A framework for initialising a dynamic clustering algorithm: ART2-A

Algorithms in the Adaptive Resonance Theory (ART) family adapt to structural changes in data as new information presents, making it an exciting candidate for dynamic online clustering of big health data. Its use however has largely been restricted to the signal processing field. In this paper we introduce an refinement of the ART2-A method within an adapted separation and concordance (SeCo) framework which has been shown to identify stable and reproducible solutions from repeated initialisations that also provides evidence for an appropriate number of initial clusters that best calibrates the algorithm with the data presented. The results show stable, reproducible solutions for a mix of real-world heath related datasets and well known benchmark datasets, selecting solutions which better represent the underlying structure of the data than using a single measure of separation. The scalability of the method and it's facility for dynamic online clustering makes it suitable for finding structure in big data

The new very small angle neutron scattering spectrometer at Laboratoire Leon Brillouin

The design and characteristics of the new very small angle neutron scattering spectrometer under construction at the Laboratoire Leon Brillouin is described. Its goal is to extend the range of scattering vectors magnitudes towards 2x10{-4} /A. The unique feature of this new spectrometer is a high resolution two dimensional image plate detector sensitive to neutrons. The wavelength selection is achieved by a double reflection supermirror monochromator and the collimator uses a novel multibeam design

A framework approach to initialisation dependent clustering methodologies

Clustering algorithms are commonly used for exploratory data analysis and data mining and used correctly are powerful tools for gaining insights into the underlying structure of data. It is known however that some of these algorithms are dependent upon the parameters with which they start, giving differing results as these vary. Often there is an element of randomness in the initialisation process greatly increasing the difficulty of selecting an appropriately initialised solution. Effective use of these algorithms depends upon the correct choice of appropriate initialisations, however when exploring new data it is often difficult to objectively obtain values appropriate to the problem. The use of initialisation strategies to maximise the performance of the algorithm are therefore important to ensure solutions identified are both consistent with the structure of the data and reproducible. This thesis introduces a coherent strategy for dealing with initialisation in the form of chosen parameter selection and randomness. A Separation Concordance (SeCo) framework is developed which uses a dual measure approach to evaluating the solutions from resampling of starting conditions. This SeCo framework also allows for the inference of an appropriate number of partitions within the data and introduces a SeCo map for visualising the solution space. The performance of these visualisations compared and contrasted with the existing methods in use through an exhaustive series of experiments for both algorithms tested, and is shown to be effectivein the selection of a repeatable solution with high concordance to the underlying structure of the data. These results are benchmarked using a range of synthetic and real world data-sets whose composition ranges from trivial to complex

Why big brains? A comparison of models for both primate and carnivore brain size evolution

Despite decades of research, much uncertainty remains regarding the selection pressures responsible for brain size variation. Whilst the influential social brain hypothesis once garnered extensive support, more recent studies have failed to find support for a link between brain size and sociality. Instead, it appears there is now substantial evidence suggesting ecology better predicts brain size in both primates and carnivores. Here, different models of brain evolution were tested, and the relative importance of social, ecological, and life-history traits were assessed on both overall encephalisation and specific brain regions. In primates, evidence is found for consistent associations between brain size and ecological factors, particularly diet; however, evidence was also found advocating sociality as a selection pressure driving brain size. In carnivores, evidence suggests ecological variables, most notably home range size, are influencing brain size; whereas, no support is found for the social brain hypothesis, perhaps reflecting the fact sociality appears to be limited to a select few taxa. Life-history associations reveal complex selection mechanisms to be counterbalancing the costs associated with expensive brain tissue through extended developmental periods, reduced fertility, and extended maximum lifespan. Future studies should give careful consideration of the methods chosen for measuring brain size, investigate both whole brain and specific brain regions where possible, and look to integrate multiple variables, thus fully capturing all of the potential factors influencing brain size

Use of q-values to Improve a Genetic Algorithm to Identify Robust Gene Signatures

Several approaches have been proposed for the analysis of DNA microarray datasets, focusing on the performance and robustness of the final feature subsets. The novelty of this paper arises in the use of q-values to pre-filter the features of a DNA microarray dataset identifying the most significant ones and including this information into a genetic algorithm for further feature selection. This method is applied to a lung cancer microarray dataset resulting in similar performance rates and greater robustness in terms of selected features (on average a 36.21% of robustness improvement) when compared to results of the standard algorithm

Digital literacy linked to engagement and psychological benefits among breast cancer survivors in Internet-based peer support groups

© 2019 John Wiley & Sons Ltd Objective: Internet-based peer support groups (ISGs) represent an innovative, scalable approach to addressing information and support needs of cancer survivors. However, this innovation may not benefit survivors equally due to population variance in digital literacy. This study examined how digital literacy influences level of engagement in and psychological benefits from participating in ISGs for breast cancer (N = 183). Methods: Secondary analysis of data from a randomised trial of ISGs that included behavioural measures of engagement, subjective ratings and psychological distress symptoms. Results: Digital literacy was positively related to education level (p =.005). Relative to women with high digital literacy, those with lower digital literacy were more likely to report difficulties using the ISG and to value the user's guide and facilitator assistance (all p's <.05). Digital literacy was negatively correlated with computer anxiety pre-intervention, distress before and after online chat during the intervention and post-intervention depressive symptoms (all p's <.05). Conclusion: Low digital literacy is associated with computer anxiety and barriers to ISG use, as well as distress during and after ISG use. Digital literacy must be taken into account when designing or delivering innovative digital interventions for cancer survivors

Using MCDA to generate and interpret evidence to inform local government investment in public health

Smoking is the single biggest cause of preventable death in the Uited Kingdom (UK) and is a major cause of coronary heart disease, some cancers, and respiratory disease, including chronic obstructive pulmonary disease. At the time of initiating the project, smoking prevalence had not changed across four local government areas in South Yorkshire for some years. Most spending had been focussed on helping people quit, an intervention where there was clear evidence of effectiveness. A number of changes occurred in public health structures and targets, requiring a reappraisal of the range of interventions offered. This was challenging due to a lack of clear evidence for some of the areas’ alternative interventions. The aim of this paper is to describe the use of a multi-criteria decision analysis (MCDA) approach to support the health priority setting in local authorities to reduce smoking prevalence. There were three phases to this process: (1) problem structuring; (2) the multiple criteria decision analysis; (3) and using the MCDA results to influence decision making at the local government level. The MCDA approach was used to collate information in a consistent and transparent manner, using expert, stakeholder and public opinion to fill known gaps in evidence. Fifteen interventions (such as stop smoking support services, smoke-free spaces, communication and marketing exercises, and increased investment in enforcement) were ranked across eight criteria (relating to reductions in prevalence across relevant groups, as well as aspects relating to equity and feasibility), allowing a range of relevant concerns to be incorporated. Subsequent steps were taken to translate the results of this stage into workable policy options. The results differed significantly from current practice. Sensitivity analysis showed that the findings were robust to changes in preference weights. These results informed subsequent changes to the interventions offered across the four boroughs. The ability of MCDA techniques to incorporate data and both qualitative and quantitative judgements in a formal manner mean that they are well suited to support public health decision making, where evidence is often only partially available and many policies are value driven. MCDA methods, if used, should be chosen carefully based on their resource/time constraints, scientific validity, and the significance and broader context of the decision problem.This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s40070-016-0059-

Extremely red radio galaxies

At least half the radio galaxies at z>1 in the 7C Redshift Survey have extremely red colours (R-K>5), consistent with stellar populations which formed at high redshift (z>5). We discuss the implications of this for the evolution of massive galaxies in general and for the fraction of near-IR-selected EROs which host AGN, a result which is now being tested by deep, hard X-ray surveys. The conclusion is that many massive galaxies undergo at least two active phases: one at z~5 when the black hole and stellar bulge formed and another at z~1-2 when activity is triggered by an event such as an interaction or merger.Comment: 6 pages, 2 figures, to appear in the proceedings of the workshop on "QSO hosts and their environments", IAA, Granada, 10-12 Jan 2001, Ed. I. Marque

The endoplasmic reticulum remains functionally connected by vesicular transport after its fragmentation in cells expressing Z-alpha(1)-antitrypsin

α1-Antitrypsin is a serine protease inhibitor produced in the liver that is responsible for the regulation of pulmonary inflammation. The commonest pathogenic gene mutation yields Z-α1-antitrypsin, which has a propensity to self-associate forming polymers that become trapped in inclusions of endoplasmic reticulum (ER). It is unclear whether these inclusions are connected to the main ER network in Z-α1-antitrypsin-expressing cells. Using live cell imaging, we found that despite inclusions containing an immobile matrix of polymeric α1-antitrypsin, small ER resident proteins can diffuse freely within them. Inclusions have many features to suggest they represent fragmented ER, and some are physically separated from the tubular ER network, yet we observed cargo to be transported between them in a cytosol-dependent fashion that is sensitive to N-ethylmaleimide and dependent on Sar1 and sec22B. We conclude that protein recycling occurs between ER inclusions despite their physical separation.—Dickens, J. A., Ordóñez, A., Chambers, J. E., Beckett, A. J., Patel, V., Malzer, E., Dominicus, C. S., Bradley, J., Peden, A. A., Prior, I. A., Lomas, D. A., Marciniak, S. J. The endoplasmic reticulum remains functionally connected by vesicular transport after its fragmentation in cells expressing Z-α1-antitrypsin

The role of mindfulness in distress and quality of life for men with advanced prostate cancer.

OBJECTIVE: To examine the extent to which mindfulness skills influence psychological distress and health-related quality of life (HRQOL) in men with metastatic or castration-resistant biochemical progression of prostate cancer. PATIENTS AND METHODS: A cross-sectional survey of 190 men (46 % response; mean age 71 years, SD = 8.7, range 40-91 years) with advanced prostate cancer, assessed psychological and cancer-specific distress, HRQOL. Mindfulness skills were assessed as potential predictors of adjustment outcomes. RESULTS: Overall, 39 % of men reported high psychological distress. One third had accessed psychological support previously although only 10 % were under current psychological care. One quarter had accessed a prostate cancer support group in the past six months. Higher HRQOL and lower cancer-specific and global psychological distress were related to non-judging of inner experience (p < 0.001). Higher HRQOL and lower psychological distress were related to acting with awareness (p < 0.001). Lower distress was also related to higher non-reactivity to inner experience and a lower level of observing (p < 0.05). CONCLUSIONS: Men with advanced prostate cancer are at risk of poor psychological outcomes. Psychological flexibility may be a promising target for interventions to improve adjustment outcomes in this patient group. CLINICAL TRIAL REGISTRY: Trial Registration: ACTRN12612000306819