The purpose of this talk is to initially provide an over-view of classical approaches used in bacterial identification, microbial forensics and biodetection. There will then be a brief discussion of bacterial biochemistry and taxonomy. This will be followed by recent examples of morphological (electron microscopy) and analytical chemical (mass spectrometry) bacterial characterization from our own research. This will lay down a framework for considerations in the use of spectroscopy (which is more developmental in nature) to provide equivalent or alternative information.

The US Army Edgewood Chemical Biological Center is the leader in development of military systems for chemical and biological defense, in collaboration with all Services, other Government laboratories, academia, and industry. Chemical and biological optical sensing principles, unique capabilities, state-of-the-art sensors, and emerging technologies will be discussed. Exciting new results will be presented on standoff biodiscrimination using infrared (IR) depolarization lidar and long-wave IR (LWIR) lidar.

Visible extinction and Surface Enhanced Raman Scattering (SERS) spectra using quartz-bound Au nanoparticle substrates are used to identify substrate production-related sources of spectral variability. Hydrosol Au nanoparticle size distributions are known to affect SERS enhancement, but the effect of spatial orientation and nanoparticle physiosorption during substrate preparation on spectral reproducibility and performance are not well understood. Experiments varying quartz slide orientation and Au nanoparticle delivery method show significant concentrationgradient and physiosorption-related aggregation effects in the substrate extinction spectra and SERS spectra of R6G applied to spatially mapped substrate regions. Additionally, applying multiple Au hydrosol treatments to functionalized quartz substrates reveals interesting relationships between Au nanoparticle thickness and substrate extinction and SERS spectra. Of the many factors affecting substrate spectral reproducibility, minimizing concentration gradients and optimizing the rate of Au nanoparticle-quartz physiosorption allow improvements in SERS active substrate spectral reproducibility.

A multilayer surface-enhanced Raman scattering (SERS) substrate geometry providing significantly greater SERS enhancements, longer active lifetimes, better reproducibility, and lower detection limits for trace chemical analysis than traditional SERS substrates has been developed. We have fabricated and characterized this novel class of multilayered metal film-based SERS substrates, which are capable of enhancing SERS signals over an order of magnitude relative to conventional metal film over nanostructure substrates. These multilayer enhanced metal film substrates are fabricated by repeated vapor deposition of metal films over nanometer sized structures. Different sizes of nanostructures were evaluated in order to obtain the optimal SERS enhancements. Meanwhile, different dielectric coatings were fabricated between silver layers, and SERS enhancements were evaluated for each type. Additionally, different metals, such as gold, were used to further optimize the stability and reproducibility of these novel substrates. Silver oxide layers produced at elevated temperatures were also investigated to accelerate the fabrication rate of these multilayer substrates. Finally, this paper also discusses the application of these novel multilayer substrates for trace detection of chemical agents and simulants.

Photoacoustic spectroscopy is a useful monitoring technique that is well suited for trace gas detection. The technique also possesses favorable detection characteristics when the system dimensions are scaled to a micro-system design. The objective of present work is to incorporate two strengths of the Army Research Laboratory (ARL), Quantum Cascade Laser (QCL) source development and chemical and biological sensing into a monolithic micro-electromechanical systems (MEMS) photoacoutic trace gas sensor. Past examination of a one quarter scale photoacoustic (PA) macro-cell has indicated a pathway to incorporate a photoacoustic resonance structure in a micro-mechanical platform. Initial studies involve the incorporation of a QCL source operating @ ~3.45 μm into the PA macro-cell system as a means to discern proper operational characteristics in relation to the photoacoustic cell design. Results will be presented describing beam conditioning, modulation control and wavelength selection associated with the QCL source. Some preliminary information regarding MEMS-scale designs based off of hybrid concept, involving commercially available microphone and fully fabricated MEMS photoacoustic resonator will be described.

General Dynamics ATP (GDATP) and Sionex Corporation (Sionex) are carrying out a cooperative development for a handheld chemical agent detector, being called JUNO^TM, which will have lower false positives, higher sensitivity, and improved interference rejection compared with presently available detectors. This enhanced performance is made possible by the use of a new principle of ion separation called Differential Mobility Spectrometry (DMS). The enhanced selectivity is provided by the field tunable nature of the Sionex differential mobility technology (microDMxTM) which forms the analytical heart of the JUNO system and enables fingerprinting of molecules by characterization of the ionized molecular behavior under multiple electric field conditions. This enhanced selectivity is valuable in addressing not only the traditional list of chemical warfare agents (CWA) but also the substantial list of Toxic Industrial Compounds (TICs) and Toxic Industrial Materials (TIMs) which may be released in warfare or terrorist situations. Experimental results showing the ability of the microDMx to reject interferences, detect and resolve live agents are presented. An additional breakthrough in the technology was realized by operating the device at a reduced pressure of around 0.5 atmospheres. This reduced pressure operation resulted in roughly doubling the spectrometers resolution over what has previously been reported [1]. Advances have also been made in power consumption and packaging leading to a device suitable for portable, handheld, applications. Experimental results illustrating the performance of the microDMx technology employed in JUNO are highlighted.

Detection performance of LWIR passive standoff chemical agent sensors is strongly influenced by various scene parameters, such as atmospheric conditions, temperature contrast, concentration-path length product (CL), agent absorption coefficient, and scene spectral variability. Although temperature contrast, CL, and agent absorption coefficient affect the detected signal in a predictable manner, fluctuations in background scene spectral radiance have less intuitive consequences. The spectral nature of the scene is not problematic in and of itself; instead it is spatial and temporal fluctuations in the scene spectral radiance that cannot be entirely corrected for with data processing. In addition, the consequence of such variability is a function of the spectral signature of the agent that is being detected and is thus different for each agent. To bracket the performance of background-limited (low sensor NEDN), passive standoff chemical sensors in the range of relevant conditions, assessment of real scene data is necessary¹. Currently, such data is not widely available². To begin to span the range of relevant scene conditions, we have acquired high fidelity scene spectral radiance measurements with a Telops FTIR imaging spectrometer³. We have acquired data in a variety of indoor and outdoor locations at different times of day and year. Some locations include indoor office environments, airports, urban and suburban scenes, waterways, and forest. We report agent-dependent clutter measurements for three of these backgrounds.

In developing algorithms for remote sensing of chemical and biological warfare agents, it is imperative to have a good understanding of the background radiance signal and environmental characteristics that influence detection. Factors such as thermal contrast, interferent atmospheric constituents, spatial clutter, and temporal variations should all be investigated for both the development and performance modeling of field sensors. To aid in the investigation of these topics as well as to provide data for current simulation tools, JHU/APL has constructed an automated data collection suite capable of simultaneous radiometric measurements in the longwave IR (8μm - 12μm) and midwave IR (3μm - 5μm) while also measuring a host of relevant atmospheric parameters. The primary radiometric sensor, an ABB Bomem MR304, is mounted on a pan/tilt system that is used to scan regions of interest while periodically generating calibration data. This paper describes the system design requirements, specifications of the individual components, and the overall system performance. In addition, data from field exercises are presented.

UV Raman spectroscopy is being applied to the detection of natural and man-made surfaces contaminated with chemical agents. In support of these efforts, we have measured the UV Raman signatures of chemical agents and their simulants. In addition, we have measured both the UV Raman and UV absorption cross sections of these compounds for determining their relative limits of detection. The UV Raman measurements were made using a doubled Argon ion laser operating at 248 nm. Spectra were collected on an echelle spectrograph equipped with a CCD array detector. Based on the data collected, we also discuss the suitability of currently accepted agent simulants for testing UV Raman detection instruments.

The BioAerosol Mass Spectrometry (BAMS) system is a rapidly fieldable, fully autonomous instrument that can perform correlated measurements of multiple orthogonal properties of individual aerosol particles. The BAMS front end uses optical techniques to nondestructively measure a particle's aerodynamic diameter and fluorescence properties. Fluorescence can be excited at 266nm or 355nm and is detected in two broad wavelength bands. Individual particles with appropriate size and fluorescence properties can then be analyzed more thoroughly in a dual-polarity time-of-flight mass spectrometer. Over the course of two deployments to the San Francisco International Airport, more than 6.5 million individual aerosol particles were fully analyzed by the system. Analysis of the resulting data has provided a number of important insights relevant to rapid bioaerosol detection, which are described here.

The Bio-Aerosol Mass Spectrometry (BAMS) system is an instrument used for the real time detection and identification of biological aerosols. Particles are drawn from the atmosphere directly into vacuum and tracked as they scatter light from several continuous wave lasers. After tracking, the fluorescence of individual particles is excited by a pulsed 266nm or 355nm laser. Molecules from those particles with appropriate fluorescence properties are subsequently desorbed and ionized using a pulsed 266nm laser. Resulting ions are analyzed in a dual polarity mass spectrometer. During two field deployments at the San Francisco International Airport, millions of ambient particles were analyzed and a small but significant fraction were found to have fluorescent properties similar to Bacillus spores and vegetative cells. Further separation of non-biological background particles from potential biological particles was accomplished using laser desorption/ionization mass spectrometry. This has been shown to enable some level of species differentiation in specific cases, but the creation and observation of higher mass ions is needed to enable a higher level of specificity across more species. A soft ionization technique, matrix-assisted laser desorption/ionization (MALDI) is being investigated for this purpose. MALDI is particularly well suited for mass analysis of biomolecules since it allows for the generation of molecular ions from large mass compounds that would fragment under normal irradiation. Some of the initial results from a modified BAMS system utilizing this technique are described.

A number of strategies to meet the need for a small and inexpensive biosensor that mitigates military and civilian vulnerabilities to biological weapons are currently being pursued. Among them is UV induced biological fluorescence. UV induced biofluorescence is a potentially successful strategy because it involves no chemical consumables and it is an "on-line" detection method where particles can be interrogated without impaction onto a substrate or into a liquid. Indeed, there are already existing fluorescence based sensors already in place, yet these are limited by the cost and power consumption of the laser based UV excitation sources. Fortunately, inexpensive and low power solid state UV sources arising from the Defense Advanced Research Projects Agency's (DARPA) Semiconductor UV Optical Sources (SUVOS) project have become commercially available in wavelengths capable of exciting aromatic amino acids (e.g. tryptophan) and metabolic products (e.g. NADH). The TAC-Bio Sensor is capable of exploiting either source wavelength and will ultimately include both source wavelengths within a single sensor. Initial work with the deep UV sources involves the correct optical filtering for the devices. The primary emission from both the 280 nm and 340 nm devices occurs at the design wavelength and is about 20 nm FWHM, however, there is a tail extending to the longer wavelengths that interferes with the fluorescence signal. A system of optical filters that sufficiently removes the long wavelength component from the UV source is designed and tested for the deep UV sources. Ongoing work with the sensor has confirmed that sensitivity to small biological particles is enhanced with the deeper wavelengths. When the 340 nm sources are placed in the TAC-Bio, it is capable of detecting 4 micron diameter Bacillus globigii (BG, Dugway, washed 4X) spore agglomerates. The deep UV sources show an improvement in signal to noise of 2, permitting the detection of 3 micron diameter BG agglomerates.

The development of an integrated sensor device BiSAM (Biological Sampling and Analysing Module) is presented which is designed for rapid detection of aerosol or dust particles potentially loaded with biological warfare agents. All functional steps from aerosol collection via immuno analysis to display of results are fully automated. The core component of the sensor device is an ultra sensitive rapid analyser PBA (Portable Benchtop Analyser) based on a 3 dimensional immuno filtration column of large internal area, Poly HRP marker technology and kinetic optical detection. High sensitivity despite of the short measuring time, high chemical stability of the micro column and robustness against interferents make the PBA an ideal tool for fielded sensor devices. It is especially favourable to combine the PBA with a bio collector because virtually no sample preparation is necessary. Overall, the BiSAM device is capable to detect and identify living micro organisms (bacteria, spores, viruses) as well as toxins in a measuring cycle of typically half an hour duration. In each batch up to 12 different tests can be run in parallel together with positive and negative controls to keep the false alarm rate low.

The events of September 11, 2001 represented an escalation in the means and effects of terrorist attacks and raised awareness of the vulnerability of major infrastructures such as transportation, finance, power and energy, communications, food, and water. A re-examination of the security of critical assets was initiated. Actions were taken in the United States to protect our drinking water. Anti-terrorism monitoring systems that allow us to take action before contaminated water can reach the consumer have been under development since then. This presentation will discuss the current performance of a laser-based, multi-angle light scattering (MALS) technology for continuous, real-time detection and classification of microorganisms for security applications in all drinking and process water applications inclusive of protection of major assets, potable and distributed water. Field test data for a number of waterborne pathogens will also be presented.

Surface-enhanced Raman scattering (SERS) provides rapid fingerprinting of biomaterial in a non-destructive manner. The problem of tissue fluorescence, which can overwhelm a normal Raman signal from biological samples, is largely overcome by treatment of biomaterials with colloidal silver. This work presents a study into the applicability of qualitative SER spectroscopy with principal component analysis (PCA) for the discrimination of four biological threat simulants; Bacillus globigii, Pantoea agglomerans, Brucella noetomae, and Yersinia rohdei. We also demonstrate differentiation of gram-negative and gram-positive species and as well as spores and vegetative cells of Bacillus globigii.

The Joint Biological Standoff Detection System (JBSDS) Program has developed a lidar system for detecting and discriminating biological clouds at a standoff range. The lidar typically scans near the horizon to detect a cloud and then "stares" at the cloud for a time period to ensure adequate signal-to-noise ratio (SNR) to discriminate if the cloud is biological. This paper proposes an alternative to the scan-and-stare approach; i.e., to scan only. The analysis results of lidar data obtained from field tests conducted in 2004 at Dugway Proving Ground (DPG) in Utah suggest that scan-only operations without staring would improve SNR for detection and discrimination and provide operational advantages.

Most organic and many inorganic materials absorb strongly in specific wavelength ranges in the deep UV between about 220nm and 300nm. Excitation within these absorption bands results in native fluorescence emission. Each compound or composite material, such as a bacterial spore, has a unique excitation-emission fingerprint that can be used to provide information about the material. The sensitivity and specificity with which these materials can be detected and identified depends on the excitation wavelength and the number and location of observation wavelengths. We will present data on our deep ultraviolet Targeted Ultraviolet Chemical Sensors that demonstrate the sensitivity and specificity of the sensors. In particular, we will demonstrate the ability to quantitatively differentiate a wide range of biochemical agent targets against a wide range of background materials. We will describe the relationship between spectral resolution and specificity in target identification, as well as simple, fast, algorithms to identify materials. Hand-held, battery operated instruments using a deep UV laser and multi-band detection have been developed and deployed on missions to the Antarctic, the Arctic, and the deep ocean with the capability of detecting a single bacterial spore and to differentiate a wide range of organic and biological compounds.

Colloidal crystal hydrogels with tunable optical properties are useful materials to construct thermoresponsive tunable photonic crystals¹ and optical switch that responds in nanoseconds². We report here instead, the fabrication of optically tunable colloidal crystal hydrogel material that can also behave as biosensors, responding to the presence of antigens or antibodies. Optically tunable hydrogel material was fabricated from submicron sized, thermoresponsive poly-N-isopropyl acrylamide (pNIPAM) particles and pNIPAM-co-acrylic acid (pNIPAM-AA) copolymer particles. The color of this translucent hydrogel material can be tuned from blue to green to red by changing the concentration of pNIPAM particles. PNIPAM-AA with covalently linked protein A (pNIPAM-AA-protein A) formed light blue colored hydrogel material that respond to the presence of anti-Protein A antibodies to form an opaque, crosslinked networked hydrogel particles aggregate which could be detected visually. A similar response was observed when pNIPAM-AA particles, with attached anti-protein A antibodies (pNIPAM-AA-Anti Protein A), were treated with protein A. The lowest limit of detection, based on visual observation of aggregate, of anti-protein A antibodies was 0.5μg or a concentration of 1μg/μl, whereas for protein A the lowest limit of detection was 0.25μg or a concentration of 0.5μg/μl. We expect this optically tunable hydrogel biosensor material to find applications as photonic crystals that respond to the presence of microbial antigens or human antibodies, to control laser transmission and the optically switching of devices.

Apoptosis is an evolutionary conserved cellular process that plays an important role during development, but it is also involved in tissue homeostasis and in many diseases. To study the characteristics of suicide gene system of the herpes simplex virus thymidine kinase (HSV-tk) gene in tumor cells and explore the apoptosis phenomena in this system and its effect on the human adenoid cystic carcinoma line ACC-M cell, we detected apoptosis of CD3- (ECFP-CRS-DsRed) and TK-GFP-expressing ACC-M (ACC-M-TK-GFP-CD3) cells induced by acyclovir (ACV) using fluorescence resonance energy transfer (FRET) technique. CD3 is a FRET-based indicator for activity of caspase-3, which is composed of an enhanced cyan fluorescent protein, a caspase-3 sensitive linker, and a red fluorescent protein from Discosoma with efficient maturation property. FRET from ECFP to DsRed could be detected in normal ACC-M-TK-GFP-CD3 cells, and the FRET efficient was remarkably decreased and then disappeared during the cells apoptosis induced by ACV. It was due to the activated caspase-3 cleaved the CD3 fusion protein. In this study, the results suggested that the ACV-induced apoptosis of ACC-M-TK-GFP-CD3 cells was through caspase-3 pathway.

The thickness shear mode (TSM) resonator attached with living cells has been shown to be an effective functional biosensing device to monitor the process of cell adhesion to a surface. In this study, we first monitored the dynamic process of cell attachment and spreading as a function of cell seeding densities. Based on the steady state of cell adhesion to the substrate, a multilayer sensor structure model including a quartz substrate, a cell-substrate interfacial layer and a cell layer was constructed. The thickness of cell-substrate interfacial layer and the viscoelastic properties of human skin fibroblasts (HSF) were then determined by fitting experimental results with the theoretical model. It has been obtained that the thickness of the cell-substrate interfacial layer is 60-80 nm, and the elastic module and viscosity of cell layers are about 13 KPa and 3-4 mPa's respectively. These results are in a good agreement with those measured by other techniques, such as magnetic bead microrheometry, atomic force microscopy (AFM) and Surface Plasmon Resonance Microscopy (SPRM). In addition, knowing that the actin cytoskeleton is important for the mechanical properties of living cells, we investigated the motional resistance change caused by the disruption of actin cytoskeleton induced by fungal toxin Cytochalasin D in the human skin fibroblasts. The results indeed indicate the direct correlation between resistance changes and the disruption of actin cytoskeleton, which are again consistent with the results observed by fluorescence images.

Raman and surface-enhanced Raman spectroscopy (SERS) studies of bacteria have reported a wide range of vibrational mode assignments associated with biological material. We present Raman and SER spectra of the amino acids phenylalanine, tyrosine, tryptophan, glutamine, cysteine, alanine, proline, methionine, asparagine, threonine, valine, glycine, serine, leucine, isoleucine, aspartic acid and glutamic acid and the nucleic acid bases adenosine, guanosine, thymidine, and uridine to better characterize biological vibrational mode assignments for bacterial target identification. We also report spectra of the bacteria Bacillus globigii, Pantoea agglomerans, and Yersinia rhodei along with band assignments determined from the reference spectra obtained.

This paper presents an algorithm, based on principal component analysis for the detection of biological threats using General Dynamics Canada's 4WARN Sentry 3000 biodetection system. The proposed method employs a statistical method for estimating background biological activity so as to make the algorithm adaptive to varying background situations. The method attempts to characterize the pattern of change that occurs in the fluorescent particle counts distribution and uses the information to suppress false-alarms. The performance of the method was evaluated using a total of 68 tests including 51 releases of Bacillus Globigii (BG), six releases of BG in the presence of obscurants, six releases of obscurants only, and five releases of ovalbumin at the Ambient Breeze Tunnel Test facility, Battelle, OH. The peak one-minute average concentration of BG used in the tests ranged from 10 - 65 Agent Containing Particles per Liter of Air (ACPLA). The obscurants used in the tests included diesel smoke, white grenade smoke, and salt solution. The method successfully detected BG at a sensitivity of 10 ACPLA and resulted in an overall probability of detection of 94% for BG without generating any false-alarms for obscurants at a detection threshold of 0.6 on a scale of 0 to 1. Also, the method successfully detected BG in the presence of diesel smoke and salt water fumes. The system successfully responded to all the five ovalbumin releases with noticeable trends in algorithm output and alarmed for two releases at the selected detection threshold.

A fractal analysis is presented for the detection of pathogens such as Franscisela tularensis, and Yersinia pestis (the bacterium that causes plague) using a CANARY (cellular analysis and notification of antigens risks and yields) biosensor (Rider et al., 2003). In general, the binding and dissociation rate coefficients may be adequately described by either a single- or a dual-fractal analysis. An attempt is made to relate the binding rate coefficient to the degree of heterogeneity (fractal dimension value) present on the biosensor surface. Binding and dissociation rate coefficient values obtained are presented. The kinetics aspects along with the affinity values presented are of interest, and should along with the rate coefficients presented for the binding and the dissociation phase be of significant interest in help designing better biosensors for an application area that is bound to gain increasing importance in the future.

A portable and extensible multisensor testbed for long-term multi-point aerosol background data collections has been developed. The primary objective of the testbed is to support investigations related to the information fusion, machine-intelligence based CB decision support architectrure, now under development at MIT Lincoln Laboratory. This paper describes major design features of the testbed and concentrates on the analysis and the results of multiple indoor data collections. Specifically, two deployments of the testbed for extensive indoor data collection campaigns are described. The indoor background characterization results are presented.

A model is developed which consists of a horizontal atmospheric path divided into N+2 regions. The observer (sensor) is located in region 0, the cloud in an arbitrary region labeled ℓ , and the terminating object in region N+1. A simple expression is obtained for the cloud transmittance in a region labeled ℓwhich requires two observations: the radiance with no cloud present (background) and the radiance in region ℓ(some distance from the sensor). The analysis is then extended to a situation where two clouds (vapor or aerosol) are present. The scatter properties of the aerosol cloud must now be considered. The two clouds are now located in sections labeled ℓ and k. In order to determine the transmittance of each cloud, we shall show that three measurements are required. The resulting model enables one to determine the transmittance through each cloud from measured radiances.

Passive radiometric remote sensing of the lower atmospheric is an attractive alternative to conventional techniques such as balloonsondes and active radiometric sensing with lidars. Measurements can be made with high sampling frequency, with complete safety and covertness, and with no loss in performance during day or night operation. A recently developed inversion algorithm generates vertical profiles of temperature and water vapor partial pressure from midwave infrared downwelling radiance to a ground based spectroradiometer. The technique is fast allowing real-time profile computation. Profiles up to 1 km altitude can be obtained for temperature and up to 0.5 to 1 km altitude for water vapor depending on the level of relative humidity. As with any new technique a verification and validation process must be performed to achieve acceptance. The verification is based on the fact that sound physical principles are employed with accepted databases (HITRAN spectroscopic database). The validation is based on comparisons with balloonsonde, MET tower, and Raman lidar measurements, and comparisons with MODTRAN 4 calculations of downwelling radiance using known profiles. An introduction to the inversion algorithm emphasizing verification is presented. This is followed by a discussion of the results from the comparison study.

The Army is currently developing acoustic sensor systems that will provide extended range surveillance, detection, and identification for force protection and tactical security. A network of such sensors remotely deployed in conjunction with a central processing node (or gateway) will provide early warning and assessment of enemy threats, near real-time situational awareness to commanders, and may reduce potential hazards to the soldier. In contrast, the current detection of chemical/biological (CB) agents expelled into a battlefield environment is limited to the response of chemical sensors that must be located within close proximity to the CB agent. Since chemical sensors detect hazardous agents through contact, the sensor range to an airburst is the key-limiting factor in identifying a potential CB weapon attack. The associated sensor reporting latencies must be minimized to give sufficient preparation time to field commanders, who must assess if an attack is about to occur, has occurred, or if occurred, the type of agent that soldiers might be exposed to. The long-range propagation of acoustic blast waves from heavy artillery blasts, which are typical in a battlefield environment, introduces a feature for using acoustics and other sensor suite technologies for the early detection and identification of CB threats. Employing disparate sensor technologies implies that warning of a potential CB attack can be provided to the solider more rapidly and from a safer distance when compared to current conventional methods. Distinct characteristics arise within the different airburst signatures because High Explosive (HE) warheads emphasize concussive and shrapnel effects, while chemical/biological warheads are designed to disperse their contents over immense areas, therefore utilizing a slower burning, less intensive explosion to mix and distribute their contents. Highly reliable discrimination (100%) has been demonstrated at the Portable Area Warning Surveillance System (PAWSS) Limited Objective Experiment (LOE) conducted by Joint Project Manager for Nuclear Biological Contamination Avoidance (JPM NBC CA) and a matrixed team from Edgewood Chemical and Biological Center (ECBC) at ranges exceeding 3km. The details of the fieldtest experiment and real-time implementation/integration of the standalone acoustic sensor system are discussed herein.

This article presents a contactless, remote sensing Salmonella typhimurium sensor based on the principle of magnetostriction. Magnetostrictive materials have been used widely for various types of sensor systems. In this work, the use of a magnetostrictive material for the detection of Salmonella typhimurium has been established. The mass of the bacteria attached to the sensor causes changes in the resonance frequency of the sensor. Filamentous bacteriophage was used as a probe order to ensure specific and selective binding of the bacteria onto the sensor surface. Thus changes in response of the sensor due to the mass added onto the sensor caused by specific attachment of bacteria can be monitored in absence of any contact to the sensor. The response of the sensor due to increasing concentrations (from 5x10¹ to 5x10⁸ cfu/ml) of the bacteria was studied. A reduction in the physical dimensions enhances the sensitivity of these sensors and hence different dimensions of the sensor ribbons were studied. For a 2mm x 0.1mm x 0.02mm the detection limit was observed to be of the order of 10⁴ cfu/mL and for a sensor of 1mm x 0.2mm x 0.02mm a reduced detection limit of 10³ cfu/mL was achieved.

Grating-based optical waveguide devices offer label-free biodetection capabilities relying on optical response to adsorption of analytes and corresponding changes of refractive index. Various configurations of this measurement approach were explored with the goal of obtaining a miniaturized system. In particular, we evaluated the use of a two-dimensional grating coupler both experimentally and theoretically. Design criteria for optimized sensing structures are presented.

A zeolite-fiber integrated chemical sensor was developed for in situ point detection of chemical warfare agents. The sensor was made by fine-polishing the MFI polycrystalline zeolite thin film synthesized on the endface of the single mode optical fiber. The sensor device operates by measuring the optical thickness changes of the zeolite thin film caused by the adsorption of analytes into the zeolite channels. The sensor was demonstrated for sensitive detection of toluene and dimethyl methylphosphonate (DMMP).

The measurement of binding forces between specific antigen-antibody pairs presents a powerful tool for sensitive detection with applications in medical diagnostics, bioagent sensing, and environmental monitoring. The ability to detect single molecular binding events with an AFM, using the technique of dynamic force spectroscopy, is a known capability; however, reliance on traditional AFM architectures limits the use of this method to laboratory environments. The approach presented here uses active piezoelectric microcantilevers, providing electronic output for detection of molecular binding. Functionalization of this device with specific antibodies provides a platform for a stand-alone detection device. As the microcantilever can be operated as both a sensor and an actuator, the detection scheme includes actuating the cantilever to present an antibody bound to the cantilever tip to a second antibody bound to a fixed substrate. If a target antigen is present in solution, the cantilever detects the mechanical strain and vibrational response created by the binding force and subsequent rupture of the antigen-antibody pair. This detection strategy distinguishes this work from resonance-based cantilever devices that respond to changes in cantilever mass based on adsorption of numerous antigen molecules. In this research, piezoelectric microcantilevers were fabricated, and initial results were obtained demonstrating transient response caused by rupture of nonspecific adhesion forces in air and water environments. Analytical results are also presented relating geometrical parameters with sensor performance.

A fractal analysis is presented for the binding and dissociation of different heart-related compounds in solution to receptors immobilized on biosensor surfaces. The data analyzed include LCAT (lecithin cholesterol acyl transferase) concentrations in solution to egg-white apoA-I rHDL immobilized on a biosensor chip surface.¹ Single- and dual- fractal models were employed to fit the data. Values of the binding and the dissociation rate coefficient(s), affinity values, and the fractal dimensions were obtained from the regression analysis provided by Corel Quattro Pro 8.0 (Corel Corporation Limited).² The binding rate coefficients are quite sensitive to the degree of heterogeneity on the sensor chip surface. Predictive equations are developed for the binding rate coefficient as a function of the degree of heterogeneity present on the sensor chip surface and on the LCAT concentration in solution, and for the affinity as a function of the ratio of fractal dimensions present in the binding and the dissociation phases. The analysis presented provided physical insights into these analyte-receptor reactions occurring on different biosensor surfaces.

The Arousal Meter (AM) is a gauge derived from heart-rate variability designed to measure autonomic arousal. The purpose of this study was to determine the extent to which the AM could differentiate state shifts in arousal that occurred in response to workload changes. A state shift was considered to be a statistically significant change in the level of arousal relative to the level of workload. Participants (n = 56) were engaged in a dual-task paradigm continuously for 31 minutes that consisted of one of two primary tasks - one high workload (shooting game) and one low workload (surveillance task) - paired with a secondary task (mental arithmetic). The experimental paradigm shifted from high workload (shooting) to low workload (surveillance) for time intervals of 30 seconds, 1 minute, 2 minutes, 4 minutes, and 8 minutes. Participants experienced each time interval twice corresponding to each level of workload. Arousal was averaged across each time interval for each workload level. Means between the low and high workload conditions for the 2, 4, and 8 minute intervals were significantly different in the expected direction (t = 2.20, p < .05; t = 3.82, p < .01; t = 5.85, p < .01). These results indicate that the gauge resolution is approximately 2 minutes. Hence, it appears that the AM is able to differentiate tasks from one another if the tasks are greater than 2 minutes in duration. Results are promising considering the type of tasks the gauge is likely to be used with are longer in nature. Possible applications include mitigation of task characteristics to optimize arousal and subsequently performance.

Quantifying the accuracy of physiological data measured by a Vital Signs Detection System (VSDS) plays a key role in making trustworthy decisions about the physiological status of a soldier. We developed an algorithm to report VSDSmeasured heart and respiratory rates and their associated confidence levels. Heart and respiratory rates were measured about every 2 seconds for about 4 hours, while subjects engaged in low (e.g., sitting), medium (e.g., sit-ups), and high intensity (e.g., running) activities. The mean heart and median respiratory rates are calculated every 15 seconds by an in-house developed algorithm, and associated confidence levels for each variable are estimated simultaneously using a fuzzy-logic-based algorithm. Inputs into the algorithm are features that represent two types of information; the quality of each variable, and the relationship between the variables. Faulty data points are separated from good measures by setting a threshold. When data with pre-classified faults are tested with the confidence level threshold set at 0.5, the sensitivity and specificity of the algorithm for heart rate are 91% and 97%, respectively. For respiratory rate, because of the intrinsically noisy property of the data, the sensitivity and specificity are 87% and 93%, respectively. These preliminary results demonstrate that the fuzzy logic algorithm can accurately qualify heart and respiratory rates measured by a VSDS.

Geospatial Intelligence Analysts are currently faced with an enormous volume of imagery, only a fraction of which can be processed or reviewed in a timely operational manner. Computer-based target detection efforts have failed to yield the speed, flexibility and accuracy of the human visual system. Rather than focus solely on artificial systems, we hypothesize that the human visual system is still the best target detection apparatus currently in use, and with the addition of neuroscience-based measurement capabilities it can surpass the throughput of the unaided human severalfold. Using electroencephalography (EEG), Thorpe et al¹ described a fast signal in the brain associated with the early detection of targets in static imagery using a Rapid Serial Visual Presentation (RSVP) paradigm. This finding suggests that it may be possible to extract target detection signals from complex imagery in real time utilizing non-invasive neurophysiological assessment tools. To transform this phenomenon into a capability for defense applications, the Defense Advanced Research Projects Agency (DARPA) currently is sponsoring an effort titled Neurotechnology for Intelligence Analysts (NIA). The vision of the NIA program is to revolutionize the way that analysts handle intelligence imagery, increasing both the throughput of imagery to the analyst and overall accuracy of the assessments. Successful development of a neurobiologically-based image triage system will enable image analysts to train more effectively and process imagery with greater speed and precision.

We simulate a notional Navy SEAL rebreather diver on an extended mission using Model Predictive Control (MPC) theory. A mathematical framework for enabling physiological HUMS (Health Usage Management Systems) is shown. A rebreather simulation is used to derive MPC baseline Data Models of diver status by converting the simulation first into differential equations and then into lookup tables (LUT). When abnormal readings are indicated, sensor data from the diver is published to the ad hoc network, enabling intermittent upload. Mission success confidence is updated and determined during the mission. A novel method of converting MPC Data Models into lookup tables worn by the diver is given.