The multiple pheromone ant clustering algorithm and its application to real world domains

The Multiple Pheromone Ant Clustering Algorithm (MPACA) models the collective behaviour of ants to find clusters in data and to assign objects to the most appropriate class. It is an ant colony optimisation approach that uses pheromones to mark paths linking objects that are similar and potentially members of the same cluster or class. Its novelty is in the way it uses separate pheromones for each descriptive attribute of the object rather than a single pheromone representing the whole object. Ants that encounter other ants frequently enough can combine the attribute values they are detecting, which enables the MPACA to learn influential variable interactions. This paper applies the model to real-world data from two domains. One is logistics, focusing on resource allocation rather than the more traditional vehicle-routing problem. The other is mental-health risk assessment. The task for the MPACA in each domain was to predict class membership where the classes for the logistics domain were the levels of demand on haulage company resources and the mental-health classes were levels of suicide risk. Results on these noisy real-world data were promising, demonstrating the ability of the MPACA to find patterns in the data with accuracy comparable to more traditional linear regression models

Moderating the Influence of Current Intention to Improve Suicide Risk Prediction

When assessors evaluate a person's risk of completing suicide, the person's expressed current intention is one of the most influential factors. However, if people say they have no intention, this may not be true for a number of reasons. This paper explores the reliability of negative intention in data provided by mental-health services using the GRiST decision support system in England. It identifies features within a risk assessment record that can classify a negative statement regarding current intention of suicide as being reliable or unreliable. The algorithm is tested on previously conducted assessments, where outcomes found in later assessments do or do not match the initially stated intention. Test results show significant separation between the two classes. It means suicide predictions could be made more accurate by modifying the assessment process and associated risk judgement in accordance with a better understanding of the person's true intention

The influence of patient's age on clinical decision-making about coronary heart disease in the USA and the UK

This paper examines UK and US primary care doctors' decision-making about older (aged 75 years) and midlife (aged 55 years) patients presenting with coronary heart disease (CHD). Using an analytic approach based on conceptualising clinical decision-making as a classification process, it explores the ways in which doctors' cognitive processes contribute to ageism in health-care at three key decision points during consultations. In each country, 56 randomly selected doctors were shown videotaped vignettes of actors portraying patients with CHD. The patients' ages (55 or 75 years), gender, ethnicity and social class were varied systematically. During the interviews, doctors gave free-recall accounts of their decision-making. The results do not establish that there was substantial ageism in the doctors' decisions, but rather suggest that diagnostic processes pay insufficient attention to the significance of older patients' age and its association with the likelihood of co-morbidity and atypical disease presentations. The doctors also demonstrated more limited use of ‘knowledge structures’ when diagnosing older than midlife patients. With respect to interventions, differences in the national health-care systems rather than patients' age accounted for the differences in doctors' decisions. US doctors were significantly more concerned about the potential for adverse outcomes if important diagnoses were untreated, while UK general practitioners cited greater difficulty in accessing diagnostic tests

Designing multiple user perspectives and functionality for clinical decision support systems

Clinical Decision Support Systems (CDSSs) need to disseminate expertise in formats that suit different end users and with functionality tuned to the context of assessment. This paper reports research into a method for designing and implementing knowledge structures that facilitate the required flexibility. A psychological model of expertise is represented using a series of formally specified and linked XML trees that capture increasing elements of the model, starting with hierarchical structuring, incorporating reasoning with uncertainty, and ending with delivering the final CDSS. The method was applied to the Galatean Risk and Safety Tool, GRiST, which is a web-based clinical decision support system (www.egrist.org) for assessing mental-health risks. Results of its clinical implementation demonstrate that the method can produce a system that is able to deliver expertise targetted and formatted for specific patient groups, different clinical disciplines, and alternative assessment settings. The approach may be useful for developing other real-world systems using human expertise and is currently being applied to a logistics domain

A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron (MLP) is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term Memory (LSTM) are also explored, and Adaptive Boosting of the two types of models is tested for each problem. Three experiments are provided for comparison using different classifiers: One for attention state classification, one for emotional sentiment classification, and a third experiment in which the goal is to guess the number a subject is thinking of. The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. In particular, the accuracy of the nonboosted DEvo MLP was of 79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the experiments were gathered using a Muse EEG headband with four electrodes corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG placement standard. The EEG MindBigData digits dataset was gathered from the TP9, FP1, FP2, and TP10 locations

Cues and knowledge structures used by mental-health professionals when making risk assessments

Background: Research into mental-health risks has tended to focus on epidemiological approaches and to consider pieces of evidence in isolation. Less is known about the particular factors and their patterns of occurrence that influence clinicians’ risk judgements in practice. Aims: To identify the cues used by clinicians to make risk judgements and to explore how these combine within clinicians’ psychological representations of suicide, self-harm, self-neglect, and harm to others. Method: Content analysis was applied to semi-structured interviews conducted with 46 practitioners from various mental-health disciplines, using mind maps to represent the hierarchical relationships of data and concepts. Results: Strong consensus between experts meant their knowledge could be integrated into a single hierarchical structure for each risk. This revealed contrasting emphases between data and concepts underpinning risks, including: reflection and forethought for suicide; motivation for self-harm; situation and context for harm to others; and current presentation for self-neglect. Conclusions: Analysis of experts’ risk-assessment knowledge identified influential cues and their relationships to risks. It can inform development of valid risk-screening decision support systems that combine actuarial evidence with clinical expertise

Uses and Attitudes of Old and Oldest Adults towards Self-Monitoring Health Systems

Oldest adults (80 years and over) are the fastest growing group in the total world population. This is putting pressure on national healthcare budgets, as the distribution of healthcare expenses is strongly age-dependent. One way of mitigating this burden may be to let older adults contribute to their own health directly by using self-management health systems (SMHS). SMHS might help older, including oldest, adults gain insight into their health status, and invite them to take action. However, while many studies report on user evaluations of older adults with one specific sensor system, fewer studies report on older adults’ uses and attitudes towards integrated SMHS. Moreover, most studies include participants with mean ages of 65 rather than 80. In this paper, we report on a qualitative study, consisting of a focus group interview and a user evaluation of an SMHS by 12 participants with a median age of 85 years. Three main findings were derived: Older adults (1) showed heterogeneity in computer skills, (2) found health technologies useful for others – not yet for themselves, and (3) perceived health technologies as a threat to social interaction. These findings suggest that health technologies are not ready for adoption by older adults yet, and further research on making them more accessible and desirable is required

Leptin fails to blunt the lipopolysaccharide-induced activation of the hypothalamic-pituitary-adrenal axis in rats

Copyright @ 2013 The authors. This work is licensed under a Creative Commons Attribution 3.0 Unported License.Obesity is a risk factor for sepsis morbidity and mortality, whereas the hypothalamic-pituitary-adrenal (HPA) axis plays a protective role in the body's defence against sepsis. Sepsis induces a profound systemic immune response and cytokines serve as excellent markers for sepsis as they act as mediators of the immune response. Evidence suggests that the adipokine leptin may play a pathogenic role in sepsis. Mouse endotoxaemic models present with elevated leptin levels and exogenously added leptin increased mortality whereas human septic patients have elevated circulating levels of the soluble leptin receptor (Ob-Re). Evidence suggests that leptin can inhibit the regulation of the HPA axis. Thus, leptin may suppress the HPA axis, impairing its protective role in sepsis.We hypothesised that leptin would attenuate the HPA axis response to sepsis.We investigated the direct effects of an i.p. injection of 2 mg/kg leptin on the HPA axis response to intraperitoneally injected 25 μg/kg lipopolysaccharide (LPS) in the male Wistar rat. We found that LPS potently activated the HPA axis, as shown by significantly increased plasma stress hormones, ACTH and corticosterone, and increased plasma interleukin 1β (IL1β) levels, 2 h after administration. Pre-treatment with leptin, 2 h before LPS administration, did not influence the HPA axis response to LPS. In turn, LPS did not affect plasma leptin levels. Our findings suggest that leptin does not influence HPA function or IL1b secretion in a rat model of LPS-induced sepsis, and thus that leptin is unlikely to be involved in the acute-phase endocrine response to bacterial infection in rats.The section is funded by grants from the MRC, BBSRC, NIHR and an Integrative Mammalian Biology (IMB) Capacity Building Award, and by a FP7-HEALTH-2009-241592 EuroCHIP grant and is supported by the NIHR Imperial Biomedical Research Centre Funding Scheme. This work is supported by a BBSRC Doctoral Training-Strategic Skills Award grant (BB/F017340/1)

The Homeobox Transcription Factor Barx2 Regulates Plasticity of Young Primary Myofibers

Adult mammalian muscle retains incredible plasticity. Muscle growth and repair involves the activation of undifferentiated myogenic precursors called satellite cells. In some circumstances, it has been proposed that existing myofibers may also cleave and produce a pool of proliferative cells that can re-differentiate into new fibers. Such myofiber dedifferentiation has been observed in the salamander blastema where it may occur in parallel with satellite cell activation. Moreover, ectopic expression of the homeodomain transcription factor Msx1 in differentiated C2C12 myotubes has been shown to induce their dedifferentiation. While it remains unclear whether dedifferentiation and redifferentiaton occurs endogenously in mammalian muscle, there is considerable interest in induced dedifferentiation as a possible regenerative tool.We previously showed that the homeobox protein Barx2 promotes myoblast differentiation. Here we report that ectopic expression of Barx2 in young immature myotubes derived from cell lines and primary mouse myoblasts, caused cleavage of the syncytium and downregulation of differentiation markers. Microinjection of Barx2 cDNA into immature myotubes derived from primary cells led to cleavage and formation of mononucleated cells that were able to proliferate. However, injection of Barx2 cDNA into mature myotubes did not cause cleavage. Barx2 expression in C2C12 myotubes increased the expression of cyclin D1, which may promote cell cycle re-entry. We also observed differential muscle gene regulation by Barx2 at early and late stages of muscle differentiation which may be due to differential recruitment of transcriptional activator or repressor complexes to muscle specific genes by Barx2.We show that Barx2 regulates plasticity of immature myofibers and might act as a molecular switch controlling cell differentiation and proliferation

Anticoagulation for non-valvular atrial aibrillation – towards a new beginning with ximelagatran

OBJECTIVES: Ximelagatran is a novel oral direct thrombin inhibitor. It has favorable pharmacodynamic properties, with a broad therapeutic range without the need for anticoagulation monitoring. We aimed to discover whether ximelagatran offers a genuine future replacement to warfarin for patients in persistent atrial fibrillation (AF). MATERIALS AND METHODS: We provide an evidence-based review of the relative merits and disadvantages of warfarin and aspirin. We subsequently present an overview of the evidence for the utility of ximelagatran in the treatment of AF. RESULTS: Adjusted dose warfarin is recommended over aspirin for patients in AF at high risk of future stroke. Some of this benefit is partially offset by the higher bleeding risks associated with warfarin therapy. The SPORTIF III and V studies have shown that ximelagatran is not inferior to warfarin in the prevention of all strokes in patients with AF (both persistent and paroxysmal). This benefit was partially offset by the finding of a significant elevation of liver transaminases (>3 × normal) in 6% of patients. CONCLUSIONS: Current data would suggest that ximelagatran might represent a future alternative to warfarin. The lack of need for anticoagulant monitoring has been partially offset by a need for regular monitoring of liver function. Further data from randomized clinical trials is clearly needed