinforcement learning algorithms with the adaptive structure that neural networks provide results in theoretically optimal controllers that have more flexibility, and thus are more general, than current adaptive control techniques. However, for reinforcement learning networks to be practical, the efficiency with which they learn must be improved. In previous work, the PI identified one cause of slow learning to be difficulty of discovering useful features by the hidden units of the network. This difficulty has also been recognized within the supervisedlearning paradigm and a number of alternatives to the common error back propagation algorithm have been shown to significantly reduce learning time. These ideas will be extended to the reinforcement learning paradigm and their potential for reducing the learning time of reinforcement based networks will be explored. The objective is to alleviate the problem of training hidden units and to identify any remaining limitations of reinforcement learning networks that restrict their generality and practicality as real time control techniques. The methods will include both simulation studies and implementations as controllers of physical systems.

This is the first year of a three year continuing effort. This research is to study the feasibility of human-computer interaction through the use of on-line recognition of electroencephalogram (EEG) patterns. A system that can identify, on-line, which of several mental tasks a person is performing could provide an alphabet with which a severely disabled person can compose commands to devices like a wheel chair. Most prior research has focused on recognition accuracy rather than on fast, real-time responses. The goals of this research are to characterize the limitations of current EEG recognition methods, to increase the accuracy and decrease the computation time of EEG recognition by use of a neural network approach, and to develop a practical real-time method. The performance of conventional methods and of neural network learning methods designed for high- dimensional data will be compared. Evaluations of feature and classifier characteristics will be based on classification accuracy, robustness and computation speed. This project has relevance to the field of pattern recognition in general, and to the neural network and human-computer interaction fields in particular.

9353099 Anderson The project is a three year investigation of collaborative problem solving in science for the purpose of developing accounts of learning and problem solving by middle school students and developing instructional strategies and materials that improve both students' conceptual understanding of science and their capacities for self-regulation in complex problem solving situations. Year 1 is an intervention study investigating the comparative effects of three instructional conditions: a) mediated independent problem solving, b) group problem solving, and c) mediated group problem solving. This investigation will be conducted in eight classrooms as the students study a unit on the nature and properties of matter. Analyses of Year 1 data will occur in Year 2 for the purpose of: a) evaluating the success with which the desired outcomes were achieved across the various conditions in terms of conceptual understanding, self-regulatory abilities, and internalization of the social norms practiced across the three conditions, b) evaluating the reasons for the outcomes, principally through microanalysis of classroom interactions, and c) revising the instructional procedures. In Year 3, we will investigate the implementation of the revised instructional program in eight classrooms. The proposed study will contribute both to research on problem solving and conceptual understanding in science and to the improvement of educational practice, focusing on the opportunities for teachers and students to work together in "learning communities." ***

9401249 Hittle The objective of this research project is to increase the efficiency of control methods for energy systems in buildings. IN meeting this objective, novel combinations of feedback controllers and neural network learning algorithms will be developed. to achieve the objective, recent developments in reinforcement learning methods for neural networks will be adapted to the HVAC system control problem. Neural networks will not be the sole source of control decisions for such a complex system. Adaptive schemes are notorious for leading to instabilities. to lessen the possibility of introducing instability, adaptive schemes will be integrated with existing, fixed controllers. the adaptive methods will only augment the control actions taken by the fixed controllers and a limit will be enforced on the degree to which the fixed controller's decision can be modified. This will result in a control system with an initial performance at the level provided by the original control designer. Then, with experience, the learning mechanism will be able to increase the performance of the controller beyond its initial ability. Results will certainly be of interest to building engineers, but will also advance the sate-of-the-art in neural networks for control. ***

9422007 Beveridge This award is to purchase two shared-memory multiprocessor systems, one with accelerated 3D graphics as required by the following research projects: 1) Computer Vision and Graphics; 2) Combinatorial Optimization Using Local and Global Search; 3) Machine Learning for Classification and Control; 4) Planning Agents in Dynamic Simulated Environments; 5) Comparing Functional Programming Languages. Essential to these projeats is the computational power of the multiprocessors. The equipment will provide a means to gain experience mapping algorithms onto coarse grain parallel hardware. Many parallel algorithms in these projects require a shared memory multiprocessor architecture. The vision, graphics, learning and planning projects explicitly benefit from the 3D graphics capability of the hardware. In addition to the above projects, this equipment will be available for exploration of new areas of research. ***

The primary objectives of the proposed research are: 1) Develop a new controller design methodology based around a combination of robust control and neural network reinforcement learning techniques. The strategy will combine the best aspects of robust control and reinforcement learning and yield a combined control design that is far more powerful than either acting alone. 2) Provide a theoretical framework for precisely analyzing the stability and performance properties of reinforcement learning controllers by exploiting and extending results from robust control theory. 3) Apply these results to the design of closed loop controllers by exploiting and extending results from robust control theory. 4) Implement and test these ideas on an experimental HVAC system. 5) Educate practitioners in industry about this new technology. 6) Integrate these ideas into systems and control curriculum. To accomplish these objectives, an interdisciplinary team has been formed consisting of a specialist in robust control from the Electrical Engineering Department, a specialist in reinforcement learning for neural networks from the Department of Computer Science, and a specialist in design, modeling and control of HVAC systems from the Mechanical Engineering Department. This interdisciplinary approach will advance the state-of-the-art in the theory of robust reinforcement learning control design, demonstrate these new methods on an experimental HVAC system and provide much needed improved methods for controlling HVAC systems in buildings. Eventual wide spread implementation of these schemes in buildings around the world will reduce energy consumption and extend equipment life.

The goal of this project is to develop novel electroencephalogram (EEG) classification methods that result in a practical, real-time brain-computer interfaces (BCI) system. BCIs are hardware and software systems that sample EEG signals from electrodes placed on the scalp and extract patterns from EEG that indicate the mental activity being performed by the person. The long-term goal of this line of research is a new mode of communication for victims of diseases and injuries resulting in the loss of voluntary muscle control, such as amyotrophic lateral sclerosis (ALS), high-level spinal cord injuries or severe cerebral palsy. The autonomic and intellectual functions of such subjects continue to be active. This can result in a "locked-in" syndrome in which a person is unable to communicate to the outside world. The interpretation of information contained in EEG may lead to a new mode of communication with which subjects can communicate with their care givers or directly control devices such as televisions, wheel chairs, speech synthesizers and computers.

The objectives of this project are the design and testing of an EEG system for experimentation in real-time EEG pattern analysis constructed of off-the-shelf components for under $5,000, development of new techniques for a novel approach to studying the cognitive components of mental tasks and how they vary in time and across subjects, demonstration that real-time feedback to the subject will produce a biofeedback situation in which the subject can learn to modify their EEG to increase classification accuracy, proof by demonstration that accuracy and classification time will be sufficient for two persons to interact over the net in a simple game controlled by two BCI systems. The evaluation of the results of this project in light of these objectives will be based on the accuracy of EEG classification, the speed with which the classification can be performed, and the expense of the EEG system and of its maintenance and extendibility.

The most significant impact of this project to the disabled community will be an easier to use, affordable BCI system. The inclusion of a wide range of mental tasks will result in a better understanding of which mental tasks are easiest for subjects to consistently perform and for detection algorithms to reliably identify. Better BCI systems will also be significant for other classes of users who can benefit from augmented communication interfaces in applications that require extremely fast commands. The significance of this project to the BCI research community is the specification and testing of the inexpensive system for experimentation with EEG signal analysis. The system based on off-the-shelf components and software to be developed and made publicly available is expected to allow a number of additional research groups to enter the BCI field. Also, this project's results on the analysis of cognitive components in EEG measured during a wide range of mental tasks will broaden the set of mental activities available to users of BCI systems.

This project will develop of new control strategies for heating, ventilating, and air-conditioning (HVAC) systems that combine artificial intelligence and robust control theory. A primary objective of the work is to develop learning controllers, which provide (a-priori) guaranteed closed-loop stability even while the controller trains online. It is a follow-on project to NSF project 9804757, i8Robust Learning Control for Heating, Ventilating and Air-Conditioning Systems, it where several significant advancements in the state of the art were achieved.

The primary objectives of the proposed research are:

New analysis tools and algorithms, which allow for new theoretical approaches for learning including: Continuous learning algorithms; recurrent dynamic networks; and learned dynamic models.

New theoretical tools, including enhanced Integral Quadratic Constraints (IQCs) for improved speed and accuracy the more general classes of networks, and advanced systematic algorithms for tunneling, to facilitate navigation of the learning around regions of instability.

Design and test of more advanced robust reinforcement learning algorithms;

Extensive experimental study of the new tools being developed here for multi-input, multi-output (MIMO) robust reinforcement learning control, utilizing a recently developed experimental platform for heating, ventilating, and air-conditioning (HVAC) system control.

Disseminate in through conference and journal publications and by assisting colleagues at Korea Institute for Energy Research in conducting their own tests of these methods.

To accomplish these objectives, an interdisciplinary team has been formed consisting of a specialist in robust control from the Electrical & Computer Engineering Department, a specialist in reinforcement learning for neural networks from the Department of Computer Science, and a specialist in design, modeling and control of HVAC systems from the Mechanical Engineering Department.

ABSTRACT
PI: Michael Kirby
proposal: 0434351

This proposal develops new algebro-geometric tools for data analysis, and in particular, for the investigation and understanding of massive data sets arising in computer science, where both the ambient dimension and number of samples may be large. Particular examples of interest to the team include brain-computer interface (BCI) problems and computer-vision and recognition problems. Specifically, the team is relating secant varieties (of arbitrary order) to the determination of optimal projections of data sets; they inquire what order of variety is appropriate and are investigating the relationship between varieties of different orders. Secant varieties also play a role in understanding canonical forms and decompositions of higher-order tensors, which in turn are of fundamental interest in signal-processing applications such as the BCI problem. Algebraic geometry provides a framework for developing algorithms for higher-order tensors. Finally, very recent developments in the Schubert calculus on Grassmannians have dramatically increased the potential applicability of this theory. The investigators are studying how to exploit these ideas to develop efficient algorithms for finding near-optimal projectors subject to constraints.

The research, which this team is carrying out, has distinctive impacts on the mathematical and computer science communities. By bringing together groups of researchers from seemingly disparate areas of expertise, it aims for cross-fertilization among these fields. Computer science provides the problems for which the team is developing new algorithms. Conversely, the team expects new mathematical conjectures to arise from the practical problems being addressed by this research. Furthermore, the graduate students (all working towards the Ph.D.) will receive innovative training during the course of this project that will prepare them for jobs in either academia or industry where they will have tools and preparation essentially unlike any of their peers. This should greatly enhance their ability to contribute new and innovative ideas to the team research environment. Beyond this effect on the professional communities, the applications driving the proposed research have clear and immediate impact on aspects of such disparate issues as national security and the broadening of the ability of disabled individuals to participate more fully in society.

This project will study the development of a learning progression: a coherent account of the development of students' reasoning about the role of carbon in environmental systems, based on old and new research. The role of carbon in environmental systems includes physical and chemical changes in matter, growth and decay of plants and animals, photosynthesis and cellular respiration, matter cycling and energy flow in ecosystems, and the effects of humans and human technologies on these processes. Products of this project will include:
1. A longitudinal description of children's learning that documents strands in the development of elementary through high school students' conceptual understanding and ability to use this knowledge. This account will be based in part on a synthesis of existing research, including research done by the project PI's and consultants. The project will conduct new research that builds on prior work, fills in gaps, and validates findings for a sample of American students that spans major subgroups.
2. Validated assessments that measure student understanding of matter and energy transformations in biogeochemical systems at the upper elementary, middle school, and high school levels. The assessment system is based on progress variables defined in the longitudinal description of children's learning. The system will include both embedded assessments for use in classrooms and link tests that form the basis of a systematically designed large-scale assessment program. These assessments will be used for a survey of student reasoning in a sample of rural, urban, and suburban classrooms. The assessments will be made available to large-scale assessment developers and classroom teachers through the BEAR Assessment System.
3. Reports of teaching experiments focusing on students' reasoning when they encounter challenging questions about environmental systems in resource-rich classrooms. These reports will capture the dynamics of learning-both successful learning by students who master key conceptual tools and practices and barriers to successful learning. The teaching experiments will be conducted in rural, urban, and suburban classrooms at the upper elementary, middle, and high school levels.

This proposal describes a Track 1 project designed to bring inquiry-based learning to schools in rural Michigan. Eight graduate Fellows will connect with teachers to learn pedagogical skills and bring ecological concepts to schools. Eleven Faculty at Kellogg Biological Station will be involved in several aspects of the project.

DRL-0815993
PI: Andy Anderson

PROJECT ABSTRACT

This empirical study develops a science learning progression that extends from 4th grade through the first year of college and focuses on key bio-geo-chemical processes in socio-ecological systems at multiples scales, including cellular and organisms, metabolism, ecosystems, energetics and carbon cycling, carbon sequestration, and combustion of fossil fuels.

The project investigates the learning progression hypothesis that there are patterns in the development of students' knowledge and practice that are both conceptually coherent and empirically verifiable. Using an iterative, design-based research process, the project develops: (1) a validated framework; (2) assessment resources; and (3) studies of sequences and mechanisms of conceptual change based on teaching experiments at the elementary, middle, high school and college levels.

Driven by an environmental science literacy framework around learning progressions within core science and mathematics concepts complemented with citizenship, this targeted partnership connects the research and education prowess in the environmental sciences of universities and sites within the NSF-funded Long Term Ecological Research (LTER) Network with teacher professional development in science and mathematics of partner middle schools and high schools. The project extends across the nation, involving four LTER research sites and 22 K-12 schools/districts with direct impacts on over 250 science and mathematics teachers and 70,000 students of highly diverse backgrounds. The work focuses on coupled human-ecosystem interactions in the context of socio-ecological systems as a framework to develop a culturally relevant ecology from both a scientific and educational perspective. The following research, professional development and institutional goals unify the project:

Research Goals -
1: Refine and extend current frameworks and assessments for learning progressions leading to environmental science literacy and associated mathematics that focus on carbon cycling, water systems, and biodiversity in socio-ecological systems.
2: Assess the relationship between students' learning about socio-ecological systems and engagement in citizenship practices and local socio-ecological factors, including students' culture, socioeconomic status, region, and educational level.
3: Use teaching experiments conducted by participating teachers and graduate students to develop and disseminate improved frameworks, assessments, and teaching resources for environmental science literacy.

Professional Development (PD) Goals -
4: Develop models of PD that integrate the research efforts of scientists with the professional development needs of K-12 teachers to include a teacher-in-residence program, research internships for teachers, placement of graduate students into K-12 classrooms, professional development workshops leading to graduate credit and/or graduate degrees, and professional learning communities.
5: Develop models of culturally responsive engagement that connect culturally relevant ecological content with different populations of learners.

Institutional Goals -
6: Integrate culturally relevant, environmental content-based, PD models for K-12 teachers into district and university programs and outreach activities through the development of graduate training and degrees and in service-learning tracks.
7: Increase the awareness, opportunities and participation of K-12 teachers in content-based professional development activities.

This project builds on a one year Phase 1 project in which ecology faculty began to use Diagnostic Question Clusters (DQCs) to: 1. organize an entire introductory course around key concepts and biologically principled thinking based on an overarching framework and 2. identify and gauge student progress on specific principles especially problematic for them. The DQCs focus on energy and matter across all levels of biological organization. Faculty from universities, including Historically Black Colleges, plus four year and community colleges contributed to the original DQC research. Adopting an expanding networks approach for broader dissemination and impact, this project extends this model to more faculty and also introduces modifications to the faculty workshops based on the "lessons learned" from the previous project.

Intellectual Merit: The intellectual merit of this project stems from the value of the material being produced and the lessons concerning effective dissemination techniques being learned from the outcomes of the workshops.

Broader Impact: The ability of the workshops to help faculty inspect their own teaching practices is potentially transformative.

Abstract: Using the STEM Dimensions of Bioenergy Sustainability
to Bring Leading-edge Graduate Research to K-12 Learning Settings.

The intellectual focus of this new GK-12 project at the W.K. Kellogg Biological Station (KBS) is on the ecological dimensions of bioenergy sustainability. Graduate students in Michigan State University?s Ecology, Evolutionary Biology & Behavior and Environmental Science & Public Policy programs who are engaged in STEM research at KBS will partner with teachers in the KBS K-12 Partnership for Science Literacy, the new Department of Energy Great Lakes Bioenergy Research Center (GLBRC), and the NSF Long-Term Ecological Research (LTER) project on the Ecology of Agricultural Landscapes. Project activities include establishing schoolyard science research plots in K-12 Partner districts that mimic aspects of GLBRC research plots and serve as the foundation for a schoolyard research network. Fellows will work collaboratively with each other, their advisors, and project partners to incorporate their own research into K-12 research and inquiry activities that address Michigan and national science education standards.

Fellows will improve their ability to place their research in its broader societal and global contexts, to collaborate across disciplines, to integrate their research and teaching, and to communicate their research to professional, K-12 and public audiences. The opportunity to work collaboratively with fellows on authentic research related to pressing national needs will enhance the professional development of the K-12 partner teachers and enrich the education of K-12 students. This project will also enhance ongoing efforts at KBS to recruit a greater number and diversity of young people into STEM science disciplines.

Interdisciplinary (99)
This project is tracing future teachers understanding of foundational big ideas (those that span all science disciplines) as the teachers engage in the phenomena and practice of science within the context of specific topics in life, earth, and physical science in the K-8 science curriculum. Based on the analysis of students learning progressions, three on-line learning modules are being developed for use in a range of pre-service courses. The foundational big ideas are conservation of matter and energy and the causative role of energy in changing matter. The modules focus on specific topics in life science (photosynthesis), earth science (weathering), and physical science (combustion) that are essential to an elementary teachers understanding and provide an opportunity to apply the big ideas within and across disciplines.

Each learning module includes three components. An introductory knowledge component includes a sequenced framework of disciplinary big ideas tied to the foundational big ideas along with common misconceptions. The second component is a system of assessments that allows for eliciting misconceptions and gaps in understanding at multiple time points in students learning progressions. A third component consists of a set of evidence-based teaching strategies developed from assessment data, misconceptions, and big ideas. These teaching strategies provide faculty with a range of responses at specific points in students learning progressions. The learning modules are based on following the learning progressions of 450 pre-service elementary teachers and using that assessment data to develop evidence-based teaching strategies and learning materials. Continual assessment is being used to track individuals learning progressions toward understanding the big ideas in a broad range of disciplinary phenomena. Clinical interviews and focus groups are being used to provide a deeper exploration of student understanding and of the teaching strategies and learning materials that promote understanding in individual students. An iterative process of assessment and analysis of student understanding is being used to inform subsequent design and implementation of teaching strategies. This design-based approach ultimately will lead to the development of the learning modules for use by other institutions.

This exploratory project, led by faculty at the University of Montana, Michigan State University, and the University of Arizona, collaborating with teachers from the Missoula, MT schools, builds on current learning progression research to study the effects of teaching Tools for Reasoning on development of middle school students' capacities to understand the Earth's hydrologic systems. The project applies a design-based research approach using iterative cycles of Tool design/revision, teacher workshops, and small-scale pilot tests of Tools through classroom experiments with teachers and students in Montana and Arizona.

The central research question being addressed is: How can learning progression-based Reasoning Tools support students in using models and representations to engage in principled reasoning about hydrologic systems? This question will be answered by analysis of data from assessments of student learning, student clinical interviews, teacher assessments, classroom observations, and teacher focus groups.

The Reasoning Tools project will contribute insight into the challenge of developing students' environmental science literacy and the reasoning skills needed to make informed citizenship decisions about 21st century water issues. Project outcomes will include materials for teaching middle school students to reason about hydrologic systems, theoretical and practical insights into the effects of teaching Tools for Reasoning, strategies for supporting students and teachers in use of the Tools, and refinements of a water systems learning progression framework.

This project--led by science educators at Michigan State University, the National Geographic Society, the Natural Resource Ecology Laboratory (NREL) at Colorado State University, the Berkeley Evaluation and Assessment Research (BEAR) Center, and AAAS Project 2061, and including schools in California, Colorado, Maryland, Michigan, and Washington--builds on prior efforts with learning progressions, and is focused on key carbon-transforming processes in socio-ecological systems at multiple scales, including cellular and organismal metabolism, ecosystem energetics and carbon cycling, carbon sequestration, and combustion of fossil fuels.

The project uses an iterative design research process to develop and refine a suite of tools for reasoning and test efficacy of those tools in geographically and culturally diverse schools. The project team is:

1. Refining and validating a detailed learning progression framework covering the middle and high school years; ultimately, the framework will describe the development of students' capacity to use fundamental principles such as conservation of matter and energy to reason about carbon-transforming processes at multiple scales.

2. Refining 'Tools for Reasoning' that make hidden scientific principles - matter, energy, and scale - visible to students; the power of these tools lies in their flexible use for different processes, systems, scales, and curricular contexts.

3. Developing and refining flexible teaching strategies that engage students in cognitive apprenticeship in the practices of environmental science literacy: a) inquiry and argumentation, b) explanations and predictions, and c) decision-making about environmental issues.

4. Using and refining existing summative assessments, and developing and testing formative assessment tools; these assessment tools will provide teachers and researchers with immediate information about their students' intellectual resources and will be linked to the learning progression framework.

5. Developing, field testing, and assessing the effectiveness of six middle school and six high school units that use project tools and enact project principles; the units introduce students to fundamental principles, engage them in reasoning about carbon-transforming processes at organismal scale, and at landscape and global scales. Each unit includes a) an online formative assessment and b) activity sequences that use tools for reasoning and teaching strategies.

6. Developing, field testing, and assessing professional development materials in both face-to-face and facilitated online forms; the materials introduce teachers to learning progressions in environmental science literacy, assessment tools, tools for reasoning, teaching strategies, and teaching materials and activities, and also address difficulties that teachers encounter in using learning progressions and enacting teaching strategies.

The primary project outcomes will be coordinated instructional tools that are useful to professionals at all levels in the science education system--classroom teachers, professional developers, and developers of curricula, standards and assessments.

Through the "Learning Progressions Footprint Conference," leading researchers in science and mathematics education are bringing together leaders in the field for a workshop - to take place in Washington, DC, in July, 2011 - that will enable them to take stock of the progress of learning progressions research to date and to suggest productive directions for future research and development. The year 2011 promises to be a watershed year in science and mathematics education as many important developments are currently taking place that will shape mathematics and science education for years to come. In particular, new national standards are under development in both mathematics and science education, with revisions in state standards and assessments to follow. Learning progressions are informing Common Core standards and assessments for mathematics and are expected to play a central role in the conceptual framework for new National Science Education Standards. As new standards and assessments are introduced to the field, it will be critical (a) to assess current standards and contributions of learning progressions research, and (b) to turn attention to research on how to support implementation of practices and related products and technologies informed by a learning progressions perspective.

Learning progressions research seeks to establish empirically grounded accounts of the development of students' scientific and mathematical knowledge and practice over broad spans of time, and of the instructional means of support that enable that development. Well-grounded learning progressions or learning trajectories can serve as valuable resources for science and mathematics education researchers, developers of standards documents, assessment developers, and curriculum developers. A critical evaluation of learning progressions research at this time is enabling the field to set priorities and invest resources wisely. The conference is framed to focus on these issues and is to be attended by about 40 leading researchers, developers, and practitioners in mathematics and science education, as well as representatives of NSF and other federal agencies. In preparation for the conference, participants are considering five key questions:
1. What is the important work that has taken place over the last 20 years? How strong is the evidence base and how can it be strengthened?
2. What are learning progressions good for? How have they been used in practice, and with what consequences? What additional uses might they legitimately have?
3. What methodological advances have been made in learning progressions work? To what extent are current standards of evidence adequate and where are further refinements needed?
4. How can we develop the interdisciplinary teams, possibly including potential users, to do this type of work?
5. What recommendations do conference members have for policies and priorities for future learning progressions work?

The products of the conference will include: (a) a conference report that assesses the evidence base around learning progressions, evaluates methodological approaches, and considers implications for policy and practice, (b) proposals for sessions at professional conferences, and (c) position papers to be submitted to journals for publication.

Brain-computer interfaces (BCIs) are hardware and software systems that allow users to interact with computer applications by changing their mental activity, which causes variations in weak electrical voltages produced by the brain. BCIs measure these voltages in one of two ways: invasive methods use electrodes implanted in the brain, while noninvasive methods use electrodes resting on the scalp that are part of a cap worn by the user. A long-term goal of BCI research is a new mode of communication for subjects with diseases and injuries resulting in the loss of voluntary muscle control, such as amyotrophic lateral sclerosis (ALS), multiple sclerosis, high-level spinal cord injuries or severe cerebral palsy. If all voluntary muscle control is lost, a locked-in syndrome results in which a person is unable to communicate with the outside world. BCIs can provide a new way for users to communicate with their caregivers and to control devices such as televisions, wheelchairs, speech synthesizers and computers. While BCI technology holds great promise, most BCI systems remain in research labs. The goal of this project is to remove barriers to practical, noninvasive, BCI technology that exist in current approaches, and to field test the resulting BCI systems in the homes of users who suffer from motor impairments. Limitations of current BCI systems that will be addressed include the difficulty of applying an electrode cap, signal artifacts due to other assistive technology in the user's environment, and long computer and user training times required to calibrate current EEG classification algorithms.

A key barrier to practical BCI systems is the lack of methods for reliable, fast classification of EEG signals. In this project, this limitation will be addressed by conducting experiments in three areas. One set of experiments will investigate the quality of EEG signals recorded in subjects' homes and the performance of BCI applications in real-time in the homes. The second set of experiments will involve new algorithms for EEG artifact removal and signal classification that are tailored for EEG recorded in subjects' homes and for real-time use. For the second set of experiments, new user interfaces will be studied and compared to currently available interfaces. For the third set of experiments, several different user interface designs for BCI applications will be developed and studied. The effectiveness of visual and auditory feedback provided to the user in real-time will be investigated.

This interdisciplinary project involves a team of investigators and students from diverse backgrounds. Faculty and students in computer science will design and implement algorithms and the BCI user interface. Faculty and students in occupational therapy will guide the field testing of BCI systems and will guide the evaluation of these experiments. Progress will be evaluated in a number of ways, including experiments comparing EEG signal representations and classifiers by accuracy, reliability, and training time, and field tests of BCI systems. Ultimately, the project's success will be measured by new or improved means of individuals interacting with computers in their homes for purposes of communication with others and control of assistive devices like wheelchairs.

Broader Impacts: This project will develop a new technology for sensing and analyzing electroencephalogram signals (EEG) from human subjects. The resulting technology will help advance brain imaging and its application. The long term goal of this research is a new brain-computer interface based on EEG signals with which persons can use a computer to communicate with others in their vicinity or remotely over the net, to surf the net, and to control environmental entertainment, and assistive devices. The new technology will be simple enough for any person with minimal training to use. The project will also play a strong role in the education of future researchers and health professionals in this interdisciplinary field by involving graduate and undergraduate students from multiple departments as research assistants, by teaching a new course in BCI for students from a variety of backgrounds, and by providing fieldwork experiences.

DESCRIPTION (provided by applicant): Autism spectrum disorder (ASD) affects as many as 1 in 150 children. It is a complex developmental disorder characterized by social deficits, language impairment, and restricted interests (Newschaffer et al., 2007;Levitt and Campbell, 2009). Genetic linkage techniques recently revealed that individuals with ASD are three times more likely to have a mutation in the promoter region of the gene for MET receptor tyrosine kinase (hereafter MET), which results in reduced MET expression (Campbel et al., 2006;Campbel et al., 2007). The MET signaling pathway is important for the normal development of the cerebral cortex (Powell et al., 2001;Powell et al., 2003;Levitt et al., 2004;Gutierrez et al., 2004). Interference with this pathway affects cortical development in a number of ways. Still unknown however, is the extent to which the functional organization of cortical circuits is affected by MET dysfunction. Of particular interest to ASD research is the synaptic organization of the frontal cortex, which is involved in many higher order cognitive aspects of behavior and executive functioning. Evidence suggests long-range under-connectivity between cortical areas in ASD (Horwitz et al., 198;Courchesne and Pierce, 2005;Kana et al., 2007). From this view, ASD is a disorder of cortical circuits. It has been proposed that there is also an over-strengthening of local connectivity in ASD (Courchesne and Pierce, 2005), but this has yet to be directly measured. The availability of a MET- knockout (MET-KO) mouse will allow me to measure changes in synaptic connectivity that result from interference with the MET signaling pathway. I hypothesize that local synaptic connections in frontal cortex are over-strengthened as a result of MET- KO. I will test this hypothesis by measuring the circuit abnormalities of corticostriatal neurons with high throughput circuit mapping techniques (Weiler et al., 2008;Yu et al., 2008;Wood et al., 2009, Anderson 2010). I will focus on corticostriatal neurons because these neurons provide long-range input to other cortical areas (Wilson, 1987;Reiner et al., 2003) and the projection is a key component in loops linking the frontal cortex with the basal ganglia and the thalamus important for the selection and initiation of behavior (Albin et al., 1989). Understanding altered cortical connectivity in MET-KO will be an important step towards characterizing the nature of cortical circuit disorders associated with ASD. It will provide a basis for understanding the mechanisms underlying the changes to the brain in ASD. PUBLIC HEALTH RELEVANCE: As many 1 in 150 children are diagnosed with autism spectrum disorder (ASD), which is a complex developmental disorder characterized by abnormal social interactions, language deficits, and restricted interests. Evidence implicates alterations to the circuits of the frontal cortex in ASD, but to date, no detailed, cell-level resolution of synaptic connectivity in non-syndromic ASD has been obtained. We will characterize the cortical circuits of a mouse model of cortical circuit dysfunction in ASD, which will yield important insight into the mechanism underlying differences in the synaptic organization of the frontal cortex, and help understand the changes that occur in the brains of people with ASD.

This Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) project from Colorado State University will develop a state-of-the-art system integrating greenhouse gas (GHG) emissions modeling, energy modeling, and life cycle assessment (LCA) for the built environment. This new Carbon Footprint Metric (CFM) system will mesh seamlessly with existing design and construction management tools that are widely used by architects, engineers and builders. The system will provide dynamic, real-time, 'on the-fly' carbon footprint measurements, all the way from the building concept stage through to construction and building completion and operation.

The broader impacts of this research begin with the opportunity to help reduce the environmental footprint of buildings and the built environment, which currently produce about 50% of total U.S. GHG emissions. A key element to changing behavior on the part of both building professionals and consumers are better environmental impact measurements. The availability of easily used yet rigorous and standardized tools for quantifying GHG metrics can dramatically change architectural and construction best practices. Once validated for each class of building, the CFM system will permit architects and construction practitioners to evaluate and alter building design in order to reduce energy use and GHG emissions over the life cycle of the building. Moreover, constructors can better evaluate construction methods and materials and building managers can exploit improved efficiencies to reduce GHGs. This will help the competitiveness and performance of the many small businesses that are among the pioneers in the "green" building industry. The system could further serve as a platform for urban planners and environmental managers to evaluate and plan improvements in a given built environment which encompasses many buildings and building types. The research and product development also have the potential to help transform the education and training of the next generation of architects, engineers, and product designers, who will be called upon to help build a more sustainable urban environment.


Partners at the inception of the project are Colorado State University, with participants from four Colleges (Agricultural Sciences, Health and Human Sciences, Engineering, and Natural Sciences), along with three small business partners: 1) Living Homes (Santa Monica, CA) that has as its mission the design and creation of homes and communities which set a standard for the positive impact on soil, water, energy and health; 2) The Neenan Company (Fort Collins, CO) that has as its mission the design and building of highly sustainable commercial buildings, schools, and hospitals; and 3) Nemetschek Vectorworks (Columbia, MD) that has as its mission the production of versatile, intuitive, and affordable Building Information Modeling (BIM) software for professional design solutions to the architecture, engineering, and construction industries. Other partner: The National Renewable Energy Laboratory (NREL) is a non-business partner that has as its mission research and development of alternative clean energy technology. Supporting institutions, which are leaders in advancing a more sustainable built environment, include the following: the American Institute of Architects, the U.S. Green Building Council, the National Institute for Building Science, Architecture 2030, and the Rocky Mountain Institute (RMI).

DESCRIPTION (provided by applicant): Tinnitus - the perception of phantom sounds -is frequently caused by acoustic trauma. This widespread neurological condition affects approximately 40 million people in the U.S. Recent evidence suggests that the auditory brainstem, and in particular, the dorsal cochlear nucleus (DCN), plays a crucial role in the induction of tinnitus. The DCN displays hyperexcitability in tinnitus, hypothesized to result from endogenous compensatory mechanisms in response to acoustic trauma, termed maladaptive plasticity. Multiple mechanisms have been proposed to account for changes to the DCN in tinnitus, but to date, there has been no consensus as to how this brainstem nucleus transitions into a pathological state during this disorder. The DCN has a well-defined synaptic organization and contains multiple inhibitory and excitatory pathways that shape the response properties of this structure to sound. There are a rich variety of synaptic plasticity mechanisms present in the DCN, so factors that influence synaptic plasticity are potential substrates for the chronic hyperexcitability observed in the DCN during tinnitus. The DCN is unique among the auditory brainstem nuclei because it contains high levels of synaptic zinc - a strong regulator of long-term plasticity. Zinc is released from glutamatergic terminals during synaptic transmission, and because it potently inhibits NMDA receptors, it is poised to have a dramatic effect on synaptic signaling. My preliminary data indicate that mice with behavioral evidence of tinnitus have a dramatic reduction of synaptic zinc released from the DCN. This is a novel neurophysiological correlate of tinnitus. I hypothesize that synaptic zinc is critical for the normal functioning of the DCN and tht the loss of zinc is crucial feature of the pathology of the DCN during tinnitus. My preliminary data suggest that reduced synaptic zinc release leads to reduced inhibitory drive in the DCN. This has the potential to change to the balance of excitation and inhibition in the DCN, and be a contributing factor to the hyperexcitability of this structure in tinnitus. I will use newly developed ratiometric fluorescent zinc sensors and chelators in combination with brain slice electrophysiology to examine the role of synaptic zinc in the DCN in normal hearing and in tinnitus. My results will shed new light onto the mechanisms by which the DCN transitions into a state of pathological hyperactivity and potentially offer new strategies for therapeutic interventions for tinnitus.

Successful science teachers need high quality teaching materials, sustained professional development opportunities, and a school structure that aligns local goals and policies, and supports sustained teacher networks. This project addresses all three of these essential elements in the context of a key topic in the sciences: the role of carbon in the flow of materials and energy through living systems, human engineered systems, and Earth systems at multiple scales. The project builds on previously funded projects that have developed student learning progressions for these topics, and it will develop and test a new professional development model for teachers that is based on a teacher learning progression framework. The framework is based on four core teaching and learning practices advocated by the Next Generation Science Standards (NGSS): formative assessment, inquiry, explanations, and decision making. Online and in-person teacher networks will also be developed and studied for their effects on teacher knowledge and practices, and on student learning.

The project engages the University of Michigan, the National Geographic Society, the Seattle Public Schools, the Institute for Learning at the University of Pittsburgh, and others in a partnership spanning schools in four states, in diverse sociocultural settings, and located in urban, suburban, and rural environments. Case-study methods will be used to develop the teacher learning progression, including analyses of written assessments, online data capture techniques, interviews, and classroom videotaping. Collected data and analyses will be used to develop a professional development model with blended online and face-to-face experiences. A design-based implementation research approach will be used to develop and test teacher implementation networks. Longitudinal and online network data will be used to identify the conditions under which teachers are influenced by others in their networks, and how those influences affect student outcomes. Findings from this project are expected to provide new knowledge on how to sustain responsive and rigorous science teaching that is anchored in the NGSS and situated in the culture of typical middle schools and high schools.

Proteins perform many cellular functions, made possible by complex networks of interactions; knowing the location of the interaction sites on the proteins is key for understanding exactly how they work. Important applications include designing drugs and therapeutic agents. Experimental techniques for determining the interfaces between proteins are expensive and time consuming, so computational structural biologists seek to predict these mathematically. Current prediction methods use a limited number of features hand-crafted by an expert. An alternate approach is to learn the important features directly from all of the data, using a method called deep neural networks. This proposal explores a combined approach: use expert intuition for some features but add the power of unsupervised learning with deep neural networks to learn additional, novel features. The results will enrich the way protein structural features are understood in terms of their functional properties, whether those are catalytic sites, protein-binding sites or other sites important to the protein structure. Certainly in the prediction of protein structure itself machine learning scoring methods are showing great promise. Aspects of the research will be used in courses offered through a recently awarded NSF-NRT training grant, The training grant establishes an interdisciplinary program at the interfaces of biology, engineering, math/statistics and computer science. The program prepares students for a variety of career paths. Research and education experiences will provide students with valuable expertise in a computational area that is highly valued by top technology firms, such as Google and Facebook, which have research teams exploring the possibilities of deep neural networks.

This work proposes a paradigm shift in the field of protein interface prediction and scoring: from hand-crafted features and standard off-the-shelf classifiers to an approach that augments existing features with automatic learning of the features that characterize the 3-d structures of proteins, combined with the use of learning algorithms that are specifically designed for the characteristics of the problem. The proposed approach has multiple novel aspects: the proposed learning approach leverages information contained in the entire protein data bank (PDB) to learn features that characterize protein structures at multiple scales and levels of abstraction. It introduces a novel neural network architecture and regularization terms that constrain the solution towards biologically relevant results. The primary alternative to this machine learning-based interface prediction uses docking simulations; however, current docking energy functions are not accurate enough, so that a near-native solution is often not ranked high enough on the list of outcomes to be useful. Extensions of the proposed architectures for interface prediction will be employed for re-scoring docking solutions to improve their predictive success. A workflow that integrates docking and machine learning-based interface prediction and scoring is proposed to explore the synergism between these tasks. Information on the progress made on the project is available through the project website: http://www.cs.colostate.edu/~asa/projects.html.

An award is made to The Pennsylvania State University, University Park campus to purchase a super-resolution microscope that will enable the capture of images of plant and animal cells, as well as complex chemical samples, at the scale of single molecules. This microscope will reveal new insights into how living and chemical systems are organized and work. The project will also generate new image analysis tools for the scientific community. The microscope will enable interdisciplinary research training and enhance education through coursework and outreach to other Penn State campuses and other institutions. Integration of this microscope into a core microscopy facility will make it available to undergraduate, graduate and postdoctoral trainees, and regular imaging workshops will be offered by Penn State. New teaching modules for K-12 and undergraduate educators demonstrating the science of size and the potential of super-resolution microscopy will be developed. Access and training will be assured for underrepresented students through programs including the Summer Experience in the Eberly College of Science, McNair Scholars, Women in Science and Engineering Research, and Minority Undergraduate Research Experience. Public understanding of super-resolution microscopy and its advantages will be catalyzed by multiple outreach activities and venues, including The Franklin Institute (science museum) and Penn State's Ag Progress Days, which together will expose this cutting-edge imaging technology to tens of thousands of people. The discoveries enabled by this microscope will advance the study of plant and animal development, sustainable agriculture and energy production, and the chemical interactions that define our physical environment.

Super-resolution microscopy is leading to dramatic new research opportunities in the life sciences, and has emerging capabilities in chemistry and materials research. Penn State will advance our optical imaging-based research through the purchase of a super-resolution microscope with both Structured Illumination Microscopy (SIM) and Stochastic Optical Reconstruction Microscopy (STORM) capabilities. Both imaging modules are combined on a single microscope platform, and use high-magnification total internal reflection fluorescence (TIRF) objectives, an automated focus retention system, and an integrated multi-channel solid state laser launch. The ease of use of this commercial super-resolution microscope is a particularly appealing feature for equipment residing within Penn State's Huck Institutes of the Life Sciences Microscopy & Cytometry Facility, a multi-user core facility. Research and training with this microscope will focus on four main areas: (a) plant cell biology; (b) animal cell biology; (c) chemistry; and (d) advanced image processing and pattern recognition. Researchers in groups (a)-(c) will leverage the increased resolution offered by this system to push their research into hitherto unreachable areas of fine-scale biological processes and molecular interactions. In parallel, group (d) will collaborate with those generating super-resolution imaging data to advance our ability to recognize structures and patterns from molecular localization data. The combined SIM/STORM microscope will maximally benefit the largest number of researchers in the life and chemical sciences at Penn State by offering a high degree of flexibility for sample labeling and imaging. As an integral component of this project, faculty with research interests in advanced image analysis and pattern recognition will generate new algorithms for analyzing SIM and STORM imaging data that will be widely applicable to super-resolution microscopy and will be freely disseminated to the global scientific community.

Brain-computer interface (BCI) technology is a potentially powerful communication and control option for people with severe motor disabilities, as well as a hands-free, voice-free control option for mainstream users. The success of this exciting work depends on close and productive multidisciplinary interactions among researchers working in neuroscience, psychology, engineering, mathematics, computer science, and clinical rehabilitation. For this reason, the Sixth International Brain-Computer Interface meeting , themed "BCI Past, Present, and Future", will be held May 30 - June 3, 2016, at the Asilomar Conference Grounds, Pacific Grove, California. The 2016 BCI Meeting will retain the unique, highly interactive, student-friendly, retreat-like atmosphere and high-impact activities that are the hallmarks of the BCI Meeting series, starting with the first BCI meeting in 1999.

This NSF award will provide live streaming of presentations made at the meeting. This will provide access to people who have interests in BCI but are unable to attend. This includes all who may not have the time or funds to attend, but also the large population of people with impairments that hinder or prevent their attendance, exactly the population that would most benefit from the material presented at this meeting. NSF funds will also support the editing and archiving of the presentations for viewing on-line after the meeting.

This project seeks to determine how the carbohydrate-based cell walls of guard cells dynamically change shape to control stomatal pore size, thus allowing plants to control carbon dioxide (CO2) uptake and water loss. Stomata are small openings in the surfaces of plants that regulate the photosynthetic conversion of CO2 into plant biomass, which serves as a renewable source of food, materials, and bioenergy. A deeper understanding of cell wall structure, mechanics, and dynamics in stomatal guard cells will help identify plants that can more efficiently use water, a major limiting factor in global agricultural production. The computational image analysis and modeling tools that will be developed in this project will provide scientists with new ways of interpreting and understanding experimental data. Because stomatal guard cells are an amazing example of cellular engineering by plants and are accessible and observable by scientists of all ages, a learning module will be developed and deployed that allows 4th through 8th graders to observe stomatal dynamics first-hand and challenges them to construct and optimize functioning macro-scale models of stomatal guard cells, helping to inspire future scientists and engineers. This project will also train two PhD students and a research associate in interdisciplinary research skills that cross the boundaries of biology, computer and information science, and engineering.

In plants, stomatal guard cells function as dynamic gatekeepers that control CO2 and water flux to maintain homeostasis. To control transpiration and photosynthesis, stomatal development, morphology, and mechanics are tightly regulated. However, two large gaps exist in our knowledge of how stomata develop and function. First, stomatal pores form via controlled separation of sister guard cells, but how this is accomplished is unknown. Second, the walls of guard cells must be highly flexible to enable repeated stomatal opening and closing, but strong enough to withstand the enormous turgor pressure that drives their deformation. How guard cell walls are molecularly constructed to meet these competing requirements remains largely undefined. This project will analyze the molecular and mechanical requirements for stomatal pore formation, and the dynamic molecular architecture of guard cell walls that underlie their unique mechanical properties, using a complementary set of approaches including molecular genetics, high-resolution microscopy, and computational image analysis. The data and insights gained from these analyses will be used to construct computational mechanical models of guard cell walls that can be iteratively refined with new experimental results, ultimately resulting in the ability to predict guard cell dynamics across a range of species, wall compositions, and signaling inputs.

Brain-computer interface (BCI) research explores avenues of controlling devices directly from brain signals. Thus, BCI technology is a powerful control option for neuro-prosthetic limbs, as well as a potential communication option for people with severe motor disabilities or disorders such as amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy, and spinal cord injury, who may have little or even no muscle control and therefore no means of communication with the external world. The International Brain-Computer Interface (IBCI) meeting is the flagship conference for the field, the seventh of which will be held May 21-25, 2018, on the Asilomar Conference Grounds in Pacific Grove,, California. Effective BCI research requires interdisciplinary interactions involving neuroscience, psychology, engineering, mathematics, computer science, and clinical rehabilitation, and the IBCI meeting serves as a critical catalyst for technology dissemination, new collaborations, and educational opportunities for students. The primary source of funding for the meeting is the National Institutes of Health (NIH). This NSF funding will enable another 22 students, including undergraduate and graduate students and postdoctoral fellows, to attend and participate in the conference. The organizers are planning innovations to make the 2018 event the most valuable yet. For the first time the conference will include a series of 12 one-hour masterclasses, six of which will meet in parallel on Wednesday evening, and the other six will meet in parallel on Thursday afternoon. Each masterclass will have a senior researcher as the "master" along with two students each of whom will present his/her research for 10-15 minutes, after which the master will provide wise and constructive comments, with audience participation encouraged in an informal session intended to give students access to senior expertise and to get good tips for their research. Another new event at this year's meeting with be the "BCI Hackathon" organized and sponsored by G.Tec Medical Engineering GmbH, during which participants will work in teams on a number of BCI applications and will then present their work to the hackathon community. NSF funding will be used to support student travel, registration, and student-only event costs. Student participation in previous IBCI meetings has been very fruitful; a large number of those students have now graduated and are prominent researchers in the BCI field. The organizers are actively recruiting student attendees from traditionally underrepresented groups. More information about the conference may be found online at http://bcisociety.org/meetings/bci-2018-welcome/.

Because of the growth in the field of BCI research, 300 or more participants are expected to attend this meeting, including investigators from at least 100 BCI research groups. All attendees commit to the entire meeting, from the opening reception and dinner on the evening of Monday, May 21 through the final summary discussion at breakfast the morning of Friday, May 25. A main objective of the conference is to give students a significant educational and professional experience in the BCI field, and to provide opportunities for them to gain depth in their specific interest areas. The conference will begin with a specialized student colloquium on the afternoon before the full conference starts, which will provide lectures from prominent experts in the BCI field. This will give the students a solid foundation for understanding the rest of the conference. There will also be a student poster session and prizes for the best student research projects, as well as giving students access to research leaders for informal question-and-answer sessions at the end of each day. The students will participate fully in the main conference as well. Exposure to prominent researchers in the BCI field will allow students to receive invaluable feedback on their work, and to make connections that may result in new research directions; it will also provide students with access to potential mentors, committee members, and experts to review their future work. A full and detailed schedule of planned conference sessions and events, along with any updates, can be found on the conference website. With all participants housed on site and all meals for all attendees taken together on site, there will be ample opportunity for informal discussions. This creates a unique opportunity for students and trainees to mingle with and learn from established researchers.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Summary Many regions of the brain including the cortex, hippocampus, basal ganglia, and limbic structures are highly enriched with synaptic zinc. Synaptic zinc (as Zn2+) is loaded into presynaptic vesicles by zinc transporter 3 (ZnT3), where it is coreleased with glutamate during synaptic transmission. Since synaptic zinc inhibits AMPA and NMDA receptors ? which mediate the majority of excitatory glutamatergic transmission in the brain ? synaptic zinc can modulate excitatory synaptic signaling. ZnT3 KO mice (which lack synaptic zinc) display a range of cognitive and sensory impairments and demonstrate behavioral deficits associated with autism and schizophrenia. Mounting evidence from human populations shows that mutations in certain zinc transporters are linked with major neurological disorders such as schizophrenia. Together, these findings strongly suggest that synaptic zinc signaling is important for neuronal processing. The goals of this project are to understand how synaptic zinc contributes to normal neuronal function and how disruptions in zinc signaling are linked to pathological neuronal conditions. We will take three complimentary experimental approaches to these questions. 1) Using ex vivo brain slice preparations and optogenetic stimulation paradigms, we dissect the roles of synaptic zinc in shaping the dynamics of synaptic transmission at specific synaptic connections in cortical microcircuits. 2) Using in vivo 2-photon calcium imaging, we assess the roles of synaptic zinc in shaping the sensory-evoked responses of specific classes of auditory cortical neurons in awake mice. 3) Using in vitro high-throughput screening assays and rational compound design approaches, we are designing novel tools to modulate the function of specific zinc transport proteins. Together these approaches will allow us to answer fundamental questions concerning the role of synaptic zinc in brain function and provide new mechanistic insights into endogenous mechanisms that shape synaptic and neural processing.

Brain-computer interface (BCI) research explores avenues of controlling devices directly from brain signals. Thus, BCI technology is a powerful control option for neuro-prosthetic limbs, as well as a potential communication option for people with severe motor disabilities or disorders such as amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy, and spinal cord injury, who may have little or even no muscle control and therefore no means of communication with the external world. The International Brain-Computer Interface (IBCI) meeting is the flagship conference for the field. Effective BCI research requires interdisciplinary interactions involving neuroscience, psychology, engineering, mathematics, computer science, and clinical rehabilitation, and the IBCI meetings serve as critical catalysts for technology dissemination, new collaborations, and educational opportunities for students. The IBCI conferences are under the auspices of the BCI Society, and in light of the significant progress that has been made towards restoring communication and mobility the 2020 International BCI meeting will focus on emerging applications and techniques with the theme "BCIs: The Next Frontier." NSF funding will enable an additional 25 students, including undergraduate and graduate students and postdoctoral fellows, to participate in the conference. Student participation in previous IBCI meetings has been very fruitful; many of those students have now graduated and are prominent researchers in the BCI field. The organizers are actively working to recruit student attendees from traditionally underrepresented groups.

Reflecting the growth of the field of BCI research, 400 or more participants are expected to attend this year's meeting, including investigators from at least 100 BCI research groups. All attendees commit to the entire meeting. A main objective of the conference is to give students a significant educational and professional experience in the BCI field, and to provide opportunities for them to gain depth in their specific interest areas. To these ends, and guided by feedback from a survey of 2018 IBCI attendees, the conference will include interactive events such as workshops and poster sessions along with 7 plenary keynote talks which will be complemented by research sessions and master classes, a BCI users forum as well as BCI didactics sessions, and a Women in BCI social.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

In plants, stomatal guard cells influence both photosynthesis and water transport and are thus essential for growth and efficient water use. However, our understanding of guard cell function at the cellular and molecular levels is limited. This project studies how guard cells dynamically expand and shrink to open and close stomatal pores, controlling the uptake of carbon dioxide and the release of oxygen and water by the plant. Understanding how guard cells function will aid in the development of resilient, high-yielding crops that can grow in hot, dry environments and more effectively remove carbon dioxide from the atmosphere. The image analysis and modeling tools created in this project will allow researchers to predict the behavior of a wide range of mechanical behaviors and responses by cells. This multidisciplinary project will train three PhD students as future leaders across the topics of cell biology, computational image analysis, and mechanical testing and modeling of biological systems, and will spread knowledge of guard cells and plant biology using learning modules and research experiences for K-12 and undergraduate students.

Despite decades of research interest, the molecular and biophysical mechanisms by which stomatal guard cells function remain incompletely understood. In particular, the roles of the cell wall and water flux in guard cells and neighboring cells in stomatal mechanics are not fully defined. This project combines molecular genetics, cell biology, advanced microscopy, computational image analysis, nanoindentation, and computational modeling to measure and model turgor pressure and wall mechanics in wild type and mutant stomatal complexes of the model plant Arabidopsis thaliana. These analyses will be used to examine the biomechanics of guard cells and the dynamic mechanical and functional relationships between guard cells and their neighboring cells. Another major goal of the project is to use super-resolution microscopy to develop a clear picture of the molecular composition and architecture of the guard cell wall, and to use these data to construct detailed, accurate, and experimentally testable finite element and multiscale, multiphysics models of stomatal guard cells. Together, this work will enable the prediction of stomatal dynamics across a range of species, wall compositions, stomatal geometries, and signaling inputs.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The long-term goal of brain-computer interface (BCI) research is to establish a new mode of communication for individuals who have lost some or all voluntary muscle control due to injury or degenerative diseases such as amyotrophic lateral sclerosis, multiple sclerosis, and severe cerebral palsy. If all voluntary muscle control is lost, a locked-in syndrome results in which a person is unable to communicate with the outside world. BCIs could potentially provide a way for these individuals to communicate with their caregivers and to control devices such as televisions, wheelchairs, and robot assistants. While BCI technology holds great promise, non-invasive BCI systems are not yet practical, primarily due to limitations in signal quality provided by current electroencephalogram (EEG) scalp electrodes. This project will explore initial steps towards a research plan that will transform BCI technology in ways that will enable breakthroughs in the reliability and accuracy of BCI applications. After years of limited advances in BCI accuracy and reliability, project outcomes will accelerate the design of new BCI applications to significantly improve the quality of life for many persons who are in dire need of help. The project will also play a strong role in the interdisciplinary education of computer science and biomedical students.

In this exploratory project, equipment will be acquired to enable the recording of high-quality EEG signals generated by a new non-invasive tripolar concentric ring electrode EEG sensor being developed by Dr. Walter Besio of the University of Rhode Island, which enables the recording of brain activity with much more spatial and temporal precision than what is possible with conventional EEG electrodes. The EEG data thus obtained will provide the information needed by novel deep learning algorithms to translate brain activity to intended arm and hand movements, and experiments will be performed to demonstrate the feasibility of detecting real and imagined individual finger movements. The belief for decades has been that detecting finger movements requires invasive, implanted electrodes to avoid degradation of brain signals as they pass through cerebral-spinal fluid, skull and skin. This research will be the first to try a new end-to-end deep learning approach to translating brain activity to arm and hand movements.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.