Non-linear hierarchical visualisation

This thesis applies a hierarchical latent trait model system to a large quantity of data. The motivation for it was lack of viable approaches to analyse High Throughput Screening datasets which maybe include thousands of data points with high dimensions. High Throughput Screening (HTS) is an important tool in the pharmaceutical industry for discovering leads which can be optimised and further developed into candidate drugs. Since the development of new robotic technologies, the ability to test the activities of compounds has considerably increased in recent years. Traditional methods, looking at tables and graphical plots for analysing relationships between measured activities and the structure of compounds, have not been feasible when facing a large HTS dataset. Instead, data visualisation provides a method for analysing such large datasets, especially with high dimensions. So far, a few visualisation techniques for drug design have been developed, but most of them just cope with several properties of compounds at one time. We believe that a latent variable model (LTM) with a non-linear mapping from the latent space to the data space is a preferred choice for visualising a complex high-dimensional data set. As a type of latent variable model, the latent trait model can deal with either continuous data or discrete data, which makes it particularly useful in this domain. In addition, with the aid of differential geometry, we can imagine the distribution of data from magnification factor and curvature plots. Rather than obtaining the useful information just from a single plot, a hierarchical LTM arranges a set of LTMs and their corresponding plots in a tree structure. We model the whole data set with a LTM at the top level, which is broken down into clusters at deeper levels of t.he hierarchy. In this manner, the refined visualisation plots can be displayed in deeper levels and sub-clusters may be found. Hierarchy of LTMs is trained using expectation-maximisation (EM) algorithm to maximise its likelihood with respect to the data sample. Training proceeds interactively in a recursive fashion (top-down). The user subjectively identifies interesting regions on the visualisation plot that they would like to model in a greater detail. At each stage of hierarchical LTM construction, the EM algorithm alternates between the E- and M-step. Another problem that can occur when visualising a large data set is that there may be significant overlaps of data clusters. It is very difficult for the user to judge where centres of regions of interest should be put. We address this problem by employing the minimum message length technique, which can help the user to decide the optimal structure of the model. In this thesis we also demonstrate the applicability of the hierarchy of latent trait models in the field of document data mining.

Abnormal amygdala-prefrontal effective connectivity to happy faces differentiates bipolar from major depression

Background - Bipolar disorder is frequently misdiagnosed as major depressive disorder, delaying appropriate treatment and worsening outcome for many bipolar individuals. Emotion dysregulation is a core feature of bipolar disorder. Measures of dysfunction in neural systems supporting emotion regulation might therefore help discriminate bipolar from major depressive disorder. Methods - Thirty-one depressed individuals—15 bipolar depressed (BD) and 16 major depressed (MDD), DSM-IV diagnostic criteria, ages 18–55 years, matched for age, age of illness onset, illness duration, and depression severity—and 16 age- and gender-matched healthy control subjects performed two event-related paradigms: labeling the emotional intensity of happy and sad faces, respectively. We employed dynamic causal modeling to examine significant among-group alterations in effective connectivity (EC) between right- and left-sided neural regions supporting emotion regulation: amygdala and orbitomedial prefrontal cortex (OMPFC). Results - During classification of happy faces, we found profound and asymmetrical differences in EC between the OMPFC and amygdala. Left-sided differences involved top-down connections and discriminated between depressed and control subjects. Furthermore, greater medication load was associated with an amelioration of this abnormal top-down EC. Conversely, on the right side the abnormality was in bottom-up EC that was specific to bipolar disorder. These effects replicated when we considered only female subjects. Conclusions - Abnormal, left-sided, top-down OMPFC–amygdala and right-sided, bottom-up, amygdala–OMPFC EC during happy labeling distinguish BD and MDD, suggesting different pathophysiological mechanisms associated with the two types of depression.

The functional anatomy of the McCollough contingent colour after-effect

We report two functional magnetic resonance imaging (fMRI) experiments which reveal a cortical network activated when perceiving coloured grids, and experiencing the McCollough effect (ME). Our results show that perception of red-black and green-black grids activate the right fusiform gyrus (area V4) plus the left and right lingual gyri, right striate cortex (V1) and left insula. The ME activated the left anterior fusiform gyrus as well as the ventrolateral prefrontal cortex, and in common with colour perception, the left insula. These data confirm the critical role of the fusiform gyrus in actual and illusory colour perception as well as revealing localized frontal cortical activation associated with the ME, which would suggest that a 'top-down' mechanism is implicated in this illusion.

Hierarchical classification of G-protein-coupled receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.

Evolving forms of manufacturing strategy development:evidence and implications

This paper outlines, using evidence from several case studies, the use of alternative forms of manufacturing strategy processes. Our investigation shows that the manufacturing strategy development practices of manufacturers are evolving in many directions; we found several alternatives to the formal top-down planning process. Manufacturers use one or more of the following alternatives with or without the top-down manufacturing strategy process: a coherent pattern of actions; manufacturing/process improvement programs; or the pursuit of core manufacturing capabilities. It appears that the various manufacturing strategy development processes may be tied to the strategic role of manufacturing in a company. This paper offers a framework that captures the relationship between the strategic role of manufacturing and the process of manufacturing strategy development. An in-depth case from a UK company illustrates the evolving forms of manufacturing strategy development processes.

Sustainable development, shock and awe? London 2012 and Civil Society

There is a disconnection between the top-down, elite, nature of sports mega-events and the ostensible redistributive and participatory sustainable development agendas staked out by BINGOs (Business-based International Non-Governmental Organizations) such as the contemporary International Olympic Committee (IOC). Focusing specifically on the London 2012 Summer Olympic and Paralympic Games, we argue that, for all the environmental technology advances offered by sports mega-events, their dominant model remains one of a hollowed-out form of sustainable development. Despite significant technical and methodological innovations in environmental stewardship, the development model of the London Olympics remains predicated on the satisfaction of transnational investment flows. We discuss what this means for claims about the staging of a ‘green’ Olympic Games.

Team working in intensive care:current evidence and future endeavors

Purpose of review: It has recently been argued that the future of intensive care medicine will rely on high quality management and teamwork. Therefore, this review takes an organizational psychology perspective to examine the most recent research on the relationship between teamwork, care processes, and patient outcomes in intensive care. Recent findings: Interdisciplinary communication within a team is crucial for the development of negotiated shared treatment goals and short-team patient outcomes. Interventions for maximizing team communication have received substantial interest in recent literature. Intensive care coordination is not a linear process, and intensive care teams often fail to discuss how to implement goals, trigger and align activities, or reflect on their performance. Despite a move toward interdisciplinary team working, clinical decision-making is still problematic and continues to be perceived as a top-down and authoritative process. The topic of team leadership in intensive care is underexplored and requires further research. Summary: Based on findings from the most recent research evidence in medicine and management, four principles are identified for improving the effectiveness of team working in intensive care: engender professional efficacy, create stable teams and leaders, develop trust and participative safety, and enable frequent team reflexivity.

Visual hallucinations in the psychosis spectrum and comparative information from neurodegenerative disorders and eye disease

Much of the research on visual hallucinations (VHs) has been conducted in the context of eye disease and neurodegenerative conditions, but little is known about these phenomena in psychiatric and nonclinical populations. The purpose of this article is to bring together current knowledge regarding VHs in the psychosis phenotype and contrast this data with the literature drawn from neurodegenerative disorders and eye disease. The evidence challenges the traditional views that VHs are atypical or uncommon in psychosis. The weighted mean for VHs is 27% in schizophrenia, 15% in affective psychosis, and 7.3% in the general community. VHs are linked to a more severe psychopathological profile and less favorable outcome in psychosis and neurodegenerative conditions. VHs typically co-occur with auditory hallucinations, suggesting a common etiological cause. VHs in psychosis are also remarkably complex, negative in content, and are interpreted to have personal relevance. The cognitive mechanisms of VHs in psychosis have rarely been investigated, but existing studies point to source-monitoring deficits and distortions in top-down mechanisms, although evidence for visual processing deficits, which feature strongly in the organic literature, is lacking. Brain imaging studies point to the activation of visual cortex during hallucinations on a background of structural and connectivity changes within wider brain networks. The relationship between VHs in psychosis, eye disease, and neurodegeneration remains unclear, although the pattern of similarities and differences described in this review suggests that comparative studies may have potentially important clinical and theoretical implications. © 2014 The Author.

Disturbing visual working memory:electrophysiological evidence for a role of the prefrontal cortex in recovery from interference

Single cell recordings in monkeys support the notion that the lateral prefrontal cortex (PFC) controls reactivation of visual working memory representations when rehearsal is disrupted. In contrast, recent fMRI findings yielded a double dissociation for PFC and the medial temporal lobe (MTL) in a letter working memory task. PFC was engaged in interference protection during reactivation while MTL was prominently involved in the retrieval of the letter representations. We present event-related potential data (ERP) that support PFC involvement in the top-down control of reactivation during a visual working memory task with endogenously triggered recovery after visual interference. A differentiating view is proposed for the role of PFC in working memory with respect to endogenous/exogenous control and to stimulus type. General implications for binding and retention mechanisms are discussed.

Outsourcing:an Irish perspective

This paper investigates three case studies with regards their outsourcing initiatives from an Irish perspective, the benefits and problems they encountered, the contracts they have constructed, and where they have outsourced and the reasons behind them. The research took place over a period of two years, and the information for the case studies was gleaned from a number of sources including: published papers about the companies, the company websites, annual financial reports, the questionnaire they filled out, and interviews with key decision making personnel in the company. Follow up interviews were conducted where it was felt they were required. Key findings were that: it is very important to have the outsourcing initiative driven from the top down, and it is imperative to construct a contract that promotes a relationship based on mutual trust and respect, the use of Ireland for outsourcing, and the benefits and problem companies have experienced.

Understanding data collection behaviour of mental health practitioners

Failure to detect patients at risk of attempting suicide can result in tragic consequences. Identifying risks earlier and more accurately helps prevent serious incidents occurring and is the objective of the GRiST clinical decision support system (CDSS). One of the problems it faces is high variability in the type and quantity of data submitted for patients, who are assessed in multiple contexts along the care pathway. Although GRiST identifies up to 138 patient cues to collect, only about half of them are relevant for any one patient and their roles may not be for risk evaluation but more for risk management. This paper explores the data collection behaviour of clinicians using GRiST to see whether it can elucidate which variables are important for risk evaluations and when. The GRiST CDSS is based on a cognitive model of human expertise manifested by a sophisticated hierarchical knowledge structure or tree. This structure is used by the GRiST interface to provide top-down controlled access to the patient data. Our research explores relationships between the answers given to these higher-level 'branch' questions to see whether they can help direct assessors to the most important data, depending on the patient profile and assessment context. The outcome is a model for dynamic data collection driven by the knowledge hierarchy. It has potential for improving other clinical decision support systems operating in domains with high dimensional data that are only partially collected and in a variety of combinations.

Planning the neighbourhood economy:land-use plans and the economic potential of urban residential neighbourhoods in the Netherlands

This article investigates the relationship between zoning by-laws, as put forward in governmental land-use plans and the viability of urban residential neighbourhood economies. The Dutch planning tradition has long been characterized by strict separation of functions and top-down planning. We argue that profound changes in social and economic structures make land-use planning practices less suitable for the current policy formula of "mixed urban milieus". Although the residential neighbourhood might not be the location of large firms, it definitely attracts small ones, and facilitates starting businesses whose presence (and potential growth) can be beneficial to the city as a whole. We present a typology of spatial patterns of neighbourhood economies based on land-use plans and describe whether these are related to the distinctive economic development of the neighbourhood over the period 1999-2007. © 2013 Taylor & Francis.

Parsing mathematical constructs:results from a preliminary eye tracking study

The semantic model described in this paper is based on ones developed for arithmetic (e.g. McCloskey et al. 1985, Cohene and Dehaene 1995), natural language processing (Fodor 1975, Chomsky 1981) and work by the author on how learners parse mathematical structures. The semantic model highlights the importance of the parsing process and the relationship between this process and the mathematical lexicon/grammar. It concludes by demonstrating that for a learner to become an efficient, competent mathematician a process of top-down parsing is essential.

Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach

DNA-binding proteins are crucial for various cellular processes and hence have become an important target for both basic research and drug development. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to establish an automated method for rapidly and accurately identifying DNA-binding proteins based on their sequence information alone. Owing to the fact that all biological species have developed beginning from a very limited number of ancestral species, it is important to take into account the evolutionary information in developing such a high-throughput tool. In view of this, a new predictor was proposed by incorporating the evolutionary information into the general form of pseudo amino acid composition via the top-n-gram approach. It was observed by comparing the new predictor with the existing methods via both jackknife test and independent data-set test that the new predictor outperformed its counterparts. It is anticipated that the new predictor may become a useful vehicle for identifying DNA-binding proteins. It has not escaped our notice that the novel approach to extract evolutionary information into the formulation of statistical samples can be used to identify many other protein attributes as well.

A hierarchical latent variable model for data visualization

Visualization has proven to be a powerful and widely-applicable tool the analysis and interpretation of data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and sub-clusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach first on a toy data set, and then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multi-phase flows in oil pipelines and to data in 36 dimensions derived from satellite images.