The heteromeric 5-HT3A/B receptor:the effect of 5-HT3B subunits on receptor structure and ligand recognition

The 5-HT3 receptors are members of the cys-loop family of ligand-gated ion channels. Two functional subtypes are known, the homomeric 5HT3A and the heteromeric 5HT3A/B receptors, which exhibit distinct biophysical characteristics but are difficult to differentiate pharmacologically. Atomic force microscopy has been used to determine the stoichiometry and architecture of the heteromeric 5HT3A/B receptor. Each subunit was engineered to express a unique C-terminal epitope tag, together with six sequential histidine residues to facilitate nickel affinity purification. The 5-HT3 receptors, ectopically expressed in HEK293 cells, were solubilised, purified and decorated with antibodies to the subunit specific epitope tags. Imaging of individual receptors by atomic force microscopy revealed a pentameric arrangement of subunits in the order BBABA, reading anti-clockwise when viewed from the extracellular face. Homology models for the heteromeric receptor were then constructed using both the electron microscopic structure of the nicotinic acetylcholine receptor, from Torpedo marmorata, and the X-ray crystallographic structure of the soluble acetylcholine binding protein, from Lymnaea stagnalis, as templates. These homology models were used, together with equivalent models constructed for the homomeric receptor, to interpret mutagenesis experiments designed to explore the minimal recognition differences of both the natural agonist, 5-HT, and the competitive antagonist, granisetron, for the two human receptor subtypes. The results of this work revealed that the 5-HT3B subunit residues within the ligand binding site, for both the agonist and antagonist, are accommodating to conservative mutations. They are consistent with the view that the 5-HT3A subunit provides the principal and the 5-HT38 subunit the complementary recognition interactions at the binding interface.

Footwear for gender recognition

Conventionally, biometrics resources, such as face, gait silhouette, footprint, and pressure, have been utilized in gender recognition systems. However, the acquisition and processing time of these biometrics data makes the analysis difficult. This letter demonstrates for the first time how effective the footwear appearance is for gender recognition as a biometrics resource. A footwear database is also established with reprehensive shoes (footwears). Preliminary experimental results suggest that footwear appearance is a promising resource for gender recognition. Moreover, it also has the potential to be used jointly with other developed biometrics resources to boost performance.

Dimensions of perception and recognition of danger

Despite abundant literature on human behaviour in the face of danger, much remains to be discovered. Some descriptive models of behaviour in the face of danger are reviewed in order to identify areas where documentation is lacking. It is argued that little is known about recognition and assessment of danger and yet, these are important aspects of cognitive processes. Speculative arguments about hazard assessment are reviewed and tested against the results of previous studies. Once hypotheses are formulated, the reason for retaining the reportory grid as the main research instrument are outlined, and the choice of data analysis techniques is described. Whilst all samples used repertory grids, the rating scales were different between samples; therefore, an analysis is performed of the way in which rating scales were used in the various samples and of some reasons why the scales were used differently. Then, individual grids are looked into and compared between respondents within each sample; consensus grids are also discussed. the major results from all samples are then contrasted and compared. It was hypothesized that hazard assessment would encompass three main dimensions, i.e. 'controllability', 'severity of consequences' and 'likelihood of occurrence', which would emerge in that order. the results suggest that these dimensions are but facets of two broader dimensions labelled 'scope of human intervention' and 'dangerousness'. It seems that these two dimensions encompass a number of more specific dimensions some of which can be further fragmented. Thus, hazard assessment appears to be a more complex process about which much remains to be discovered. Some of the ways in which further discovery might proceed are discussed.

Live 3D shape reconstruction, recognition and registration

We present a video-based system which interactively captures the geometry of a 3D object in the form of a point cloud, then recognizes and registers known objects in this point cloud in a matter of seconds (fig. 1). In order to achieve interactive speed, we exploit both efficient inference algorithms and parallel computation, often on a GPU. The system can be broken down into two distinct phases: geometry capture, and object inference. We now discuss these in further detail. © 2011 IEEE.

Development of configural 3D object recognition

There is evidence for the late development in humans of configural face and animal recognition. We show that the recognition of artificial three-dimensional (3D) objects from part configurations develops similarly late. We also demonstrate that the cross-modal integration of object information reinforces the development of configural recognition more than the intra-modal integration does. Multimodal object representations in the brain may therefore play a role in configural object recognition. © 2003 Elsevier B.V. All rights reserved.

Late development of metric part-relational processing in object recognition

Four experiments with unfamiliar objects examined the remarkably late consolidation of part-relational relative to part-based object recognition (Jüttner, Wakui, Petters, Kaur, & Davidoff, 2013). Our results indicate a particularly protracted developmental trajectory for the processing of metric part relations. Schoolchildren aged 7 to 14 years and adults were tested in 3-Alternative-Forced-Choice tasks to judge the correct appearance of upright and inverted newly learned multipart objects that had been manipulated in terms of individual parts or part relations. Experiment 1 showed that even the youngest tested children were close to adult levels of performance for recognizing categorical changes of individual parts and relative part position. By contrast, Experiment 2 demonstrated that performance for detecting metric changes of relative part position was distinctly reduced in young children compared with recognizing metric changes of individual parts, and did not approach the latter until 11 to 12 years. A similar developmental dissociation was observed in Experiment 3, which contrasted the detection of metric relative-size changes and metric part changes. Experiment 4 showed that manipulations of metric size that were perceived as part (rather than part-relational) changes eliminated this dissociation. Implications for theories of object recognition and similarities to the development of face perception are discussed. © 2014 American Psychological Association.

Emotion recognition and alexithymia in females with non-clinical disordered eating

Objectives: The aims were to determine if emotion recognition deficits observed in eating disorders generalise to non-clinical disordered eating and to establish if other psychopathological and personality factors contributed to, or accounted for, these deficits. Design: Females with high (n=23) and low (n=22) scores on the Eating Disorder Inventory (EDI) were assessed on their ability to recognise emotion from videotaped social interactions. Participants also completed a face memory task, a Stroop task, and self-report measures of alexithymia, depression and anxiety. Results: Relative to the low EDI group, high EDI participants exhibited a general deficit in recognition of emotion, which was related to their scores on the alexithymia measure and the bulimia subscale of the EDI. They also exhibited a specific deficit in the recognition of anger, which was related to their scores on the body dissatisfaction subscale of the EDI. Conclusions: In line with clinical eating disorders, non-clinical disordered eating is associated with emotion recognition deficits. However, the nature of these deficits appears to be dependent upon the type of eating psychopathology and the degree of co-morbid alexithymia.

On Structural Resource of Monotone Recognition

Algorithmic resources are considered for elaboration and identification of monotone functions and some alternate structures are brought, which are more explicit in sense of structure and quantities and which can serve as elements of practical identification algorithms. General monotone recognition is considered on multi- dimensional grid structure. Particular reconstructing problem is reduced to the monotone recognition through the multi-dimensional grid partitioning into the set of binary cubes.

The Knowledge: Its Presentation and Role in Recognition Systems

The concept of knowledge is the central one used when solving the various problems of data mining and pattern recognition in finite spaces of Boolean or multi-valued attributes. A special form of knowledge representation, called implicative regularities, is proposed for applying in two powerful tools of modern logic: the inductive inference and the deductive inference. The first one is used for extracting the knowledge from the data. The second is applied when the knowledge is used for calculation of the goal attribute values. A set of efficient algorithms was developed for that, dealing with Boolean functions and finite predicates represented by logical vectors and matrices.

Neural-Like Growing Networks in Intelligent System of Recognition of Images

The neural-like growing networks used in the intelligent system of recognition of images are under consideration in this paper. All operations made over the image on a pre-design stage and also classification and storage of the information about the images and their further identification are made extremely by mechanisms of neural-like networks without usage of complex algorithms requiring considerable volumes of calculus. At the conforming hardware support the neural network methods allow considerably to increase the effectiveness of the solution of the given class of problems, saving a high accuracy of result and high level of response, both in a mode of training, and in a mode of identification.

One Approach for the Optimization of Estimates Calculating Algorithms

In this article the new approach for optimization of estimations calculating algorithms is suggested. It can be used for finding the correct algorithm of minimal complexity in the context of algebraic approach for pattern recognition.

Optimal Decision Rules in Logical Recognition Models

The task of smooth and stable decision rules construction in logical recognition models is considered. Logical regularities of classes are defined as conjunctions of one-place predicates that determine the membership of features values in an intervals of the real axis. The conjunctions are true on a special no extending subsets of reference objects of some class and are optimal. The standard approach of linear decision rules construction for given sets of logical regularities consists in realization of voting schemes. The weighting coefficients of voting procedures are done as heuristic ones or are as solutions of complex optimization task. The modifications of linear decision rules are proposed that are based on the search of maximal estimations of standard objects for their classes and use approximations of logical regularities by smooth sigmoid functions.

"AVO-Polynom" Recognition Algorithm

Estimates Calculating Algorithms have a long story of application to recognition problems. Furthermore they have formed a basis for algebraic recognition theory. Yet use of ECA polynomials was limited to theoretical reasoning because of complexity of their construction and optimization. The new recognition method “AVO- polynom” based upon ECA polynomial of simple structure is described.

Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty

Modern advances in technology have led to more complex manufacturing processes whose success centres on the ability to control these processes with a very high level of accuracy. Plant complexity inevitably leads to poor models that exhibit a high degree of parametric or functional uncertainty. The situation becomes even more complex if the plant to be controlled is characterised by a multivalued function or even if it exhibits a number of modes of behaviour during its operation. Since an intelligent controller is expected to operate and guarantee the best performance where complexity and uncertainty coexist and interact, control engineers and theorists have recently developed new control techniques under the framework of intelligent control to enhance the performance of the controller for more complex and uncertain plants. These techniques are based on incorporating model uncertainty. The newly developed control algorithms for incorporating model uncertainty are proven to give more accurate control results under uncertain conditions. In this paper, we survey some approaches that appear to be promising for enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.