Convergence of the iterative algorithm for a general Hammerstein system identification

The convergence of the iterative identification algorithm for a general Hammerstein system has been an open problem for a long time. In this paper, it is shown that the convergence can be achieved by incorporating a regularization procedure on the nonlinearity in addition to a normalization step on the parameters.

Robust automated tumour segmentation on histological and immunohistochemical tissue images

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.

Body mass-abundance relationships are robust to cascading effects in marine food webs

Body mass has been shown to scale negatively with abundance in a wide range of habitats and ecosystems. It is believed that this relationship has important consequences for the distribution and maintenance of energy in natural communities. Some studies have shown that the relationship between body mass and abundance may be robust to major food web perturbations, fuelling the belief that natural processes may preserve the slope of this relationship and the associated cycling of energy and nutrients. Here, we use data from a long-term experimental food web manipulation to examine this issue in a semi-natural environment. Similar communities were developed in large experimental mesocosms over a six month period. Some of the mesocosms were then subjected to species removals, based on the mean strength of their trophic interactions in the communities. In treatments where the strongest interactors were removed, a community-level trophic cascade occurred. The biomass density of invertebrates increased dramatically in these communities, which led to a suppression of primary production. In spite of these widespread changes in ecosystem functioning, the slope of the relationship between body mass and abundance remained unchanged. This was the case whether average species body mass and abundance or individual organism size spectra were considered. An examination of changes in species composition before and after the experimental manipulations revealed an important mechanism for maintaining the body mass-abundance relationship. The manipulated communities all had a higher species turnover than the intact communities, with the highest turnover in communities that experienced cascading effects. As some species increased in body mass and abundance, new species filled the available size-abundance niches that were created. This maintained the overall body mass-abundance relationship and provided a stabilising structure to these experimental communities.

Convergence/Divergence: Party political discourse in Northern Ireland's transition from conflict

This paper illuminates the role of political language in a peace process through analysing the discourse used by political parties in Northern Ireland. What matters, it seems, is not whether party discourses converge or diverge but rather how, and in what ways, they do so. In the case of Northern Ireland, there remains strong divergence between discourses regarding the ethos of unionist and nationalist parties. As a consequence, core definitions of identity, culture, norms and principle remain common grounds for competition within nationalism and unionism. There has, however, been a significant shift towards convergence between unionist and nationalist parties in their discourses on power and governance, specifically among the now predominant (hardline) and the smaller (moderate) parties. The argument thus elaborated is that political transition from conflict need not necessarily entail the creation of a “shared discourse” between all parties. Indeed, points of divergence between parties’ discourses of power and ethos are as important for a healthy post-conflict democratic environment as the elements of convergence between them.

A Robust Co-Localisation Measurement Utilising Z-Stack Image Intensity Similarities for Biological Studies

Background: Co-localisation is a widely used measurement in immunohistochemical analysis to determine if fluorescently labelled biological entities, such as cells, proteins or molecules share a same location. However the measurement of co-localisation is challenging due to the complex nature of such fluorescent images, especially when multiple focal planes are captured. The current state-of-art co-localisation measurements of 3-dimensional (3D) image stacks are biased by noise and cross-overs from non-consecutive planes.

Method: In this study, we have developed Co-localisation Intensity Coefficients (CICs) and Co-localisation Binary Coefficients (CBCs), which uses rich z-stack data from neighbouring focal planes to identify similarities between image intensities of two and potentially more fluorescently-labelled biological entities. This was developed using z-stack images from murine organotypic slice cultures from central nervous system tissue, and two sets of pseudo-data. A large amount of non-specific cross-over situations are excluded using this method. This proposed method is also proven to be robust in recognising co-localisations even when images are polluted with a range of noises.

Results: The proposed CBCs and CICs produce robust co-localisation measurements which are easy to interpret, resilient to noise and capable of removing a large amount of false positivity, such as non-specific cross-overs. Performance of this method of measurement is significantly more accurate than existing measurements, as determined statistically using pseudo datasets of known values. This method provides an important and reliable tool for fluorescent 3D neurobiological studies, and will benefit other biological studies which measure fluorescence co-localisation in 3D.

Exploiting spatio-temporal constraints for robust 2D pose tracking

We present a Spatio-temporal 2D Models Framework (STMF) for 2D-Pose tracking. Space and time are discretized and a mixture of probabilistic "local models" is learnt associating 2D Shapes and 2D Stick Figures. Those spatio-temporal models generalize well for a particular viewpoint and state of the tracked action but some spatio-temporal discontinuities can appear along a sequence, as a direct consequence of the discretization. To overcome the problem, we propose to apply a Rao-Blackwellized Particle Filter (RBPF) in the 2D-Pose eigenspace, thus interpolating unseen data between view-based clusters. The fitness to the images of the predicted 2D-Poses is evaluated combining our STMF with spatio-temporal constraints. A robust, fast and smooth human motion tracker is obtained by tracking only the few most important dimensions of the state space and by refining deterministically with our STMF.

Investigating the efficiency of fitting Coxian phase-type distributions to health care data

Coxian phase-type distributions are becoming a popular means of representing survival times within a health care environment. They are favoured as they show a distribution as a system of phases and can allow for an easy visual representation of the rate of flow of patients through a system. Difficulties arise, however, in determining the parameter estimates of the Coxian phase-type distribution. This paper examines ways of making the fitting of the Coxian phase-type distribution less cumbersome by outlining different software packages and algorithms available to perform the fit and assessing their capabilities through a number of performance measures. The performance measures rate each of the methods and help in identifying the more efficient. Conclusions drawn from these performance measures suggest SAS to be the most robust package. It has a high rate of convergence in each of the four example model fits considered, short computational times, detailed output, convergence criteria options, along with a succinct ability to switch between different algorithms.