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TECHNOLOGY AREAS: Chemical/Bio Defense ACQUISITION PROGRAM: PEO CB DEFENSE OBJECTIVE: Analysis of the proteome is the next great challenge in biology following sequencing of the genome. Current techniques rely on 2-dimensional gel electrophoresis followed by mass spectrometry, and have difficulty distinguishing among proteins which differ only in post translational modifications. The objective is to develop a multi-dimensional separation technique with broad applicability in proteomic analysis. DESCRIPTION: Proteins are the functional end products of gene expression and the sequencing of human, bacterial and other genomes will result in next generation biological materials for the Objective Force. Applications, described in detail by recent National Academy and Office of Net Assessment studies, include armor, power, soldier health and performance and sensors. There are two key issues: identification of a useful protein, and its subsequent production and purification. When working with proteins from samples where genomic information is available (e.g., E. coli), proteins are separated by charge (pI) and molecular weight (MW), i.e., in two dimensions. The protein spot is then subjected to MALDI-TOF mass spectrometry and peaks matched against expected peptides. In the absence of genomic information one automatically assigns different spots to different proteins and performs laborious and expensive mass spectrometric analysis. Extraction and purification of proteins currently relies on traditional protein biochemistry techniques which are sufficient for many high yield and non-modified proteins. However, when the protein is post-translationally modified (e.g., glycosylation, phosphorylation, amidation) or expressed in low abundance, these techniques are insufficient. Since these modifications are often responsible for the protein’s biological activity, this issue is key. The problem is to separate proteins on more than two-dimensional criteria of MW and pI in order to identify those whose amino acid sequences may be identical but which differ in post-translational modifications, hence functionality. Multi-dimensionality might be added by including functional criteria using approaches other than gels, such as MEMs or array devices. PHASE I: Propose a set of dimensions in addition to traditional charge and molecular weight to functionally separate proteins. Select a family of proteins whose subspecies differ only in level of expression and degree of post-translational modification. PHASE II: The objective of Phase II is to design a platform or device capable of separating proteins in more than two dimensions. An example of an additional dimension might be protease activity, but others are required. Such a device could be based on ¾ but is not limited to ¾ microelectromechanical systems. The approach should include a complete protocol including extraction, purification and storage conditions and must be demonstrably applicable to different tissue types and protein species. PHASE III: A multi-dimensional system for protein separation would have virtually unlimited applications in all aspects of proteomics, from drug discovery to biological assays. REFERENCES: 1) Cumming, D.A. (1999) Structural heterogeneity and functional differentiation: a rationale for glycosylation analysis of recombinant therapeutics. Semin Cell Biol. 2(5):273-9. 2) Fukui, S., Feiz, T., Galustian, C., Lawson, A.M., and Chai, W. (2002) Oligosaccharide microarrays for high-throughput detection and specificity assignments of carbohydrate-protein interactions. Nature Biotechnol. 20(10):1011-1017. 3) Hirabayashi J, Kasai K. (2002) Separation technologies for glycomics. J. Chromatography B Analyt Technol Biomed Life Sci.771(1-2):67-87. 4) Prutsch A, Lohaus C, Green B, Meyer H E, Lubben M. Multiple posttranslational modifications at distinct sites contribute to heterogeneity of the lipoprotein cytochrome bo(3). (2000) Biochemistry 39(21):6554-63. 5) Regnier F E, Riggs L, Zhang R, Xiong L, Liu P, Chakraborty A, Seeley E, Sioma C, Thompson RA (2002) Comparative proteomics based on stable isotope labeling and affinity selection. J Mass Spectrom.37(2):133-45. 6) Soderquist A M, Todderud G, Carpenter G. (1988) The role of carbohydrate as a post-translational modification of the receptor for epidermal growth factor. Adv Exp Med Biol. 231:569-82. 7) Opportunities in Biotechnology for Future Army Applications, Board on Army Science and Technology, National Research Council, National Academy of Sciences (2001). 8) Exploring Biotechnology, Opportunities for the Department of Defense, Office of Secretary of Defense – Net Assessment (2001).
A03-127 TITLE: Buried Mine/Unexploded (UXO) Detection and Identification Improvement Through Characterization and Innovative Incorporation of Sensor Background Noise/Clutter Signals TECHNOLOGY AREAS: Sensors OBJECTIVE: Develop, demonstrate, and validate a quantitative Inquiry Signal to Soil to Detector (ISSD) model that is accurately predictive of the noise/clutter signals expected for a wide variety of soil types/conditions when using various inquiry signals. The focus of this topic is on the development of innovative prototype signal processing methods to enhance mine/UXO sensor performance and, as such, success would result in a critically enabling technological capability. DESCRIPTION: Detection, location, and accurate identification of buried mines/unexploded ordnance (UXO), without resorting to extensive excavation, has always been difficult because of the lack of a quantitative, accurately predictive, and widely applicable model of the interaction of inquiry signals with the soil, and then with a detector. With such a model that accurately predicts and quantitatively characterizes the noise/clutter expected from any intended environment, it will be possible to greatly improve the signal-to-noise (S/N) ratio of any sensor system. The associated benefits include an enhanced detection capability while also reducing the “false positives” (i.e., improving the assuredness of target identification). The inquiry signals include infrared, nuclear quadrapole resonance (NQR), other electromagnetic techniques including ground penetrating radar (GPR), chemical detection, and acoustics based methods. The soil component of the interaction model is intended to cover all naturally occurring soil types present on the surface and near surface of the earth. This includes all combinations of sand-silt-clay, organic matter, moisture, compaction, and inclusions (e.g., gravel, rocks, roots, voids, sea shells, bones, macro-level life forms, etc.). Owing to the difficulty of the problem this ISSD model should first be considered for a situation free of any targets. Next, the more difficult case of including differencing approaches and interactions with targets can be treated. For this topic the targets shall consist of one or more of: 1) anti-personnel mines, 2) anti-tank mines, and, 3) a wide variety of shallow UXO but typically with metallic casings. Burial depth ranges from shallow sub-surface to 20 centimeters (cm) below ground surface. Soil type and conditions are extremely variable. For landmines the characteristic size dimension ranges from 4.5 cm to 38 cm; the emphasis shall be on low-metal (1 – 100 grams (g)) within a non-metallic case varieties; the explosive fill is typically Trinitrotoluene (TNT), Royal Dutch (Demolition) Explosive (RDX), or cyclotrimethylenetrinitramine, and Pentaerythritol Tetranitrate (PETN); and fuse mechanisms can include pressure, tilt rod, magnetic influence, seismic/acoustic or other. Much of the challenge and need for innovative thought associated with this topic involves creating a widely useable approach or framework that can easily encompass all of the applicable and potentially applicable variables for the various inquiry signals. Both active signals (i.e., purposefully introduced artificially) and passive signals (i.e., naturally occurring but still detectable) are to be considered. It is expected that, at a minimum, broad and incisive knowledge of the areas of physics, soil mechanics and chemistry, and signal processing will be needed.
1) Journal of Magnetic Resonance, 144, 305-315 (2000) A. J. Blauch, J. L. Schiano, M. D. Ginsberg, "Detection of Nuclear Resonance Signals: Modification of Receiver Operating Characteristics Using Feedback". 2) Liang-Liang Xie, P. R. Kumar, "A Network Information Theory for Wireless Communication: Scaling Laws and Optimal Operation", submitted to IEEE Transactions on Information Theory. 3) A. Ephremides and B. Hajek, "Information theory and communications networks: An unconsummated union," IEEE Trans. Inform. Theory, vol. 44, pp. 2416-2434, 1998. 4) Dwight K. Butler et al. “Multisensor Methods for Buried Unexploded Ordnance Detection, Discrimination, and Identification”, Tech Report SERDP-98-10, Sept 98. 5) Jose L. Llopis et al., “Site Characterization Investigations in Support of UXO Technology Demonstrations, Jefferson Proving Ground, Indiana”, Tech Report GL-98-20, Sept 98. KEYWORDS: signal processing, modeling, geosciences, soils, mine detection, UXO detection, identification, noise, clutter A03-128 TITLE: Implementation of a Geospatial 3-dimensional Topology Model TECHNOLOGY AREAS: Sensors ACQUISITION PROGRAM: Combat Terrain Information Systems (CTIS) OBJECTIVE: To design and develop a three-dimensional model of topology for inclusion into Geographic Information System Technology. DESCRIPTION: As the world population grows and urban land is used with growing intensity, there is increasing emphasis to utilize space under and above the surface for such purposes as urban transit, employment centers, and high-density housing. With this focus, there is a need to develop and maintain computational models that master the complexity of three-dimensional relationships above, below, and across the land surface. Current Geographic Information System (GIS) technology is based upon 2 dimensional (2-D) models of topology that detail and track connected spatial relationships between physical entities. Current GIS technology projects our 3-dimensional (3D) world onto a two dimensional plane to form topology. This limiting 2-D approach cannot begin to model the complexity of dense 3-D urban areas where 50% of the world's population is projected to live in coming years. This lack of a developed model of 3-D topology precludes our computational ability to perform accurate spatial analysis of objects above and below the earth, and with regard to their relation to the surrounding surfaces. Examples of potential applications enabled by the development of 3-D topology are: cellular communication tower location planning, complex building fire analysis, and chemical weapon dispersion models. Furthermore, the ability to visualize scenes from a 3-D perspective is constrained to being a "pretty picture" without computational ability to detect errors in 3-D geometry models or perform complex computational 3-D analysis operations. The Government seeks the capability to store, manage, and use 3-dimensional feature data in concert with 3-D topological spatial relationships. The contractor shall either internally store 3-D geometry and topology within a GIS database or interface to a COTS GIS database through external data files and access methods. This shall be implemented in a manner to facilitate both transition to commercial applications and adoption by Government for uses in Homeland Defense and the Army Future Combat System. The solution must address problems and handle such 3-D features as tunnels, vertical cliffs, caves, and building interiors. The Government seeks a contractor to investigate methods and develop software to implement a 3-dimensional topology software application. PHASE I: The contractor will accomplish the following research goals: 1) develop and show feasibility of a conceptual design for storing and exploiting 3 dimensional topology, and 2) initiate development of key 3-D concepts and relationships. The contractor will provide a report documenting their conceptual 3-D topology design and algorithms. PHASE II: The contractor shall develop and deliver an implemented 3-dimensional topology software application that interoperates with commercial geographic information systems. The contractor will demonstrate ability to model complex topology relationships, access topological primitives, and demonstrate these topological relationships between objects above, below, and across the land surface, in addition, to demonstrating system interoperability. PHASE III: This SBIR would result in a technology with broad applications in the civil and military communities by providing a new unique commercial capability to model, access, and utilize complex 3-dimensional geospatial geometry and topology. REFERENCES: 1) Draft International Standard ISO/DIS 19107 Geographic information - Spatial schema, International Organization for Standardization. www.iso.ch 2) "Incorporating 3D Geo-objects into a 2D Geo-DBMS", Stoter, Jantien and Oosterom, Peter van, ACSM-ASPRS 2002 Annual Conference Proceedings, Washington DC 2002.
A03-129 TITLE: Spatial Data Mining TECHNOLOGY AREAS: Battlespace ACQUISITION PROGRAM: Combat Terrain Information Systems (CTIS) OBJECTIVE: Army commanders need to quickly and accurately see, decide and act in order to have the advantage over future adversaries. In the DoD cognitive hierarchy, spatial data mining discovers relationships that produce information that results in knowledge through evaluation and analysis. The objective of this research is to develop tools for: a) discovering relationships from diverse sources of spatial data that will provide or predict essential terrain features and attribution, and b) predicting patterns based on observation of dynamic spatial behavior (troops, snipers, weapons, on- and off-road vehicles, etc.). Meeting this objective is important for the intelligence preparation of the battlefield (IPB) for the Future Combat System (FCS). These tools will make it possible to see relationships in complex datasets which could not be seen in a reasonable time, or perhaps ever, by operators simply looking at the data. Data mining is included as a tool in the FCS Situational Understanding Specification. DESCRIPTION: The Army seeks to develop automated spatial data mining techniques that will provide information superiority to the warfighter. Army commanders need to see, decide and act in order to gain and maintain advantage over future adversaries. In the DoD cognitive hierarchy, spatial data mining provides relationships that are used to derive information that, through evaluation and analysis, results in knowledge. The commander then uses this knowledge to gain understanding that is used to decide and to act. The Army, Homeland Security, and the commercial sector will use the relationships derived through spatial data mining to rapidly predict, augment, and verify information, as well as to detect errors. Army systems need terrain data at a greater fidelity than is provided by NIMA’s Country Database. To achieve greater fidelity, additional terrain information is needed in the form of a Mission Specific Data Set (MSDS). Spatial data mining will assist in achieving the needed fidelity. Examples of capabilities essential to the Future Combat System (FCS) which require greater fidelity than are provided by the Country Database are Tactical Decision Aids (TDA’s) and detailed mission-planning and rehearsal. Manual extraction of essential attribution and features is a labor-intensive process. The Army seeks to develop automated spatial data mining techniques that discover relationships among diverse sources of spatial data and that use these relationships to discover attribution and features, needed in a MSDS, that are not found in the Country Database. Examples of essential mission-specific terrain features and attribution that are not found in the Country Database are soils, streams, cropland attribution, grassland attribution, and urban attribution. As an example of spatial data mining applied to the battlefield, a commander needs to decide the best approach to a military objective such as an unfriendly command center. In this case, spatial data mining tools will provide information of greater fidelity to the TDAs that will improve the commanders understanding of terrain, trafficability, cover, slope, distance, predicted weather, troop and weapons movement, air cover, and firepower. This enhanced understanding will enable the commander to make improved maneuver decisions. As an example of spatial data mining applied to Homeland Security, a sniper is terrorizing a metropolitan area in a manner that has an obvious spatial component. Spatial data mining techniques automatically detect behavior patterns, predict future sites of activity, and narrow the list of suspects. Key scientific challenges revolve around the discovery, enumeration, and digital encapsulation of relationships between differing components within our physical spatial world. The proliferation of geospatial data holds the promise of having many representations of attributed feature data over the same physical location, either in vector or raster form. Little use has been made of the relationships that might exist among all data sources that exist over a particular location. Relationships can be viewed as vertical, looking down at all data over a location, horizontal, looking at spatial relationships such as adjacency that exist in one or more data sets, or time-related, looking at the change in position of entities within the spatial domain. Spatial data mining seeks to discover the vertical, horizontal, and temporal relationships that exist among various sources of spatial data and to use these relationships to discover useful information. The primary emphasis of this research should be to develop spatial data mining tools for terrain features and attribution. However, proposals encompassing spatial data mining applied to dynamic situations are also encouraged. The Army seeks the capability to discover relationships among co-located spatial data and to use these relationships to predict or verify the occurrence of spatial features and their attributes. Spatial data mining makes explicit or implicit use of coordinates. This is not to be seen as an exercise in extracting features from imagery. The Army seeks a contractor to investigate methods and to develop software to perform spatial data mining so that it can be quickly and efficiently integrated into Army systems. Emphasis is placed on predicting or verifying features and attributes, which are useful to the FCS. The contractor must have the ability to interface with a geographic information system (GIS) to insure compatibility with both the commercial sector and with the FCS Lead System Integrator (LSI). PHASE I: The contractor will develop a design, and demonstrate limited prototype software that predicts the occurrence of terrain features or attributes and/or the dynamic components within the terrain. The prototype software can use real or simulated data. The contractor will provide a report documenting the design, including the data mining algorithms and the discovered relationships. PHASE II: The contractor will develop and demonstrate data mining prototype software and demonstrate the accuracy of the information it predicts. At the conclusion of Phase II, the contractor will demonstrate and deliver mature, robust spatial data mining software that is interoperable with a commercial Geographic Information System (GIS). Algorithms and methodologies will be documented so that they can be integrated into the Army systems. PHASE III: This SBIR would result in a technology with broad applications in the civil and military communities by providing datasets of improved quality and resolution. The FCS would use spatial data mining technology for force deployment, intelligence preparation of the battlefield, and training. In the commercial sector this SBIR would be used for emergency management, community planning, and infrastructure development. REFERENCES: 1) Fayyad, Usama, et all, editors, Advances in Knowledge Discovery and Data Mining, AAAI Press, 1996. 2) Han, Jiawei and Kamber, Micheline, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001. 3) Miller, Harvey J and Han, Jiawei, editors, Geographic Data Mining and Knowledge Discovery, Taylor and Francis, 2001. KEYWORDS: Data Mining A03-130 TITLE: Sensors for Rapid Chemical Biological Radiological (CBR) Detection and Countermeasure Activation to Protect Water Distribution Systems TECHNOLOGY AREAS: Chemical/Bio Defense ACQUISITION PROGRAM: PEO CBD OBJECTIVE: To develop technological solutions to support Physical Security Planning Area and prevent, isolate or mitigate chemical, biological, (CB) to water-based utility systems. To maintain water supply safety and health requirements by providing early warning capabilities to protect against CB threats to drinking water. DESCRIPTION: Existing Department of Defense (DoD) installation utility systems typically lack consideration for counter-terrorism, do not have supervisory control and data acquisition systems (e.g., commercial sector Supervisory Control and Data Acquisition System (SCADA)), are not set up for alternate source or cross feeds/supply or do not have updated emergency response plans. PHASE 1: Terrorist attacks on water based utility systems such as potable water, water based fire protection systems have the potential for greatest direct impact to personnel. The thrust of Phase I is to investigate and adapt novel sensors for use in detecting CBR threats to water based utility systems. The challenge is to identify and adapt innovative detector technologies that will survive and operate in a continuously submerged, high-pressure environment to provide a low cost, high reliability sensor to detect low contaminant concentrations. The sensor should be capable of detection levels on the order of 10 to the minus 12th to 10 to the minus 11th. The CB sensor based network would provide an effective early warning system and countermeasure/treatment activation for water-based utilities. Developed sensor systems should be compatible with commonly available SCADA and building automation systems. 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