Multiplex biotoxin surface plasmon resonance method for marine biotoxins in algal and seawater samples

A multiplex surface plasmon resonance (SPR) biosensor method for the detection of paralytic shellfish poisoning (PSP) toxins, okadaic acid (and analogues) and domoic acid was developed. This method was compared to enzyme-linked immunosorbent assay (ELISA) methods. Seawater samples (n?=?256) from around Europe were collected by the consortia of an EU project MIcroarrays for the Detection of Toxic Algae (MIDTAL) and evaluated using each method. A simple sample preparation procedure was developed which involved lysing and releasing the toxins from the algal cells with glass beads followed by centrifugation and filtering the extract before testing for marine biotoxins by both multi-SPR and ELISA. Method detection limits based on IC20 values for PSP, okadaic acid and domoic acid toxins were 0.82, 0.36 and 1.66 ng/ml, respectively, for the prototype multiplex SPR biosensor. Evaluation by SPR for seawater samples has shown that 47, 59 and 61 % of total seawater samples tested positive (result greater than the IC20) for PSP, okadaic acid (and analogues) and domoic acid toxins, respectively. Toxic samples were received mainly from Spain and Ireland. This work has demonstrated the potential of multiplex analysis for marine biotoxins in algal and seawater samples with results available for 24 samples within a 7 h period for three groups of key marine biotoxins. Multiplex immunological methods could therefore be used as early warning monitoring tools for a variety of marine biotoxins in seawater samples.

The Effects of Lead Sources on Oral Bioaccessibility in Soil and Implications for Contaminated Land Risk Management

Lead (Pb) is a non-threshold toxin capable of inducing toxic effects at any blood level but availability of soil screening criteria for assessing potential health risks is limited. The oral bioaccessibility of Pb in 163 soil samples was attributed to sources through solubility estimation and domain identification. Samples were extracted following the Unified BARGE Method. Urban, mineralisation, peat and granite domains accounted for elevated Pb concentrations compared to rural samples. High Pb solubility explained moderate-high gastric (G) bioaccessible fractions throughout the study area. Higher maximum G concentrations were measured in urban (97.6 mg kg−1) and mineralisation (199.8 mg kg−1) domains. Higher average G concentrations occurred in mineralisation (36.4 mg kg−1) and granite (36.0 mg kg−1) domains. Findings suggest diffuse anthropogenic and widespread geogenic contamination could be capable of presenting health risks, having implications for land management decisions in jurisdictions where guidance advises these forms of pollution should not be regarded as contaminated land.

Dynamic State Estimation of Power Systems with Quantization Eects: A Recursive Filter Approach

In this paper, a recursive filter algorithm is developed to deal with the state estimation problem for power systems with quantized nonlinear measurements. The measurements from both the remote terminal units and the phasor measurement unit are subject to quantizations described by a logarithmic quantizer. Attention is focused on the design of a recursive filter such that, in the simultaneous presence of nonlinear measurements and quantization effects, an upper bound for the estimation error covariance is guaranteed and subsequently minimized. Instead of using the traditional approximation methods in nonlinear estimation that simply ignore the linearization errors, we treat both the linearization and quantization errors as norm-bounded uncertainties in the algorithm development so as to improve the performance of the estimator. For the power system with such kind of introduced uncertainties, a filter is designed in the framework of robust recursive estimation, and the developed filter algorithm is tested on the IEEE benchmark power system to demonstrate its effectiveness.

Deep Head Pose: Gaze-Direction Estimation in Multimodal Video

In this paper we present a convolutional neuralnetwork (CNN)-based model for human head pose estimation inlow-resolution multi-modal RGB-D data. We pose the problemas one of classification of human gazing direction. We furtherfine-tune a regressor based on the learned deep classifier. Next wecombine the two models (classification and regression) to estimateapproximate regression confidence. We present state-of-the-artresults in datasets that span the range of high-resolution humanrobot interaction (close up faces plus depth information) data tochallenging low resolution outdoor surveillance data. We buildupon our robust head-pose estimation and further introduce anew visual attention model to recover interaction with theenvironment. Using this probabilistic model, we show thatmany higher level scene understanding like human-human/sceneinteraction detection can be achieved. Our solution runs inreal-time on commercial hardware

Massive MIMO with I/Q imbalance: Performance analysis and compensation

In this paper, we consider the uplink of a single-cell massive multiple-input multiple-output (MIMO) system with inphase and quadrature-phase imbalance (IQI). This scenario is of particular importance in massive MIMO systems, where the deployment of lower-cost, lower-quality components is desirable to make massive MIMO a viable technology. Particularly, we investigate the effect of IQI on the performance of massive MIMO employing maximum-ratio combining (MRC) receivers. In order to study how IQI affects channel estimation, we derive a new channel estimator for the IQI-impaired model and show that IQI can substantially downgrade the performance of MRC receivers. Moreover, a low-complexity IQI compensation scheme, suitable for massive MIMO, is proposed which is based on the IQI coefficients' estimation and it is independent of the channel gain. The performance of the proposed compensation scheme is analytically evaluated by deriving a tractable approximation of the ergodic achievable rate and providing the asymptotic power scaling laws assuming transmission over Rayleigh fading channels with log-normal large-scale fading. Finally, we show that massive MIMO effectively suppresses the residual IQI effects, as long as, the compensation scheme is applied.

Judging Time Intevals using a model of perceptuo-motor control

Estimating a time interval and temporally coordinating movements in space are fundamental skills, but the relationships between these different forms of timing, and the neural processes that they incur, are not well understood. While different theories have been proposed to account for time perception, time estimation, and the temporal patterns of coordination, there are no general mechanisms which unify these various timing skills. This study considers whether a model of perceptuo-motor timing, the tau(GUIDE), can also describe how certain judgements of elapsed time are made. To evaluate this, an equation for determining interval estimates was derived from the tau(GUIDE) model and tested in a task where participants had to throw a ball and estimate when it would hit the floor. The results showed that in accordance with the model, very accurate judgements could be made without vision (mean timing error -19.24 msec), and the model was a good predictor of skilled participants' estimate timing. It was concluded that since the tau(GUIDE) principle provides temporal information in a generic form, it could be a unitary process that links different forms of timing.

Multiplier-less realization of a poly-phase filter using LUT-based FPGAs

In DSP applications such as fixed transforms and filtering, the full flexibility of a general-purpose multiplier is not required and only a limited range of values is needed on one of the multiplier inputs. A new design technique has been developed for deriving multipliers that operate on a limited range of multiplicands. This can be used to produce FPGA implementations of DSP systems where area is dramatically improved. The paper describes the technique and its application to the design of a poly-phase filter on a Virtex FPGA. A 62% area reduction and 7% speed increase is gained when compared to an equivalent design using general purpose multipliers. It is also compared favourably to other known fixed coefficient approaches.

The Y-procedure: How to extract the chemical transformation rate from reaction-diffusion data with no assumption on the kinetic model

The paper presents a new method to extract the chemical transformation rate from reaction–diffusion data with no assumption on the kinetic model (“kinetic model-free procedure”). It is a new non-steady-state kinetic characterization procedure for heterogeneous catalysts. The mathematical foundation of the Y-procedure is a Laplace-domain analysis of the two inert zones in a TZTR followed by transposition to the Fourier domain. When combined with time discretization and filtering the Y-procedure leads to an efficient practical method for reconstructing the concentration and reaction rate in the active zone. Using the Y-procedure the concentration and reaction rate of a non-steady state catalytic process can be determined without any pre-assumption regarding the type of kinetic dependence. The Y-procedure is the basis for advanced software for non-steady state kinetic data interpretation. The Y-procedure can be used to relate changes in the catalytic reaction rate and kinetic parameters to changes in the surface composition (storage) of a catalyst.

Experimenting with the Coxian Phase-Type Distribution to Uncover Suitable Fits

In the past few decades, Coxian phase-type distributions have become increasingly more popular as a means of representing survival times. In healthcare, they are considered suitable for modelling the length of stay of patients in hospital and more recently for modelling the patient waiting times in Accident and Emergency Departments. The Coxian phase-type distribution has not only been shown to provide a good representation of real survival data, but its interpretation seems reasonably initiative to the medical experts. The drawback, however, is fitting the distribution to the data. There have been many attempts at accurately estimating the Coxian phase-type parameters. This paper wishes to examine the most promising of the approaches reported in the literature to determine the most accurate. Three performance measures are introduced to assess the fitting process of the algorithms along with the likelihood values and AIC to examine the goodness of fit and complexity of the model. Previous research suggests that the fitting process is strongly influenced by the initial parameter estimates and the data itself being quite variable. To overcome this, one experiment in this research paper will use the same initial parameter values for each estimation and perform the fits on the data simulated from a Coxian phase-type distribution with known parameters.

On MIMO Radar Subarrayed Transmit Beamforming

Multiple-input-multiple-output (MIMO) radar schemes whereby the transmit array is partitioned into subarrays have recently been proposed in the literature to combine advantages of phased array and MIMO radar technology. In this work, we utilize this architecture to significantly simplify a transmit procedure in which the covariance matrix across the MIMO radar array is optimized to improve the Cramer-Rao bound (CRB) on target parameter estimation. The MIMO effective array for regular subarrayed transmit apertures is studied, and necessary conditions to obtain a filled effective aperture are presented, which is important for maintaining nonambiguous, low sidelobe beampatterns. The performance of the subarrayed transmit approach is evaluated in terms of the CRB on target parameter estimation, and the optimisation of the beamformer applied to the subarrays to minimize the CRB is considered. The subarrayed transmit scheme is found to have a CRB which is suboptimal to the full diversity transmission, as expected, but is solvable in a small fraction of the time using an iterative beamspace algorithm developed here.

Modelling Transition Due to Backward Facing Steps Using the Laminar Kinetic Energy Concept

Boundary layer transition estimation and modelling is essential for the design of many engineering products across many industries. In this paper, the Reynolds-averaged Navier–Stokes are solved in conjunction with three additional transport equations to model and predict boundary layer transition. The transition model (referred to as the kTkT–kLkL–ωω model) is based on the kk–ωω framework with an additional transport equation to incorporate the effects low-frequency flow oscillations in the form of a laminar kinetic energy (kLkL). Firstly, a number of rectifications are made to the original kTkT–kLkL–ωω framework in order to ensure an appropriate response to the free-stream turbulence level and to improve near wall predictions. Additionally, the model is extended to incorporate the capability to model transition due to surface irregularities in the form of backward-facing steps with maximum non-dimensional step sizes of approximately 1.5 times the local displacement thickness of the boundary layer where the irregularity is located (i.e k/δ∗⪅1.5k/δ∗⪅1.5) at upstream turbulence intensities in the range 0.01<Tu(%)<0.80.01<Tu(%)<0.8. A novel function is proposed to incorporate transition sensitivity due to aft-facing steps. This paper details the rationale behind the development of this new function and demonstrates its suitability for transition onset estimation on a flat plate at zero pressure gradient.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Ophthalmic epidemiology in Europe: the “European Eye Epidemiology” (E3) consortium

The European Eye Epidemiology (E3) consortium is a recently formed consortium of 29 groups from 12 European countries. It already comprises 21 population-based studies and 20 other studies (case-control, cases only, randomized trials), providing ophthalmological data on approximately 170,000 European participants. The aim of the consortium is to promote and sustain collaboration and sharing of data and knowledge in the field of ophthalmic epidemiology in Europe, with particular focus on the harmonization of methods for future research, estimation and projection of frequency and impact of visual outcomes in European populations (including temporal trends and European subregions), identification of risk factors and pathways for eye diseases (lifestyle, vascular and metabolic factors, genetics, epigenetics and biomarkers) and development and validation of prediction models for eye diseases. Coordinating these existing data will allow a detailed study of the risk factors and consequences of eye diseases and visual impairment, including study of international geographical variation which is not possible in individual studies. It is expected that collaborative work on these existing data will provide additional knowledge, despite the fact that the risk factors and the methods for collecting them differ somewhat among the participating studies. Most studies also include biobanks of various biological samples, which will enable identification of biomarkers to detect and predict occurrence and progression of eye diseases. This article outlines the rationale of the consortium, its design and presents a summary of the methodology.

Novel tools for conservation genomics: comparing two high-throughput approaches for SNP discovery in the transcriptome of the European hake

The growing accessibility to genomic resources using next-generation sequencing (NGS) technologies has revolutionized the application of molecular genetic tools to ecology and evolutionary studies in non-model organisms. Here we present the case study of the European hake (Merluccius merluccius), one of the most important demersal resources of European fisheries. Two sequencing platforms, the Roche 454 FLX (454) and the Illumina Genome Analyzer (GAII), were used for Single Nucleotide Polymorphisms (SNPs) discovery in the hake muscle transcriptome. De novo transcriptome assembly into unique contigs, annotation, and in silico SNP detection were carried out in parallel for 454 and GAII sequence data. High-throughput genotyping using the Illumina GoldenGate assay was performed for validating 1,536 putative SNPs. Validation results were analysed to compare the performances of 454 and GAII methods and to evaluate the role of several variables (e.g. sequencing depth, intron-exon structure, sequence quality and annotation). Despite well-known differences in sequence length and throughput, the two approaches showed similar assay conversion rates (approximately 43%) and percentages of polymorphic loci (67.5% and 63.3% for GAII and 454, respectively). Both NGS platforms therefore demonstrated to be suitable for large scale identification of SNPs in transcribed regions of non-model species, although the lack of a reference genome profoundly affects the genotyping success rate. The overall efficiency, however, can be improved using strict quality and filtering criteria for SNP selection (sequence quality, intron-exon structure, target region score).

A frontal view gait recognition based on 3D imaging using a time of flight camera

Studies have been carried out to recognize individuals from a frontal view using their gait patterns. In previous work, gait sequences were captured using either single or stereo RGB camera systems or the Kinect 1.0 camera system. In this research, we used a new frontal view gait recognition method using a laser based Time of Flight (ToF) camera. In addition to the new gait data set, other contributions include enhancement of the silhouette segmentation, gait cycle estimation and gait image representations. We propose four new gait image representations namely Gait Depth Energy Image (GDE), Partial GDE (PGDE), Discrete Cosine Transform GDE (DGDE) and Partial DGDE (PDGDE). The experimental results show that all the proposed gait image representations produce better accuracy than the previous methods. In addition, we have also developed Fusion GDEs (FGDEs) which achieve better overall accuracy and outperform the previous methods.