First Responder Pathogen Detection System (FiRPaDS) Investigator: Bhaskar DasGupta, Computer Science

Prime Grant Support: NSF (including a CAREER grant)

Problem Statement and Motivation

Technical Approach Key Achievements and Future Goals

• Need to identify unknown virus sequences during events such as epidemic or biological warfare

• We only have a database of known virus sequences

• Few complications of the real-world problem:

• Sequence has mutated (possibly maliciously)

• Impossibility to obtain entire DNA sequence

• Sample may be contaminated and/or contains mixture of sequences.

• Rapid amplification of the collected genetic material, e.g., via degenerate oligonucleotide primer based multiplex PCR

• A pathogen fingerprinting and/or barcoding component built around universal DNA tag arrays

• Rapid and robust computational procedures to compute barcodes that produces short signatures of sequences

• Two possible approaches to design FiRPaDS:

• Target based FiRPaDS

• Primer based FiRPaDS

• Developed efficient barcoding algorithms using combinatorial techniques

Software available from http://www.cs.uic.edu/~dasgupta/professional/software.html

• Will extend barcoding approaches for more complicated scenarios such as mixture of samples

• Will generate an efficient solution for a combinatorial or graph-theoretic formulation for the degenerate multiplexed PCR minimization problem

• Will investigate applications of universal DNA tag arrays for helpful coordination with barcoding or fingerprinting steps

Virtual Reality and Robots in Stroke RecoveryInvestigators: Robert V. Kenyon, Computer Science; James L. Patton, RIC

Prime Grant Support: NIH, NIDRR

Mission: Mission:

To evaluate the utility of simple robotic To evaluate the utility of simple robotic devices for providing rehabilitation devices for providing rehabilitation therapy after hemispheric stroke. The therapy after hemispheric stroke. The integration of virtual reality and robot integration of virtual reality and robot technology increases flexibility in technology increases flexibility in training for patients recovering from training for patients recovering from stroke. Promoting innovative stroke. Promoting innovative techniques to train the nervous system techniques to train the nervous system for the recovery of functional for the recovery of functional movement.movement.

Technical Approach:Technical Approach: Key Achievements and Future Goals:Key Achievements and Future Goals:• Personal Augmented Reality Immersive System (PARIS):

•Virtual and physical objects seen by user.

• Robotic systems: PHANToM, Haptic Master, WAM:

•These back-drivable robots provide force to the subject only when commanded to do so.

• Software integration:

•Real-time interactivity requires rapid communication between the different components of the rehabilitation system and must contain consistent representations of what the user should feel and see.

•The robot’s control must quickly communicate with the display control so that graphics are synchronized with the robot’s state.

•This system provides a platform for exploring how the nervous system controls movements, teaches new movements, explores novel strategies for training and rehabilitation, assesses and tracks functional recovery, and tests and challenges existing theories of rehabilitation.

•Such a system will determine the necessary levels of quality for future design cycles and related technology.

•Future designs will lead the way to new modes of clinical practice and to the commercialization of such systems.

PROJECT:PROJECT: Development Of A Development Of A Robotic System Robotic System With An With An Augmented Reality Augmented Reality Interface For Interface For Rehabilitation Of Rehabilitation Of Brain Injured Brain Injured IndividualsIndividuals

Computational Tools for Population BiologyTanya Berger-Wolf, Computer Science, UIC; Daniel Rubenstein, Ecology and

Evolutionary Biology, Princeton; Jared Saia, Computer Science, U New MexicoSupported by NSF

Technical Approach

• Collect explicitly dynamic social data: sensor collars on animals, disease logs, synthetic population simulations, cellphone and email communications

• Represent a time series of observation snapshots as a layered graph. Questions about persistence and strength of social connections and about criticality of individuals and times can be answered using standard and novel graph connectivity algorithms

• Validate theoretical predictions derived from the abstract graph representation by simulations on collected data and controlled experiments on real populations

Key Achievements and Future Goals

• A formal computational framework for analysis of dynamic social interactions

• Valid and tested computational criteria for identifying• Individuals critical for spreading processes in a population• Times of social and behavioral transition• Implicit communities of individuals

• Preliminary results on Grevy’s zebra and wild donkeys data show that addressing dynamics of the population produces more accurate conclusions

• Extend and test our framework and computational tools to other problems and other data

Problem Statement and Motivation

Of the three existing species of zebra, one, the Grevy's zebra, is endangered while another, the plains zebra, is extremely abundant. The two species are similar in almost all but one key characteristic: their social organization.

Finding patterns of social interaction within a population has applications from epidemiology and marketing to conservation biology and behavioral ecology. One of the intrinsic characteristics of societies is their continual change. Yet, there are few analysis methods that are explicitly dynamic.

Our goal is to develop a novel conceptual and computational framework to accurately describe the social context of an individual at time scales matching changes in individual and group activity.

Zebra with a sensor collar

A snapshot of zebra population and the corresponding abstract representation