Promoting normality in the management of the perineum during the second stage of labour

The management of the perineum during birth has multiple long-term effects on women and their families. The midwife has a key role to play and often the techniques they employ vary significantly, as does their justification of these practices. This article seeks to examine current evidence to explore what is known to contribute to lower perineal trauma rates and what practices should be avoided to protect childbearing women. The conclusions drawn show that the updating of
practice and antenatal education may be required so that woman are given the information they need to make an informed choice as to what they want for their own body, child and experience.

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.

Two-Stage Transmitter Precoding Based on Data-Driven Code Hopping and Partial Zero Forcing Beamforming for MC-DCMA Communications

This paper proposes a hybrid transmission technique based on adaptive code-to-user allocation and linear precoding for the downlink of phase shift keying (PSK) based multi-carrier code division multiple access (MC-CDMA) systems. The proposed scheme is based on the separation of the instantaneous multiple access interference (MAI) into constructive and destructive components taking into account the dependency on both the channel variation and the instantaneous symbol values of the active users. The first stage of the proposed technique is to adaptively distribute the available spreading sequences to the users on a symbol-by-symbol basis in the form of codehopping with the objective to steer the users' instantaneous crosscorrelations to yield a favourable constructive to destructive MAI ratio. The second stage is to employ a partial transmitter based zero forcing (ZF) scheme specifically designed for the exploitation of constructive MAI. The partial ZF processing decorrelates destructive interferers, while users that interfere constructively remain correlated. This results in a signal to interference-plus-noise ratio (SINR) enhancement without the need for additional power-per-user investment. It will be shown in the results section that significant bit error rate (BER) performance benefits can be achieved with this technique.

A multi-output two-stage locally regularized model construction method using the extreme learning machine

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.

SORBS1 gene, a new candidate for diabetic nephropathy: results from a multi-stage genome-wide association study in patients with type 1 diabetes

Aims/hypothesis

Getting to the root of neuronal signalling in plant-parasitic nematodes using RNA interference

A variety of genes expressed in preparasitic second-stage juveniles (J2) of plant-parasitic nematodes appear to be vulnerable to RNA interference (RNAi) in vitro by coupling double-stranded (ds)RNA soaking with the artificial stimulation of pharyngeal pumping. Also, there is mounting evidence that the in planta generation of nematode-specific double-stranded RNAs (dsRNAs) has real utility in the control of these pests. Although neuronally-expressed genes in Caenorhabditis elegans are commonly refractory to RNAi, we have discovered that neuronally-expressed genes in plant-parasitic nematodes are highly susceptible to RNAi and that silencing can be induced by simple soaking procedures without the need for pharyngeal stimulation. Since most front-line anthelmintics that are used for the control of nematode parasites of animals and humans act to disrupt neuromuscular coordination, we argue that intercellular signalling processes associated with neurons have much appeal as targets for transgenic plant-based control strategies for plant-parasitic nematodes. FMRFamide-like peptides (FLPs) are a large family of neuropeptides which are intimately associated with neuromuscular regulation, and our studies on flp gene function in plant-parasitic nematodes have revealed that their expression is central to coordinated locomotory activities. We propose that the high level of conservation in nervous systems across nematodes coupled with the RNAi-susceptibility of neuronally-expressed genes in plant-parasitic nematodes provides a valuable research tool which could be used to interrogate neuronal signalling processes in nematodes.

Reduced-complexity iterative equalization for severe time-dispersive MIMO channels

In this paper, a reduced-complexity soft-interference-cancellation minimum mean-square-error.(SIC-MMSE) iterative equalization method for severe time-dispersive multiple-input-multiple-output (MIMO) channels is proposed. To mitigate the severe time dispersiveness of the channel, a single carrier with cyclic prefix is employed, and the equalization is per-formed in the frequency domain. This simplifies the challenging problem of equalization in MIMO channels due to both the intersymbol interference (ISI) and the coantenna interference (CAI). The proposed iterative algorithm works in two stages. The first stage estimates the transmitted frequency-domain symbols using a low-complexity SIC-MMSE equalizer. The second stage converts the estimated frequency-domain symbols in the time domain and finds their means and variances to incorporate in the SIC-MMSE equalizer in the next iteration. Simulation results show the bit-/symbol-error-rate performance of the SIC-MMSE equalizer, with and without coding, for various modulation schemes.

tts: A SAT-Solver for Small, Difficult Instances

The Ternary Tree Solver (tts) is a complete solver for propositional satisfiability which was designed to have good performance on the most difficult small instances. It uses a static ternary tree data structure to represent the simplified proposition under all permissible partial assignments and maintains a database of derived propositions known to be unsatisfiable. In the SAT2007 competition version 4.0 won the silver medal for the category handmade, speciality UNSAT solvers and was the top qualifier for the second stage for handmade benchmarks, solving 11 benchmarks which were not solved by any other entrant. We describe the methods used by the solver and analyse the competition Phase 1 results on small benchmarks. We propose a first version of a comprehensive suite of small difficult satisfiability benchmarks (sdsb) and compare the worst-case performance of the competition medallists on these benchmarks.

Irish Credit Unions: Investigating performance determinants and the opportunity cost of regulatory compliance

The study investigates how producer-specific environmental factors influence the performance of Irish credit unions. The empirical analysis uses a two-stage approach. The first stage measures efficiency by a data envelopment analysis (DEA) estimator, which explicitly incorporates the production of undesirable outputs such as bad loans in the modelling, and the second stage uses truncated regression to infer how various factors influence the (bias-corrected) estimated efficiency. A key finding of the analysis is that 68% of Irish credit unions do not incur an extra opportunity cost in meeting regulatory guidance on bad debt.

An Analysis of Advertising Wars

Comparative advertising by one brand against another showcases its merits versus the demerits of the other. In a two-stage game with finitely many firms, firms decide first how much to advertise against whom. In the second stage, given the advertising configuration, firms compete as Cournot oligopolists. In the symmetric case, equilibrium advertising constitutes a clear welfare loss. In the asymmetric case, depending on parameter values, a variety of outcomes are possible in equilibrium. An a priori disadvantaged firm (in terms of advertising costs or advertising effectiveness) may advertise more. Advertising can affect firms that are not advertisers or targets themselves.

Application of Nonlinear PCA for Fault Detection in Polymer Extrusion Processes

This paper describes the application of an improved nonlinear principal component analysis (PCA) to the detection of faults in polymer extrusion processes. Since the processes are complex in nature and nonlinear relationships exist between the recorded variables, an improved nonlinear PCA, which incorporates the radial basis function (RBF) networks and principal curves, is proposed. This algorithm comprises two stages. The first stage involves the use of the serial principal curve to obtain the nonlinear scores and approximated data. The second stage is to construct two RBF networks using a fast recursive algorithm to solve the topology problem in traditional nonlinear PCA. The benefits of this improvement are demonstrated in the practical application to a polymer extrusion process.

Novel bioassays to examine the host-finding ability of plant-parasitic nematodes

We present two novel bioassays to be used in the examination of plant-parasitic nematode host-finding ability. The host-finding 'pipette-bulb assay' was constructed from modelled Pasteur pipette bulbs and connecting barrels using parafilm fastenings. This assay examines the direction of second-stage juvenile (J2) migration in response to a host seedling, through a moistened sand substrate, which underlies terminal upward-facing 'seedling bulbs', one containing a host seedling in potting compost, the other with only potting compost. An equal watering regime through both upward-facing seedling bulbs creates a directional concentration gradient of host diffusate chemotactic factors. Positive chemotactic stimuli cause the J2 to orientate and migrate towards the host plant. We present validation data collected from assays of the root-knot nematode, Meloidogyne incognita, and the potato cyst nematode, Globodera pallida, which indicate a highly significant positive attraction of J2 of both species to respective host plants. This represents a simple, quick and inexpensive method of assessing host-finding behaviour in the laboratory. We consider that the pipette-bulb assay improves on previous host-finding/chemo-attraction assays through creating a more biologically relevant environment for experimental J2; analysis is quick and easy, allowing the straightforward interpretation of results. In addition, we have developed an 'agar trough' sensory assay variant which we believe can be used rapidly to ratify nematode responses to chemical gustatory or olfactory cues. This was constructed from a water agar substrate such that two counting wells were connected by a raised central trough, all flooded with water. Two small water agar plugs were dehydrated briefly in an oven and then hydrated in either an attractant, repellent or water control; these plugs were then placed in the terminal counting wells and subsequently leached the attractant or repellent to form a concentration gradient along the central trough, which contained the initial J2 innoculum. Our data show that both M. incognita and G. pallida J2 are positively attracted to host diffusates. In addition, they displayed a strong repulsion in response to 1 M NaCl2. J2 of M. incognita displayed a mild aversion to a non-host oak root diffusate, whereas G. pallida J2 displayed a strong aversion to the same non-host diffusate; neither species responded to a compost leachate. We believe that the agar trough assay improves on previous methods by facilitating rapid diffusion of attractant or repellents. Both of the aforementioned assays were designed as tools to assess the impact of RNAi-based reverse genetics screens for gene targets involved in chemosensory orientation.

Common-sense reasoning for human action recognition

This paper presents a novel method that leverages reasoning capabilities in a computer vision system dedicated to human action recognition. The proposed methodology is decomposed into two stages. First, a machine learning based algorithm - known as bag of words - gives a first estimate of action classification from video sequences, by performing an image feature analysis. Those results are afterward passed to a common-sense reasoning system, which analyses, selects and corrects the initial estimation yielded by the machine learning algorithm. This second stage resorts to the knowledge implicit in the rationality that motivates human behaviour. Experiments are performed in realistic conditions, where poor recognition rates by the machine learning techniques are significantly improved by the second stage in which common-sense knowledge and reasoning capabilities have been leveraged. This demonstrates the value of integrating common-sense capabilities into a computer vision pipeline. © 2012 Elsevier B.V. All rights reserved.

Online Multiperson Tracking With Occlusion Reasoning and Unsupervised Track Motion Model

We address the problem of multi-target tracking in realistic crowded conditions by introducing a novel dual-stage online tracking algorithm. The problem of data-association between tracks and detections, based on appearance, is often complicated by partial occlusion. In the first stage, we address the issue of occlusion with a novel method of robust data-association, that can be used to compute the appearance similarity between tracks and detections without the need for explicit knowledge of the occluded regions. In the second stage, broken tracks are linked based on motion and appearance, using an online-learned linking model. The online-learned motion-model for track linking uses the confident tracks from the first stage tracker as training examples. The new approach has been tested on the town centre dataset and has performance comparable with the present state-of-the-art

Learner-Teacher Interaction (LTI): engaging and supporting students in achieving self-regulated learning

This report represents the second stage of a study which was part of a wide-ranging research programme conducted by the Centre for Excellence of Interprofessional Education, Queen’s University Belfast. The study was an investigation into learner-teacher interaction in the education of undergraduate medical and other healthcare students in order to inform how teachers might facilitate learning in a healthcare setting. It focused in particular on clinical and ward-based tutorials and seminars.

In order to give meaning to this second stage of the study, the report will contextualise the learner-teacher interaction study, will describe the research methods and methods of analysis developed and used to explore learner-teacher interaction. It will then focus on this second stage of the research and the results of the analysis of video sessions of clinical and ward-based tutorials and seminars. In particular it will identify examples of good practice and missed opportunities for the engagement of the learners.