Presence of Epstein-Barr virus in lymphoepithelioma-like carcinoma of the middle ear

AIM: To examine the association of Epstein-Barr virus (EBV) with carcinoma of the ear. METHODS: Five non-keratinising squamous cell carcinomas and two undifferentiated carcinomas of the ear were examined. In situ hybridisation was used to localised EBV-encoded RNAs (EBER). Immunohistochemical methods to detect LMP-1 and EBNA2 were performed in the EBER positive cases. RESULTS: Two cases were EBER positive, including one non-keratinising and one undifferentiated carcinoma. Both showed identical morphology to those arising from the nasopharynx, with abundant lymphoid stroma. They were both negative for LMP-1 and EBNA2. CONCLUSIONS: EBV associated carcinoma with the morphology of lymphoepithelioma can also arise from the middle ear.     

Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm

This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based incremental learning algorithm. The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on this feature subset can achieve the most similar clustering solution to the one obtained by an ensemble learning algorithm. In particular, a clustering solution is firstly achieved by a clustering ensembles method, then the population based incremental learning algorithm is adopted to find the feature subset that best fits the obtained clustering solution. One advantage of the proposed unsupervised feature selection algorithm is that it is dimensionality-unbiased. In addition, the proposed unsupervised feature selection algorithm leverages the consensus across multiple clustering solutions. Experimental results on several real data sets demonstrate that the proposed unsupervised feature selection algorithm is often able to obtain a better feature subset when compared with other existing unsupervised feature selection algorithms.     

A competitive associative memory model and its dynamics

Conventional associative memory networks perform "noncompetitive recognition" or "competitive recognition in distance". In this paper a "competitive recognition" associative memory model is introduced which simulates the competitive persistence of biological species. Unlike most of the conventional networks, the proposed model takes only the prototype patterns as its equilibrium points, so that the spurious points are effectively excluded. Furthermore, it is shown that, as the competitive parameters vary, the network has a unique stable equilibrium point corresponding to the winner competitive parameter and, in this case, the unique stable equilibrium state can be recalled from any initial key.     

Auditory system plasticity in children after long periods of complete deafness

Separation and identification of proteins by two-dimensional (2-D) electrophoresis can be used for protein-based gene expression analysis. In this report single protein spots, from polyvinylidene difluoride blots of micropreparative E. coli 2-D gels, were rapidly and economically identified by matching their amino acid composition, estimated pI and molecular weight against all E. coli entries in the SWISS-PROT database. Thirty proteins from an E. coli 2-D map were analyzed and identities assigned. Three of the proteins were unknown. By protein sequencing analysis, 20 of the 27 proteins were correctly identified. Importantly, correct identifications showed unambiguous "correct" score patterns. While incorrect protein identifications also showed distinctive score patterns, indicating that protein must be identified by other means. These techniques allow large-scale screening of the protein complement of simple organisms, or tissues in normal and disease states. The computer program described here is accessible via the World Wide Web at URL address (http:@expasy.hcuge.ch/).     

On The Approximation Capability of Neural Networks ‐ Dynamic System Modeling and Control

This paper discusses issues related to the approximation capability of neural networks in modeling and control. We show that neural networks are universal models and universal controllers for a class of nonlinear dynamic systems. That is, for a given dynamic system, there exists a neural network which can model the system to any degree of accuracy over time. Moreover, if the system to be controlled is stabilized by a continuous controller, then there exists a neural network which can approximate the controller such that the system controlled by the neural network is also stabilized with a given bound of output error.     

Sense abstractness, semantic activation, and word sense disambiguation

Concrete concepts are often easier to understand than abstract concepts. The notion of abstractness is thus closely tied to the organisation of our semantic memory, and more specifically our internal lexicon, which underlies our word sense disambiguation (WSD) mechanisms. State-of-the-art automatic WSD systems often draw on a variety of contextual cues and assign word senses by an optimal combination of statistical classifiers. The validity of various lexico-semantic resources as models of our internal lexicon and the cognitive aspects pertinent to the lexical sensitivity of WSD are seldom questioned. We attempt to address these issues by examining psychological evidence of the internal lexicon and its compatibility with the information available from computational lexicons. In particular, we compare the responses from a word association task against existing lexical resources, WordNet and SUMO, to explore the relation between sense abstractness and semantic activation, and thus the implications on semantic network models and the lexical sensitivity of WSD. Our results suggest that concrete senses are more readily activated than abstract senses, and broad associations are more easily triggered than narrow paradigmatic associations. The results are expected to inform the construction of lexico-semantic resources and WSD strategies.     

Genetic Parallel Programming: design and implementation

This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.     

A multi-objective genetic algorithm approach to rule mining for affective product design

A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data.     

A simple characterization of optimal ARMA predictors

The coefficients of the optimal steady state k-step ahead predictor for an ARMA process in general depend on k. It is shown that a simple formula completely characterizes all these coefficients.