Principal Component Analysis of Wide Area Phasor Measurements for Islanding Detection – A Geometric View

This paper presents a new technique for the detectionof islanding conditions in electrical power systems. This problem isespecially prevalent in systems with significant penetrations of distributedrenewable generation. The proposed technique is based onthe application of principal component analysis (PCA) to data setsof wide-area frequency measurements, recorded by phasor measurementunits. The PCA approach was able to detect islandingaccurately and quickly when compared with conventional RoCoFtechniques, as well as with the frequency difference and change-ofangledifference methods recently proposed in the literature. Thereliability and accuracy of the proposed PCA approach is demonstratedby using a number of test cases, which consider islandingand nonislanding events. The test cases are based on real data,recorded from several phasor measurement units located in theU.K. power system.

Automatic Analysis of Digital Retinal Images for Glaucoma Detection

In this paper we propose a novel automated glaucoma detection framework for mass-screening that operates on inexpensive retinal cameras. The proposed methodology is based on the assumption that discriminative features for glaucoma diagnosis can be extracted from the optical nerve head structures,
such as the cup-to-disc ratio or the neuro-retinal rim variation. After automatically segmenting the cup and optical disc, these features are feed into a machine learning classifier. Experiments were performed using two different datasets and from the obtained results the proposed technique provides
better performance than approaches based on appearance. A main advantage of our approach is that it only requires a few training samples to provide high accuracy over several different glaucoma stages.

"...and they won land among the Picts by friendly treaty or the sword": How a re-examination of early historical sources and an analysis of early medieval settlement in north Co. Antrim confirms the validity of traditional accounts of Dál Riatic migration to Scotland from Ulster

Much recent scholarship has been critical of the concept of a Dál Riatic migration to, or colonisation of, Argyll. Scepticism of the accuracy of the early medieval accounts of this population movement, arguing that these are late amendments to early sources, coupled with an apparent lack of archaeological evidence for such a migration have led to its rejection. It is argued here, however, that this rejection has been based on too narrow a reading of historical sources and that there are several early accounts which, while differing in detail, agree on one point of substance, that the origin of Scottish Dál Riata lies in Ireland. Also, the use of archaeological evidence to suggest no migration to Argyll by the Dál Riata is flawed, misunderstanding the nature of early migrations and how they might be archaeologically identified, and it's proposed that there is actually quite a lot of evidence for migration to Argyll by the Dál Riata, in the form of settlement and artefactural evidence, but that it is to be found in Ireland through the mechanism of counterstream migration, rather than in Scotland.

Successful Food Safety Analysis

Dr Kevin Cooper of the Institute for Global Food Security at Queen’s University Belfast, Northern Ireland, spoke to Kate Mosford of The
Column about the importance of accuracy, reliability, and stability in food safety analysis and the role of ultrahigh-pressure liquid chromatography tandem mass spectrometry (UHPLC–MS–MS) in his research.

Combined Analysis of Gamma-H2AX/53BP1 Foci and Caspase Activation in Lymphocyte Subsets Detects Recent and More Remote Radiation Exposures

Analysis of gamma-H2AX foci in blood lymphocytes is a promising approach for rapid dose estimation to support patient triage after a radiation accident but has one major drawback: the rapid decline of foci levels post-exposure cause major uncertainties in situations where the exact timing between exposure and blood sampling is unknown. To address this issue, radiation-induced apoptosis (RIA) in lymphocytes was investigated using fluorogenic inhibitors of caspases (FLICA) as an independent biomarker for radiation exposure, which may complement the gamma-H2AX assay. Ex vivo X-irradiated peripheral blood lymphocytes from 17 volunteers showed dose-and time-dependent increases in radiation-induced apoptosis over the first 3 days after exposure, albeit with considerable interindividual variation. Comparison with gamma-H2AX and 53BP1 foci counts suggested an inverse correlation between numbers of residual foci and radiation-induced apoptosis in lymphocytes at 24 h postirradiation (P = 0.007). In T-helper (CD4), T-cytotoxic (CD8) and B-cells (CD19), some significant differences in radiation induced DSBs or apoptosis were observed, however no correlation between foci and apoptosis in lymphocyte subsets was observed at 24 h postirradiation. While gamma-H2AX and 53BP1 foci were rapidly induced and then repaired after exposure, radiation-induced apoptosis did not become apparent until 24 h after exposure. Data from six volunteers with different ex vivo doses and post-exposure times were used to test the capability of the combined assay. Results show that simultaneous analysis of gamma-H2AX and radiation-induced apoptosis may provide a rapid and more accurate triage tool in situations where the delay between exposure and blood sampling is unknown compared to gamma-H2AX alone. This combined approach may improve the accuracy of dose estimations in cases where blood sampling is performed days after the radiation exposure. 

An EPID-based method for comprehensive verification of gantry, EPID and the MLC carriage positional accuracy in Varian linacs during arc treatments

Background

]In modern radiotherapy, it is crucial to monitor the performance of all linac components including gantry, collimation system and electronic portal imaging device (EPID) during arc deliveries. In this study, a simple EPID-based measurement method has been introduced in conjunction with an algorithm to investigate the stability of these systems during arc treatments with the aim of ensuring the accuracy of linac mechanical performance.

The Varian EPID sag, gantry sag, changes in source-to-detector distance (SDD), EPID and collimator skewness, EPID tilt, and the sag in MLC carriages as a result of linac rotation were separately investigated by acquisition of EPID images of a simple phantom comprised of 5 ball-bearings during arc delivery. A fast and robust software package was developed for automated analysis of image data. Twelve Varian linacs of different models were investigated.

The average EPID sag was within 1 mm for all tested linacs. All machines showed less than 1 mm gantry sag. Changes in SDD values were within 1.7 mm except for three linacs of one centre which were within 9 mm. Values of EPID skewness and tilt were negligible in all tested linacs. The maximum sag in MLC leaf bank assemblies was around 1 mm. The EPID sag showed a considerable improvement in TrueBeam linacs.

The methodology and software developed in this study provide a simple tool for effective investigation of the behaviour of linac components with gantry rotation. It is reproducible and accurate and can be easily performed as a routine test in clinics.

Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.

A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability

Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case. 

Automated tumour annotation and analysis for molecular pathology using TissueMark (R)

Objective: Molecular pathology relies on identifying anomalies using PCR or analysis of DNA/RNA. This is important in solid tumours where molecular stratification of patients define targeted treatment. These molecular biomarkers rely on examination of tumour, annotation for possible macro dissection/tumour cell enrichment and the estimation of % tumour. Manually marking up tumour is error prone. Method: We have developed a method for automated tumour mark-up and % cell calculations using image analysis called TissueMark® based on texture analysis for lung, colorectal and breast (cases=245, 100, 100 respectively). Pathologists marked slides for tumour and reviewed the automated analysis. A subset of slides was manually counted for tumour cells to provide a benchmark for automated image analysis. Results: There was a strong concordance between pathological and automated mark-up (100 % acceptance rate for macro-dissection). We also showed a strong concordance between manually/automatic drawn boundaries (median exclusion/inclusion error of 91.70 %/89 %). EGFR mutation analysis was precisely the same for manual and automated annotation-based macrodissection. The annotation accuracy rates in breast and colorectal cancer were 83 and 80 % respectively. Finally, region-based estimations of tumour percentage using image analysis showed significant correlation with actual cell counts. Conclusion: Image analysis can be used for macro-dissection to (i) annotate tissue for tumour and (ii) estimate the % tumour cells and represents an approach to standardising/improving molecular diagnostics.

Global Sensitivity Analysis for MAP Inference in Graphical Models

We study the sensitivity of a MAP configuration of a discrete probabilistic graphical model with respect to perturbations of its parameters. These perturbations are global, in the sense that simultaneous perturbations of all the parameters (or any chosen subset of them) are allowed. Our main contribution is an exact algorithm that can check whether the MAP configuration is robust with respect to given perturbations. Its complexity is essentially the same as that of obtaining the MAP configuration itself, so it can be promptly used with minimal effort. We use our algorithm to identify the largest global perturbation that does not induce a change in the MAP configuration, and we successfully apply this robustness measure in two practical scenarios: the prediction of facial action units with posed images and the classification of multiple real public data sets. A strong correlation between the proposed robustness measure and accuracy is verified in both scenarios.

Rapid detection of health-care-associated bloodstream infection in critical care using multipathogen real-time polymerase chain reaction technology: a diagnostic accuracy study and systematic review

Background: There is growing interest in the potential utility of real-time polymerase chain reaction (PCR) in diagnosing bloodstream infection by detecting pathogen deoxyribonucleic acid (DNA) in blood samples within a few hours. SeptiFast (Roche Diagnostics GmBH, Mannheim, Germany) is a multipathogen probe-based system targeting ribosomal DNA sequences of bacteria and fungi. It detects and identifies the commonest pathogens causing bloodstream infection. As background to this study, we report a systematic review of Phase III diagnostic accuracy studies of SeptiFast, which reveals uncertainty about its likely clinical utility based on widespread evidence of deficiencies in study design and reporting with a high risk of bias. 

Objective: Determine the accuracy of SeptiFast real-time PCR for the detection of health-care-associated bloodstream infection, against standard microbiological culture. 

Design: Prospective multicentre Phase III clinical diagnostic accuracy study using the standards for the reporting of diagnostic accuracy studies criteria. 

Setting: Critical care departments within NHS hospitals in the north-west of England. 

Participants: Adult patients requiring blood culture (BC) when developing new signs of systemic inflammation. 

Main outcome measures: SeptiFast real-time PCR results at species/genus level compared with microbiological culture in association with independent adjudication of infection. Metrics of diagnostic accuracy were derived including sensitivity, specificity, likelihood ratios and predictive values, with their 95% confidence intervals (CIs). Latent class analysis was used to explore the diagnostic performance of culture as a reference standard. 

Results: Of 1006 new patient episodes of systemic inflammation in 853 patients, 922 (92%) met the inclusion criteria and provided sufficient information for analysis. Index test assay failure occurred on 69 (7%) occasions. Adult patients had been exposed to a median of 8 days (interquartile range 4–16 days) of hospital care, had high levels of organ support activities and recent antibiotic exposure. SeptiFast real-time PCR, when compared with culture-proven bloodstream infection at species/genus level, had better specificity (85.8%, 95% CI 83.3% to 88.1%) than sensitivity (50%, 95% CI 39.1% to 60.8%). When compared with pooled diagnostic metrics derived from our systematic review, our clinical study revealed lower test accuracy of SeptiFast real-time PCR, mainly as a result of low diagnostic sensitivity. There was a low prevalence of BC-proven pathogens in these patients (9.2%, 95% CI 7.4% to 11.2%) such that the post-test probabilities of both a positive (26.3%, 95% CI 19.8% to 33.7%) and a negative SeptiFast test (5.6%, 95% CI 4.1% to 7.4%) indicate the potential limitations of this technology in the diagnosis of bloodstream infection. However, latent class analysis indicates that BC has a low sensitivity, questioning its relevance as a reference test in this setting. Using this analysis approach, the sensitivity of the SeptiFast test was low but also appeared significantly better than BC. Blood samples identified as positive by either culture or SeptiFast real-time PCR were associated with a high probability (> 95%) of infection, indicating higher diagnostic rule-in utility than was apparent using conventional analyses of diagnostic accuracy. 

Conclusion: SeptiFast real-time PCR on blood samples may have rapid rule-in utility for the diagnosis of health-care-associated bloodstream infection but the lack of sensitivity is a significant limiting factor. Innovations aimed at improved diagnostic sensitivity of real-time PCR in this setting are urgently required. Future work recommendations include technology developments to improve the efficiency of pathogen DNA extraction and the capacity to detect a much broader range of pathogens and drug resistance genes and the application of new statistical approaches able to more reliably assess test performance in situation where the reference standard (e.g. blood culture in the setting of high antimicrobial use) is prone to error.

An analysis of stage performance in automotive turbocharger centrifugal compressors

As the concept of engine downsizing becomes ever more integrated into automotive powertrain development strategies, so too does the pressure on turbocharger manufacturers to deliver improvements in map width and a reduction in the mass flow rate at which compressor surge occurs. A consequence of this development is the increasing importance of recirculating flows, both in the impeller inlet and outlet domains, on stage performance.
The current study seeks to evaluate the impact of the inclusion of impeller inlet recirculation on a meanline centrifugal compressor design tool. Using a combination of extensive test data, single passage CFD predictions, and 1-D analysis it is demonstrated how the addition of inlet recirculation modelling impacts upon stage performance close to the surge line. It is also demonstrated that, in its current configuration, the accuracy of the 1-D model prediction diminishes significantly with increasing blade tip speed.
Having ascertained that the existing model requires further work, an evaluation of the vaneless diffuser modelling method currently employed within the existing 1-D model is undertaken. The comparison of the predicted static pressure recovery coefficient with test data demonstrated the inherent inadequacies in the resulting prediction, in terms of both magnitude and variation with flow rate. A simplified alternative method based on an equivalent friction coefficient is also presented that, with further development, could provide a significantly improved stage performance prediction.

Stiffness Analysis and Experiment of a Novel 5-DoF Parallel Kinematic Machine Considering Gravitational Effects

In order to carry out high-precision machining of aerospace structural components with large size, thin wall and complex surface, this paper proposes a novel parallel kinematic machine (PKM) and formulates its semi-analytical theoretical stiffness model considering gravitational effects that is verified by stiffness experiments. From the viewpoint of topology structure, the novel PKM consists of two substructures in terms of the redundant and overconstrained parallel mechanisms that are connected by two interlinked revolute joints. The theoretical stiffness model of the novel PKM is established based upon the virtual work principle and deformation superposition principle after mapping the stiffness models of substructures from joint space to operated space by Jacobian matrices and considering the deformation contributions of interlinked revolute joints to two substructures. Meanwhile, the component gravities are treated as external payloads exerting on the end reference point of the novel PKM resorting to static equivalence principle. This approach is proved by comparing the theoretical stiffness values with experimental stiffness values in the same configurations, which also indicates equivalent gravity can be employed to describe the actual distributed gravities in an acceptable accuracy manner. Finally, on the basis of the verified theoretical stiffness model, the stiffness distributions of the novel PKM are illustrated and the contributions of component gravities to the stiffness of the novel PKM are discussed.

Cultivating Compassion through analysis of online patient narratives

Compassion is at the forefront of national and international healthcare policy, practice and educational debates as a result of a series of recent reports (Mid Staffordshire NHS Foundation Trust Inquiry, 2010, Lown et al 2011, Mannion, 2014). Arguably, this emphasis on compassion is in juxtaposition to an increasingly complex technological healthcare system focused upon outcomes, efficiency, productivity and competence. Within this fast paced and time pressured environment innovative strategies are required to cultivate and sustain compassion among healthcare professionals.

Understanding the person’s experience of illness and making an emotional connection are key processes in cultivating compassion (Dewar, 2013). The exponential growth in unsolicited patient narratives has the potential to provide invaluable insight into what matters to patients and their experience of illness. For many patients these stories ‘reclaim’ their illnesses from the traditional biomedical model of disease and reveal otherwise hidden aspects of their experience. The content though freely accessible, is however unedited and lacks safeguards in relation to the quality or accuracy of the information provided. Despite these concerns, healthcare professionals are now challenged to pay attention to these unsolicited patient stories and to consider how they can inform and improve patient care.

This paper discusses the use of online patient narratives in undergraduate nurse education to cultivate compassion. Critical analysis of online patient narratives is advocated as a potential educational strategy to cultivate compassion among future health care professionals.

References
Dewar,B. (2013) Cultivating compassionate care Nursing Standard 27, (34) 48-55

Lown B, Rosen J, Martilla J.(2011) An agenda for improving compassionate care: a survey shows about half of patients say such care is missing. Health Affairs (Millwood) 30, 1772–8.
Mannion,R. (2014) Enabling compassionate healthcare: perils, prospects and perspectives International Journal of Health Policy and Management 2, 115-7
Mid Staffordshire NHS Foundation Trust Inquiry (2010). Independent Inquiry into care provided by Mid Staffordshire NHS Foundation London: Stationery Office.

Online health information - what the newspapers tell their readers: a systematic content analysis

Background: This study investigated the nature of newspaper reporting about online health information in the UK and US. Internet users frequently search for health information online, although the accuracy of the information retrieved varies greatly and can be misleading. Newspapers have the potential to influence public health behaviours, but information has been lacking in relation to how newspapers portray online health information to their readers.

Methods: The newspaper database Nexis (R) UK was searched for articles published from 2003 - 2012 relating to online health information. Systematic content analysis of articles published in the highest circulation newspapers in the UK and US was performed. A second researcher coded a 10% sample to establish inter-rater reliability of coding.

Results: In total, 161 newspaper articles were included in the analysis. Publication was most frequent in 2003, 2008 and 2009, which coincided with global threats to public health. UK broadsheet newspapers were significantly more likely to cover online health information than UK tabloid newspapers (p = 0.04) and only one article was identified in US tabloid newspapers. Articles most frequently appeared in health sections. Among the 79 articles that linked online health information to specific diseases or health topics, diabetes was the most frequently mentioned disease, cancer the commonest group of diseases and sexual health the most frequent health topic. Articles portrayed benefits of obtaining online health information more frequently than risks. Quotations from health professionals portrayed mixed opinions regarding public access to online health information. 108 (67.1%) articles directed readers to specific health-related web sites. 135 (83.9%) articles were rated as having balanced judgement and 76 (47.2%) were judged as having excellent quality reporting. No difference was found in the quality of reporting between UK and US articles.

Conclusions: Newspaper coverage of online health information was low during the 10-year period 2003 to 2012. Journalists tended to emphasise the benefits and understate the risks of online health information and the quality of reporting varied considerably. Newspapers directed readers to sources of online health information during global epidemics although, as most articles appeared in the health sections of broadsheet newspapers, coverage was limited to a relatively small readership.