We report the development of a compact point-detection fluorescence spectroscopy system and two data analysis methods to quantify the intrinsic fluorescence redox ratio and diagnose brain cancer in an orthotopic brain tumor rat model. Our system employs one compact cw diode laser (407 nm) to excite two primary endogenous fluorophores, reduced nicotinamide adenine dinucleotide, and flavin adenine dinucleotide. The spectra were first analyzed using a spectral filtering modulation method developed previously to derive the intrinsic fluorescence redox ratio, which has the advantages of insensitivty to optical coupling and rapid data acquisition and analysis. This method represents a convenient and rapid alternative for achieving intrinsic fluorescence-based redox measurements as compared to those complicated model-based methods. It is worth noting that the method can also extract total hemoglobin concentration at the same time but only if the emission path length of fluorescence light, which depends on the illumination and collection geometry of the optical probe, is long enough so that the effect of absorption on fluorescence intensity due to hemoglobin is significant. Then a multivariate method was used to statistically classify normal tissues and tumors. Although the first method offers quantitative tissue metabolism information, the second method provides high overall classification accuracy. The two methods provide complementary capabilities for understanding cancer development and noninvasively diagnosing brain cancer. The results of our study suggest that this portable system can be potentially used to demarcate the elusive boundary between a brain tumor and the surrounding normal tissue during surgical resection.
This paper describes the use of plasmonics-based nanoprobes for detection of multidrug-resistant tuberculosis gene. The plasmonics nanoprobe is composed of a silver nanoparticle pre-coated with a stem-loop DNA probe that is tagged with a Raman label at one end of the stem region, while the other end of the probe is covalently conjugated to the nanoparticle via a thiol-silver bond. The loop region is designed to detect a specific target gene sequence. In the absence of target, the Raman label is in close proximity to the metal surface, resulting in an intense SERS signal upon laser excitation. In the presence of the target DNA sequence, hybridization between the target and probe disrupts the stem-loop configuration, separating the Raman label from the metal surface and quenching the SERS signal. In this study, we successfully demonstrated for the first time the feasibility of using plasmonics nanoprobes for the detection of multidrug-resistant tuberculosis gene.
This paper describes the development of fiber optic sensor probes and planar substrates containing patterned
nanostructures such as nanoholes in gold films, as well as gold nanoparticles, nano-pillars, nanorods, and nano-islands.
Several methods of producing gold nanofeatures on fiber tips and planar substrates were investigated such as annealing
of thin gold films and focused ion beam (FIB) milling. A Hitachi FB-2100 FIB milling machine with a gallium ion
source was employed to form the nanoparticles from 20-100 nm gold films deposited on the fiber tip. Nano-engineered
gold features were also formed by coating planar substrates and fiber tips with thin gold films (4-10 nm) and annealing
these thin films. Excitation of surface plasmons in gold nanostructures leads to substantial enhancement in the Raman
scattering signal obtained from molecules attached to the nanostructure surface. In this work, a comparison was made
between the SERS signals obtained from the gold substrates developed by employing the different procedures mentioned
above. Fiber samples and planar substrates with these nanostructures were coated with SERS active dyes such as pmercaptobenzoic
acid (pMBA) and cresyl fast violet (CFV). It was observed that the SERS signal obtained from these
gold nanofeatures was much higher than that obtained from a continuous gold film and that the SERS enhancement was
shape and size dependent.
A critical aspect of the use of nanoprobes for intracellular studies in chemical and biological sensing involves a fundamental understanding of their uptake and trajectory in cells. In this study, we describe experiments using surface-enhanced Raman scattering (SERS) spectroscopy and mapping to track cellular uptake of plasmonics-active labeled nanoparticles. Three different Raman-active labels with positive, negative, and neutral charges were conjugated to silver colloidal nanoparticles with the aim of spatially and temporally profiling intracellular delivery and tracking of nanoprobes during uptake in single mammalian cells. 1-D Raman spectra and 2-D Raman mapping are used to identify and locate the probes via their SERS signal intensities. Because Raman spectroscopy is very specific for identification of chemical and molecular signatures, the development of functionalized plasmonics-active nanoprobes capable of exploring intracellular spaces and processes has the ability to provide specific information on the effects of biological and chemical pollutants in the intracellular environment. The results indicate that this technique will allow study of when, where, and how these substances affect cells and living organisms.
The effect of layer-by-layer electrostatic self-assembly processing parameters on resulting thin-film characteristics was determined in order to optimize the thin-film structure for optical biosensing. The use of long-period fiber gratings (LPFGs) requires careful control of the refractive index of the surrounding medium, which can be achieved by tuning the refractive index and thickness of an optical thin film deposited directly on the optical fiber surface. The high-sensitivity LPFG refractometry range falls in a window just below the effective index of the relevant cladding mode. We investigated five factors at two levels using variable-angle spectroscopic ellipsometry and analysis of variance. Salt concentration and pH were found to be critical parameters for refractive index control.
A novel Intrinsic Fabry-Perot fiber-optic sensor is presented in this paper. The sensors were made through two simple steps: wet chemical etch and fusion splice. Micro air-gaps were generated inside the fibers and functioned as reflective mirrors. This procedure not only provides a simple and cost effective technology for fabricating intrinsic Fabry-Perot Interferometric (IFPI) fiber sensors, but also provides two possible IFPI structures. Both of the fiber cavity between the air-gaps or the air-gap and cleaved fiber end can be used as sensing elements. With these two structures, this sensor can be used to measure the temperature, strain, pressure, refractive index of chemicals and the thin film thickness by itself. Multi-point measurements can also be achieved by multiplexing. Furthermore, it also can be multiplexed with other sensors such as Long Period Gratings (LPG) to provide compensations for other perturbation sensing. Theoretical and experimental studies of two sensor structures are described. Experimental results show that high resolution and high sensitivity can be obtained with appropriate signal processing.
Detection and remediation of unexploded ordnance (UXO) represents a major challenge. The detection problem is exacerbated by the fact that on sites contaminated with UXO, extensive surface and sub-surface clutter and shrapnel is also present. Traditional methods used for UXO remediation have difficulty distinguishing buried UXO from these anthropic clutter items as well as from naturally occurring magnetic geologic noise, and thus incur prohibitively high false alarm rates. In this research, model-based statistical signal processing techniques are applied to field data from magnetometer and electromagnetic induction (EMI) sensors in order to determine to what degree such an approach results in false alarm mitigation. Features of the target signatures are extracted by inverting the measured sensor data associated with an anomaly using the associated physical, or forward, model. The statistical uncertainty in the feature space is explicitly treated using statistical processors, including generalized likelihood ratio tests and support vector machines, to discriminate targets from clutter. This approach has been evaluated on data collected in a recent field trial that was performed at JPG. Results are presented for one area in which ground truth was known, and for two others in which the ground truth was not known. Substantial reduction of the false alarm rate is achieved for two different platforms, the GEM-3 and the MTADS system. For example, using data from the GEM-3 in one area, the number of false targets was reduced from 181 to 20 with 100% detection of all UXO objects.
Unexploded ordnance (UXO) discrimination is investigated using the wide band electromagnetic induction (EMI) data. The main focus of this paper is on the practical phenomenological modeling for the induced wideband EMI sensor response from different targets. Modeling for the sensor response provides feature vectors to UXO classification algorithms, and it has been proven to be very important for the improvement of the overall remediation performance. A parametric model is discussed with the emphsis on multiple offset dipole centers. The measured data from several actual targets are utilized to validate the model and to demonstrate the advantage of multiple offset dipole centers vs. single dipole center. We further illustrate the application of the model with multiple dipoles in target classifications by numerical examples. We show that the classification performance might be improved substantially. Finally, we state that the nonlinear EMI dipole model can be decomposed into a linear model. Thus it benefits from the rich literature of linear algebra and signal processing. To report one of our efforts, two methods are proposed to detect the number of dipoles blindly by the information theoretic criteria, namely the Akaike information criterion (AIC) and the minimum description length (MDL). The methods are testified using measured EMI data.
With a single-crystal sapphire disk as the sensing element, a broadband polarimetric interferometer (BPI) based high temperature sensor is presented. The state of polarization of the broadband incident light is modulated by the birefringence of the sapphire disk and becomes a wavelength-encoded signal, which is detected by an optical spectrum analyzer (OSA). From the detected optical spectrum, an internally developed algorithm is employed to measure the difference of optical paths between two orthogonal linearly polarized lights in the sapphire disk, which is uniquely determined by environment temperature. A wide dynamic measurement range (from room temperature up to 1600 degrees Celsius) with a resolution less than 1 degree(s)C and accuracy 0.26% full scale is achieved. The great advantages of this sensor are its simplicity and long-term stability in the harsh environment.
Traditional algorithms for UXO remediation experience severe difficulties distinguishing buried targets from anthropic clutter, and in most cases UXO items are found among extensive surface clutter and shrapnel from ordnance operations. These problems render site mediation a very slow, labor intensive, and efficient process. While sensors have improved significantly over the past several years in their ability to detect conducting and/or permeable targets, reduction of the false alarm rate has proven to be a significantly more challenging problem. Our work has focused on the development of optimal signal processing algorithms that rigorously incorporate the underlying physics characteristics of the sensor and the anticipated UXO target in order to address the false alarm issue. In this paper, we describe several techniques for discriminating targets from clutter that have been applied to data obtained with the Multi-sensor Towed Array Detection System (MTADS) that has been developed by the Naval Research Laboratory. MTADS includes both EMI and magnetometer sensors. We describe a variety of signal processing techniques which incorporate physics-based models that have been applied to the data measured by MTADS during field demonstrations. We will compare and contrast the performance of the various algorithms as well as discussing tradeoffs, such as training requirements. The result of this analysis quantify the utility of fusing magnetometer and EMI dat. For example, the JPG-IV test, at the False Positive level obtained by NRL, one of our algorithms achieved a 13 percent improvement in True Positive level over the algorithm traditionally used for processing MTADS data.
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