Assessment of a multiplexing high throughput immunochemical SPR biosensor in measuring multiple proteins on a single biosensor chip

Multiplexed immunochemical detection platforms offer the potential to decrease labour demands, increase sample throughput and decrease overall time to result. A prototype four channel multiplexed high throughput surface plasmon resonance biosensor was previously developed, for the detection of food related contaminants. A study focused on determining the instruments performance characteristics was undertaken. This was followed by the development of a multiplexed assay for four high molecular weight proteins. The protein levels were simultaneously evaluated in serum samples of 10-week-old veal calves (n = 24) using multiple sample preparation methods. Each of the biosensor's four channels were shown to be independent of one another and produced multiplexed within run repeatability (n = 6) ranging from 2.0 to 6.7%CV, for the four tested proteins, whilst between run reproducibility (n = 4) ranged from 1.5 to 8.9%CV. Four calibration curves were successfully constructed before serum sample preparation was optimised for each protein. Multiplexed concentration analysis was successfully performed on four channels revealing that each proteins concentration was consistent across the twenty-four tested animals. Signal reproducibility (n > 19) on a further long term study revealed coefficient of variation ranging from 1.1% to 7.3% and showed that the multiplexed assay was stable for at least 480 cycles. These findings indicate that the performance characteristics fall within the range of previously published data for singleplex optical biosensors and that the multiplexing biosensor is fit-for-purpose for simultaneous concentration analysis in many different types of applications such as the multiplexed detection of markers of growth-promoter abuse and multiplexed detection of residues of concern in food safety. © 2013 Elsevier B.V.

Engineering of Vis-NIR Metamaterials towards Revolutionary Ultrasensitive and Versatile Plasmonic Biosensors

By enabling subwavelength light localization and strong electromagnetic field enhancement, plasmonic biosensors have opened up a new realm of possibilities for a broad range of chemical and biological sensing applications owing to their label-free and real-time attributes. Although significant progress has been made, many fundamental and practical challenges still remain to be addressed. For instance, the plasmonic biosensors are nonselective sensing platforms; they are not well-suited to provide information regarding conformation or chemical fingerprint of unknown biomolecules. Furthermore, tunability of the plasmonic resonance in visible frequency regime is still limited; this will prevent their efficient and reproducible exploitation in single-molecule sensitivity. Here, we show that by engineering geometry of plasmonic metamaterials,1 consisting of periodic arrays of artificial split-ring resonators (SRRs), the plasmonic resonance of metamaterials could be tuned to visible-near infrared regimes (Vis-NIR) such that it allows parallel acquisition of optical transmission and highly surface-enhanced Raman (SERS) spectra from large functionalized SRR arrays. The Au SRRs were designed in form of alphabet letters (U, V, S, H, Y) with various line width (from 80 to 30 nm). By tailoring their size and shape, plasmonic resonance wavelength of the SRRs could be actively tuned so that it gives the strongest SERS effect under given excitation energy and polarization for biological and organic molecules. On the other hand, the plasmonic tunability was also achieved for a given SRR pattern by tuning the laser wavelength to obtain the highest electromagnetic field enhancement. The geometry- and laser-tunable channels typically provide an electromagnetic field enhancement as high as 20 times. This will provide the basis of versatile and multichannel devices for identification of different conformational states of Guanine-rich DNA, detection of a cancer biomarker nucleolin, and femtomolar sensitivity detection of food and drink additives. These results show that the tunable Vis-IR metamaterials are very versatile biosensing platforms and suggest considerable promise in genomic research, disease diagnosis, and food safety analysis.

You have free access to this content 5.8 GHz full duplex amplitude shift keying retrodirective interrogator array

This article describes a practical demonstration of a complete full-duplex “amplitude shift keying (ASK)” retrodirective radio frequency identification (RFID) transceiver array.The interrogator incorporates a “retrodirective array (RDA)” with a dual-conversion phase conjugating architecture in order to achieve better performance than is possible with conventional RFID solutions. Here mixers phase conjugate the incoming signal and a carrier recovery circuit recovers incoming angle of arrival phase information of an encoded amplitude shift keyed signal. The resulting interrogator provides a receiver sensitivity level of -109 dBm. A four element square patch RDA gives a 3 dB automatic beam steering angle of acceptance of ±45°. When compared to an RFID system operating by conventional (non-retrodirective) means retrodirective action leads to improved range extension of up to 16 times at ±45°. Operator pointing accuracy requirements are also reduced due to automatic retrodirective self-pointing. These features significantly enhance deployment opportunities requiring long range low equivalent isotropic radiation power (EIRP) and/or RFID tagging of moving platforms. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:160–164, 2013; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27258

Co-operating retrodirective system

Practical demonstration of the operational advantages gained through the use of a co-operating retrodirective array (RDA) basestation and Van Atta node arrangements is discussed. The system exploits a number of inherent RDA features to provide analogue real time multifunctional operation at low physical complexity. An active dual-conversion four element RDA is used as the power distribution source (basestation) while simultaneously achieving a receive sensitivity level of ??109 dBm and 3 dB automatic beam steering angle of ??45??. When mobile units are each equipped with a semi-passive four element Van Atta array, it is shown mobile device orientation issues are mitigated and optimal energy transfer can occur because of automatic beam formation resulting from retrodirective self-pointing action. We show that operation in multipath rich environments with or without line of sight acts to reduce average power density limits in the operating volume with high energy density occurring at mobile nodes sites only. The system described can be used as a full duplex ASK communications link, or, as a means for remote node charging by wireless means, thereby enhancing deployment opportunities between unstabilised moving platforms.

CP retrodirective antenna array performance degradation at large elevation angles

At large elevation angles away from boresight the performance of planar phased antenna arrays for circularly polarized, CP, signals suffers from significant gain reduction, worsening of the circular polarization purity, increased pointing error and unwanted dominantly specular lobe radiation. The mechanisms governing this performance deterioration and suggestions for possible rectification are for the first time elaborated in this paper. The points raised in this paper are important when CP retrodirective arrays are to be deployed in self-tracking satellite and terrestrial communication systems mounted on mobile platforms.

Validation of Next Generation Sequencing Technologies in Comparison to Current Diagnostic Gold Standards for BRAF, EGFR and KRAS Mutational Analysis

Next Generation Sequencing (NGS) has the potential of becoming an important tool in clinical diagnosis and therapeutic decision-making in oncology owing to its enhanced sensitivity in DNA mutation detection, fast-turnaround of samples in comparison to current gold standard methods and the potential to sequence a large number of cancer-driving genes at the one time. We aim to test the diagnostic accuracy of current NGS technology in the analysis of mutations that represent current standard-of-care, and its reliability to generate concomitant information on other key genes in human oncogenesis. Thirteen clinical samples (8 lung adenocarcinomas, 3 colon carcinomas and 2 malignant melanomas) already genotyped for EGFR, KRAS and BRAF mutations by current standard-of-care methods (Sanger Sequencing and q-PCR), were analysed for detection of mutations in the same three genes using two NGS platforms and an additional 43 genes with one of these platforms. The results were analysed using closed platform-specific proprietary bioinformatics software as well as open third party applications. Our results indicate that the existing format of the NGS technology performed well in detecting the clinically relevant mutations stated above but may not be reliable for a broader unsupervised analysis of the wider genome in its current design. Our study represents a diagnostically lead validation of the major strengths and weaknesses of this technology before consideration for diagnostic use.

An abstract annotation model for skeletons

Multi-core and many-core platforms are becoming increasingly heterogeneous and asymmetric. This significantly increases the porting and tuning effort required for parallel codes, which in turn often leads to a growing gap between peak machine power and actual application performance. In this work a first step toward the automated optimization of high level skeleton-based parallel code is discussed. The paper presents an abstract annotation model for skeleton programs aimed at formally describing suitable mapping of parallel activities on a high-level platform representation. The derived mapping and scheduling strategies are used to generate optimized run-time code. © 2013 Springer-Verlag Berlin Heidelberg.

Establishing optimal quantitative-polymerase chain reaction assays for routine diagnosis and tracking of minimal residual disease in JAK2-V617F-associated myeloproliferative neoplasms:a joint European LeukemiaNet/MPN&MPNr-EuroNet (COST action BM0902) study

Reliable detection of JAK2-V617F is critical for accurate diagnosis of myeloproliferative neoplasms (MPNs); in addition, sensitive mutation-specific assays can be applied to monitor disease response. However, there has been no consistent approach to JAK2-V617F detection, with assays varying markedly in performance, affecting clinical utility. Therefore, we established a network of 12 laboratories from seven countries to systematically evaluate nine different DNA-based quantitative PCR (qPCR) assays, including those in widespread clinical use. Seven quality control rounds involving over 21,500 qPCR reactions were undertaken using centrally distributed cell line dilutions and plasmid controls. The two best-performing assays were tested on normal blood samples (n=100) to evaluate assay specificity, followed by analysis of serial samples from 28 patients transplanted for JAK2-V617F-positive disease. The most sensitive assay, which performed consistently across a range of qPCR platforms, predicted outcome following transplant, with the mutant allele detected a median of 22 weeks (range 6-85 weeks) before relapse. Four of seven patients achieved molecular remission following donor lymphocyte infusion, indicative of a graft vs MPN effect. This study has established a robust, reliable assay for sensitive JAK2-V617F detection, suitable for assessing response in clinical trials, predicting outcome and guiding management of patients undergoing allogeneic transplant.

FPGA Prototyping of Emerging Manycore Architectures for Parallel Programming Research using Formic Boards

Performance evaluation of parallel software and architectural exploration of innovative hardware support face a common challenge with emerging manycore platforms: they are limited by the slow running time and the low accuracy of software simulators. Manycore FPGA prototypes are difficult to build, but they offer great rewards. Software running on such prototypes runs orders of magnitude faster than current simulators. Moreover, researchers gain significant architectural insight during the modeling process. We use the Formic FPGA prototyping board [1], which specifically targets scalable and cost-efficient multi-board prototyping, to build and test a 64-board model of a 512-core, MicroBlaze-based, non-coherent hardware prototype with a full network-on-chip in a 3D-mesh topology. We expand the hardware architecture to include the ARM Versatile Express platforms and build a 520-core heterogeneous prototype of 8 Cortex-A9 cores and 512 MicroBlaze cores. We then develop an MPI library for the prototype and evaluate it extensively using several bare-metal and MPI benchmarks. We find that our processor prototype is highly scalable, models faithfully single-chip multicore architectures, and is a very efficient platform for parallel programming research, being 50,000 times faster than software simulation.

Reconfigurable Long-Range Phonon Dynamics in Optomechanical Arrays

We investigate periodic optomechanical arrays as reconfigurable platforms for engineering the coupling between multiple mechanical and electromagnetic modes and for exploring many-body phonon dynamics. Exploiting structural resonances in the coupling between light fields and collective motional modes of the array, we show that tunable effective long-range interactions between mechanical modes can be achieved. This paves the way towards the implementation of controlled phononic walks and heat transfer on densely connected graphs as well as the coherent transfer of excitations between distant elements of optomechanical arrays.

Optomechanical interface for probing matter-wave coherence

We combine matter-wave interferometry and cavity optomechanics to propose a coherent matter-light interface based on mechanical motion at the quantum level. We demonstrate a mechanism that is able to transfer non-classical features imprinted on the state of a matter-wave system to an optomechanical device, transducing them into distinctive interference fringes. This provides a reliable tool for the inference of quantum coherence in the particle beam. Moreover, we discuss how our system allows for intriguing perspectives, paving the way to the construction of a device for the encoding of quantum information in matter-wave systems. Our proposal, which highlights previously unforeseen possibilities for the synergistic exploitation of these two experimental platforms, is explicitly based on existing technology, available and widely used in current cutting-edge experiments.

Weaving behavior into feature models for embedded system families

Product Line software Engineering depends on capturing the commonality and variability within a family of products, typically using feature modeling, and using this information to evolve a generic reference architecture for the family. For embedded systems, possible variability in hardware and operating system platforms is an added complication. The design process can be facilitated by first exploring the behavior associated with features. In this paper we outline a bidirectional feature modeling scheme that supports the capture of commonality and variability in the platform environment as well as within the required software. Additionally, 'behavior' associated with features can be included in the overall model. This is achieved by integrating the UCM path notation in a way that exploits UCM's static and dynamic stubs to capture behavioral variability and link it to the feature model structure. The resulting model is a richer source of information to support the architecture development process.

Monitoring a toxic bloom of Alexandrium minutum using novel microarray and multiplex surface plasmon resonance biosensor technology

Blooms of Alexandrium occur annually during the summer months in the North Channel of Cork Harbour on the south coast of Ireland. This study monitored an extensive bloom of the toxin producing Alexandrium minutum during the summer of 2011 with the use of the MIDTAL (Microarrays for the Detection of Toxic Algae) microarray and a prototype multiplex surface plasmon resonance (multi SPR) biosensor. Microarray signal intensities and toxin results from three testing platforms of the prototype multi SPR biosensor, commercial (CER) enzyme-linked immunosorbent assay (ELISA) and high performance liquid chromatography (HPLC) were compared against light microscopy counts. The main aim was to demonstrate the use of these methodologies to support national monitoring agencies by providing a faster and more accurate means of identifying and quantifying the harmful phytoplankton community and their toxins in natural water samples. Both the microarray signals and multi SPR biosensor results followed a significant trend with light microscopy results and both techniques indicated detection limits of <4000 cells of A. minutum in natural seawater samples.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Shortening design time through multiplatform simulations with a portable OpenCL golden-model: the LDPC decoder case

Hardware designers and engineers typically need to explore a multi-parametric design space in order to find the best configuration for their designs using simulations that can take weeks to months to complete. For example, designers of special purpose chips need to explore parameters such as the optimal bitwidth and data representation. This is the case for the development of complex algorithms such as Low-Density Parity-Check (LDPC) decoders used in modern communication systems. Currently, high-performance computing offers a wide set of acceleration options, that range from multicore CPUs to graphics processing units (GPUs) and FPGAs. Depending on the simulation requirements, the ideal architecture to use can vary. In this paper we propose a new design flow based on OpenCL, a unified multiplatform programming model, which accelerates LDPC decoding simulations, thereby significantly reducing architectural exploration and design time. OpenCL-based parallel kernels are used without modifications or code tuning on multicore CPUs, GPUs and FPGAs. We use SOpenCL (Silicon to OpenCL), a tool that automatically converts OpenCL kernels to RTL for mapping the simulations into FPGAs. To the best of our knowledge, this is the first time that a single, unmodified OpenCL code is used to target those three different platforms. We show that, depending on the design parameters to be explored in the simulation, on the dimension and phase of the design, the GPU or the FPGA may suit different purposes more conveniently, providing different acceleration factors. For example, although simulations can typically execute more than 3x faster on FPGAs than on GPUs, the overhead of circuit synthesis often outweighs the benefits of FPGA-accelerated execution.