Kay A. Robbins - US grants

Distributed computing is an important aspect of many fields of computer science including architecture, operating systems, programming languages, data base management, algorithms, and artificial intelligence. Undergraduates are usually introduced to this subject in an operating systems course during a study of process control and concurrency. There is, however, a lack of programming assignments which give students practical experience with these concepts within a single semester course. This project establishes a laboratory and develops an innovative laboratory curriculum which can serve as a model for undergraduate operating systems courses in other programs. The laboratory is workstation-based and uses the UNIX operating system in a windowing environment. Fourteen workstations and a server have been purchased and networked. A series of experiments and student projects based on virtual rings are being implemented, and a laboratory manual is being produced which describes student assignments covering most of the topics contained in a standard operating systems course. In addition, debugging tools are being integrated with the windows to allow students to monitor concurrent computations. The award is being matched by an equal amount from the principal investigator's institution.

The objectives of this project are to develop practical tools for online analysis and visualization of experimental video data that should be applicable in both research and teaching laboratories. The system will have a familiar web-based interface, promote collaborative visualization and remote access and take advantage of distributed computing resources to attain near-realtime performance. The system will be particularly oriented towards the seamless acquisition and analysis of video data in order to detech transitions during the running of experiments. Fast display techniques and spatial Karhunen-Loeve reconstructions will be among the techniques implemented to allow experimentalists to map out bifurcation structure as experimental parameters are changed.

EIA-0217884
Robbins, Kay A
Univ of Texas

CRCNS: Collaborative Research : How is Information Coded in Turtle Visual Cortex ?

Visual stimuli evoke a propagating wave of activity in the visual cortex of freshwater turtles. Preliminary work suggested that information about the position of stimuli in visual space is coded in the spatiotemporal dynamics of these waves. Effectively, there may be a map of visual space to the dynamics of the visual cortex. This hypothesis is being examined in a collaborative effort involving three laboratories. David Senseman in San Antonio is using voltage sensitive dye methods to record the waves produced by presenting spots of light at 35 spots on the retina. These studies will characterize the features of the map based on repeated presentations of stimuli at 35 loci. Philip Ulinski in Chicago is developing a large-scale model of the visual pathway of turtles. Models of individual retinal ganglion cells that combine both classic filter-based approaches to modeling ganglion cells, with compartmental modeling of ganglion cells are being constructed. They are being used to construct 35 patches of a model retina that match the 35 loci. Physiological studies of the biophysics of neurons in the lateral geniculate complex of turtles are being carried out. They are used to develop a model of the lateral geniculate complex, which is the last step in modeling the retino-geniculate-cortical pathway. Bijoy Ghosh in St. Louis is developing refined estimation techniques that allow the position of a visual stimulus to be estimated from the dynamics of the cortical waves. This work is providing the mathematical framework needed to characterize a potential map of visual space to the dynamics of the wave. This work is significant because it is characterizing a novel method of coding information in visual cortex that may apply to higher order cortical areas in mammals, as well as turtles.

bioimaging /biomedical imaging; computational neuroscience; minority institution research support; biomedical facility;

The UMB Scholars program will provide stipend and research support for 12 students (6 from Biology and 6 from Math/Statistics) to work collaboratively on advanced problems in Computational Neuroscience and Bioinformatics under the mentorship of established investigators. To facilitate this collaborative research, UMB Scholars majoring in biology will be required to add a minor in math/statistics; those majoring in math/statistics will be required to add a minor in biology. This approach offers the best way to ensure that all students are exposed to a coherent, logical and systematic series of foundational and more advanced courses in both fields. From a broader perspective, the UMB Scholars program will showcase to the much larger university community the importance and value of quantitative thinking and approaches in modern biological science. Successes of UMB Scholars following their graduation from UTSA will underscore the message that adding an academic minor in biology, math or statistics to their course of study is the best way for a student to prepare for a future in biological research. The UMB Program will serve as a catalyst for a comprehensive, coordinated and integrated restructuring the of current undergraduate curricula in biology, math, statistics and computer science to eliminate deficiencies and constraints that limit quantitative approaches in undergraduate education at this institution .The University of Texas at San Antonio (UTSA) is a public institution serving San Antonio and South Texas. Students targeted for this UMB program are the University's large Hispanic population. Currently, UTSA ranks first in the nation in the number of baccalaureate degrees awarded to Hispanics in biology and ninth in the number awarded in mathematics.

Biology (61)

This project supports the development and assessment of curricular materials for a unique Computation for Scientists and Engineers course that uses MATLAB to teach programming to biologists through data analysis and visualization. Three teaching innovations form the central core of this development effort: (1) Teach computation by working with data rather than by working with formulas. MATLAB and other computational science courses generally target advanced students with some mathematical training. Such courses typically begin by explaining how MATLAB performs vector and matrix operations - an approach that is obviously inappropriate for many students, especially those with limited math and logic skills. A more engaging alternative is to ask these students to use MATLAB to visualize and explore meaningful data sets that are presented as simple lists and tables. By working with real data instead of abstract mathematical equations, students quickly appreciate the practical benefits and central role of computing in their discipline. (2) Use a hybrid approach to teaching programming. The course focuses on programming tasks that can be easily mastered in a single semester course and are useful in subsequent courses. Facilities such as MATLAB's plottools are exploited to teach programming skills in a hybrid fashion. MATLAB's plottools allow students to examine and plot data through an intuitive "point and click" GUI interface, and show them the programming code that produced the plot. Students are asked to modify this code in order to visualize the data differently or to explore completely different data sets. (3) Integrate real science to highlight relevance, interaction and discovery. The materials rely on real data as much as possible, and several significant case studies are developed in support of the curriculum. Important scientific papers are used for examples. Side notes are created to explain how the paper is structured, what the results are, and how the data supported the results. These materials are generated in web format with links to the references and notes.

By emphasizing hands-on data analysis and being required early in their program of study, this course has a significant impact on the quantitative skills of science students. Furthermore, the curricular content and teaching materials are made broadly available to other institutions using an open source model and linkage to the National Science Digital Library (NSDL).

DESCRIPTION (provided by applicant): The rapid evolution of sophisticated data-intensive technologies has created a growing need for well-trained informatics scientists and computational biologists particularly in the field of cancer research. An inter-institutional effort to support state-of-the art training in computational biology and bioinformatics in San Antonio has assembled an experienced group of faculty in order to develop a unique educational training program in bioinformatics and computational biology with an emphasis on the needs of the cancer research community. This program will provide opportunities for students and faculty at the University of Texas at San Antonio (UTSA), a minority serving institution, to gain relevant experience by interacting directly with cancer center members at the Cancer Therapy and Research Center (CTRC) at the University of Texas Health Science Center at San Antonio (UTHSCSA), an NCI designated Cancer Center. The interaction will also provide UTHSCSA cancer researchers with needed computational analysis and modeling assistance from quantitative scientists across both campuses. Additional opportunities will be provided for intensive short courses/workshops for computational biology training aimed for a mixed audience of biologists and quantitative scientists is also planned. Thus, a total of three programs are proposed that are all focused on education. The programs will include: 1) Computation Biology/Bioinformatics Graduate Education; 2) Paid Internship Program; and 3) Continuing Education Opportunities. A unique aspect of the proposal is the use of real data and research questions in cancer and health disparities to provide a context for the graduate education of program 1, the basic skill sets for participants in program 2, and the hands-on activities for the broader audience of program 3. This multifocal approach should strengthen the interaction between the cancer center and the minority servicing institution. These efforts will ultimately lead to the submission of an R25 application.

Project summary Brain dynamics that drive variability within and between patients are an important, but poorly understood, element of many cognitive disorders. The long-term goal of this research project is to develop technology that will identify brain activity patterns associated with successful performance on a given task, and use this pattern as a target for brain-computer interface (BCI) training. The overarching hypothesis is that using BCI training to more often have a brain state that is spontaneously correlated to good performance will, in turn, improve overall performance. This approach could be developed into a powerful tool for rehabilitation and therapy for many neurological and psychiatric disorders. Here we will investigate persistent developmental stuttering (PDS) as a model to study brain dynamics associated with successful vs. unsuccessful performance. PDS is a speech disorder where fluent speech is punctuated to various degrees by stuttering. Individuals with PDS are otherwise neurologically in the normal range, which avoids complicating factors in most patient populations. Stuttering is intermittent; thus on some occasions the brain is in a state conducive to fluent speech and at other times it is not. We propose to use EEG activity shortly before speaking to predict whether somebody with PDS will stutter or speak fluently. Preliminary data are given to show proof of concept with traditional EEG analysis methods. This approach will be expanded by first using advanced methods such as common spatial pattern analysis and machine learning over multiple subject sessions to identify EEG signals that distinguish fluent vs. dysfluent trials (Aim 1). PDS subjects will then be trained to produce and maintain their EEG pattern that is most strongly associated with fluent speech by using BCI methods. We hypothesize that individuals will learn to modulate EEG features to be more consistent with fluent trials, which in turn will significantly reduce stuttering rate. After successful completion of this project we envision a new BCI-based intervention that can be used to encourage neural states conducive to fluent speech in those who stutter. The BCI intervention would complement traditional speech therapy using behavioral methods. The ?two-step approach? of first identifying brain states associated with a patient?s best performance followed by BCI training to enter that state more often can be applied to rehabilitation in many other neurological and psychiatric disorders, such as Alzheimer?s disease, traumatic brain injury, and mood disorders, to name a few.