Management of dry eye in UK pharmacies

Purpose - To investigate the ability of pharmacy staff in the United Kingdom (UK) to diagnose and treat dry eye. Methods - A mystery shopper technique to simulate a patient with presumed dry eye was used in 50 pharmacy practices in major towns and cities across the UK. Pharmacies were unaware of their involvement in the study. With the exception of a predetermined opening statement to initiate the consultation, no further information was volunteered. Questions asked, diagnoses given, management strategy advised and staff type was recorded immediately after the consultation. Results - The mean number of questions was 4.5 (SD 1.7; range 1–10). The most common question was the duration of symptoms (56%) and the least common was whether the patient had a history of headaches (2%). All pharmacy staff gave a diagnosis, but the majority were incorrect (58%), with only 42% correctly identifying dry eye. Treatment was advised by 92% of pharmacy staff, with the remaining 8% advising referral directly to the patient's GP or optometrist. Dry eye treatments involved topical ocular lubrication via eye drops (90%) and lipid based sprays (10%). However, only 10% gave administration advice, 10% gave dosage advice, 9% asked about contact lens wear, and none offered follow up although 15% also advised GP or optometrist referral. Conclusions - There is a need for improved ophthalmological training amongst pharmacists and pharmacy staff and establishment of cross referral relationships between pharmacies and optometry practices.

Towards the in silico identification of class II restricted T-cell epitopes:a partial least squares iterative self-consistent algorithm for affinity prediction

Motivation: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. Results: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 pep- tides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r pred =0.593 and r pred=0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. Availability: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHC- Pred

The 'moving targets' training algorithm

A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any training problem in discrete-time networks with arbitrary feedback. The method resembles back-propagation in that it is a least-squares, gradient-based optimization method, but the optimization is carried out in the hidden part of state space instead of weight space. A straightforward adaptation of this method to feedforward networks offers an alternative to training by conventional back-propagation. Computational results are presented for simple dynamical training problems, with varied success. The failures appear to arise when the method converges to a chaotic attractor. A patch-up for this problem is proposed. The patch-up involves a technique for implementing inequality constraints which may be of interest in its own right.

A projected process kriging algorithm for sensor networks with heterogeneous error characteristics

Large monitoring networks are becoming increasingly common and can generate large datasets from thousands to millions of observations in size, often with high temporal resolution. Processing large datasets using traditional geostatistical methods is prohibitively slow and in real world applications different types of sensor can be found across a monitoring network. Heterogeneities in the error characteristics of different sensors, both in terms of distribution and magnitude, presents problems for generating coherent maps. An assumption in traditional geostatistics is that observations are made directly of the underlying process being studied and that the observations are contaminated with Gaussian errors. Under this assumption, sub–optimal predictions will be obtained if the error characteristics of the sensor are effectively non–Gaussian. One method, model based geostatistics, assumes that a Gaussian process prior is imposed over the (latent) process being studied and that the sensor model forms part of the likelihood term. One problem with this type of approach is that the corresponding posterior distribution will be non–Gaussian and computationally demanding as Monte Carlo methods have to be used. An extension of a sequential, approximate Bayesian inference method enables observations with arbitrary likelihoods to be treated, in a projected process kriging framework which is less computationally intensive. The approach is illustrated using a simulated dataset with a range of sensor models and error characteristics.

Common variance in amplitude envelope perception tasks and their impact on phoneme duration perception and reading and spelling in Finnish children with reading disabilities

Our goal was to investigate auditory and speech perception abilities of children with and without reading disability (RD) and associations between auditory, speech perception, reading, and spelling skills. Participants were 9-year-old, Finnish-speaking children with RD (N = 30) and typically reading children (N = 30). Results showed significant group differences between the groups in phoneme duration discrimination but not in perception of amplitude modulation and rise time. Correlations among rise time discrimination, phoneme duration, and spelling accuracy were found for children with RD. Those children with poor rise time discrimination were also poor in phoneme duration discrimination and in spelling. Results suggest that auditory processing abilities could, at least in some children, affect speech perception skills, which in turn would lead to phonological processing deficits and dyslexia.

Methodology of photic stimulation revisited:updated European algorithm for visual stimulation in the EEG laboratory

Intermittent photic stimulation (IPS) is a common procedure performed in the electroencephalography (EEG) laboratory in children and adults to detect abnormal epileptogenic sensitivity to flickering light (i.e., photosensitivity). In practice, substantial variability in outcome is anecdotally found due to the many different methods used per laboratory and country. We believe that standardization of procedure, based on scientific and clinical data, should permit reproducible identification and quantification of photosensitivity. We hope that the use of our new algorithm will help in standardizing the IPS procedure, which in turn may more clearly identify and assist monitoring of patients with epilepsy and photosensitivity. Our algorithm goes far beyond that published in 1999 (Epilepsia, 1999a, 40, 75; Neurophysiol Clin, 1999b, 29, 318): it has substantially increased content, detailing technical and logistical aspects of IPS testing and the rationale for many of the steps in the IPS procedure. Furthermore, our latest algorithm incorporates the consensus of repeated scientific meetings of European experts in this field over a period of 6 years with feedback from general neurologists and epileptologists to improve its validity and utility. Accordingly, our European group has provided herein updated algorithms for two different levels of methodology: (1) requirements for defining photosensitivity in patients and in family members of known photosensitive patients and (2) requirements for tailored studies in patients with a clear history of visually induced seizures or complaints, and in those already known to be photosensitive.

Summarization of scientific documents by detecting common facts in citations

Reading scientific articles is more time-consuming than reading news because readers need to search and read many citations. This paper proposes a citation guided method for summarizing multiple scientific papers. A phenomenon we can observe is that citation sentences in one paragraph or section usually talk about a common fact, which is usually represented as a set of noun phrases co-occurring in citation texts and it is usually discussed from different aspects. We design a multi-document summarization system based on common fact detection. One challenge is that citations may not use the same terms to refer to a common fact. We thus use term association discovering algorithm to expand terms based on a large set of scientific article abstracts. Then, citations can be clustered based on common facts. The common fact is used as a salient term set to get relevant sentences from the corresponding cited articles to form a summary. Experiments show that our method outperforms three baseline methods by ROUGE metric.©2013 Elsevier B.V. All rights reserved.

An efficient, approximate path-following algorithm for elastic net based nonlinear spike enhancement

Unwanted spike noise in a digital signal is a common problem in digital filtering. However, sometimes the spikes are wanted and other, superimposed, signals are unwanted, and linear, time invariant (LTI) filtering is ineffective because the spikes are wideband - overlapping with independent noise in the frequency domain. So, no LTI filter can separate them, necessitating nonlinear filtering. However, there are applications in which the noise includes drift or smooth signals for which LTI filters are ideal. We describe a nonlinear filter formulated as the solution to an elastic net regularization problem, which attenuates band-limited signals and independent noise, while enhancing superimposed spikes. Making use of known analytic solutions a novel, approximate path-following algorithm is given that provides a good, filtered output with reduced computational effort by comparison to standard convex optimization methods. Accurate performance is shown on real, noisy electrophysiological recordings of neural spikes.

Handling high level of censoring for endovascular aortic repair risk prediction

Feature selection is important in medical field for many reasons. However, selecting important variables is a difficult task with the presence of censoring that is a unique feature in survival data analysis. This paper proposed an approach to deal with the censoring problem in endovascular aortic repair survival data through Bayesian networks. It was merged and embedded with a hybrid feature selection process that combines cox's univariate analysis with machine learning approaches such as ensemble artificial neural networks to select the most relevant predictive variables. The proposed algorithm was compared with common survival variable selection approaches such as; least absolute shrinkage and selection operator LASSO, and Akaike information criterion AIC methods. The results showed that it was capable of dealing with high censoring in the datasets. Moreover, ensemble classifiers increased the area under the roc curves of the two datasets collected from two centers located in United Kingdom separately. Furthermore, ensembles constructed with center 1 enhanced the concordance index of center 2 prediction compared to the model built with a single network. Although the size of the final reduced model using the neural networks and its ensembles is greater than other methods, the model outperformed the others in both concordance index and sensitivity for center 2 prediction. This indicates the reduced model is more powerful for cross center prediction.

A Bayesian neural network algorithm identifies patients in whom post-EVAR surveillance may be unnecessary

Lifelong surveillance is not cost-effective after endovascular aneurysm repair (EVAR), but is required to detect aortic complications which are fatal if untreated (type 1/3 endoleak, sac expansion, device migration). Aneurysm morphology determines the probability of aortic complications and therefore the need for surveillance, but existing analyses have proven incapable of identifying patients at sufficiently low risk to justify abandoning surveillance. This study aimed to improve the prediction of aortic complications, through the application of machine-learning techniques. Patients undergoing EVAR at 2 centres were studied from 2004–2010. Aneurysm morphology had previously been studied to derive the SGVI Score for predicting aortic complications. Bayesian Neural Networks were designed using the same data, to dichotomise patients into groups at low- or high-risk of aortic complications. Network training was performed only on patients treated at centre 1. External validation was performed by assessing network performance independently of network training, on patients treated at centre 2. Discrimination was assessed by Kaplan-Meier analysis to compare aortic complications in predicted low-risk versus predicted high-risk patients. 761 patients aged 75 +/− 7 years underwent EVAR in 2 centres. Mean follow-up was 36+/− 20 months. Neural networks were created incorporating neck angu- lation/length/diameter/volume; AAA diameter/area/volume/length/tortuosity; and common iliac tortuosity/diameter. A 19-feature network predicted aor- tic complications with excellent discrimination and external validation (5-year freedom from aortic complications in predicted low-risk vs predicted high-risk patients: 97.9% vs. 63%; p < 0.0001). A Bayesian Neural-Network algorithm can identify patients in whom it may be safe to abandon surveillance after EVAR. This proposal requires prospective study.