Real-time detection of DNA interactions with long-period fiber-grating-based biosensor

Using an optical biosensor based on a dual-peak long-period fiber grating, we have demonstrated the detection of interactions between biomolecules in real time. Silanization of the grating surface was successfully realized for the covalent immobilization of probe DNA, which was subsequently hybridized with the complementary target DNA sequence. It is interesting to note that the DNA biosensor was reusable after being stripped off the hybridized target DNA from the grating surface, demonstrating a function of multiple usability.

DNA hybridisation biosensor based on dual-peak long-period grating

Using an optical biosensor based on dual-peak long-period fibre grating, we demonstrate the detection of interactions between DNA biomolecules in real-time, showing a high sensitivity and reusability function.

Optical fibre-based detection of DNA hybridization

A dual-peak LPFG (long-period fibre grating), inscribed in an optical fibre, has been employed to sense DNA hybridization in real time, over a 1 h period. One strand of the DNA was immobilized on the fibre, while the other was free in solution. After hybridization, the fibre was stripped and repeated detection of hybridization was achieved, so demonstrating reusability of the device. Neither strand of DNA was fluorescently or otherwise labelled. The present paper will provide an overview of our early-stage experimental data and methodology, examine the potential of fibre gratings for use as biosensors to monitor both nucleic acid and other biomolecular interactions and then give a summary of the theory and fabrication of fibre gratings from a biological standpoint. Finally, the potential of improving signal strength and possible future directions of fibre grating biosensors will be addressed.

Reporter ion-based mass spectrometry approaches for the detection of non-enzymatic protein modifications in biological samples

Development of mass spectrometry techniques to detect protein oxidation, which contributes to signalling and inflammation, is important. Label-free approaches have the advantage of reduced sample manipulation, but are challenging in complex samples owing to undirected analysis of large data sets using statistical search engines. To identify oxidised proteins in biological samples, we previously developed a targeted approach involving precursor ion scanning for diagnostic MS3 ions from oxidised residues. Here, we tested this approach for other oxidations, and compared it with an alternative approach involving the use of extracted ion chromatograms (XICs) generated from high-resolution MSMS data using very narrow mass windows. This accurate mass XIC data methodology was effective at identifying nitrotyrosine, chlorotyrosine, and oxidative deamination of lysine, and for tyrosine oxidations highlighted more modified peptide species than precursor ion scanning or statistical database searches. Although some false positive peaks still occurred in the XICs, these could be identified by comparative assessment of the peak intensities. The method has the advantage that a number of different modifications can be analysed simultaneously in a single LC-MSMS run. This article is part of a Special Issue entitled: Posttranslational Protein modifications in biology and Medicine. Biological significance: The use of accurate mass extracted product ion chromatograms to detect oxidised peptides could improve the identification of oxidatively damaged proteins in inflammatory conditions. © 2013 Elsevier B.V.

DNA hybridisation biosensor based on dual-peak long-period grating

Using an optical biosensor based on dual-peak long-period fibre grating, we demonstrate the detection of interactions between DNA biomolecules in real-time, showing a high sensitivity and reusability function.

Experiments in Detection and Correction of Russian Malapropisms by Means of the WEB

Malapropism is a semantic error that is hardly detectable because it usually retains syntactical links between words in the sentence but replaces one content word by a similar word with quite different meaning. A method of automatic detection of malapropisms is described, based on Web statistics and a specially defined Semantic Compatibility Index (SCI). For correction of the detected errors, special dictionaries and heuristic rules are proposed, which retains only a few highly SCI-ranked correction candidates for the user’s selection. Experiments on Web-assisted detection and correction of Russian malapropisms are reported, demonstrating efficacy of the described method.

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

Real-time detection of DNA interactions with long-period fiber-grating-based biosensor

Using an optical biosensor based on a dual-peak long-period fiber grating, we have demonstrated the detection of interactions between biomolecules in real time. Silanization of the grating surface was successfully realized for the covalent immobilization of probe DNA, which was subsequently hybridized with the complementary target DNA sequence. It is interesting to note that the DNA biosensor was reusable after being stripped off the hybridized target DNA from the grating surface, demonstrating a function of multiple usability. © 2007 Optical Society of America.

Reductions of peak-to-average power ratio and optical beat interference in cost-effective OFDMA-PONs

The peak-to-average power ratio (PAPR) and optical beat interference (OBI) effects are examined thoroughly in orthogonal frequency-division multiplexing access (OFDMA)-passive optical networks (PONs) at a signal bit rate up to ∼ 20 Gb/s per channel using cost-effective intensity-modulation and direct-detection (IM/DD). Single-channel OOFDM and upstream multichannel OFDM-PONs are investigated for up to six users. A number of techniques for mitigating the PAPR and OBI effects are presented and evaluated including adaptive-loading algorithms such as bit/power-loading, clipping for PAPR reduction, and thermal detuning (TD) for the OBI suppression. It is shown that the bit-loading algorithm is a very efficient PAPR reduction technique by reducing it at about 1.2 dB over 100 Km of transmission. It is also revealed that the optimum method for suppressing the OBI is the TD + bit-loading. For a targeted BER of 1 × 10-3, the minimum allowed channel spacing is 11 GHz when employing six users. © 2013 Springer Science+Business Media New York.

Graphene-based D-shaped polymer FBG for highly sensitive erythrocyte detection

Graphene-based silica fiber-optic sensors, with high sensitivity, fast response, and low cost, have shown great promise for gas sensing applications. In this letter, by covering a monolayer of p-doped graphene on a D-shaped microstructured polymer fiber Bragg grating (FBG), we propose and demonstrate a novel biochemical probe sensor, the graphene-based D-shaped polymer FBG (GDPFBG). Due to the graphene-based surface evanescent field enhancement, this sensor shows high sensitivity to detect surrounding biochemical parameters. By monitoring the Bragg peak locations of the GDPFBG online, human erythrocyte (red blood cell) solutions with different cellular concentrations ranging from 0 to 104 ppm were detected precisely, with the maximum resolution of sub-ppm. Such a sensor is structurally compact, is clinically acceptable, and provides good recoverability, offering a state-of-the-art polymer-fiber-based sensing platform for highly sensitive in situ and in vivo cell detection applications.

Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^

Headspace analysis of smokeless powders: Development of mass calibration methods using microdrop printing for chromatographic and ion mobility spectrometric detection

Smokeless powder additives are usually detected by their extraction from post-blast residues or unburned powder particles followed by analysis using chromatographic techniques. This work presents the first comprehensive study of the detection of the volatile and semi-volatile additives of smokeless powders using solid phase microextraction (SPME) as a sampling and pre-concentration technique. Seventy smokeless powders were studied using laboratory based chromatography techniques and a field deployable ion mobility spectrometer (IMS). The detection of diphenylamine, ethyl and methyl centralite, 2,4-dinitrotoluene, diethyl and dibutyl phthalate by IMS to associate the presence of these compounds to smokeless powders is also reported for the first time. A previously reported SPME-IMS analytical approach facilitates rapid sub-nanogram detection of the vapor phase components of smokeless powders. A mass calibration procedure for the analytical techniques used in this study was developed. Precise and accurate mass delivery of analytes in picoliter volumes was achieved using a drop-on-demand inkjet printing method. Absolute mass detection limits determined using this method for the various analytes of interest ranged between 0.03–0.8 ng for the GC-MS and between 0.03–2 ng for the IMS. Mass response graphs generated for different detection techniques help in the determination of mass extracted from the headspace of each smokeless powder. The analyte mass present in the vapor phase was sufficient for a SPME fiber to extract most analytes at amounts above the detection limits of both chromatographic techniques and the ion mobility spectrometer. Analysis of the large number of smokeless powders revealed that diphenylamine was present in the headspace of 96% of the powders. Ethyl centralite was detected in 47% of the powders and 8% of the powders had methyl centralite available for detection from the headspace sampling of the powders by SPME. Nitroglycerin was the dominant peak present in the headspace of the double-based powders. 2,4-dinitrotoluene which is another important headspace component was detected in 44% of the powders. The powders therefore have more than one headspace component and the detection of a combination of these compounds is achievable by SPME-IMS leading to an association to the presence of smokeless powders.

Peak -to -average power ratio reduced parallel interference cancellation Multicarrier-Code Division Multiple Access system with anti -interference property

Orthogonal Frequency-Division Multiplexing (OFDM) has been proved to be a promising technology that enables the transmission of higher data rate. Multicarrier Code-Division Multiple Access (MC-CDMA) is a transmission technique which combines the advantages of both OFDM and Code-Division Multiplexing Access (CDMA), so as to allow high transmission rates over severe time-dispersive multi-path channels without the need of a complex receiver implementation. Also MC-CDMA exploits frequency diversity via the different subcarriers, and therefore allows the high code rates systems to achieve good Bit Error Rate (BER) performances. Furthermore, the spreading in the frequency domain makes the time synchronization requirement much lower than traditional direct sequence CDMA schemes. There are still some problems when we use MC-CDMA. One is the high Peak-to-Average Power Ratio (PAPR) of the transmit signal. High PAPR leads to nonlinear distortion of the amplifier and results in inter-carrier self-interference plus out-of-band radiation. On the other hand, suppressing the Multiple Access Interference (MAI) is another crucial problem in the MC-CDMA system. Imperfect cross-correlation characteristics of the spreading codes and the multipath fading destroy the orthogonality among the users, and then cause MAI, which produces serious BER degradation in the system. Moreover, in uplink system the received signals at a base station are always asynchronous. This also destroys the orthogonality among the users, and hence, generates MAI which degrades the system performance. Besides those two problems, the interference should always be considered seriously for any communication system. In this dissertation, we design a novel MC-CDMA system, which has low PAPR and mitigated MAI. The new Semi-blind channel estimation and multi-user data detection based on Parallel Interference Cancellation (PIC) have been applied in the system. The Low Density Parity Codes (LDPC) has also been introduced into the system to improve the performance. Different interference models are analyzed in multi-carrier communication systems and then the effective interference suppression for MC-CDMA systems is employed in this dissertation. The experimental results indicate that our system not only significantly reduces the PAPR and MAI but also effectively suppresses the outside interference with low complexity. Finally, we present a practical cognitive application of the proposed system over the software defined radio platform.

The Effect of Sample and Sample Matrix on DNA Processing: Mechanisms for the Detection and Management of Inhibition in Forensic Samples

The presence of inhibitory substances in biological forensic samples has, and continues to affect the quality of the data generated following DNA typing processes. Although the chemistries used during the procedures have been enhanced to mitigate the effects of these deleterious compounds, some challenges remain. Inhibitors can be components of the samples, the substrate where samples were deposited or chemical(s) associated to the DNA purification step. Therefore, a thorough understanding of the extraction processes and their ability to handle the various types of inhibitory substances can help define the best analytical processing for any given sample. A series of experiments were conducted to establish the inhibition tolerance of quantification and amplification kits using common inhibitory substances in order to determine if current laboratory practices are optimal for identifying potential problems associated with inhibition. DART mass spectrometry was used to determine the amount of inhibitor carryover after sample purification, its correlation to the initial inhibitor input in the sample and the overall effect in the results. Finally, a novel alternative at gathering investigative leads from samples that would otherwise be ineffective for DNA typing due to the large amounts of inhibitory substances and/or environmental degradation was tested. This included generating data associated with microbial peak signatures to identify locations of clandestine human graves. Results demonstrate that the current methods for assessing inhibition are not necessarily accurate, as samples that appear inhibited in the quantification process can yield full DNA profiles, while those that do not indicate inhibition may suffer from lowered amplification efficiency or PCR artifacts. The extraction methods tested were able to remove >90% of the inhibitors from all samples with the exception of phenol, which was present in variable amounts whenever the organic extraction approach was utilized. Although the results attained suggested that most inhibitors produce minimal effect on downstream applications, analysts should practice caution when selecting the best extraction method for particular samples, as casework DNA samples are often present in small quantities and can contain an overwhelming amount of inhibitory substances.

Peak-to-Average Power Ratio Reduced Parallel Interference Cancellation Multicarrier-Code Division Multiple Access System with Anti-Interference Property

Orthogonal Frequency-Division Multiplexing (OFDM) has been proved to be a promising technology that enables the transmission of higher data rate. Multicarrier Code-Division Multiple Access (MC-CDMA) is a transmission technique which combines the advantages of both OFDM and Code-Division Multiplexing Access (CDMA), so as to allow high transmission rates over severe time-dispersive multi-path channels without the need of a complex receiver implementation. Also MC-CDMA exploits frequency diversity via the different subcarriers, and therefore allows the high code rates systems to achieve good Bit Error Rate (BER) performances. Furthermore, the spreading in the frequency domain makes the time synchronization requirement much lower than traditional direct sequence CDMA schemes. There are still some problems when we use MC-CDMA. One is the high Peak-to-Average Power Ratio (PAPR) of the transmit signal. High PAPR leads to nonlinear distortion of the amplifier and results in inter-carrier self-interference plus out-of-band radiation. On the other hand, suppressing the Multiple Access Interference (MAI) is another crucial problem in the MC-CDMA system. Imperfect cross-correlation characteristics of the spreading codes and the multipath fading destroy the orthogonality among the users, and then cause MAI, which produces serious BER degradation in the system. Moreover, in uplink system the received signals at a base station are always asynchronous. This also destroys the orthogonality among the users, and hence, generates MAI which degrades the system performance. Besides those two problems, the interference should always be considered seriously for any communication system. In this dissertation, we design a novel MC-CDMA system, which has low PAPR and mitigated MAI. The new Semi-blind channel estimation and multi-user data detection based on Parallel Interference Cancellation (PIC) have been applied in the system. The Low Density Parity Codes (LDPC) has also been introduced into the system to improve the performance. Different interference models are analyzed in multi-carrier communication systems and then the effective interference suppression for MC-CDMA systems is employed in this dissertation. The experimental results indicate that our system not only significantly reduces the PAPR and MAI but also effectively suppresses the outside interference with low complexity. Finally, we present a practical cognitive application of the proposed system over the software defined radio platform.