Development and validation of a fast monoclonal based disequilibrium enzyme-linked immunosorbent assay for the detection of triphenylmethane dyes and their metabolites in fish

Malachite Green (MG), Crystal Violet (CV) and Brilliant Green (BC) are antibacterial, antifungal and antiparasitic agents that have been used for treatment and prevention of diseases in fish. These dyes are metabolized into reduced leuco forms (LMG, LCV, LBG) that can be present in fish muscles for a long period. Due to the carcinogenic properties they are banned for use in fish for human consumption in many countries including the European Union and the United States. HPLC and LC-MS techniques are generally used for the detection of these compounds and their metabolites in fish. This study presents the development of a fast enzyme-linked immunosorbent assay (ELISA) method as an alternative for screening purposes. A first monoclonal cell line producing antibodies to MG was generated using a hybridoma technique. The antibody had good cross-reactivates with related chromatic forms of triphenylmethane dyes such as CV, BC, Methyl Green, Methyl Violet and Victoria Blue R. The monoclonal antibody (mAb) was used to develop a fast (20 min) disequilibrium ELISA screening method for the detection of triphenylmethanes in fish. By introducing an oxidation step with 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ) during sample extraction the assay was also used to detect the presence of the reduced metabolites of triphenylmethanes. The detection capability of the assay was 1 ng g(-1) for MG, LMG, CV, LCV and BC which was below the minimum required performance limit (MRPL) for the detection method of total MG (sum of MG and LMG) set by the Commission Decision 2004/25/EC (2 ng g(-1)). The mean recoveries for fish samples spiked at 0.5 MRPL and MRPL levels with MG and LMG were between 74.9 and 117.0% and inter- and intra-assay coefficients of variation between 4.7 and 25.7%. The validated method allows the analysis of a batch of 20 samples in two to three hours. Additionally, this procedure is substantially faster than other ELISA methods developed for MG/LMG thus far. The stable and efficient monoclonal cell line obtained is an unlimited source of sensitive and specific antibody to MG and other triphenylmethanes. (C) 2011 Elsevier B.V. All rights reserved.

Online Tracking of Thévenin Equivalent Parameters Using PMU Measurements

This paper presents a new method for tracking Thévenin equivalent parameters for a power system at a node using local Phasor Measurement Unit (PMU) measurements. Three consecutive phasor measurements for voltage and current, recorded at one location, are used. The phase drifts caused by the measurement slip frequency are first determined and phase angles of the measured phasors are corrected so that the corrected phasors are synchronized to the same reference. The synchronized phasors are then used to determine the equivalent Thévenin parameters of the system.

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Neural Network Based Attack on a Masked Implementation of AES

Masked implementations of cryptographic algorithms are often used in commercial embedded cryptographic devices to increase their resistance to side channel attacks. In this work we show how neural networks can be used to both identify the mask value, and to subsequently identify the secret key value with a single attack trace with high probability. We propose the use of a pre-processing step using principal component analysis (PCA) to significantly increase the success of the attack. We have developed a classifier that can correctly identify the mask for each trace, hence removing the security provided by that mask and reducing the attack to being equivalent to an attack against an unprotected implementation. The attack is performed on the freely available differential power analysis (DPA) contest data set to allow our work to be easily reproducible. We show that neural networks allow for a robust and efficient classification in the context of side-channel attacks.

Elastodynamic Modeling and Analysis for an Exechon Parallel Kinematic Machine

As a newly invented parallel kinematic machine (PKM), Exechon has attracted intensive attention from both academic and industrial fields due to its conceptual high performance. Nevertheless, the dynamic behaviors of Exechon PKM have not been thoroughly investigated because of its structural and kinematic complexities. To identify the dynamic characteristics of Exechon PKM, an elastodynamic model is proposed with the substructure synthesis technique in this paper. The Exechon PKM is divided into a moving platform subsystem, a fixed base subsystem and three limb subsystems according to its structural features. Differential equations of motion for the limb subsystem are derived through finite element (FE) formulations by modeling the complex limb structure as a spatial beam with corresponding geometric cross sections. Meanwhile, revolute, universal, and spherical joints are simplified into virtual lumped springs associated with equivalent stiffnesses and mass at their geometric centers. Differential equations of motion for the moving platform are derived with Newton's second law after treating the platform as a rigid body due to its comparatively high rigidity. After introducing the deformation compatibility conditions between the platform and the limbs, governing differential equations of motion for Exechon PKM are derived. The solution to characteristic equations leads to natural frequencies and corresponding modal shapes of the PKM at any typical configuration. In order to predict the dynamic behaviors in a quick manner, an algorithm is proposed to numerically compute the distributions of natural frequencies throughout the workspace. Simulation results reveal that the lower natural frequencies are strongly position-dependent and distributed axial-symmetrically due to the structure symmetry of the limbs. At the last stage, a parametric analysis is carried out to identify the effects of structural, dimensional, and stiffness parameters on the system's dynamic characteristics with the purpose of providing useful information for optimal design and performance improvement of the Exechon PKM. The elastodynamic modeling methodology and dynamic analysis procedure can be well extended to other overconstrained PKMs with minor modifications.

Feature recognition & tool path generation for 5 axis step-nc machining of free form / irregular contoured surfaces

This research paper presents a five step algorithm to generate tool paths for machining Free form / Irregular Contoured Surface(s) (FICS) by adopting STEP-NC (AP-238) format. In the first step, a parametrized CAD model with FICS is created or imported in UG-NX6.0 CAD package. The second step recognizes the features and calculates a Closeness Index (CI) by comparing them with the B-Splines / Bezier surfaces. The third step utilizes the CI and extracts the necessary data to formulate the blending functions for identified features. In the fourth step Z-level 5 axis tool paths are generated by adopting flat and ball end mill cutters. Finally, in the fifth step, tool paths are integrated with STEP-NC format and validated. All these steps are discussed and explained through a validated industrial component.

Identification of Microturbine Model for Long-term Dynamic Analysis of Distribution Networks

As one of the most successfully commercialized distributed energy resources, the long-term effects of microturbines (MTs) on the distribution network has not been fully investigated due to the complex thermo-fluid-mechanical energy conversion processes. This is further complicated by the fact that the parameter and internal data of MTs are not always available to the electric utility, due to different ownerships and confidentiality concerns. To address this issue, a general modeling approach for MTs is proposed in this paper, which allows for the long-term simulation of the distribution network with multiple MTs. First, the feasibility of deriving a simplified MT model for long-term dynamic analysis of the distribution network is discussed, based on the physical understanding of dynamic processes that occurred within MTs. Then a three-stage identification method is developed in order to obtain a piecewise MT model and predict electro-mechanical system behaviors with saturation. Next, assisted with the electric power flow calculation tool, a fast simulation methodology is proposed to evaluate the long-term impact of multiple MTs on the distribution network. Finally, the model is verified by using Capstone C30 microturbine experiments, and further applied to the dynamic simulation of a modified IEEE 37-node test feeder with promising results.

Dynalog: an automated dynamic analysis framework for characterizing android applications

Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.

Robustness of Damping Control Implemented by Energy Storage Systems Installed in Power Systems

An energy storage system (ESS) installed in a power system can effectively damp power system oscillations through controlling exchange of either active or reactive power between the ESS and power system. This paper investigates the robustness of damping control implemented by the ESS to the variations of power system operating conditions. It proposes a new analytical method based on the well-known equal-area criterion and small-signal stability analysis. By using the proposed method, it is concluded in the paper that damping control implemented by the ESS through controlling its active power exchange with the power system is robust to the changes of power system operating conditions. While if the ESS damping control is realized by controlling its reactive power exchange with the power system, effectiveness of damping control changes with variations of power system operating condition. In the paper, an example power system installed with a battery ESS (BESS) is presented. Simulation results confirm the analytical conclusions made in the paper about the robustness of ESS damping control. Laboratory experiment of a physical power system installed with a 35kJ/7kW SMES (Superconducting Magnetic Energy Storage) was carried out to evaluate theoretical study. Results are given in the paper, which demonstrate that effectiveness of SMES damping control realized through regulating active power is robust to changes of load conditions of the physical power system.

Oxidation of N,N,N ',N '-tetraalkyl-para-phenylenediamines in a series of room temperature ionic liquids incorporating the bis(trifluoromethylsulfonyl)imide anion

The five room temperature ionic liquids: 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([CnMIM][N(Tf)(2)], n = 2, 4, 8, 10) and n-hexyltriethylammonium bis(trifluoromethylsulfonyl)imide ([N-6222][N(Tf)(2)]) were investigated as solvents in which to study the electrochemical oxidation of N,N,N',N'-tetramethyl-para-phenylenediamine (TMPD) and N,N,N',N'-tetrabutyl-paraphenylenediamine (TBPD), using 20 mul micro-samples under vacuum conditions. The effect of dissolved atmospheric gases on the accessible electrochemical window was probed and determined to be less significant than seen previously for ionic liquids containing alternative anions. Chronoamperometric transients recorded at a microdisk electrode were analysed via a process of non-linear curve fitting to yield values for the diffusion coefficients of the electroactive species without requiring a knowledge of their initial concentration. Comparison of experimental and simulated cyclic voltammetry was then employed to corroborate these results and allow diffusion coefficients for the electrogenerated species to be estimated. The diffusion coefficients obtained for the neutral compounds in the five ionic liquids via this analysis were, in units of 10(-11) m(2) s(-1), 2.62, 1.87, 1.12, 1.13 and 0.70 for TMPD. and 1.23, 0.80, 0.40, 0.52 and 0.24 for TBPD (listed using the same order for the ionic liquids as stated above). The most significant consequence of changing the cationic component of the ionic liquid was found to be its effect on the solvent viscosity; the diffusion coefficient of each species was found to be approximately inversely proportional to viscosity across the series of ionic liquids, in accordance with Walden's rule. (C) 2003 Elsevier B.V. All rights reserved.

Development of a monoclonal antibody binding okadaic acid and dinophysistoxins-1, -2 in proportion to their toxicity equivalence factors

Okadaic acid (OA) and structurally related toxins dinophysistoxin-1 (DTX-1), and DTX-2, are lipophilic marine biotoxins. The current reference method for the analysis of these toxins is the mouse bioassay (MBA). This method is under increasing criticism both from an ethical point of view and because of its limited sensitivity and specificity. Alternative replacement methods must be rapid, robust, cost effective, specific and sensitive. Although published immuno-based detection techniques have good sensitivities, they are restricted in their use because of their inability to: (i) detect all of the OA toxins that contribute to contamination; and (ii) factor in the relative toxicities of each contaminant. Monoclonal antibodies (MAbs) were produced to OA and an automated biosensor screening assay developed and compared with ELISA techniques. The screening assay was designed to increase the probability of identifying a MAb capable of detecting all OA toxins. The result was the generation of a unique MAb which not only cross-reacted with both DTX-1 and DTX-2 but had a cross-reactivity profile in buffer that reflected exactly the intrinsic toxic potency of the OA group of toxins. Preliminary matrix studies reflected these results. This antibody is an excellent candidate for the development of a range of functional immunochemical-based detection assays for this group of toxins.

Systematic random sampling of the comet assay

The comet assay is a technique used to quantify DNA damage and repair at a cellular level. In the assay, cells are embedded in agarose and the cellular content is stripped away leaving only the DNA trapped in an agarose cavity which can then be electrophoresed. The damaged DNA can enter the agarose and migrate while the undamaged DNA cannot and is retained. DNA damage is measured as the proportion of the migratory ‘tail’ DNA compared to the total DNA in the cell. The fundamental basis of these arbitrary values is obtained in the comet acquisition phase using fluorescence microscopy with a stoichiometric stain in tandem with image analysis software. Current methods deployed in such an acquisition are expected to be both objectively and randomly obtained. In this paper we examine the ‘randomness’ of the acquisition phase and suggest an alternative method that offers both objective and unbiased comet selection. In order to achieve this, we have adopted a survey sampling approach widely used in stereology, which offers a method of systematic random sampling (SRS). This is desirable as it offers an impartial and reproducible method of comet analysis that can be used both manually or automated. By making use of an unbiased sampling frame and using microscope verniers, we are able to increase the precision of estimates of DNA damage. Results obtained from a multiple-user pooled variation experiment showed that the SRS technique attained a lower variability than that of the traditional approach. The analysis of a single user with repetition experiment showed greater individual variances while not being detrimental to overall averages. This would suggest that the SRS method offers a better reflection of DNA damage for a given slide and also offers better user reproducibility.

History of Child Welfare and Protection Social Work in Northern Ireland: Finding Continuity amongst Discontinuity in Case Files from 1950-1968

This article reports on research carried out on 200 child welfare files from the largest welfare authority in Northern Ireland from 1950-1968. The literature review provides a commentary on some of the major debates surrounding child welfare and protection social work from the perspective of its historical development. The report of the research which follows offers an insight into one core, and less well-known period of child welfare history in Northern Ireland between the two Children and Young Persons Acts (1950 & 1968). Using a method of discourse analysis influenced by Michel Foucault, a detailed description of the nature of practice is offered. This paper is offered as a work in progress, with further work being planned for dissemination of more detailed analysis of the method and outcomes. The research seeks to ask a few core questions based on problems identified in the present with our current understandings of child welfare and protection histories. While recognising the limitations of this study and the need for broader analysis of the wider context surrounding child welfare practice at the moment, it is argued that some salient conclusions can be drawn about continuity and discontinuity in practice which are of interest to practitioners and students of child welfare social work.