Colorimetric anion sensor based on receptor having indole- and thiourea-binding sites

A new colorimetric sensor L containing nitro-substituted indole and bisthiocarbonohydrazone units for selective fluoride and acetate ions is designed and synthesized. The receptor L shows well-defined color change in the visible region of the spectrum with an absorption band at similar to 515 nm and 506 nm, respectively, for the F- and CH3COO- ions in an acetonitrile solution. Job's plots indicated the formation of 1 : 1 (L with CH3COO-) and 1 : 2 (L with F-) complexes. The interaction of L with the F- ion undergoes a deprotonation process and release of HX2](-), whereas with the CH3COO- ion, it forms a stable LH2(...)X](-) complex. The relative affinities of the anions with L are rationalized using computational studies.

Detection of Target DNA using Photo-Reactive Protoporphyrin Moeity on a Nanocomposite Substrate

Detection of pathogens from infected biological samples through conventional process involves cell lysis and purification. The main objective of this work is to minimize the time and sample loss, as well as to increase the efficiency of detection of biomolecules. Electrical lysis of medical sample is performed in a closed microfluidic channel in a single integrated platform where the downstream analysis of the sample is possible. The device functions involve, in a sequence, flow of lysate from lysis chamber passed through a thermal denaturation counter where dsDNA is denatured to ssDNA, which is controlled by heater unit. A functionalized binding chamber of ssDNA is prepared by using ZnO nanorods as the matrix and functionalized with bifunctional carboxylic acid, 16-(2-pyridyldithiol) hexadecanoic acid (PDHA) which is further attached to a linker molecule 1-ethyl-3-(3-dimethylaminopropyl) (EDC). Linker moeity is then covalently bound to photoreactive protoporphyrin (PPP) molecule. The photolabile molecule protoporphyrin interacts with -NH2 labeled single stranded DNA (ssDNA) which thus acts as a probe to detect complimentary ssDNA from target organisms. Thereafter the bound DNA with protoporphyrin is exposed to an LED of particular wavelength for a definite period of time and DNA was eluted and analyzed. UV/Vis spectroscopic analysis at 260/280 nm wavelength confirms the purity and peak at 260 nm is reconfirmed for the elution of target DNA. Quantitative and qualitative data obtained from the current experiments show highly selective detection of biomolecule such as DNA which have large number of future applications in Point-of-Care devices.

Titania nanotube-modified screen printed carbon electrodes enhance the sensitivity in the electrochemical detection of proteins

The use of titania nanotubes (TiO2-NT) as the working electrode provides a substantial improvement in the electrochemical detection of proteins. A biosensor designed using this strategy provided a robust method to detect protein samples at very low concentrations (C-protein ca 1 ng/mu l). Reproducible measurements on protein samples at this concentration (I-p,I-a of 80 +/- 1.2 mu A) could be achieved using a sample volume of ca 30 mu l. We demonstrate the feasibility of this strategy for the accurate detection of penicillin binding protein, PBP2a, a marker for methicillin resistant Staphylococcus aureus (MRSA). The selectivity and efficiency of this sensor were also validated using other diverse protein preparations such as a recombinant protein tyrosine phosphatase (PTP10D) and bovine serum albumin (BSA). This electrochemical method also presents a substantial improvement in the time taken (few minutes) when compared to conventional enzyme-linked immunosorbent assay (ELISA) protocols. It is envisaged that this sensor could substantially aid in the rapid diagnosis of bacterial infections in resource strapped environments. (C) 2014 Elsevier B.V. All rights reserved.

Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning

It is well known that the impulse response of a wide-band wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this paper, we consider the estimation of the unknown channel coefficients and its support in OFDM systems using a sparse Bayesian learning (SBL) framework for exact inference. In a quasi-static, block-fading scenario, we employ the SBL algorithm for channel estimation and propose a joint SBL (J-SBL) and a low-complexity recursive J-SBL algorithm for joint channel estimation and data detection. In a time-varying scenario, we use a first-order autoregressive model for the wireless channel and propose a novel, recursive, low-complexity Kalman filtering-based SBL (KSBL) algorithm for channel estimation. We generalize the KSBL algorithm to obtain the recursive joint KSBL algorithm that performs joint channel estimation and data detection. Our algorithms can efficiently recover a group of approximately sparse vectors even when the measurement matrix is partially unknown due to the presence of unknown data symbols. Moreover, the algorithms can fully exploit the correlation structure in the multiple measurements. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the mean-square error and bit error rate performance.

Spectrum sensing using distributed sequential detection via noisy reporting MAC

This paper considers cooperative spectrum sensing algorithms for Cognitive Radios which focus on reducing the number of samples to make a reliable detection. We propose algorithms based on decentralized sequential hypothesis testing in which the Cognitive Radios sequentially collect the observations, make local decisions and send them to the fusion center for further processing to make a final decision on spectrum usage. The reporting channel between the Cognitive Radios and the fusion center is assumed more realistically as a Multiple Access Channel (MAC) with receiver noise. Furthermore the communication for reporting is limited, thereby reducing the communication cost. We start with an algorithm where the fusion center uses an SPRT-like (Sequential Probability Ratio Test) procedure and theoretically analyze its performance. Asymptotically, its performance is close to the optimal centralized test without fusion center noise. We further modify this algorithm to improve its performance at practical operating points. Later we generalize these algorithms to handle uncertainties in SNR and fading. (C) 2014 Elsevier B.V. All rights reserved.

Race Detection for Android Applications

Programming environments for smartphones expose a concurrency model that combines multi-threading and asynchronous event-based dispatch. While this enables the development of efficient and feature-rich applications, unforeseen thread interleavings coupled with non-deterministic reorderings of asynchronous tasks can lead to subtle concurrency errors in the applications. In this paper, we formalize the concurrency semantics of the Android programming model. We further define the happens-before relation for Android applications, and develop a dynamic race detection technique based on this relation. Our relation generalizes the so far independently studied happens-before relations for multi-threaded programs and single-threaded event-driven programs. Additionally, our race detection technique uses a model of the Android runtime environment to reduce false positives. We have implemented a tool called DROIDRACER. It generates execution traces by systematically testing Android applications and detects data races by computing the happens-before relation on the traces. We analyzed 1 5 Android applications including popular applications such as Facebook, Twitter and K-9 Mail. Our results indicate that data races are prevalent in Android applications, and that DROIDRACER is an effective tool to identify data races.

Multiscale Symmetry Detection in Scalar Fields by Clustering Contours

The complexity in visualizing volumetric data often limits the scope of direct exploration of scalar fields. Isocontour extraction is a popular method for exploring scalar fields because of its simplicity in presenting features in the data. In this paper, we present a novel representation of contours with the aim of studying the similarity relationship between the contours. The representation maps contours to points in a high-dimensional transformation-invariant descriptor space. We leverage the power of this representation to design a clustering based algorithm for detecting symmetric regions in a scalar field. Symmetry detection is a challenging problem because it demands both segmentation of the data and identification of transformation invariant segments. While the former task can be addressed using topological analysis of scalar fields, the latter requires geometry based solutions. Our approach combines the two by utilizing the contour tree for segmenting the data and the descriptor space for determining transformation invariance. We discuss two applications, query driven exploration and asymmetry visualization, that demonstrate the effectiveness of the approach.

A fluorescent organic cage for picric acid detection

Dynamic covalent imine chemistry has been utilized to synthesize a fluorescent 3+2] self-assembled nanoscopic organic cage. The fluorescent nature of the reduced analogue of the cage was further exploited for the highly selective detection of the explosive picric acid (PA).

Optical detection of submicromolar levels of nitro explosives by a submicron sized metal-organic phosphor material

Two isomorphous submicron sized metal-organic network compounds, Y-2(PDA)(3)(H2O)1]center dot 2H(2)O (PDA = 1,4-phenylenediacetate), 1 and Y1.8Tb0.2(PDA)(3)(H2O)1]center dot 2H(2)O, Tb@1 have been synthesized by employing solvent assisted liquid grinding followed by heating at 180 degrees C for 1' min and washing with water. Single crystal X-ray data of bulk 1 confirmed a three dimensional porous structure. The structure and morphology of 1 and Tb@1 were systematically characterized by PXRD, TGA, DSC, IR, SEM and EDX analysis. Dehydrated Tb@1 Tb@1'] shows a high intense visible green emission upon exposure to UV light. The green emission of Tb@1' was used for the detection of nitro explosives, such as 2,4,6-trinitrophenol (TNP), 1,3-dinitro benzene (DNB), 2,4-dinitro toluene (DNT), nitro benzene (NB), and 4-nitro toluene (NT) in acetonitrile. The results show that the emission intensity of dehydrated Tb@1' can be quenched by all the nitro analytes used in the present work. Remarkably, Tb@1' exhibited a high efficiency for TNP, DNB and DNT detection with K-SV K-SV = quenching constant based on linear Stern-Volmer plot] values of 70 920, 44 000 and 35 430 M-1, respectively, which are the highest values amongst known metal-organic materials. Using this material submicromolar level (equivalent to 0.18 ppm), a detection of nitro explosives has been achieved.

Enriching the annotation of Mycobacterium tuberculosis H37Rv proteome using remote homology detection approaches: Insights into structure and function

The availability of the genome sequence of Mycobacterium tuberculosis H37Rv has encouraged determination of large numbers of protein structures and detailed definition of the biological information encoded therein; yet, the functions of many proteins in M. tuberculosis remain unknown. The emergence of multidrug resistant strains makes it a priority to exploit recent advances in homology recognition and structure prediction to re-analyse its gene products. Here we report the structural and functional characterization of gene products encoded in the M. tuberculosis genome, with the help of sensitive profile-based remote homology search and fold recognition algorithms resulting in an enhanced annotation of the proteome where 95% of the M. tuberculosis proteins were identified wholly or partly with information on structure or function. New information includes association of 244 proteins with 205 domain families and a separate set of new association of folds to 64 proteins. Extending structural information across uncharacterized protein families represented in the M. tuberculosis proteome, by determining superfamily relationships between families of known and unknown structures, has contributed to an enhancement in the knowledge of structural content. In retrospect, such superfamily relationships have facilitated recognition of probable structure and/or function for several uncharacterized protein families, eventually aiding recognition of probable functions for homologous proteins corresponding to such families. Gene products unique to mycobacteria for which no functions could be identified are 183. Of these 18 were determined to be M. tuberculosis specific. Such pathogen-specific proteins are speculated to harbour virulence factors required for pathogenesis. A re-annotated proteome of M. tuberculosis, with greater completeness of annotated proteins and domain assigned regions, provides a valuable basis for experimental endeavours designed to obtain a better understanding of pathogenesis and to accelerate the process of drug target discovery. (C) 2014 Elsevier Ltd. All rights reserved.

Matrix Crack Detection in Composite Plate with Spatially Random Material Properties using Fractal Dimension

Fractal dimension based damage detection method is investigated for a composite plate with random material properties. Composite material shows spatially varying random material properties because of complex manufacturing processes. Matrix cracks are considered as damage in the composite plate. Such cracks are often seen as the initial damage mechanism in composites under fatigue loading and also occur due to low velocity impact. Static deflection of the cantilevered composite plate with uniform loading is calculated using the finite element method. Damage detection is carried out based on sliding window fractal dimension operator using the static deflection. Two dimensional homogeneous Gaussian random field is generated using Karhunen-Loeve (KL) expansion to represent the spatial variation of composite material property. The robustness of fractal dimension based damage detection method is demonstrated considering the composite material properties as a two dimensional random field.

Sensitive detection of C-reactive protein using optical fiber Bragg gratings

An accurate and highly sensitive sensor platform has been demonstrated for the detection of C-reactive protein (CRP) using optical fiber Bragg gratings (FBGs). The CRP detection has been carried out by monitoring the shift in Bragg wavelength (Delta lambda(B)) of an etched FBG (eFBG) coated with an anti-CRP antibody (aCRP)-graphene oxide (GO) complex. The complex is characterized by Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy and atomic force microscopy. A limit of detection of 0.01 mg/L has been achieved with a linear range of detection from 0.01 mg/L to 100 mg/L which includes clinical range of CRP. The eFBG sensor coated with only aCRP (without GO) show much less sensitivity than that of aCRP-GO complex coated eFBG. The eFBG sensors show high specificity to CRP even in the presence of other interfering factors such as urea, creatinine and glucose. The affinity constant of similar to 1.1 x 10(10) M-1 has been extracted from the data of normalized shift (Delta lambda(B)/lambda(B)) as a function of CRP concentration. (C) 2014 Elsevier B.V. All rights reserved.

Multithreaded Test Synthesis for Deadlock Detection

Designing and implementing thread-safe multithreaded libraries can be a daunting task as developers of these libraries need to ensure that their implementations are free from concurrency bugs, including deadlocks. The usual practice involves employing software testing and/or dynamic analysis to detect. deadlocks. Their effectiveness is dependent on well-designed multithreaded test cases. Unsurprisingly, developing multithreaded tests is significantly harder than developing sequential tests for obvious reasons. In this paper, we address the problem of automatically synthesizing multithreaded tests that can induce deadlocks. The key insight to our approach is that a subset of the properties observed when a deadlock manifests in a concurrent execution can also be observed in a single threaded execution. We design a novel, automatic, scalable and directed approach that identifies these properties and synthesizes a deadlock revealing multithreaded test. The input to our approach is the library implementation under consideration and the output is a set of deadlock revealing multithreaded tests. We have implemented our approach as part of a tool, named OMEN1. OMEN is able to synthesize multithreaded tests on many multithreaded Java libraries. Applying a dynamic deadlock detector on the execution of the synthesized tests results in the detection of a number of deadlocks, including 35 real deadlocks in classes documented as thread-safe. Moreover, our experimental results show that dynamic analysis on multithreaded tests that are either synthesized randomly or developed by third-party programmers are ineffective in detecting the deadlocks.

Trace Driven Dynamic Deadlock Detection and Reproduction

Dynamic analysis techniques have been proposed to detect potential deadlocks. Analyzing and comprehending each potential deadlock to determine whether the deadlock is feasible in a real execution requires significant programmer effort. Moreover, empirical evidence shows that existing analyses are quite imprecise. This imprecision of the analyses further void the manual effort invested in reasoning about non-existent defects. In this paper, we address the problems of imprecision of existing analyses and the subsequent manual effort necessary to reason about deadlocks. We propose a novel approach for deadlock detection by designing a dynamic analysis that intelligently leverages execution traces. To reduce the manual effort, we replay the program by making the execution follow a schedule derived based on the observed trace. For a real deadlock, its feasibility is automatically verified if the replay causes the execution to deadlock. We have implemented our approach as part of WOLF and have analyzed many large (upto 160KLoC) Java programs. Our experimental results show that we are able to identify 74% of the reported defects as true (or false) positives automatically leaving very few defects for manual analysis. The overhead of our approach is negligible making it a compelling tool for practical adoption.

Matrix crack detection in spatially random composite structures using fractal dimension

Fractal dimension based damage detection method is studied for a composite structure with random material properties. A composite plate with localized matrix crack is considered. Matrix cracks are often seen as the initial damage mechanism in composites. Fractal dimension based method is applied to the static deformation curve of the structure to detect localized damage. Static deflection of a cantilevered composite plate under uniform loading is calculated using the finite element method. Composite material shows spatially varying random material properties because of complex manufacturing processes. Spatial variation of material property is represented as a two dimensional homogeneous Gaussian random field. Karhunen-Loeve (KL) expansion is used to generate a random field. The robustness of fractal dimension based damage detection methods is studied considering the composite plate with spatial variation in material properties.