Evaluating the effectiveness of Bayesian feature selection

A practical Bayesian approach for inference in neural network models has been available for ten years, and yet it is not used frequently in medical applications. In this chapter we show how both regularisation and feature selection can bring significant benefits in diagnostic tasks through two case studies: heart arrhythmia classification based on ECG data and the prognosis of lupus. In the first of these, the number of variables was reduced by two thirds without significantly affecting performance, while in the second, only the Bayesian models had an acceptable accuracy. In both tasks, neural networks outperformed other pattern recognition approaches.

Neural networks: A principled perspective

Introductory accounts of artificial neural networks often rely for motivation on analogies with models of information processing in biological networks. One limitation of such an approach is that it offers little guidance on how to find optimal algorithms, or how to verify the correct performance of neural network systems. A central goal of this paper is to draw attention to a quite different viewpoint in which neural networks are seen as algorithms for statistical pattern recognition based on a principled, i.e. theoretically well-founded, framework. We illustrate the concept of a principled viewpoint by considering a specific issue concerned with the interpretation of the outputs of a trained network. Finally, we discuss the relevance of such an approach to the issue of the validation and verification of neural network systems.

Activation of the left inferior frontal gyrus in the first 200 ms of reading:evidence from magnetoencephalography (MEG)

Background - It is well established that the left inferior frontal gyrus plays a key role in the cerebral cortical network that supports reading and visual word recognition. Less clear is when in time this contribution begins. We used magnetoencephalography (MEG), which has both good spatial and excellent temporal resolution, to address this question. Methodology/Principal Findings - MEG data were recorded during a passive viewing paradigm, chosen to emphasize the stimulus-driven component of the cortical response, in which right-handed participants were presented words, consonant strings, and unfamiliar faces to central vision. Time-frequency analyses showed a left-lateralized inferior frontal gyrus (pars opercularis) response to words between 100–250 ms in the beta frequency band that was significantly stronger than the response to consonant strings or faces. The left inferior frontal gyrus response to words peaked at ~130 ms. This response was significantly later in time than the left middle occipital gyrus, which peaked at ~115 ms, but not significantly different from the peak response in the left mid fusiform gyrus, which peaked at ~140 ms, at a location coincident with the fMRI–defined visual word form area (VWFA). Significant responses were also detected to words in other parts of the reading network, including the anterior middle temporal gyrus, the left posterior middle temporal gyrus, the angular and supramarginal gyri, and the left superior temporal gyrus. Conclusions/Significance - These findings suggest very early interactions between the vision and language domains during visual word recognition, with speech motor areas being activated at the same time as the orthographic word-form is being resolved within the fusiform gyrus. This challenges the conventional view of a temporally serial processing sequence for visual word recognition in which letter forms are initially decoded, interact with their phonological and semantic representations, and only then gain access to a speech code.

A smart sensing platform for the classification of ambulatory patterns

This paper describes an innovative sensing approach allowing capture, discrimination, and classification of transients automatically in gait. A walking platform is described, which offers an alternative design to that of standard force plates with advantages that include mechanical simplicity and less restriction on dimensions. The scope of the work is to investigate as an experiment the sensitivity of the distributive tactile sensing method with the potential to address flexibility on gait assessment, including patient targeting and the extension to a variety of ambulatory applications. Using infrared sensors to measure plate deflection, gait patterns are compared with stored templates using a pattern recognition algorithm. This information is input into a neural network to classify normal and affected walking events, with a classification accuracy of just under 90 per cent achieved. The system developed has potential applications in gait analysis and rehabilitation, whereby it can be used as a tool for early diagnosis of walking disorders or to determine changes between pre- and post-operative gait.

Progressive structural analysis for dynamic recognition of on-line handwritten mathematical expressions

Structural analysis in handwritten mathematical expressions focuses on interpreting the recognized symbols using geometrical information such as relative sizes and positions of the symbols. Most existing approaches rely on hand-crafted grammar rules to identify semantic relationships among the recognized mathematical symbols. They could easily fail when writing errors occurred. Moreover, they assume the availability of the whole mathematical expression before being able to analyze the semantic information of the expression. To tackle these problems, we propose a progressive structural analysis (PSA) approach for dynamic recognition of handwritten mathematical expressions. The proposed PSA approach is able to provide analysis result immediately after each written input symbol. This has an advantage that users are able to detect any recognition errors immediately and correct only the mis-recognized symbols rather than the whole expression. Experiments conducted on 57 most commonly used mathematical expressions have shown that the PSA approach is able to achieve very good performance results.

Innate recognition of apoptotic cells:novel apoptotic cell-associated molecular patterns revealed by crossreactivity of anti-LPS antibodies

Cells dying by apoptosis are normally cleared by phagocytes through mechanisms that can suppress inflammation and immunity. Molecules of the innate immune system, the pattern recognition receptors (PRRs), are able to interact not only with conserved structures on microbes (pathogen-associated molecular patterns, PAMPs) but also with ligands displayed by apoptotic cells. We reasoned that PRRs might therefore interact with structures on apoptotic cells-apoptotic cell-associated molecular patterns (ACAMPs)-that are analogous to PAMPs. Here we show that certain monoclonal antibodies raised against the prototypic PAMP, lipopolysaccharide (LPS), can crossreact with apoptotic cells. We demonstrate that one such antibody interacts with a constitutively expressed intracellular protein, laminin-binding protein, which translocates to the cell surface during apoptosis and can interact with cells expressing the prototypic PRR, mCD14 as well as with CD14-negative cells. Anti-LPS cross reactive epitopes on apoptotic cells colocalised with annexin V-and C1q-binding sites on vesicular regions of apoptotic cell surfaces and were released associated with apoptotic cell-derived microvesicles (MVs). These results confirm that apoptotic cells and microbes can interact with the immune system through common elements and suggest that anti-PAMP antibodies could be used strategically to characterise novel ACAMPs associated not only with apoptotic cells but also with derived MVs. © 2013 Macmillan Publishers Limited All rights reserved.

Examination of the predictive value of structural magnetic resonance scans in bipolar disorder:a pattern classification approach

Background - Bipolar disorder (BD) is one of the leading causes of disability worldwide. Patients are further disadvantaged by delays in accurate diagnosis ranging between 5 and 10 years. We applied Gaussian process classifiers (GPCs) to structural magnetic resonance imaging (sMRI) data to evaluate the feasibility of using pattern recognition techniques for the diagnostic classification of patients with BD. Method - GPCs were applied to gray (GM) and white matter (WM) sMRI data derived from two independent samples of patients with BD (cohort 1: n = 26; cohort 2: n = 14). Within each cohort patients were matched on age, sex and IQ to an equal number of healthy controls. Results - The diagnostic accuracy of the GPC for GM was 73% in cohort 1 and 72% in cohort 2; the sensitivity and specificity of the GM classification were respectively 69% and 77% in cohort 1 and 64% and 99% in cohort 2. The diagnostic accuracy of the GPC for WM was 69% in cohort 1 and 78% in cohort 2; the sensitivity and specificity of the WM classification were both 69% in cohort 1 and 71% and 86% respectively in cohort 2. In both samples, GM and WM clusters discriminating between patients and controls were localized within cortical and subcortical structures implicated in BD. Conclusions - Our results demonstrate the predictive value of neuroanatomical data in discriminating patients with BD from healthy individuals. The overlap between discriminative networks and regions implicated in the pathophysiology of BD supports the biological plausibility of the classifiers.

A stable graph-based representation for object recognition through high-order matching

Many Object recognition techniques perform some flavour of point pattern matching between a model and a scene. Such points are usually selected through a feature detection algorithm that is robust to a class of image transformations and a suitable descriptor is computed over them in order to get a reliable matching. Moreover, some approaches take an additional step by casting the correspondence problem into a matching between graphs defined over feature points. The motivation is that the relational model would add more discriminative power, however the overall effectiveness strongly depends on the ability to build a graph that is stable with respect to both changes in the object appearance and spatial distribution of interest points. In fact, widely used graph-based representations, have shown to suffer some limitations, especially with respect to changes in the Euclidean organization of the feature points. In this paper we introduce a technique to build relational structures over corner points that does not depend on the spatial distribution of the features. © 2012 ICPR Org Committee.

Approximate axial symmetries from continuous time quantum walks

The analysis of complex networks is usually based on key properties such as small-worldness and vertex degree distribution. The presence of symmetric motifs on the other hand has been related to redundancy and thus robustness of the networks. In this paper we propose a method for detecting approximate axial symmetries in networks. For each pair of nodes, we define a continuous-time quantum walk which is evolved through time. By measuring the probability that the quantum walker to visits each node of the network in this time frame, we are able to determine whether the two vertices are symmetrical with respect to any axis of the graph. Moreover, we show that we are able to successfully detect approximate axial symmetries too. We show the efficacy of our approach by analysing both synthetic and real-world data. © 2012 Springer-Verlag Berlin Heidelberg.

Discriminating ambulation using a smart sensing plate

The automated sensing scheme described in this paper has the potential to automatically capture, discriminate and classify transients in gait. The mechanical simplicity of the walking platform offers advantages over standard force plates. There is less restriction on dimensions offering the opportunity for multi-contact and multiple steps. This addresses the challenge of patient targeting and the evaluation of patients in a variety of ambulatory applications. In this work the sensitivity of the distributive tactile sensing method has been investigated experimentally. Using coupled time series data from a small number of sensors, gait patterns are compared with stored templates using a pattern recognition algorithm. By using a neural network these patterns were interpreted classifying normal and affected walking events with an accuracy of just under 90%. This system has potential in gait analysis and rehabilitation as a tool for early diagnosis in walking disorders, for determining response to therapy and for identifying changes between pre and post operative gait. Copyright © 2009 by ASME.

The role of the innate immune system in the clearance of apoptotic cells

Rapid clearance of dying cells is a vital feature of apoptosis throughout development, tissue homeostasis and resolution of inflammation. The phagocytic removal of apoptotic cells is mediated by both professional and amateur phagocytes, armed with a series of pattern recognition receptors that participate in host defence and apoptotic cell clearance. CD14 is one such molecule. It is involved in apoptotic cell clearance (known to be immunosuppressive and anti-inflammatory) and binding of the pathogen-associated molecular pattern, lipopolysaccharides (a pro-inflammatory event). Thus CD14 is involved in the assembly of two distinct ligand-dependent macrophage responses. This project sought to characterise the involvement of the innate immune system, particularly CD14, in the removal of apoptotic cells. The role of non-myeloid CD14 was also considered and the data suggests that the expression of CD14 by phagocytes may define their professional status as phagocytes. To assess if differential CD14 ligation causes the ligand-dependent divergence in macrophage responses, a series of CD14 point mutants were used to map the binding of apoptotic cells and lipopolysaccharides. Monoclonal antibodies, 61D3 and MEM18, known to interfere with ligand-binding and responses, were also mapped. Data suggests that residue 11 of CD14, is key for the binding of 61D3 (but not MEM18), LPS and apoptotic cells, indicating lipopolysaccharides and apoptotic cells bind to similar residues. Furthermore using an NF-kB reporter, results show lipopolysaccharides but not apoptotic cells stimulate NF-kB. Taken together these data suggests ligand-dependent CD14 responses occur via a mechanism that occurs downstream of CD14 ligation but upstream of NF-?B activation. Alternatively apoptotic cell ligation of CD14 may not result in any signalling event, possibly by exclusion of TLR-4, suggesting that engulfment receptors, (e.g. TIM-4, BAI1 and Stablin-2) are required to mediate the uptake of apoptotic cells and the associated anti-inflammatory response.

Developments of the generative topographic mapping

The generative topographic mapping (GTM) model was introduced by Bishop et al. (1998, Neural Comput. 10(1), 215-234) as a probabilistic re- formulation of the self-organizing map (SOM). It offers a number of advantages compared with the standard SOM, and has already been used in a variety of applications. In this paper we report on several extensions of the GTM, including an incremental version of the EM algorithm for estimating the model parameters, the use of local subspace models, extensions to mixed discrete and continuous data, semi-linear models which permit the use of high-dimensional manifolds whilst avoiding computational intractability, Bayesian inference applied to hyper-parameters, and an alternative framework for the GTM based on Gaussian processes. All of these developments directly exploit the probabilistic structure of the GTM, thereby allowing the underlying modelling assumptions to be made explicit. They also highlight the advantages of adopting a consistent probabilistic framework for the formulation of pattern recognition algorithms.

Structured codebooks for SCS watermarking

Digital watermarking aims at embedding information in digital data. The watermark is usually required to be imperceptible, unremovable and to have a high information content. Unfortunately, these three requirements are contradicting. For example, having a more robust watermark makes it either more perceptible or/and less informative. For Gaussian data and additive white Gaussian noise, an optimal but also impractical scheme has already be devised. Since then, many practical schemes have tried to approach the theoretical limits. This paper investigate improvements to current state-of-the-art embedding schemes.

Non-rigid object tracking in complex scenes

A recently proposed colour based tracking algorithm has been established to track objects in real circumstances [Zivkovic, Z., Krose, B. 2004. An EM-like algorithm for color-histogram-based object tracking. In: Proc, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 798-803]. To improve the performance of this technique in complex scenes, in this paper we propose a new algorithm for optimally adapting the ellipse outlining the objects of interest. This paper presents a Lagrangian based method to integrate a regularising component into the covariance matrix to be computed. Technically, we intend to reduce the residuals between the estimated probability distribution and the expected one. We argue that, by doing this, the shape of the ellipse can be properly adapted in the tracking stage. Experimental results show that the proposed method has favourable performance in shape adaption and object localisation.

The innate immune system and the clearance of apoptotic cells

Apoptosis, programmed cell death, is used by multicellular organisms to remove cells that are in excess, damaged or diseased. Activation of the apoptosis programme generates "eat me" signals on the surface of the apoptotic cell that mediate recognition and clearance by the innate immune system. CD14, a pattern recognition receptor expressed on macrophages, is widely known for its ability to recognise the pathogen-associated molecular pattern lipopolysaccharide (LPS) and promote inflammation. However, CD14 has also been shown to mediate binding and removal of apoptotic cells in a process that is anti-inflammatory suggesting CD14 is capable of producing two distinct, ligand-dependent macrophage responses. Whilst the molecular basis for this dichotomy has yet to be defined it is clear that CD14 defines a point of interest on the macrophage surface where we may study ligand-specific responses of macrophages. Our work seeks to define the molecular mechanisms underlying the involvement of CD14 in the non-inflammatory clearance of apoptotic cells. Here we used three different differentiation strategies to generate macrophages from the monocytic cell line THP-1. The resultant macrophage models were characterised to assess the expression and function of CD14 within each model system. Whilst each macrophage model shows increased levels of surface CD14 expression, our results demonstrate significant differences in the various models’ abilities to respond to LPS and clear apoptotic cells in a CD14-dependent manner. TLR4 levels correlated positively with LPS responsiveness but not CD14-dependent apoptotic cell clearance or anti-inflammatory responses to apoptotic cells. These observations suggest CD14-dependent apoptotic cell clearance is not dependent on TLR4. Taken together our data support the notion that the CD14 ligand-dependent responses to LPS and apoptotic cells derive from changes at the macrophage surface. The nature and composition of the CD14-co-receptor complex for LPS and apoptotic cell binding and responses is the subject of further study.