Awardees

Awardee: Albany State University

Toward a Systematic, High-Throughput Screening of the Toxicity of Various Organophosphate Compound
Principal Investigator (PI): Dr. Seong S. Seo

This work seeks to study the cellular and molecular effects of organophosphorus compounds (OPC) in different types of cells in order to better understand their mechanism in those cells. Different cell types will be exposed to toxic organophosphorus compounds, and then gene expression studies will be conducted to get detailed information on how the organophosphorus compounds affect the cells. These studies will help generate paradigms for cell toxicity of organophosphorus compounds. We will use commercially available toxic pesticides such as parathion, fonofos, mevinphos and trichlorfon for toxicity assays in various cells. We will use cell lines that include human SHSY5Y neuroblastoma cells and mouse NB41A3 neuroblastoma cells because AChE is known to be present in these cells. We will also include mouse muscle C2 cell lines, in which ACh is hydrolyzed at the neuromuscular junction by AChE. Further, we will use Chinese hamster V79-4 cell lines.

Awardee: California State University San Bernadino (CSUSB)

Predictive Toxicology of Organophosphates utilizing Drosophila embryonic stem cells and cell line
Principal Investigator (PI): Dr. Nicole Bournias-Vardiabasis

The project addresses the need to develop novel high-throughput methods to assess the toxicity of organophosphates. A thoroughly tested and vetted developmental toxicology assay that utilizes Drosophila embryonic stem cells will be modified so that it can robustly identify organophosphates(OPs) as toxicants. Drosophila cell lines will also be utilized to assess toxicity and if successful they will allow for a quicker throughput process. The assay will identify toxicants by changes that take place (as a result of cell specific toxicity) at the cellular (morphological) and molecular (biomarker protein expression) level. The employment and refinement of these two in vitro assays will allow the determination of toxicity risk of notable and heavily used OPs. Furthermore, both assays have transgenic GFP biosensor Drosophila constructs that allow for quantitative assessment. The assay formats include cytotoxicity, reporter gene expression and high-content imaging of cellular phenotypes.

Awardee: The City College of New York (CCNY)

Rapid Processing of a Biopolymeric Co-Continuous Filtration Membrane
Principal Investigator (PI): Dr. Raymond Tu

This work is based on the development of a new scalable protocol for the production of a co-continuous filtration membrane composed of regenerated silk fibroin. The outcomes of this research will be applied to the design of a process for the fabrication of a single pass, low flow (~5-50 LPM) membrane for chemical and biological respiration filtration. The scope of the proposed work explores the fundamental scientific issues of biopolymer phase separation under confinement, where the processing parameters (membrane thickness, solvent evaporation rate and thermal load) will determine quasi-periodic length scale of the membrane.

Absorbents for use in Building-Integrated Plant-Based Dynamic Filtration Media for Removing Chemical Warfare Agents
Principal Investigator (PI): Dr. Elizabeth Biddinger

The work proposes the use of self-regenerating, botanically-based air purifying systems that would be integrated into the air handling of the building. This would both purify the air intake from outside and allow for a reduced outdoor air intake (and even a closed system when needed) through purification of recirculated air. In the botanically-based air purifying systems activated carbon (AC) is commonly used as the adsorbent. While the AC has good adsorption ability to warfare agents and can be used in plant systems, functionalizing the AC could enhance the capture ability significantly. Ionic liquids (ILs), salts with melting points below 100°C, have been found to be good absorbents for a variety of gaseous species. The absorption selectivity and capacity can be tuned by changing the anion-cation pair. By tethering ILs to AC, the beneficial properties of gas absorption from ILs can be integrated into a solid system.

AI and Game Theory Based Autonomous CND Software Agents for Dynamic Trust Evaluation
Principal Investigator (PI): Dr. Muharrem Umit Uyar

We propose to develop a Cyber Security Assurance System (CySAS) to generate a secure software agent (SSA) that will run autonomously in each MANET node to provide computer network defense (CND) by dynamically generating and updating comprehensive attack detection, counter measures and recovery. The proposed SSA will dynamically classify trust level of neighboring nodes in ever changing tactical MANET environment to mitigate network vulnerabilities and counteract possible exploitations. CySAS will be capable of handling necessary counter measures for different types of mobile nodes executing tasks with different types of priorities and security requirements. CySAS will be run by each mobile node and utilize periodically updated information about local neighbors. As MANET nodes interact with each other over time, CySAS employs AI (e.g., grammatical evolution) to generate SSA, game theory for analyzing repeated interactions between local neighbors and Bayesian updating for neighbor reputations.

Noise-Aware, Low-Cost, Low-Power Baseband DSP Hardware using Stochastic Computing
Principal Investigator (PI): Dr. Bo Yuan

The objective of this project is to design noise-aware low-cost low-power hardware for baseband digital signal processing tasks in wireless communications. To overcome the aforementioned challenges, a novel computing paradigm, namely, stochastic computing (SC) is leveraged in the proposed project. This computing paradigm, which uses unique data representation and processing techniques, has the potential to enable the design of noise-aware low-cost low-power baseband DSP with orders-of-magnitude improvements in energy efficiency, resource efficiency and error resiliency. The first task of this project is to develop a series of high-performance stochastic accelerators for various baseband DSP algorithms. Dedicated optimizations are performed to maximally improve the performance of those accelerators in terms of area, power and error resilience. Then, system integration and overall optimization are conducted to develop the entire stochastic baseband DSP systems. The second task of this project is to exploit the novel techniques to address the potential risks arisen with the use of stochastic computing, including high interface overhead, long processing latency and sensitiveness to correlation. The overcoming of these risks eliminates the obstacles that may impede the applications of stochastic computing, thereby unlocking the potential of the deployment of the proposed stochastic computing-based baseband DSP in practical wireless communication systems. The third task of this project is to develop a comprehensive statistical modeling and analysis framework to rigorously quantify the system performance (especially for error resilience and computation accuracy) according to various statistical criteria, thereby facilitating the simulation and configuration of the entire stochastic baseband DSP systems.

Cyber Security Techniques in the SCADA Military Environment
Principal Investigator (PI): Dr. Tarek Saadawi

CCNY will research, identify and test potential vulnerabilities associated with smart grid implementations involving major communication protocols including DNP3, DNP3 with Secured Authentication, IEC-61850 and Modbus that are primarily used in SCADA operations and environment. During the initial stages of the project, vulnerabilities will be identify vulnerabilities in the SCADA technology.  Smart grid testbed and pilot project implementation will be the follow-on task for analyzing vulnerabilities and performing real penetration testing to identify possible threats associated with smart grid and ultimately this will prepare us for the next phases (year 2 & 3) to overcome the threats by adopting different mitigation techniques, software development, verification of the developed algorithm, and testing of the add-ons software tools for Intrusion Detection System, Mitigation and Tolerance. We will also harden the smart grid security infrastructure by establishing and verifying different algorithms, techniques and tools developed throughout the project experimentally on a laboratory-based testbed and during the simulation phase.

Using the Hard and Soft, Acids and Bases (HSAB) Theory to Predict Organophosphate – Target Interactions
Principal Investigator (PI): Dr. Urs Jans

This study is directed towards understanding the reactivity of organophosphates with nucleophiles and enzymes. Many chemical toxicants and their active metabolites are electrophiles that lead to cell injury by forming covalent bonds with nucleophilic targets on biological macromolecules (e.g., enzymes). However, covalent reactions between nucleophilic and electrophilic centers are discriminatory since there is a significant degree of selectivity associated with these interactions. The Hard and Soft, Acids and Bases (HSAB) theory is one quantum chemical theory that has been proven to be a useful tool in predicting the outcome of a nucleophilic attack at an electrophilic center. This theory uses the inherent electronic characteristic of polarizability to define electrophiles and nucleophiles as either soft or hard. This HSAB theory has been successfully applied to chemically induced toxicity in biological systems (e.g., aldehyde toxicity). According to the HSAB theory, a toxic electrophile reacts preferentially with a biological target of similar hardness or softness. The HSAB classification of xenobiotic electrophiles such as organophosphates has obvious utility in discerning plausible biological targets and molecular mechanisms of toxicity. CCNY proposes to use the HSAB theory of electrophiles and nucleophiles within the toxicological framework of organophosphates.

 

Awardee: Fisk University

Modeling Organophosphate Toxicity in C. Elegans through a Scalable Combined In Vivo and In Silico Approach
Principal Investigator (PI): Dr. Brian Nelms

Fisk University will develop and test an efficient in vivo toxicological screening system using the model organism C. elegans.  Fisk will use this system to test acute toxicity, particularly of organophosphates, and use the resulting datasets to inform computational and mathematical predictions about the effects of novel organophosphates, extendable to other types of chemicals. We will design and test this platform with scalability in mind, working on prototype microfluidics (“worms-on-a-chip”) solutions in parallel with our “proof-of-concept” designs for the general work flow and data acquisition of multiple relevant outputs. Fisk will measure acute toxicity via a multipronged approach examining at least 5 outputs:  1) general lethality, 2) swimming frequency / paralysis, 3) cholinergic motor neuron and muscle activity, 4) cell death and oxidative stress, and 5) surface-enhanced Raman spectroscopy (SERS) data.  Fisk will collect data from these assays and develop mathematical and computational models.  At the same time, Fisk will perform mathematical modeling based on collected data and data available from several recent studies (e.g. Lewis et al., 2013; Leelaja and Rajini 2013).

Multifunctional Materials for Air and Liquid Protection
Principal Investigator (PI): Dr. Richard Mu

This project is designed to create a new type of dynamic, biofunctionalized nanomaterial to provide real time detection of specific biological and chemical agents through the fusion of multiple, collocated sensing modalities.

 

Awardee: Florida A&M University (FAMU)

Modeling, Imaging and Optimal Design in Nano-Optics
Principal Investigator (PI): Dr. Hongmei Chi

Nanostructured materials, possessing optical qualities based upon their physical structure and chemical composition (i.e. metamaterials) are not a new concept in the scientific community. However, with the continual advancement of metal, dielectric, and semiconductor fabrication and manufacturing techniques, new doors are opening to this class of optical devices for solutions ranging from the reduction of weight in optical systems through the replacement of numerous optical elements (i.e. lenses) with single-element, numerically-designed, optical devices, to optical surface coatings for increased optical transmission and coupling efficiency of wider angles of effect than classically achievable through current industry standard methods. Advances in the Design, Analysis, and Optimization of such technologies would have immediate and beneficial impact to both the Military, and Industry.

 

Awardee: Howard University

Irregular Warfare Decision Assist System for Determining Threat
Principal Investigator (PI): Dr. Charles Kim

Howard University will develop a decision-assist system for determining and predicting threats for field commanders in irregular warfare using not only local populace environment data such as ethnicity, culture, religion, and geography but also all available human terrain and sociocultural data such as PMESII, ASCOPE, and other sources for the region and the other parts of the world. The brain of the decision assist system is an inductive inference algorithm which extracts rules and probability for threat level determination with the human terrain variables and produces the uncertainty level of the probability itself.  The main theory behind the inductive inference is entropy maximum and minimum (minimax) principle.  Entropy is interpreted as a measure of uncertainty in induced probability of threat, and therefore, entropy is smallest when all of the information has been extracted from the available data, which leads to derive a decision rule for affirming or negating a threat. On the other hand, an unbiased probability and its margin of error are obtained with the entropy maximize. Once implemented, since new data can be easily added to the system, the inductive inference produces new rules with updated probability and uncertainty from additional sample data, and thus makes a learning decision-assist system. The task of the project is focused on the development of an information entropy based inductive inference algorithm for rule extraction and probability induction and of the implementation of the algorithm. The coding environment will be a Windows-based platform or a sponsor designated/suggested integrated programming development environment. The implemented code will be verified with available sample data.

 

Awardee: North Carolina Agricultural and Technical State University (NC A&T)

High-Content Organ-On-A-Chip Assay: Predictive Nerve Toxicity Model for Organophosphates
Principal Investigator (PI): Dr. Yeoheung Yun

NCAT proposes to develop two 3D in vitro assay models; 1) a static culture model (RTI) and 2) dynamic culture model (NC A&T). These platforms will integrate the key cellular components of the BBB (endothelial cell and astrocytes) with cells that mediate brain injury responses (microglia) and surrogates for the primary targets of OP toxicity (neurons). These high-content, high-throughput platforms will then be used to screen a test set of OP agents for concentration-dependent effects on: 1) overall cell viability/toxicity within the construct, 2) barrier integrity (measured by electrical resistance), 3) penetration of OP across the model BBB, and 4) inhibition of acetylcholinesterase (AChE) activity in target cells following exposure through the model BBB. Successful completion of these studies will result in a new approach to testing OP toxicity in vitro, and ultimately will enable rapid testing of potential countermeasures in a physiologically relevant yet manipulatable, in vitro system. NCAT will develop a computational model, based on experimental data, to identify the most critical pathways that mediate OP acute neurotoxicity.

Human Liver-Neural Real-time In vivo Correlation of Organophosphate Toxicity
Principal Investigator (PI): Dr. Narayan Bhattarai

NCAT will construct and validate a high content analysis (HCA) human liver detoxifying-neural toxicity testing device that instantaneously detects the level of OP neural toxicity and real-time human hepatic detoxification. This device will permit the in vivo human correlation of OP toxicity and identify the human phenotype that is most susceptible and lacking the hepatic detoxification capacity to OP toxicity. The device can be shipped, stored, and ready for use anywhere in the world. The device will contain mini human livers in a hepatocyte-alginate spheroid format, which will ultimately be produced from multiple phenotyped human liver donors. These human hepatocyte-alginate spheroids will rest at the bottom of a 96-well plate on a bed of agarose containing acetycholinesterase that is activated upon binding OPs and will be monitored colorimetrically to relate neural toxicity data to human detoxification capacity. Ideally the spheroids will be cryopreserved prior to use and the 96-well microplates shipped with the colorimetric assay contained. All one must do is thaw the beads, place them in the 96 wells with culture media and add the toxic agent either to the water, or if a volatile OP, then inject through a resealable port. Then use a microplate spectrophotometer to monitor the colors. The slope of this dissipation curve represents the detoxification capacity of the human liver phenotype contained in the specific well in the 96-well plate. The keys to the success of this device are: (1) maintaining an environment conducive to hepatic metabolic functionality, (2) validating existing acetylcholinesterase colorimetric assay in 96-well format, (3) ensuring metabolic functionality and viability after freezing, and (4) robusting characterizing metabolic functionality during development.

Reduced Weight Polymer Based Composite for Sabots on Anti-Tank Rounds
Principal Investigator (PI): Dr. Ram Mohan

This work will focus on material solutions, development, design, analysis, fabrication, and prototyping and analysis of a reduced weight polymer based composite system used in sabots on anti-tank rounds. Timely and cost effective development of high performance materials with reduced parasitic weight that can meet the service conditions at reduced weight will have critical and determinant impact on Army mission. In case of anti-tank rounds, alternate material solutions with recent and current advances in polymer composites are proposed for reduced weight polymer based composite for sabots that would result in a reduced weight while generating significantly increased velocity of the projectile and associated kinetic energy resulting in higher lethality. The material solutions would also provide a validated and demonstrated manufacturing process involving alternate composite materials for to meet sabot requirements and potential alternate vendor as noted in the program objective. Accordingly, technical R&D developments aligned in three phases for systematic development, evaluation, and risk mitigation; materials down selection are proposed. R&D developments in each phase will cover a period of one year with success and lessons learned from each year leading to progress and further activities in future years with corrective actions.

 

Awardee: North Carolina Central University (NCCU)

Fast, Large-Scale, Inexpensive Nanoscale Fabrication
Principal Investigator (PI): Dr. Goran Rasic

To enhance the capability of munitions and weapon systems the Army is investigating the use of additive manufacture and materials printing. These enabling technologies are expected to enhance the performance of power systems, fuzing, guidance systems and soldier systems, through miniaturization, conformal electronics and reduced power consumption. Novel fabrication methodologies are needed to produce economically viable components of high reliability.

Exploring Chemical and Bio- Sensors Operating at the Quantum Frontiers
Principal Investigator (PI): Dr. Abdennaceur Karoui

This research focuses on the development of frontier chemical and bio- (CB) nanosensors. The results will benefit the US National Security as it will allow mitigation of possible man-made disasters or naturally expanded ones, such as epidemic viral outbreaks. The miniature sensors are suitable for large scale monitoring of hazard materials in cities, and early detection of CB warfare (CBW) agents in war theatres. Because the sensors are ultra-sensitive, miniature, accurate, and fast they are suitable for nanomedicine, an emerging field that requires measurements of molecular entities. The characterization of such materials has been a major challenge in today medical research. This project combines quantum mechanics with nanotechnology to develop the nanosensors.

 

Awardee: San Diego State University (SDSU)

Stable Manufacturing of Advanced Powder Components by Ultra-Rapid Pressure- and Assisted Sintering
Principal Investigator (PI): Dr. Eugene Olevsky

Spark Plasma Sintering (SPS) aka Field Assisted Sintering (FAS) has recently emerged as a viable manufacturing technology, capable of reducing sintering times from hours to minutes for nearly all classes of materials. However, there have been challenges with achieving near-net- shaped parts, as well as developing new tooling materials to address challenges with graphite such as limited strength and inherent carbonization of samples. Microwave sintering has also shown promise for reducing sintering times and energy, but has always lacked a pressure component to aid in obtaining higher density and better uniformity. There is a need to investigate the integration of SPS/FAS with microwave sintering to push the boundaries of sintering science.

RF/Optical Receiver Sensitivity Degradation from Exposure to High-Power Electromagnetic Pulses or Microware Signals
Principal Investigator (PI): Dr. Madhu Gupta

Every simulation or calculation of the performance of anRF communication system (including receivers; transceivers; radars; and sensors) requires noise models for the devices and components employed in that system. Such models are not available for devices and components that are exposed to verhigh electromagnetic fields. Existing equilibrium and quasistatic models are inadequate, since they assume nearequilibrium conditions, and do not account for the effectof very high fields. On the other hand, Monte Carlo simulations at the carrier transport level are excessively time consuming.  In the presence of very high fields, the level of noise generated in a device rises, both due to carrier avalanching in the semiconductor, and due to existence of a nonequilibrium stateWe will develop models that are analytical (rather than computational or numerical), and based on physical principles (rather than empirical curvefitting), for determining the noise generated in semiconductor devices under nonequilibrium conditions, created by the presence of large electromagnetic fields. Our models will permit the circuit simulators to calculate the noise performance, and system simulators testablish the threshold level of high electromagnetic field exposure which will render a receiver ineffective as a result of receiver sensitivity degradation. 

 

Awardee: University of North Carolina at Charlotte (UNCC)

Optical Properties of Retroreflections (OPRA)
Principal Investigator (PI): Dr. Glenn Boreman

Spark Plasma Sintering (SPS) aka Field Assisted Sintering (FAS) has recently emerged as a viable manufacturing technology, capable of reducing sintering times from hours to minutes for nearly all classes of materials. However, there have been challenges with achieving near-net- shaped parts, as well as developing new tooling materials to address challenges with graphite such as limited strength and inherent carbonization of samples. Microwave sintering has also shown promise for reducing sintering times and energy, but has always lacked a pressure component to aid in obtaining higher density and better uniformity. There is a need to investigate the integration of SPS/FAS with microwave sintering to push the boundaries of sintering science.

 

Awardee: The University of Texas Rio Grande Valley

Hazardous Radiation and Microbial Protective Integrated Fabrics for Advanced Protection Under Uncontrolled Environment
Principal Investigator (PI): Dr. Mohammed J. Uddin

This project targets the development of a novel functional system: Photocatalysis and Radiation Sensing (PRS). In addition to built-in antimicrobial, UV-protective and self-cleaning capabilities, the system will use woven functional micro-fibers (FMF) to enable the real-time detection of proximate nuclear radiation. The system is comprised of FMF, which typically are 25 μm in diameter (diameter of cotton fibers is 15 μm); a novel transport mechanism consisting of carbon fiber, or relatively low-cost carbon nanofibers; and optoelectronic materials to create biocompatible optoelectronic weavable fibers as an outlet for the sensing data. The PRS system will be applicable to fabrics constructed of conventional and biocompatible materials (e.g., cotton), and polymeric materials (nylon, etc.), into which the real time sensing and protective technology can be woven. This multifunctional integrated fabric will monitor any nuclear radiation from “cradle-to-grave.”  The photocatalytically active functional fibers will protect the body from biological and radiation hazards, and FMF will interact with incoming photons (ionizing radiation) of varied intensities and continuously provide an electrical output.

 

Awardee: The University of Texas San Antonio

PAM: A Pattern-Based Measurement Model for Improving Software Reliability
Principal Investigator (PI): Dr. Jianwei Niu

The proposed project will develop a pattern-based measurement model (PAM), which checks the software artifacts (e.g., class diagrams, code base) for violations of positive-patterns and existence of negative-patterns, and estimates the risk of software failures and their impact. The basic idea of PAM is to quantitatively estimate the reliability of software based on the detected positive-pattern violations and negative-pattern instances in software artifacts, as well as the importance and the activation probability of the patterns and document findings. Recent studies show that software reuse and code clones are prevalent throughout software enclaves. In addition, more and more software projects are becoming based on existing software frameworks which have limited number of usage patterns. Thereby, it is reasonable to assume that most of the design fragments and code portions of a new software product follow existing patterns. Furthermore, the reliability of a software application can be predicted by combining effects of all violations of positive design and instances of negative bug patterns. The project will yield a learning engine to mine online bug repositories (e.g., JIRA, Bugzilla) and project hosting websites (e.g., Github); a pattern checker to detect the existence and invocation of patterns and invocation; and a model for the estimation of the reliability of a software project based on the pattern instances/violations existing in the project and their invocation probabilities

Novel Technology for Detection and Prediction of Spreading of Air-Borne Chemicals
Principal Investigator (PI): Dr. Kiran Bhaganagar

An UTSA interdisciplinary team proposes – for the first time – a novel approach for tracking trajectory of dynamically moving (spatial and in time) chemical plume using cooperative unmanned aerial vehicles (UAV’s) with onboard chemical sensors that can provide real-time data to an atmospheric model with a capability of simulating the transport and dispersion of chemical agents and atmospheric conditions. Accurate representation of air conditions will be achieved through site-specific weather/chemistry multiscale model that accurately accounts for meteorological and transport and dispersion physics. The real-time simulation tool for plume tracking using UAV’s will serve as an important contribution – in the advancement of the science of UAV’s for hazardous plume tracking – and also contributes to – a robust and novel methodology to predict the trajectory of any impending chemical threats. The simulation platform will be tested using real-time measurements and UAV data assimilation methods. However, as an intermediate step, we propose to use existing networks of meteorological sensors and interface the sensor data with plume diffusion model. Finally, real-time UAV data will be integrated with transport and diffusion modeling to predict the spatial/temporal evolution of the chemical plume. The numerical approach involves using real-time measurements from UAV’s at different elevations coupled with site-specific weather/chemistry multi-scale model that accurately accounts for meteorological and topographical physics – to detect chemical agents, and also to calculate the transport of the agents in space and in time.