Microbial Recognition Via Toll-Like Receptor-Dependent and -Independent Pathways Determines the Cytokine Response of Murine Dendritic Cell Subsets to CD40 Triggering

Dendritic cells (DC) can produce Th-polarizing cytokines and direct the class of the adaptive immune response. Microbial stimuli, cytokines, chemokines, and T cell-derived signals all have been shown to trigger cytokine synthesis by DC, but it remains unclear whether these signals are functionally equivalent and whether they determine the nature of the cytokine produced or simply initiate a preprogrammed pattern of cytokine production, which may be DC subtype specific. Here, we demonstrate that microbial and T cell-derived stimuli can synergize to induce production of high levels of IL-12 p70 or IL-10 by individual murine DC subsets but that the choice of cytokine is dictated by the microbial pattern recognition receptor engaged. We show that bacterial components such as CpG-containing DNA or extracts from Mycobacterium tuberculosis predispose CD8alpha(+) and CD8alpha(-)CD4(-) DC to make IL-12 p70. In contrast, exposure of CD8alpha(+), CD4(+) and CD8alpha(-)CD4(-) DC to heat-killed yeasts leads to production of IL-10. In both cases, secretion of high levels of cytokine requires a second signal from T cells, which can be replaced by CD40 ligand. Consistent with their differential effects on cytokine production, extracts from M. tuberculosis promote IL-12 production primarily via Toll-like receptor 2 and an MyD88-dependent pathway, whereas heat-killed yeasts activate DC via a Toll-like receptor 2-, MyD88-, and Toll/IL-1R domain containing protein-independent pathway. These results show that T cell feedback amplifies innate signals for cytokine production by DC and suggest that pattern recognition rather than ontogeny determines the production of cytokines by individual DC subsets.

EU free movement law and the legal recognition of same-sex relationships: the case for mutual recognition

This article seeks to examine the cross-border legal recognition of same-sex relationships in the EU. Although the Member States maintain an exclusive competence in the field of family law and, thus, it is up to them to determine whether they will provide a legal status to same-sex couples within their territory, they need to exercise their powers in that field in a way that does not violate EU law. This, it is suggested, requires that Member States mutually recognize the legal status of same-sex couples and do not treat same-sex couples worse than opposite-sex couples, if the basis of the differentiation is, merely, the (homosexual) sexual orientation of the two spouses/partners. Nonetheless, the current legal framework does not make it clear that Member States are under such an obligation. The main argument of the article, therefore, is that the EU must adopt a more hands-on approach towards this issue.

Combining 3D and 2D for less constrained periocular recognition

Periocular recognition has recently become an active topic in biometrics. Typically it uses 2D image data of the periocular region. This paper is the first description of combining 3D shape structure with 2D texture. A simple and effective technique using iterative closest point (ICP) was applied for 3D periocular region matching. It proved its strength for relatively unconstrained eye region capture, and does not require any training. Local binary patterns (LBP) were applied for 2D image based periocular matching. The two modalities were combined at the score-level. This approach was evaluated using the Bosphorus 3D face database, which contains large variations in facial expressions, head poses and occlusions. The rank-1 accuracy achieved from the 3D data (80%) was better than that for 2D (58%), and the best accuracy (83%) was achieved by fusing the two types of data. This suggests that significant improvements to periocular recognition systems could be achieved using the 3D structure information that is now available from small and inexpensive sensors.

University teaching: recognition and reward

Teaching in universities has increased in importance in recent years which, in part, is a consequence of the change in funding of universities from block grants to student tuition fees. Various initiatives have been made which serve to raise the profile of teaching and give it greater recognition. It is also important that teaching is recognised even more fully and widely, and crucially that it is rewarded accordingly. We propose a mechanism for recognising and rewarding university teaching that is based on a review process that is supported by documented evidence whose outcomes can be fed into performance and development reviews, and used to inform decisions about reward and promotion, as well as the review of probationary status where appropriate.

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks

Anti-spoofing is attracting growing interest in biometrics, considering the variety of fake materials and new means to attack biometric recognition systems. New unseen materials continuously challenge state-of-the-art spoofing detectors, suggesting for additional systematic approaches to target anti-spoofing. By incorporating liveness scores into the biometric fusion process, recognition accuracy can be enhanced, but traditional sum-rule based fusion algorithms are known to be highly sensitive to single spoofed instances. This paper investigates 1-median filtering as a spoofing-resistant generalised alternative to the sum-rule targeting the problem of partial multibiometric spoofing where m out of n biometric sources to be combined are attacked. Augmenting previous work, this paper investigates the dynamic detection and rejection of livenessrecognition pair outliers for spoofed samples in true multi-modal configuration with its inherent challenge of normalisation. As a further contribution, bootstrap aggregating (bagging) classifiers for fingerprint spoof-detection algorithm is presented. Experiments on the latest face video databases (Idiap Replay- Attack Database and CASIA Face Anti-Spoofing Database), and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2013) illustrate the efficiency of proposed techniques.

On fusion for multispectral iris recognition

Multispectral iris recognition uses information from multiple bands of the electromagnetic spectrum to better represent certain physiological characteristics of the iris texture and enhance obtained recognition accuracy. This paper addresses the questions of single versus cross spectral performance and compares score-level fusion accuracy for different feature types, combining different wavelengths to overcome limitations in less constrained recording environments. Further it is investigated whether Doddington's “goats” (users who are particularly difficult to recognize) in one spectrum also extend to other spectra. Focusing on the question of feature stability at different wavelengths, this work uses manual ground truth segmentation, avoiding bias by segmentation impact. Experiments on the public UTIRIS multispectral iris dataset using 4 feature extraction techniques reveal a significant enhancement when combining NIR + Red for 2-channel and NIR + Red + Blue for 3-channel fusion, across different feature types. Selective feature-level fusion is investigated and shown to improve overall and especially cross-spectral performance without increasing the overall length of the iris code.

Efficiency of evolutionary algorithms in water network pipe sizing

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.

Selecting algorithms for Earth observation of climate within the European Space Agency Climate Change initiative: introduction to a special issue

This special issue is focused on the assessment of algorithms for the observation of Earth’s climate from environ- mental satellites. Climate data records derived by remote sensing are increasingly a key source of insight into the workings of and changes in Earth’s climate system. Producers of data sets must devote considerable effort and expertise to maximise the true climate signals in their products and minimise effects of data processing choices and changing sensors. A key choice is the selection of algorithm(s) for classification and/or retrieval of the climate variable. Within the European Space Agency Climate Change Initiative, science teams undertook systematic assessment of algorithms for a range of essential climate variables. The papers in the special issue report some of these exercises (for ocean colour, aerosol, ozone, greenhouse gases, clouds, soil moisture, sea surface temper- ature and glaciers). The contributions show that assessment exercises must be designed with care, considering issues such as the relative importance of different aspects of data quality (accuracy, precision, stability, sensitivity, coverage, etc.), the availability and degree of independence of validation data and the limitations of validation in characterising some important aspects of data (such as long-term stability or spatial coherence). As well as re- quiring a significant investment of expertise and effort, systematic comparisons are found to be highly valuable. They reveal the relative strengths and weaknesses of different algorithmic approaches under different observa- tional contexts, and help ensure that scientific conclusions drawn from climate data records are not influenced by observational artifacts, but are robust.

Designing care bundle documentation to support the recognition and treatment of acute kidney injury: a route to quality improvement

The chapter describes development of care bundle documentation, through an iterative, user-centred design process, to support the recognition and treatment of acute kidney injury (AKI). The chapter details stages of user and stakeholder consultation, employed to develop a design response that was sensitive to user experience and need, culminating in simulation testing of a near final prototype. The development of supplementary awareness-raising materials, relating to the main care bundle tool is also discussed. This information design response to a complex clinical decision-making process is contrasted to other approaches to promoting AKI care. The need for different but related approaches to the working tool itself and the tool’s communication are discussed. More general recommendations are made for the development of communication tools to support complex clinical processes.

An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.

Conflicts of law and the mutual recognition of same-sex unions in the EU

My thesis uses legal arguments to demonstrate a requirement for recognition of same-sex marriages and registered partnerships between EU Member States. I draw on the US experience, where arguments for recognition of marriages void in some states previously arose in relation to interracial marriages. I show how there the issue of recognition today depends on conflicts of law and its interface with US constitutional freedoms against discrimination. I introduce the themes of the importance of domicile, the role of the public policy exception, vested rights, and relevant US constitutional freedoms. Recognition in the EU also depends on managing the tension between private international law and freedoms guaranteed by higher norms, in this case the EU Treaties and the European Convention on Human Rights. I set out the inconsistencies between various private international law systems and the problems this creates. Other difficulties are caused by the use of nationality as a connecting factor to determine personal capacity, and the overuse of the public policy exception. I argue that EU Law can constrain the use of conflicts law or public policy by any Member State where these are used to deny effect to same-sex unions validly formed elsewhere. I address the fact that family law falls only partly within Union competence, that existing EU Directives have had limited success at achieving full equality and that powers to implement new measures have not been used to their full potential. However, Treaty provisions outlawing discrimination on grounds of nationality can be interpreted so as to require recognition in many cases. Treaty citizenship rights can also be interpreted favourably to mandate recognition, once private international law is itself recognised as an obstacle to free movement. Finally, evolving interpretations of the European Convention on Human Rights may also support claims for cross-border recognition of existing relationships.

Activation of G-protein-coupled receptor 30 is sufficient to enhance spatial recognition memory in ovariectomized rats

In ovariectomized rats, administration of estradiol, or selective estrogen receptor agonists that activate either the alpha or beta isoforms, have been shown to enhance spatial cognition on a variety of learning and memory tasks, including those that capitalize on the preference of rats to seek out novelty. Although the effects of the putative estrogen G-protein-coupled receptor 30 (GPR30) on hippocampus-based tasks have been reported using food-motivated tasks, the effects of activation of GPR30 receptors on tasks that depend on the preference of rats to seek out spatial novelty remain to be determined. Therefore, the aim of the current study was to determine if short-term treatment of ovariectomized rats with G-1, an agonist for GPR30, would mimic the effects on spatial recognition memory observed following short-term estradiol treatment. In Experiment 1, ovariectomized rats treated with a low dose (1mug) of estradiol 48h and 24h prior to the information trial of a Y-maze task exhibited a preference for the arm associated with the novel environment on the retention trial conducted 48h later. In Experiment 2, treatment of ovariectomized rats with G-1 (25mug) 48h and 24h prior to the information trial of a Y-maze task resulted in a greater preference for the arm associated with the novel environment on the retention trial. Collectively, the results indicated that short-term treatment of ovariectomized rats with a GPR30 agonist was sufficient to enhance spatial recognition memory, an effect that also occurred following short-term treatment with a low dose of estradiol.

Zero attracting recursive least squares algorithms

The l1-norm sparsity constraint is a widely used technique for constructing sparse models. In this contribution, two zero-attracting recursive least squares algorithms, referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the l1-norm of parameter vector constraint to facilitate the model sparsity. In order to achieve a closed-form solution, the l1-norm of the parameter vector is approximated by an adaptively weighted l2-norm, in which the weighting factors are set as the inversion of the associated l1-norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra as well as the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.

The relation between speech recognition in noise and the speech-evoked brainstem response in normal-hearing and hearing-impaired individuals

Little is known about the way speech in noise is processed along the auditory pathway. The purpose of this study was to evaluate the relation between listening in noise using the R-Space system and the neurophysiologic response of the speech-evoked auditory brainstem when recorded in quiet and noise in adult participants with mild to moderate hearing loss and normal hearing.

Speech recognition in reverberation in biomodal cochlear implant users

The purpose of the present study was to evaluate the effects of bimodal (implant plus hearing aid) listening on speech recognition in four different environment conditions. Results indicate that there was little difference in the cochlear implant only and bimodal conditions.