Novel Self Assembly of siRNA for Efficient and Safe Delivery

Start Date: 12/1/2009

Award Number: 933966

Program(s): BIOMEDICAL ENGINEERING

Principal Investigator: He, Huixin

Co-PI Name(s): Tamara Minko

PI Email Address: huixinhe@newark.rutgers.edu

Abstract: 0933966 He This proposal describes an integrated approach for interdisciplinary research, education, and outreach in nanotechnology and biomedical engineering. The objective of the research is to develop an innovative and nontoxic delivery platform that will enable cell specific delivery of small interference RNAs (siRNAs) to silence their targeted oncogenes both in vitro and in vivo. The other objective of this research is to seek an extensive understanding of the fundamental physicochemical characteristics, especially nanomechanical properties, of siRNA nanoparticles with their biological performance. To reach these goals, the proposal has three associated specific aims: (1) To develop a novel approach for assembly and delivery of siRNA without toxicity using labile Au nanoparticles modified with several low generation dendrimers. (2) To engineer the surface of siRNA nanoparticles for target delivery, and to study the mechanical properties of the individual siRNA nanoparticles (both with and without Au nanoparticles encapsulated, and both engineered and non-engineered). (3) To determine structure/property-biodistribution, biological potency, and toxicity relationships of the siRNA nanoparticles in vitro and in vivo. Intellectual Merit: A novel siRNA assembly approach where Au nanopartices (Au NPs) will be used to dramatically enhance non toxic, low-generation dendrimers to efficiently condense siRNA to discrete nanoparticles. However, the Au NPs can be controlled "in" or "out" of the final siRNA complexes, which is the key difference from previous reports. The potential toxic problem accompanied with the Au NPs will be solved by selectively removing the Au NPs before the siRNA complexes are delivered. In addition, to satisfy the requirements for in vivo targeted delivery of siRNA through a systemic route, the formed siRNA nanoparticles will be engineered by a layer-by-layer modular approach to enable them for spatially- and temporally- controlled release in specific sub-cellular compartments. These properties will add additional therapeutic activities and further decrease the side effects of RNAi-based therapy. In addition to the physicochemical properties, the nanomechanical properties of the individual siRNA nanoparticles from various formulations will be studied by single force microscopy. By combining the biological investigation of these siRNA nanoparticles, this proposal will link, for the first time, the physicochemical properties and the nanomechanical properties of the siRNA nanoparticles with their cellular internalization, circulation, biodistribution, and therefore, their targeting and therapeutic efficacy during in vitro and in vivo systemic delivery. The improved understanding of these relationships will lead to future design and development of new materials and strategies for efficient and safe delivery of siRNA, and therefore help to realizing its full therapeutic potentials. Successful completion of this research will also provide critical understanding how we can utilize information obtained from various multifunctional nanomedicine platforms which are constructed by engineered inorganic nanocarriers (relatively hard) to guide the development of efficient organic nanocarriers (relatively soft) and vice versa. Broader Impacts: The proposed investigations are fundamentally and practically important for efficient and safe siRNA delivery. The proposal focuses on design multifunctional siRNA nanoparticles capable of cell specific delivery and silencing of gene expression of EZH2 genes for breast cancer therapy. Given the widespread applications of siRNA in numerous fundamental and therapeutic applications, the knowledge gained from this project will have far reaching scientific and economic impacts on pharmaceutical and biotechnology industry and health care. The educational plan will bring nanoscience tools and concepts to a wide range of students on two campus of Rutgers known as the most diverse in the nation. The inherently interdisciplinary nature of this research will produce students with exceptional training in nanotechnology, biomedical engineering, molecular biology, and drug delivery. Research activities designed for undergraduates and high school students will promote more gifted minority students into the nanoscience ranks. Extensive outreach to the Newark area, a minority-dominated region, will raise the public awareness of the impact of nanoscience and nanotechnology.

MRI: Acquisition of a State-of-the-Art X-Ray Photoelectron Spectrometer for Research, Training and Education

Start Date: 10/1/2009

Award Number: 923246

Program(s): MAJOR RESEARCH INSTRUMENTATION

Principal Investigator: Bartynski, Robert

Co-PI Name(s): Prabhas Moghe, Sang-Wook Cheong, Kathryn Uhrich, Vitaly Podzorov

PI Email Address: bart@physics.rutgers.edu

Abstract: 0923246 Bartynski Rutgers U. New Brunswick Technical Summary: X-ray photoelectron spectroscopy (XPS) is widely used as an analytical technique to determine the nature of the near-surface region of a material. Shifts in the core level binding energies of atoms at or near the surface of a material can reveal changes in oxidation state, surface potential or band bending, chemical or physical inhomogeneity, or dynamic response (i.e., screening) that are distinct from those of the bulk of the material. However, a growing number of modern applications employ materials in complicated structures that are laterally inhomogeneous and thus it is critical to perform XPS in a spatially resolved manner, along with high photon flux, and high energy resolution. Examples, that are currently active research areas at Rutgers include the study of: (i) transition metal ions and their diffusion in ZnO for room temperature spintronics, (ii) surface modification of organic single crystal surfaces, (iii) surface functionalization and characterization of novel nanocrystals used to enhance biomolecule imaging, (iv) surface characterization of plasma-treated and chemically-modified polymer films for cellular and related bioactivity studies, and (v) interface properties of nanoscale self-assembled solid state systems. The Rutgers Laboratory for Surface Modification (LSM) is a multidisciplinary research center that hosts a comprehensive set of facilities used to examine surfaces, interfaces, thin films, and nanoscale materials, and has strong collaborations with state-of-the-art research and development laboratories around the world. A gap in our suite of tools is lack of a modern high resolution XPS system that can adequately address the key issues in the study of modern materials systems. The state-of-the-art instrumentation requested in this proposal would replace a 20-year-old machine and will significantly strengthen our capabilities by enabling high energy resolution studies, in parallel with high resolution lateral imaging and depth profiling. These features are central to the diverse research and education activities both within Rutgers as well as the regional community. Non-Technical Summary: Materials interact with their surroundings through their surfaces. Very often, the chemical or physical environment of surface atoms is significantly different from those of the bulk A powerful way to probe surface properties is to expose a material to X-rays of a specific wavelength and study the electrons that are emitted from surface. As only electrons that originate from the first one or two nanometers of the surface are able to escape the material, this technique is very surface sensitive. Moreover, these electrons escape with well-defined energies that not only depend upon the atomic species, but also exhibit small variations depending on the environment of the atom. The study of these electrons, known as X-ray photoelectron spectroscopy (XPS), enables one to determine the chemical and physical state of these near-surface atoms. In modern materials systems, such as nanoscale crystals used to enhance imaging of biological systems, or potentially revolutionary semiconductors made entirely of organic molecules, the atomic environment in one region of the surface can differ from that of another region. Therefore, it is critical to perform XPS studies in a spatially resolved manner. The Rutgers Laboratory for Surface Modification (LSM) is a multidisciplinary research center that hosts a comprehensive set of facilities used to examine surfaces, interfaces, thin films, and nanoscale materials, and has strong collaborations with state-of-the-art research and development laboratories around the world. A gap in our suite of tools is lack of a modern high resolution XPS system that can adequately address the key issues in the study of modern materials systems. The state-of-the-art instrumentation requested in this proposal would replace a 20-year-old machine and will significantly strengthen our capabilities by enabling high energy resolution studies, in parallel with high resolution lateral imaging and depth profiling. These features are central to the diverse research and education activities both within Rutgers as well as the regional community.

Collaborative Research: CDI-Type II: Extracting Population and Stochastic Effects on Signaling Activity from Transcription Factor Profiles

Start Date: 10/1/2009

Award Number: 941287

Program(s): CDI TYPE II

Principal Investigator: Yarmush, Martin

Co-PI Name(s):

PI Email Address: yarmush@rci.rutgers.edu

Abstract: This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Proposal Number: 0941313 PI: Juergen Hahn Institution: Texas Engineering Experiment Station Proposal Number: 0941287 PI: Martin Yarmush Institution: Rutgers University Signal transduction pathways play a key role in many cellular functions as well as intercellular communication. However, elucidating the exact mechanisms involved in signal transduction pathways is non-trivial: crosstalk exists between different pathways, the response within a population of cells can vary significantly, and only limited measurement capabilities are available for observing intracellular signals. One specific example highlighting the importance of signal transduction and how it is affected by cell population is stem cell differentiation. The resulting cell type is affected by the cell population and intercellular communication that activates different signal transduction pathways. This project is focused on the development of a new computational framework that enable the PIs to investigate the role of cell populations on signal transduction. In order to do so, they will derive techniques that allow them to distinguish between stochastic components and population effects. Unlike their past work, which dealt with average properties only, they will focus on developing techniques that consider information about individual cells within a population and use this information for investigating population effects on signal transduction activity. Intellectual Merit: This work includes the following portions: (a) Development of problem formulations and algorithms that can solve inverse problems considering cell populations, rather than just bulk averages, subject to the high level of measurement noise commonly found when studying signal transduction pathways. (b) Derivation of a new approach for determining the optimal set of parameters to estimate in a nonlinear signal transduction pathway model given the available data for a distribution of cells and considering uncertainty in the model. (c) Development of a computational technique for large-scale parameter estimation across populations to determine how intercellular communication affects signal transduction in individual cells, leading to a greater understanding of cellular behavior and improved experimental design. This includes determining the number of cells and their spatial location in experiments in order to avoid results that are skewed because cell population effects have not been considered. In summary, this work will develop and integrate mathematical, computational, and experimental approaches to partition stochastic and population effects with the ultimate goal of developing improved models of signal transduction pathways. These techniques will be applied to the Jak/STAT and the Erk-C/EBPâ signaling pathways which play an important role in many cellular responses, such as stem cell differentiation and the inflammatory response of the liver. Broader Impact: Synergies can be created by integrating research and teaching efforts in the area of systems biology as well as by establishing long-term collaborations between research groups involved in modeling and in the experimental life sciences. Two of the PIs coteach a senior-level undergraduate/graduate elective class on systems biology which integrates theoretical and experimental aspects required for modeling and analysis of bio-systems. The class aligns with departmental curriculum reform plans and will include several modules which can also be used in other courses and outreach activities. Interactive and web-based learning aids will be developed along with the modules and incorporated throughout the course. Additionally, significant effort will be devoted to disseminating research results in the form of software, case studies, undergraduate student education and training, and outreach programs to underrepresented groups.

An Effective Methodology for Combining Information from Independent Sources with Applications to Social and Behavioral Sciences and Medical Re

Start Date: 9/15/2009

Award Number: 851521

Program(s): METHOD, MEASURE & STATS

Principal Investigator: Xie, Minge

Co-PI Name(s):

PI Email Address: mxie@stat.rutgers.edu

Abstract: In the modern era with explosive growth of information, it is important to process information in an efficient and meaningful manner. Statistical methodology of meta-analysis is a technique for enabling this. It has broad impacts and applications in social and behavioral sciences and medical research, among other fields. Indeed, formal and meaningful ways of combining data information from independent sources are important both theoretically and practically. Combined results from multiple studies summarize overall associations, and inferences from this are more robust and reliable than inferences from any single study. The goals of this proposal are to develop a unifying framework and new methodologies for combining information from independent sources, and to demonstrate the usefulness of the methodologies in a broad range of applications. The underlying tool of the proposed framework is confidence distributions (CDs). Although CD is a fundamental statistical inference concept with a long history, recent developments have redefined it with a focus on solving more complex real-life problems. The proposed framework based on CDs can unify most information combination methods used in the current practice, including both the classical p-value combination and the model based meta-analysis approaches. Furthermore, this framework of CD combination can lead to developments of new methodologies, such as: a) a robust meta-analysis approach, which can remove a critical constraint in current practice requiring all studies be of the same type and with the exact same parameter values; and b) a frequentist Bayes compromise approach to combining expert opinions with information in observed data, which is otherwise not possible in regular frequentist inference. The unifying development can potentially lead to a common computing program for various meta-analysis approaches. It not only has theoretical values, but can also promote broader applications of meta-analysis. Advances emerging from this project will help solve the specific set of problems set forth herein, and stimulate new research and applications in statistical methodological developments.

Collaborative Research:At the Interface of LHC Theory and Experiment

Start Date: 9/15/2009

Award Number: 904069

Program(s): ELEMENTARY PARTICLE THEORY

Principal Investigator: Strassler, Matthew

Co-PI Name(s):

PI Email Address: strassler@physics.rutgers.edu

Abstract: The LHC is the world's foremost high-energy physics experimental facility, and will probe distances shorter, and energies higher, than ever before. It will examine the weak interactions, responsible for many natural phenomena, and seek the origin of the masses of the known fundamental particles, known to arise from a Higgs mechanism whose precise nature remains unclear. The LHC might also shed light on dark matter, and might revolutionize our understanding of space-time, through discovery of extra dimensions or Supersymmetry. A full LHC research program requires experts who can work at and across the interface between theory and experiment. This project is aimed at strengthening this interface at the Institute for Advanced Study (IAS) and Rutgers University theory groups, which already work closely with the Rutgers and Princeton University experimental groups and Princeton theory group. The purpose is to augment their LHC research programs in order to close the scientific gap located at the Theory/Experiment Interface, by hiring and hosting experts who can provide tools and knowledge required by both theorists and experimentalists working on LHC physics. This will greatly expand the range of LHC research possible at these institutions, especially assisting with personnel-intensive activities including standard model background studies, development of Monte Carlo tools, and investigations of search strategies for new physics. The PIs will be involved in both developing new models to describe Beyond the Standard Model Physics, as well as creating an effective theory approach for analyzing and interpreting directly the results coming from LHC. The proposed research includes studies on background removal, using Monte Carlo tools. If a novel signal is seen at the LHC, neither a model-independent approach nor a fully model-dependent approach is appropriate. The PIs propose that once a signal is seen at LHC, they will proceed to build a "model fragment", which in many cases will take the form of an "on-shell effective theory" (OSET). Model fragments were used by the PIs in their successful analysis at the first LHC Olympics workshop, and were developed further into a Monte Carlo tool for wider use. The essential idea is to build a semi-consistent theoretical framework that makes predictions for LHC signals without requiring a fully consistent TeV-scale theory, or in the case of an OSET, possibly without even a fully consistent effective Lagrangian. The PIs will also investigate the Effects of Multiple Soft Jets on Reconstruction and Analysis. The broader impact is to create the nucleus of an LHC center on the East Coast. The IAS and Rutgers, along with Princeton, will form the nucleus of such a center, helping to anchor and enhance the diffuse but substantial research program on the East Coast, which is currently scattered around many institutions. In particular, by training new students and broadening the postdocs at the Theory/Experiment Interface, it would help, over time, to strengthen the entire US particle physics community. Also, by hosting training and research workshops, it would directly benefit other institutions, particularly in the local and regional neighborhood. Most directly, the quick-reaction workshops would foster a more rapid response from the US community to events at the LHC. The PIs also plan a series of training exercises based on the successful "LHC Olympics" model to help in the process of training young students and in retraining postdocs from more formal areas of high-energy theory.

Gauged Gromov-Witten theory and holomorphic quilts

Start Date: 9/15/2009

Award Number: 904358

Program(s): GEOMETRIC ANALYSIS

Principal Investigator: Woodward, Christopher

Co-PI Name(s):

PI Email Address: ctw@math.rutgers.edu

Abstract: Abstract Award: DMS-0904358 Principal Investigator: Christopher Woodward The PI will carry out projects on functoriality for Lagrangian correspondences in Fukaya-Floer theory and functoriality for quotients in Gromov-Witten theory. The first group of projects will have applications in Gromov-Witten theory and ``cohomological'' mirror symmetry, that is, in the sense of Givental etc. With F. Ziltener and his former postdoctoral advisee E. Gonzalez he will investigate functoriality for Gromov-Witten invariants under the symplectic quotient construction. Potential applications include invariance of Gromov-Witten invariants under symplectic birational equivalence, to cohomological mirror symmetry for complete intersections of general type. With K. Wehrheim and his former student S. Mau the PI will study functoriality of Lagrangian correspondences in Floer-Fukaya theory. Applications include symplectic definitions of non-abelian Floer homology for tangles and arbitrary three-manifolds, possible generalizations of Khovanov homology and categorification of quantum groups. These projects will have applications in homological mirror symmetry and low-dimensional topology. Some of the projects have a graduate education component, and the PI also proposes several undergraduate research projects. Overall the research carried out under this grant will advance the understanding of symplectic geometry, which is the mathematical language for classical dynamical systems, and the relationship between gauge theory, representation theory, and quantum physics. Gauge theories arise naturally in a number of physical settings, such as electromagnetism. The first part of the project concerns certain gauge theories with an addition "non-linear" field taking values in a classical phase space, which have been substantially studied in the physics literature in the linear case under the name "gauged sigma models". The second part of the project concerns the structural properties of "Floer-theoretical" invariants which have been extensively studied in relation to dynamical systems in recent years.

NSCC/W: NEW ARMIES FROM OLD: MERGING COMPETING MILITARY FORCES AFTER CIVIL WARS

Start Date: 9/15/2009

Award Number: 904905

Program(s):

Principal Investigator: Licklider, Roy

Co-PI Name(s):

PI Email Address: licklide@rci.rutgers.edu

Abstract: This award was funded through the Social and Behavioral Dimensions of National Security, Conflict, and Cooperation competition, a joint venture between NSF and the Department of Defense. Until the end of the Cold War it was conventional wisdom that civil wars ended in military victories. Nonetheless over twenty negotiated settlements of civil wars have been reached after 1989 in places as disparate as El Salvador and South Africa. Some of these compromise settlements have ended civil wars and have resulted in postwar regimes that are substantially more democratic than their predecessors. These settlements have usually involved some form of power-sharing among the former contestants and other sectors of society. Many of these agreements have, as a central component, provisions to merge personnel from competing armed groups into a single national army. However, there has been little or no analysis of how people who have been killing one another with considerable skill and enthusiasm can be merged into a single military force, other than a few scattered case studies and some contradictory aggregate data analyses. This project involves a number of country specialists preparing case studies of how their countries merged competing military forces after civil wars. All of the studies respond to a common set of questions. The authors will then attend a conference, along with a number of specialists in comparative analysis, to refine their individual studies and start to draw comparisons across them. The studies will then be revised into an edited book together with chapters from some of the comparative specialists. The goal is to suggest for policymakers both when merging militaries is more or less likely to succeed and what techniques are appropriate under different circumstances.

Analysis of Functional Time Series

Start Date: 9/15/2009

Award Number: 905763

Program(s): STATISTICS

Principal Investigator: Chen, Rong

Co-PI Name(s):

PI Email Address: rongchen@stat.rutgers.edu

Abstract: In this project the investigator develops new models, methods and associated theory under a general framework of `Functional Time Series Analysis'. Analyzing time series in a functional framework is increasingly practical and is gaining importance rapidly as more and more applications involving such data sets. There are four research projects, in the board directions of functional time series driven by dynamic processes, distributional time series driven by dynamic processes, functional ARMA models, and functional regression models with functional time series errors. They are closely related but with different focuses. The combination of the projects builds a comprehensive framework for functional time series analysis. For each project, statistical properties of the underlying models, statistical inference and predictions under these models, and the theoretical properties of the inference and prediction methods are studied. Several special and important applications are studied. Functional time series analysis can be viewed as a marriage between the traditional time series analysis and the field of functional data analysis of independent functional observations. Time series analysis is mainly interested in the dependent structure of the observations over time, the understanding the dynamic nature of the underlying process and accurate predictions of the future. Modern data collection capability has lead to broader definition of `data' and more and more observations are in the form of functions, images, and distributions. The intersection between time series analysis and functional data analysis has not been systematically explored. In this project, the investigator develops a general framework of functional time series analysis that is amenable to statistical thinking and the analysis of real problems. This project paves the way for developing a completely new research area in statistics. It has broad impact in advancing our capabilities of statistical data analysis. It aims to produce advanced statistical tools for analyzing functional time series that are encountered in many important application fields including economics and finance, environmental studies, medical and neuroscience, ecology and meteorology. The project also actively engages in activities related to education and research training of graduate and undergraduate students, especially attracting minority and women students into the field of statistics and statistical applications.

Local Moment and Heavy Fermion Physics

Start Date: 9/15/2009

Award Number: 907179

Program(s): CONDENSED MATTER & MAT THEORY

Principal Investigator: Coleman, Piers

Co-PI Name(s):

PI Email Address: coleman@physics.rutgers.edu

Abstract: TECHNICAL SUMMARY This award supports theoretical research and education on heavy electron materials and local moment physics. The new concepts required to understand these low-energy materials, will, in many cases scale up in energy and temperature to describe related phenomenon in transition Metal materials, such as copper and iron-based superconductors. Many of the key questions in this area touch upon issues of fundamental importance, such as the physics of quantum criticality, mechanisms of anisotropic superconductivity, and the origins of non-Fermi liquid behavior. Motivated by the discovery of two new and unusual heavy fermion superconductors, the PI will apply a new type of large-N expansion for heavy electron superconductivity that he has recently developed. Fresh insights in heavy electron quantum criticality and the discovery of a Kondo spin liquid material inspire the PI to embark on a new set of investigations into the phase diagram of the Kondo lattice. A magnetically tuned critical end point in a multiferroic oxide has recently been discovered. The PI will pursue a new research program to study field-tuned quantum criticality in multiferroic materials. Research into the various aspects of "hard condensed matter physics" plays an essential driving role in both the development of new physics concepts, and new ideas for materials of the future. For example, the remarkable tendency of quantum critical points to nucleate superconductivity and other new phases of matter is of great interest to materials development; on the other hand, the new universality classes of quantum phase transition are of great fundamental interest and, like their classical predecessors in statistical mechanics, may enjoy generalization and future application in the realm of cosmology and particle physics. This is wonderful area for students to learn the advanced methods of theoretical physics, while maintaining an intimate contact with experimental physics and real materials. The PI is strongly committed to diversity in physics, with a long record of support for women in physics. Two women graduate students are currently working in his group. NON-TECHNICAL SUMMARY This award supports theoretical research and education on a class of materials known as heavy electron materials. The interplay of electrons in itinerant quantum states and quantum states localized around a rare-earth or actinide atom leads to an unusual metallic state with properties that are consistent with the textbooks. The properties of these materials are further complicated by the existence of quantum phase transitions - phase transitions that occur at the absolute zero of temperature but with a powerful influence that can be felt over a range of temperature. The PI will build on and use a promising new method that he has developed to gain insight into two recently discovered materials in this class that are superconductors and into heavy electron materials more generally. The PI will also study the superconducting states of these materials. The PI will further explore the role quantum phase transitions play in newly discovered multiferroic materials which are a promising class of materials for future electronic devices. The study of the superconducting states and the unusual metallic states which give way to superconductivity contributes to the intellectual foundations of materials research and superconductivity. This knowledge may lead to harnessing the ability of superconductors to carry electric current with no to very low power dissipation in a practical way leading to significant energy savings. The PI is strongly committed to diversity in physics, with a long record of support for women in physics. Two women graduate students are currently working in his group.

RI:Small:Collaborative Proposal: Computational Framework of Robust Intelligent System for Mental State Identification and Human Performance Prediction

Start Date: 9/15/2009

Award Number: 916580

Program(s): ROBUST INTELLIGENCE

Principal Investigator: Chaovalitwongse, Wanpracha

Co-PI Name(s):

PI Email Address: wchaoval@rci.rutgers.edu

Abstract: This project will integrate new cognitive models of behavioral data based on queueing theory with new machine learning techniques for analyzing neurophysiological data, specifically electroencephalogram (EEG), in order to provide a deeper and more complete understanding of mental states as well as more accurate prediction of human performance. In cognitive modeling, a new brain network architecture for human performance and mental workload, called Queuing Network-Model Human Processor (QN-MHP), will be further improved. QN-MHP with a new human-like small-scale knowledge system will be used to model the increase of myelination in the brain in cognitive development and predict human performance, in terms of subjective risk perception and confidence. In machine learning, new spatio-temporal (pattern-based) classification techniques will be developed for multidimensional time series data and used to identify human mental states (e.g., fully awake, fatigue, distracted, anger) from EEG data. The integrated framework will result in a robust intelligent system that uses machine learning to identify mental states and the queueing model of that mental state to predict the human performance as well as provide a human operator with feedback. A mind-driven intelligent transportation system will be developed as a case study in this project, where a certain type of feedback will be designed to help drivers avoid accidents and to improve system safety. This system can also be applied to other human-machine systems that require full or partial attention of human operators (e.g., in aviation, military, or manufacturing settings).

HCC: Small: Collaborative Research: Assessing Technology Requirements For Preventing Teamwork Errors in Safety-Critical Settings

Start Date: 9/15/2009

Award Number: 915871

Program(s): HUMAN-CENTERED COMPUTING

Principal Investigator: Lesk, Michael

Co-PI Name(s): Ivan Marsic, Aleksandra Sarcevic

PI Email Address: lesk@acm.org

Abstract: The staff in trauma centers are faced with complex problems under time pressure. Despite the introduction of standard protocols, the diversity of injuries that can occur requires a coordinated approach to the evaluation and treatment for each patient. Trauma care involves complex teamwork under time pressure, and teamwork errors endanger patient care and increase costs. Our proposed ethnographic study will use observation and detailed analysis of video recordings of trauma resuscitations to determine the nature and extent of teamwork errors in a trauma center. This detailed study of complex teamwork will uncover the causes of teamwork errors in collaborative high-risk environments. Methods will be developed to understand how teams work and where difficulties arise. This work will yield detailed descriptions of errors and their causes, a taxonomy of teamwork errors, information on how to improve team performance, and guides to the use of technology for teamwork support. It extends the level of detail of ethnographic research so that we can achieve precision in the understanding of procedures which are difficult to monitor automatically but where step-by-step records are essential to detect the causes of errors. Understanding and improving the effectiveness of trauma teams has direct benefit to society. Further, complex collaborations are ubiquitous in modern enterprises and these results could improve collaborations both in terms of quality and productivity across organizations. In addition, this work will serve to develop the skills needed by a new cadre of researchers with knowledge of computer-supported collaborative work, video-content analysis and cooperative research.

Collaborative proposal: Computing Dynamics of Multiparameter Systems

Start Date: 9/15/2009

Award Number: 915019

Program(s): COMPUTATIONAL MATHEMATICSAPPLIED MATHEMATICS

Principal Investigator: Mischaikow, Konstantin

Co-PI Name(s):

PI Email Address: mischaik@math.rutgers.edu

Abstract: The language and ideas of dynamical systems theory that have been developed over the last century have become ubiquitous in the applied sciences. While the analytic language of differential equations and maps is still the basis for most quantitative descriptions of scientific ideas, current scientific results are often obtained based on models which are not derived from first principles, for which many of the essential parameters have not been measured, and which often involve stochastic terms. The key objective of this project is to develop scalable computational techniques to provide correct robust information about global dynamics over large ranges of parameter values. Bifurcation theory implies that the cost for robustness is a coarse description. However, the fact that scientists and engineers are using numerical simulations of phenomenologically derived models to further their understanding of dynamic processes indicates that the information these techniques provide must be both quantitative and qualitative. Since the study of systems over broad ranges of possible parameter values produces considerable information, to be of practical use these methods must organize this information in an efficient, queriable manner. We expect the work proposed in this project will produce (1) reliable computational tools for global decompositions of dynamical systems by constructing a database in which the global dynamics is encoded in combinatorial and algebraic structures and (2) efficient methods for querying the database to identify dynamical structures and bifurcations of interest. This work will address the fundamental question of determining global decompositions of dynamical systems over varying parameters. The global dynamics is stored in the form of a database based on calculations for deterministic systems, but within the framework of these computations we will also explore how to predict the effects of noise on the observable dynamical behavior. These computational techniques will be tested on and applied to a variety of problems from mathematical biology. The biological models which will be considered are used to address central questions in biology including the role of the spatial environment in ecology and evolution and the robustness of the dynamics of signal transduction/gene regulatory networks. These activities will produce computational tools for global decompositions of dynamical systems, which will be made available to scientists and engineers for potential applications in a wide variety of disciplines.

AF:EAGER: Combinatorial Geometry, Partitioning, and Algorithms

Start Date: 9/15/2009

Award Number: 944081

Program(s): WIRELESS COMM & SIGNAL PROCESSINFORMATION TECHNOLOGY RESEARC

Principal Investigator: Steiger, William

Co-PI Name(s):

PI Email Address: steiger@cs.rutgers.edu

Abstract: Divide-and-conquer and prune-and-search are two fundamental and ubiquitous paradigms in the design of algorithms. The first refers to the process of (i) splitting a given problem into smaller sub-problems, (ii) solving each of these subproblems, and then (iii) combining these solutions to obtain the solution to the original problem. The second is a way of searching among possible solutions to a given problem whereby (a) the possible solutions are split into several groups, then (b) all but one of the groups is somehow eliminated, and finally (c) the search continues, now confined to the one remaining group. It is noteworthy that in both approaches, sets are ``split'' into smaller ones - step (i) in divide-and conquer and step (a) in prune-and-search - and that in addition, many efficient and beautiful algorithms are based on one of these approaches. A main goal of this project is the development of some unusual, new, splitting tools that may be used in these paradigms. They will be sought from within an unexpected domain - geometric partitioning theorems. Topological methods have been applied to obtain facts like the ham-sandwich theorem, but there has not been much work on their algorithmic aspects, and what results we do have suggest that they would not be very useful as splitting tools for other algorithms. However some recent partitioning results of the investigator encourage the search for more tools of this kind. Therefore this work will continue to seek new geometric partitioning results that can give novel, useful, splitting tools. Simultaneously the project will address a specific set of concrete, stubborn computational problems that arise frequently, and naturally, but have so far resisted efficient solutions. The goal is to better understand the complexity of these important and interesting problems, and to apply the new tools to obtain effective algorithms. Part of the intellectual merit of the project rests on the unusual approach to develop new algorithmic tools; in addition there is chance to make progress on a set of prevalent, hard, computational problems. Broader impacts reside in the potential to strengthen connections between geometry, combinatorics, and computation.

EAGER: Assessment of Barriers to Trusting Computer-Based Home Assistance

Start Date: 9/15/2009

Award Number: 945192

Program(s): TRUSTWORTHY COMPUTING

Principal Investigator: Kantor, Paul

Co-PI Name(s): Cecilia Gal

PI Email Address: kantor@scils.rutgers.edu

Abstract: Computer instrumentation of living environments promises to extend the independent life span of our aging populations. This technological potential will not be realized unless people are willing to trust their lives to such support systems, as a replacement for human support. Very little is known about how and why people make these important decisions. The proposed research will study this issue using a widely adopted, computer-dependent life-saving device, the Implantable Cardiac Device (ICD). This research will provide a foundation for understanding how and why people agree to place their life in the hands of computerized equipment that they cannot fully understand or control. The study will design and validate instruments for gathering data on this decision. The study will use in-depth interviews, and survey methods, and will gather data from persons who have accepted or refused implantable defibrillators. Phase I, will be an interview study, working through cardiologists, to reach their patients. Phase II will develop, an extensible Web-based survey that can be readily adapted to other patient populations and other technologies. Both graduate and undergraduate students will be involved in the research plan. In addition, there are a number of broader impacts. First, this research will enhance our understanding of the key factors in the decision to entrust one?s life to a complex computer whose workings are not understood. It will also add to the meager collection of instruments for collecting this kind of data. Second, the information gained about the decision to accept implant will be new, and can serve as a guide in the design of patient information material. Third, the information will guide the design of patient information for ?pervasive computing home environments? and will therefore be useful to scientists and engineers as they consider what will be the most useful features of any proposed design.

Collaborative Research: Linking Researchers and Graduate Students through COSEE Tools & Services

Start Date: 9/15/2009

Award Number: 943430

Program(s): CENTRES FOR OCEAN SCI EDU EXCE

Principal Investigator: McDonnell, Janice

Co-PI Name(s):

PI Email Address: mcdonnel@imcs.rutgers.edu

Abstract: This proposal will be awarded using funds made available by the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This award provides funds to conduct workshops aimed at improving the broader impacts efforts of scientists. Through professional development workshops, scientist-graduate student teams will produce interactive materials based on the ?Ocean Literacy? and ?Climate Literacy? principles that can be specifically designed for a variety of audiences. The primary target audiences for this project are the research scientists and graduate students who participate in the project workshops. Through team-building and COSEE-facilitated long-term contact, the project will provide sustained professional development training opportunities that result in deeper content understanding and/or confidence in teaching to lay audiences, for researchers and graduate students alike. The secondary audience is on-line users who will benefit from the project outputs: namely the interactive ocean-climate content and user-centered data tools developed as part of this effort. Learning and interaction data of target audiences will be collected and evaluated by participating COSEE Centers. These evaluation results will be used to refine the approach, workshop model, and resulting online products throughout the project.