Production and preliminary evaluation of Trypanosoma evansi HSP70 for antibody detection in Equids

The present immuno-diagnostic method using soluble antigens from whole cell lysate antigen for trypanosomosis have certain inherent problems like lack of standardized and reproducible antigens, as well as ethical issues due to in vivo production, that could be alleviated by in vitro production. In the present study we have identified heat shock protein 70 (HSP70) from T. evansi proteome. The nucleotide sequence of T. evansi HSP70 was 2116 bp, which encodes 690 amino acid residues. The phylogenetic analysis of T. evansi HSP70 showed that T. evansi occurred within Trypanosoma clade and is most closely related to T. brucei brucei and T. brucei gambiense, whereas T. congolense HSP70 laid in separate clade. The two partial HSP70 sequences (HSP-1 from N-terminal region and HSP-2 from C-terminal region) were expressed and evaluated as diagnostic antigens using experimentally infected equine serum samples. Both recombinant proteins detected antibody in immunoblot using serum samples from experimental infected donkeys with T. evansi. Recombinant HSP-2 showed comparable antibody response to Whole cell lysate (WCL) antigen in immunoblot and ELISA. The initial results indicated that HSP70 has potential to detect the T. evansi infection and needs further validation on large set of equine serum samples.

Non-enzymatic electronic detection of glucose using aminophenylboronic acid functionalized reduced graphene oxide

We report the non-enzymatic electronic detection of glucose using field effect transistor (FET) devices made of aminophenylboronic acid (APBA) functionalized reduced graphene oxide (RGO). Detection of glucose molecules was carried out over a wide dynamic range of concentration varying from 100 pM to 100 mM with a detection limit of similar to 2 nM using both covalently and non-covalently functionalized APBA-RGO complex. The normalized change in electrical conductance data shows that the FET devices made of non-covalently functionalized APBA-RGO complex (nc-APBA-RGO) exhibited a linear response to glucose aqueous solution of concentrations varying from 1 nM to 10 mM and showed 4 times enhanced sensitivity over the devices made of covalently functionalized APBA-RGO complex (c-APBA-RGO). Specificity of APBA-RGO complex to glucose is confirmed from the observation of negligible change in electrical conductance after exposure to 0.1 mM of lactose and other interfering factors. (C) 2015 Elsevier B.V. All rights reserved.

On the detection of human influence in extreme precipitation over India

Climate change is expected to influence extreme precipitation which in turn might affect risks of pluvial flooding. Recent studies on extreme rainfall over India vary in their definition of extremes, scales of analyses and conclusions about nature of changes in such extremes. Fingerprint-based detection and attribution (D&A) offer a formal way of investigating the presence of anthropogenic signals in hydroclimatic observations. There have been recent efforts to quantify human effects in the components of the hydrologic cycle at large scales, including precipitation extremes. This study conducts a D&A analysis on precipitation extremes over India, considering both univariate and multivariate fingerprints, using a standardized probability-based index (SPI) from annual maximum one-day (RX1D) and five-day accumulated (RX5D) rainfall. The pattern-correlation based fingerprint method is used for the D&A analysis. Transformation of annual extreme values to SPI and subsequent interpolation to coarser grids are carried out to facilitate comparison between observations and model simulations. Our results show that in spite of employing these methods to address scale and physical processes mismatch between observed and model simulated extremes, attributing changes in regional extreme precipitation to anthropogenic climate change is difficult. At very high (95%) confidence, no signals are detected for RX1D, while for the RX5D and multivariate cases only the anthropogenic (ANT) signal is detected, though the fingerprints are in general found to be noisy. The findings indicate that model simulations may underestimate regional climate system responses to increasing human forcings for extremes, and though anthropogenic factors may have a role to play in causing changes in extreme precipitation, their detection is difficult at regional scales and not statistically significant. (C) 2015 Elsevier B.V. All rights reserved.

A fast algorithm for speech polarity detection using long-term linear prediction

Speech polarity detection is a crucial first step in many speech processing techniques. In this paper, an algorithm is proposed that improvises the existing technique using the skewness of the voice source (VS) signal. Here, the integrated linear prediction residual (ILPR) is used as the VS estimate, which is obtained using linear prediction on long-term frames of the low-pass filtered speech signal. This excludes the unvoiced regions from analysis and also reduces the computation. Further, a modified skewness measure is proposed for decision, which also considers the magnitude of the skewness of the ILPR along with its sign. With the detection error rate (DER) as the performance metric, the algorithm is tested on 8 large databases and its performance (DER=0.20%) is found to be comparable to that of the best technique (DER=0.06%) on both clean and noisy speech. Further, the proposed method is found to be ten times faster than the best technique.

Sparse Representation based Anomaly Detection using HOMV in H.264 Compressed Videos

In this paper, we have proposed an anomaly detection algorithm based on Histogram of Oriented Motion Vectors (HOMV) 1] in sparse representation framework. Usual behavior is learned at each location by sparsely representing the HOMVs over learnt normal feature bases obtained using an online dictionary learning algorithm. In the end, anomaly is detected based on the likelihood of the occurrence of sparse coefficients at that location. The proposed approach is found to be robust compared to existing methods as demonstrated in the experiments on UCSD Ped1 and UCSD Ped2 datasets.

Near-Optimal Detection Thresholds for Bayesian Spectrum Sensing Under Fading

This paper considers the problem of energy-based, Bayesian spectrum sensing in cognitive radios under various fading environments. Under the well-known central limit theorem based model for energy detection, we derive analytically tractable expressions for near-optimal detection thresholds that minimize the probability of error under lognormal, Nakagami-m, and Weibull fading. For the Suzuki fading case, a generalized gamma approximation is provided, which saves on the computation of an integral. In each case, the accuracy of the theoretical expressions as compared to the optimal thresholds are illustrated through simulations.

Anomaly detection in compressed H.264/AVC video

Real time anomaly detection is the need of the hour for any security applications. In this article, we have proposed a real time anomaly detection for H.264 compressed video streams utilizing pre-encoded motion vectors (MVs). The proposed work is principally motivated by the observation that MVs have distinct characteristics during anomaly than usual. Our observation shows that H.264 MV magnitude and orientation contain relevant information which can be used to model the usual behavior (UB) effectively. This is subsequently extended to detect abnormality/anomaly based on the probability of occurrence of a behavior. The performance of the proposed algorithm was evaluated and bench-marked on UMN and Ped anomaly detection video datasets, with a detection rate of 70 frames per sec resulting in 90x and 250x speedup, along with on-par detection accuracy compared to the state-of-the-art algorithms.

Damage Detection Sensitivity, Specificity and Classification Data Analysis for SHM Systems Design, Verification and Validation

In this paper, we consider applying derived knowledge base regarding the sensitivity and specificity of damage(s) to be detected by an SHM system being designed and qualified. These efforts are necessary toward developing capabilities in SHM system to classify reliably various probable damages through sequence of monitoring, i.e., damage precursor identification, detection of damage and monitoring its progression. We consider the particular problem of visual and ultrasonic NDE based SHM system design requirements, where the damage detection sensitivity and specificity data definitions for a class of structural components are established. Methodologies for SHM system specification creation are discussed in details. Examples are shown to illustrate how the physics of damage detection scheme limits particular damage detection sensitivity and specificity and further how these information can be used in algorithms to combine various different NDE schemes in an SHM system to enhance efficiency and effectiveness. Statistical and data driven models to determine the sensitivity and probability of damage detection (POD) has been demonstrated for plate with varying one-sided line crack using optical and ultrasonic based inspection techniques.

Uncertainty Quantification in Ultrasonic Wave Based Detection of Delamination in Composite Structures

In this paper we consider the problem of guided wave scattering from delamination in laminated composite and further the problem of estimating delamination size and layer-wise location from the guided wave measurement. Damage location and region/size can be estimated from time of flight and wave packet spread, whereas depth information can be obtained from wavenumber modulation in the carrier packet. The key challenge is that these information are highly sensitive to various uncertainties. Variation in reflected and transmitted wave amplitude in a bar due to boundary/interface uncertainty is studied to illustrate such effect. Effect of uncertainty in material parameters on the time of flight are estimated for longitudinal wave propagation. To evaluate the effect of uncertainty in delamination detection, we employ a time domain spectral finite element (tSFEM) scheme where wave propagation is modeled using higher-order interpolation with shape function have spectral convergence properties. A laminated composite beam with layer-wise placement of delamination is considered in the simulation. Scattering due to the presence of delamination is analyzed. For a single delamination, two identical waveforms are created at the two fronts of the delamination, whereas waves in the two sub-laminates create two independent waveforms with different wavelengths. Scattering due to multiple delaminations in composite beam is studied.

Ultrasonic Guided Wave Based Damage Detection Using Low-dimensional Data from Laser Doppler Scan

In the current state of the art, it remains an open problem to detect damage with partial ultrasonic scan data and with measurements at coarser spatial scale when the location of damage is not known. In the present paper, a recent development of finite element based model reduction scheme in frequency domain that employs master degrees of freedom covering the surface scan region of interests is reported in context of non-contact ultrasonic guided wave based inspection. The surface scan region of interest is grouped into master and slave degrees of freedom. A finite element wise damage factor is derived which represents damage state over distributed areas or sharp condition of inter-element boundaries (for crack). Laser Doppler Vibrometer (LDV) scan data obtained from plate type structure with inaccessible surface line crack are considered along with the developed reduced order damage model to analyze the extent of scan data dimensional reduction. The proposed technique has useful application in problems where non-contact monitoring of complex structural parts are extremely important and at the same time LDV scan has to be done on accessible surfaces only.

Ultra sensitive NO2 gas detection using the reduced graphene oxide coated etched fiber Bragg gratings

We report a simple and highly sensitive methodology for the room temperature NO2 gas sensing using reduced graphene oxide (RGO) coated clad etched fiber Bragg grating (eFBG). A significant shift (>10 pm) is observed in the reflected Bragg wavelength (lambda(B)) upon exposing RGO coated on the surface of eFBG to the NO2 gas molecules of concentration 0.5 ppm. The shift in Bragg wavelength is due to the change in the refractive index of RGO by charge transfer from the adsorbing NO2 molecules. The range of NO2 concentration is tested from 0.5 ppm to 3 ppm and the estimated time taken for 50% increase in Delta lambda(B) ranges from 20 min (for 0.5 ppm) to 6 min (for 3 ppm). (C) 2015 Elsevier B.V. All rights reserved.

Plasmonic Lipid Bilayer Membranes for Enhanced Detection Sensitivity of Biolabeling Fluorophores

Plasmonics based sensing, using the surface plasmon resonance of metal nanoparticles, has been effectively demonstrated in various applications. Extending this methodology to cell and artificial lipid bilayer membranes is extremely beneficial in enhancing the sensitivity of the detection of binding and cellular transport of molecules across such membranes. Here, the creation of an artificial plasmonic biomembrane template is demonstrated and used to show the enhanced detection sensitivity of certain widely used biomarker molecules. The efficacy of these templates is explained in terms of the ability of the hydrophobic polymer grafted gold nanoparticles used to organize, penetrate, and fluidize the membranes. The enhancement of photoluminescence of the dye molecules used occurs over a reasonably large spectral range as compared to the plasmon resonance of gold nanoparticles. The results could, possibly, be extended to cellular membranes with relevant modifications, as well as to the detection of any other biological molecule appropriately labeled with fluorescent dye molecules, and demonstrate the versatility of these plasmonic bioinspired platforms as potential biochemical sensors.

Specific detection of the cleavage activity of mycobacterial enzymes using a quantum dot based DNA nanosensor

We present a quantum dot based DNA nanosensor specifically targeting the cleavage step in the reaction cycle of the essential DNA-modifying enzyme, mycobacterial topoisomerase I. The design takes advantages of the unique photophysical properties of quantum dots to generate visible fluorescence recovery upon specific cleavage by mycobacterial topoisomerase I. This report, for the first time, demonstrates the possibility to quantify the cleavage activity of the mycobacterial enzyme without the pre-processing sample purification or post-processing signal amplification. The cleavage induced signal response has also proven reliable in biological matrices, such as whole cell extracts prepared from Escherichia coli and human Caco-2 cells. It is expected that the assay may contribute to the clinical diagnostics of bacterial diseases, as well as the evaluation of treatment outcomes.

Enhanced UV detection by non-polar epitaxial GaN films

Nonpolar a-GaN (11-20) epilayers were grown on r-plane (1-102) sapphire substrates using plasma assisted molecular beam epitaxy. High resolution x-ray diffractometer confirmed the orientation of the grown film. Effect of the Ga/N ratio on the morphology and strain of a-GaN epilayers was compared and the best condition was obtained for the nitrogen flow of 1 sccm. Atomic force microscopy was used to analyze the surface morphology while the strain in the film was quantitatively measured using Raman spectroscopy and qualitatively analyzed by reciprocal space mapping technique. UV photo response of a-GaN film was measured after fabricating a metal-semiconductor-metal structure over the film with gold metal. The external quantum efficiency of the photodetectors fabricated in the (0002) polar and (11-20) nonpolar growth directions were compared in terms of responsivity and nonpolar GaN showed the best sensitivity at the cost of comparatively slow response time. (C) 2015 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

MULTI-INSTRUMENT DETECTION IN POLYPHONIC MUSIC USING GAUSSIAN MIXTURE BASED FACTORIAL HMM

We formulate the problem of detecting the constituent instruments in a polyphonic music piece as a joint decoding problem. From monophonic data, parametric Gaussian Mixture Hidden Markov Models (GM-HMM) are obtained for each instrument. We propose a method to use the above models in a factorial framework, termed as Factorial GM-HMM (F-GM-HMM). The states are jointly inferred to explain the evolution of each instrument in the mixture observation sequence. The dependencies are decoupled using variational inference technique. We show that the joint time evolution of all instruments' states can be captured using F-GM-HMM. We compare performance of proposed method with that of Student's-t mixture model (tMM) and GM-HMM in an existing latent variable framework. Experiments on two to five polyphony with 8 instrument models trained on the RWC dataset, tested on RWC and TRIOS datasets show that F-GM-HMM gives an advantage over the other considered models in segments containing co-occurring instruments.