AN IMPROVED SCANOMETRIC IMMUNOASSAY BASED ON DUAL ENLARGEMENT OF GOLD NANOPARTICLES FOR RAPID AND LOW COST PATHOGEN DETECTION

In this study, we introduce a dual enlargement of gold nanoparticles (AuNPs) for the scanometric detection of pathogenic
bacteria. After capturing the target bacteria (Campylobacter jejuni cells), the gold immunoprobes were added to create signal on a solid substrate. The signal was then amplified dually by a gold growth process and a silver enhancement resulting in stronger intensity which can easily be recognized by an unaided eye, or measured by an inexpensive flatbed scanner. The dual-enhanced nanocatalysis is herein reported for the first time, it provides valuable insight into the development of a rapid, simple and cost-effective detection format.

Compressive Sensing based Detector for Sparse Signal Modulation in Precoded OFDM

In this paper, we propose a sparse signal modulation (SSM) method for precoded orthogonal frequency division multiplexing (OFDM) systems and study the signal detection. Although a receiver is able to exploit a path diversity gain with random precoding in OFDM, the complexity of the receiver is usually high as the orthogonality is not retained due to precoding. However, with SSM, we can derive a low-complexity detector that can provide reasonably good performances with a low sparsity ratio based on the notion of compressive sensing (CS). An important feature of a CS detector is that it can estimate SSM signals with a small fraction of the received signals over sub-carriers. This feature can allow us to build a low cost receiver with a small number of demodulators.

A cluster randomised controlled trial evaluation and cost-effectiveness analysis of the Roots of Empathy schools-based programme for improving social and emotional wellbeing outcomes among 8-9 year olds in Northern Ireland

A substantial body of evidence suggest that well designed school based prevention programmes can be effective in improving a variety of social, health and academic outcomes for children and young people. This poster presents the methodology for evaluating the Roots of Empathy (ROE) programme. ROE is a universal programme delivered on a whole-class basis for one academic year. It consists of 27 lessons that run over a school year and is based around a monthly classroom visit by an infant and parent, typically recruited from the local community, whom the class 'adopts' at the start of the school year. The evaluation aims to evaluate the immediate and longer term impact of ROE on social and emotional wellbeing outcomes among 8-9 year old pupils, as well as evaluate the cost-effectiveness of the programme.

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

Investigation into DCT Feature Selection for Visual Lip-Based Biometric Authentication

This paper investigated using lip movements as a behavioural biometric for person authentication. The system was trained, evaluated and tested using the XM2VTS dataset, following the Lausanne Protocol configuration II. Features were selected from the DCT coefficients of the greyscale lip image. This paper investigated the number of DCT coefficients selected, the selection process, and static and dynamic feature combinations. Using a Gaussian Mixture Model - Universal Background Model framework an Equal Error Rate of 2.20% was achieved during evaluation and on an unseen test set a False Acceptance Rate of 1.7% and False Rejection Rate of 3.0% was achieved. This compares favourably with face authentication results on the same dataset whilst not being susceptible to spoofing attacks.

k-fold Subsampling based Sequential Backward Feature Elimination

We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid featureselection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimalfeature vector that well represents the shapes of the subjects in the images. In detail, the proposed featureselection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while thestandard linear support vector machine (SVM) is used as the classifier for human detection. We apply theproposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCALVOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approachcan improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy.Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach witharound 9% improvement in the detection accuracy

Cost-effectiveness of natriuretic peptide-based screening and collaborative care: a report from the STOP-HF (St Vincent's Screening TO Prevent Heart Failure) study

AIMS: Prevention of cardiovascular disease and heart failure (HF) in a cost-effective manner is a public health goal. This work aims to assess the cost-effectiveness of the St Vincent's Screening TO Prevent Heart Failure (STOP-HF) intervention.

METHODS AND RESULTS: This is a substudy of 1054 participants with cardiovascular risk factors [median age 65.8 years, interquartile range (IQR) 57.8:72.4, with 4.3 years, IQR 3.4:5.2, follow-up]. Annual natriuretic peptide-based screening was performed, with collaborative cardiovascular care between specialist physicians and general practitioners provided to patients with BNP levels >50 pg/mL. Analysis of cost per case prevented and cost-effectiveness per quality-adjusted life year (QALY) gained was performed. The primary clinical endpoint of LV dysfunction (LVD) with or without HF was reduced in intervention patients [odds ratio (OR) 0.60; 95% confidence interval (CI) 0.38-0.94; P = 0.026]. There were 157 deaths and/or emergency hospitalizations for major adverse cardiac events (MACE) in the control group vs. 102 in the intervention group (OR 0.68; 95% CI 0.49-0.93; P = 0.01). The cost per case of LVD/HF prevented was €9683 (sensitivity range -€843 to €20 210), whereas the cost per MACE prevented was €3471 (sensitivity range -€302 to €7245). Cardiovascular hospitalization savings offset increased outpatient and primary care costs. The cost per QALY gain was €1104 and the intervention has an 88% probability of being cost-effective at a willingness to pay threshold of €30 000.

CONCLUSION: Among patients with cardiovascular risk factors, natriuretic peptide-based screening and collaborative care reduced LVD, HF, and MACE, and has a high probability of being cost-effective.

TRIAL REGISTRATION: NCT00921960.

Non-contact Pressure-based Sleep/Wake Discrimination

Poor sleep is increasingly being recognised as an important prognostic parameter of health. For those with suspected sleep disorders, patients are referred to sleep clinics which guide treatment. However, sleep clinics are not always a viable option due to their high cost, a lack of experienced practitioners, lengthy waiting lists and an unrepresentative sleeping environment. A home-based non-contact sleep/wake monitoring system may be used as a guide for treatment potentially stratifying patients by clinical need or highlighting longitudinal changes in sleep and nocturnal patterns. This paper presents the evaluation of an under-mattress sleep monitoring system for non-contact sleep/wake discrimination. A large dataset of sensor data with concomitant sleep/wake state was collected from both younger and older adults participating in a circadian sleep study. A thorough training/testing/validation procedure was configured and optimised feature extraction and sleep/wake discrimination algorithms evaluated both within and across the two cohorts. An accuracy, sensitivity and specificity of 74.3%, 95.5%, and 53.2% is reported over all subjects using an external validation
dataset (71.9%, 87.9% and 56%, and 77.5%, 98% and 57% is reported for younger and older subjects respectively). These results compare favourably with similar research, however this system provides an ambient alternative suitable for long term continuous sleep monitoring, particularly amongst vulnerable populations.