bluuebunny/crossref_metadata_2025_split huggingface dataset:
Binding of Transmissible Gastroenteritis Coronavirus to Brush Border Membrane Sialoglycoproteins
Authors: Schwegmann-Wessels Christel , Zimmer Gert , Schröder Bernd , Breves Gerhard , Herrler Georg 
Published: October 2003

ABSTRACT
Transmissible gastroenteritis coronavirus (TGEV) is a porcine pathogen causing enteric infections that are lethal for suckling piglets. The enterotropism of TGEV is connected with the sialic acid binding activity of the viral surface protein S. Here we show that, among porcine intestinal brush border membrane proteins, TGEV recognizes a mucin-type glycoprotein designated MGP in a sialic acid-dependent fashion. Virus binding assays with cryosections of the small intestine from a suckling piglet revealed the binding of TGEV to mucin-producing goblet cells. A nonenteropathogenic mutant virus that lacked a sialic acid binding activity was unable to bind to MGP and to attach to goblet cells. Our results suggest a role of MGP in the enteropathogenicity of TGEV.
Multiple Viral Determinants Mediate Myopathogenicity in Coxsackievirus B1-Induced Chronic Inflammatory Myopathy
Authors: Tam Patricia E. , Weber-Sanders Melissa L. , Messner Ronald P. 
Published: October 2003

ABSTRACT

Mice infected with myopathic coxsackievirus B1 Tucson (CVB1
T
) develop chronic inflammatory myopathy (CIM) consisting of hind limb weakness and inflammation. Amyopathic virus variants are infectious but attenuated for CIM. In this report, viral clones, chimeras, and sequencing were used to identify viral determinants of CIM. Chimeras identified several regions involved in CIM and localized a weakness determinant to nucleotides 2493 to 3200 of VP1. Sequencing of multiple clones and viruses identified five candidate determinants that were strictly conserved in myopathic viruses with one located in the 5′ untranslated region (UTR), three in the VP1 capsid, and one in the 3C protease. Taken together, these studies implicate Tyr-87 and/or Val-136 as candidate determinants of weakness. They also indicate that there are at least two determinants of inflammation and one additional determinant of weakness encoded by myopathic CVB1
T
.
Retrotransposition and Cell-to-Cell Transfer of Foamy Viruses
Authors: Heinkelein Martin , Rammling Matthias , Juretzek Thomas , Lindemann Dirk , Rethwilm Axel 
Published: October 2003

ABSTRACT
A remarkable feature of the prototype foamy virus (PFV) replication pathway has been reported to consist of the ability to retrotranspose intracellularly with high efficiency (M. Heinkelein, T. Pietschmann, G. Jármy, M. Dressler, H. Imrich, J. Thurow, D. Lindemann, M. Bock, A. Moebes, J. Roy, O. Herchenröder, and A. Rethwilm, EMBO J. 19:3436-3345, 2000). PFV intracellular retrotransposition (IRT) was reported to be enhanced by coexpression of fusion-defective envelope protein. To investigate the possibility of cell-to-cell transfer of PFV genomes, which could mimic IRT, we performed cocultivation experiments with cells transfected with an IRT-competent and marker gene-expressing PFV vector together with cells expressing a different marker and measured cells positive for both markers. The findings corroborated the initial report on IRT of Env-deficient PFV. Furthermore, they indicated that viral cores that have incorporated fusion-deficient Env can be transferred from cell to cell in a cell type-specific manor. One possible explanation consists of a minor alternative cleavage site in Env that can be used to expose the fusion peptide of the Env transmembrane protein, which appears to be required for virus uptake.
Induction of Protective Immunity against Malaria by Priming-Boosting Immunization with Recombinant Cold-Adapted Influenza and Modified Vaccinia Ankara Viruses Expressing a CD8+-T-Cell Epitope Derived from the Circumsporozoite Protein ofPlasmodium yoelii
Authors: González-Aseguinolaza Gloria , Nakaya Yurie , Molano Alberto , Dy Edward , Esteban Mariano , Rodríguez Dolores , Rodríguez Juan Ramón , Palese Peter , García-Sastre Adolfo , Nussenzweig Ruth S. 
Published: October 2003

ABSTRACTWe immunized mice with an attenuated (cold-adapted) influenza virus followed by an attenuated vaccinia virus (modified vaccinia virus Ankara), both expressing a CD8+-T-cell epitope derived from malaria sporozoites. This vaccination regimen elicited high levels of protection against malaria. This is the first time that the vaccine efficacy of a recombinant cold-adapted influenza virus vector expressing a foreign antigen has been evaluated.
Efficient Communication in Folded Petersen Networks
Authors: Das Sajal K. , Hohndel Dirk H. , Ibel Maximilian , Öhring Sabine R. 
Published: October 2003

Fast and efficient communication is one of the most important requirements in today's multicomputers. When reaching a larger scale of processors, the probability of faults in the network increases, hence communication must be robust and fault tolerant. The recently introduced family of folded Petersen networks, constructed by iteratively applying the cartesian product operation on the well-known Petersen graph, provides a regular, node– and edge-symmetric architecture with optimal connectivity (hence maximal fault-tolerance), and logarithmic diameter. Compared to the closest sized hypercube, the folded petersen network has a smaller diameter, lower node degree and higher packing density. In this paper, we study fundamental communication primitives like single routing, permutation routing, one-to-all broadcasting, multinode-broadcasting (gossiping), personalized communications like scattering, and total exchange on the folded Petersen networks, considering two communication models, namely single link availability (SLA) and multiple link availability (MLA). We derive lower bounds for these problems and design optimal algorithms in terms of both time and the number of message transmissions. The results are based on the construction of minimal height spanning trees in the fault-free folded Petersen network. We further analyze these communication primitives in faulty networks, where processing nodes and transmission links cease working. This analysis is based on multiple arc-disjoint spanning trees, a construct also useful for analyzing other families of multicomputer networks.
Parsing Partially Ordered Multisets
Authors: Basten Twan 
Published: October 2003

A partially ordered multiset or pomset is a generalization of a string in which the total order has been relaxed to a partial order. Strings are often used as a model for sequential computation; pomsets are a natural model for parallel and distributed computation. By viewing pomsets as a generalization of strings, the question is raised whether concepts from language theory can be generalized to pomsets. An important area in the theory of languages is parsing theory. This paper develops the fundamentals of a parsing theory for pomsets, called PLR parsing. It is based on the LR-parsing technique, which is the most powerful deterministic parsing technique in language theory. The basic algorithm in the class of PLR parsing algorithms, the PLR(0) algorithm is explained in detail.
Using Neural Networks to Automatically Detect Brain Tumours in MR Images
Authors: Dickson S. , Thomas B. T. , Goddard P. 
Published: October 2003

Computer vision has been applied to many medical imaging problems with the aim of providing clinical tools to aid medical professionals. We present work being carried out to develop one such system to automatically detect a specific type of brain tumour from head MR images. The tumour under consideration is an acoustic neuroma, which is a benign tumour occurring in the acoustic canals. The hybrid system developed integrates neural networks with more conventional techniques used for computer vision tasks. A database of MR images from 50 patients has been assembled and the acoustic neuromas present in the images have been labelled by hand. Using this data, neural networks (MLPs) have been developed to classify the images at the pixel level to achieve a targeted segmentation. The data used to train and test the MLPs developed, consists of the grey levels of a square of pixels, the pixel to be classified being the centre pixel, together with its global position in the image. The initial pixel level segmentation is refined by a series of conventional techniques. It is combined with an edge-region based segmentation and a morphological operation is applied to the result. This processing produces clusters of adjacent regions, which are considered to be candidate tumour regions. For each possible combination of these regions, features are measured and presented to neural networks which have been trained to identify structures corresponding to acoustic neuromas. Using this approach, all the acoustic neuromas are identified together with three false positive errors.
A Neural Network Based Artificial Vision System for Licence Plate Recognition
Authors: Draghici Sorin 
Published: October 2003

This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solution used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%
Video Sequence Compression via Supervised Training on Cellular Neural Networks
Authors: Rodríguez Luis , Zufiria Pedro J. , Berzal J. Andrés 
Published: October 2003

In this paper, a novel approach for video sequence compression using Cellular Neural Networks (CNN's) is presented. CNN's are nets characterized by local interconnections between neurons (usually called cells), and can be modeled as dynamical systems. From among many different types, a CNN model operating in discrete-time (DT-CNN) has been chosen, its parameters being defined so that they are shared among all the cells in the network. The compression process proposed in this work is based on the possibility of replicating a given video sequence as a trajectory generated by the DT-CNN. In order for the CNN to follow a prescribed trajectory, a supervised training algorithm is implemented. Compression is achieved due to the fact that all the information contained in the sequence can be stored into a small number of parameters and initial conditions once training is stopped. Different improvements upon the basic formulation are analyzed and issues such as feasibility and complexity of the compression problem are also addressed. Finally, some examples with real video sequences illustrate the applicability of the method.
Data Mining of Inputs: Analysing Magnitude and Functional Measures
Authors: Gedeon Tamás D. 
Published: October 2003

The problem of data encoding and feature selection for training back-propagation neural networks is well known. The basic principles are to avoid encrypting the underlying structure of the data, and to avoid using irrelevant inputs. This is not easy in the real world, where we often receive data which has been processed by at least one previous user. The data may contain too many instances of some class, and too few instances of other classes. Real data sets often include many irrelevant or redundant input fields. This paper examines the use of weight matrix analysis techniques and functional measures using two real (and hence noisy) data sets. The first part of this paper examines the use of the weight matrix of the trained neural network itself to determine which inputs are significant. A new technique is introduced and compared with two other techniques from the literature. We present our experience and results on some satellite data augmented by a terrain model. The task was to predict the forest supra-type based on the available information. A brute force technique eliminating randomly selected inputs was used to validate our approach. The second part of this paper examines the use of measures to determine the functional contribution of inputs to outputs. Inputs which include minor but unique information to the network are more significant than inputs with higher magnitude contribution but providing redundant information, which is also provided by another input. A comparison is made to sensitivity analysis, where the sensitivity of outputs to input perturbation is used as a measure of the significance of inputs. This paper presents a novel functional analysis of the weight matrix based on a technique developed for determining the behavioral significance of hidden neurons. This is compared with the application of the same technique to the training and test data. Finally, a novel aggregation technique is introduced.
Modeling Volatility Using State Space Models
Authors: Timmer Jens , Weigend Andreas S. 
Published: October 2003

In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensinal hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude since they do not distinguish between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities. Data sets used. Olsen & Associates high frequency DEM/USD foreign exchange rates (8 years). Nikkei 225 index (40 years). Dow Jones Industrial Average (25 years).
Motion Planning with Complete Knowledge Using a Colored Som
Authors: Vleugels Jules , Kok Joost N. , Overmars Mark 
Published: October 2003

The motion planning problem requires that a collision-free path be determined for a robot moving amidst a fixed set of obstacles. Most neural network approaches to this problem are for the situation in which only local knowledge about the configuration space is available. The main goal of the paper is to show that neural networks are also suitable tools in situations with complete knowledge of the configuration space. In this paper we present an approach that combines a neural network and deterministic techniques. We define a colored version of Kohonen's self-organizing map that consists of two different classes of nodes. The network is presented with random configurations of the robot and, from this information, it constructs a road map of possible motions in the work space. The map is a growing network, and different nodes are used to approximate boundaries of obstacles and the Voronoi diagram of the obstacles, respectively. In a second phase, the positions of the two kinds of nodes are combined to obtain the road map. In this way a number of typical problems with small obstacles and passages are avoided, and the required number of nodes for a given accuracy is within reasonable limits. This road map is searched to find a motion connecting the given source and goal configurations of the robot. The algorithm is simple and general; the only specific computation that is required is a check for intersection of two polygons. We implemented the algorithm for planar robots allowing both translation and rotation and experiments show that compared to conventional techniques it performs well, even for difficult motion planning scenes.
ISO/MPEG Audio Coding
Authors: Noll Peter , Pan Davis 
Published: October 2003

The Moving Pictures Expert Group within the International Organization of Standardization (ISO/MPEG) has developed, and is presently developing, a series of audiovisual standards. Its audio coding standard MPEG Phase 1 is the first international standard in the field of high quality digital audio compression and has been applied in many areas, both for consumer and professional audio. Typical application areas for digital audio are in the fields of audio production, program distribution and exchange, digital sound broadcasting, digital storage, and various multimedia applications. This paper will describe in some detail the main features of MPEG Phase 1 coders. As a logical further step in digital audio a multichannel audio standard MPEG Phase 2 is being standardized to provide an improved stereophonic image for audio-only applications including teleconferencing and for improved television systems. The status of this standardization process will be covered briefly.
Lossless Compression for Text and Images
Authors: Moffat Alistair , Bell Timothy C. , Witten Ian H. 
Published: October 2003

Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as images—particularly bilevel ones, or ones arising in medical and remote-sensing applications, or ones that may be required to be certified true for legal reasons. Moreover, during the process of lossy compression, many occasions for lossless compression of coefficients or other information arise. This paper surveys techniques for lossless compression. The process of compression can be broken down into modeling and coding. We provide an extensive discussion of coding techniques, and then introduce methods of modeling that are appropriate for text and images. Standard methods used in popular utilities (in the case of text) and international standards (in the case of images) are described.
Experimental Highlights from Gran Sasso Laboratory
Authors: Bettini A. 
Published: October 2003

The Gran Sasso National Laboratory of the INFN has given important contributions to the discovery of the phenomenon of neutrino oscillations with experiments both on electron neutrinos from the Sun and muon neutrinos indirectly produced by cosmic rays interactions in the atmosphere. Other experiments in the laboratory give the best limits on electron neutrino effective Majorana mass with two different isotopes. We appear to have entered a new physics domain in which the study of neutrinos may lead us to discover new phenomena, corresponding to energy scales by much higher than those of the present accelerators. Underground laboratories are showing their relevance complementary to the colliders for the advance of basic physics. The physics program at the Gran Sasso Laboratory that we are defining will be focussed on neutrino physics with a complementary set of experiments on the muon neutrino beam from CERN (CNGS project), on solar neutrinos, on atmospheric neutrinos and on neutrinos from Supernova explosion. The relevant thermonuclear cross-sections will be measured. The Majorana vs. Dirac nature of electron neutrinos will be explored with the search for neutrino-less double beta decays in different isotopes.
Scanning Probe Microscopy of Surface Plasmons
Authors: Smolyaninov Igor I. 
Published: October 2003

Recent development of novel scanning probe techniques such as Scanning Tunneling Microscopy (STM), Atomic Force Microscopy (AFM), and Near-Field Optical Microscopy (NFOM) has opened new ways to study local field distribution of surface electromagnetic waves. A lot of experimental efforts have been concentrated on the study of surface plasmons (SP). Different techniques allow to excite and probe SPs with wavelengths from 1 nm down to the optical range along its entire dispersion curve. Large number of phenomena have been studied directly, such as SP scattering by individual defects, strong and weak localization of SP, SP induced local field enhancement, light emission from the tunneling junction, etc. Scanning probe techniques allow not only topography and field mapping but also surface modification and lithography on the nanometer scale. Combination of these features in the same experimental setup proved to be extremely useful in SP studies. For example, some prototype two dimensional optical elements able to control SP propagation have been demonstrated.
Effective Hamiltonian in the Problem of a "Central Spin" Coupled to a Spin Environment
Authors: Tupitsyn I. S. , Prokof'ev N. V. , Stamp P. C. E. 
Published: October 2003

We consider here the problem of a "giant spin", with spin quantum number S≫1, interacting with a set of microscopic spins. Interactions between the microscopic spins are ignored. This model describes the low-energy properties of magnetic grains or magnetic macromolecules (ferromagnetically or antiferromagnetically ordered) interacting with a surrounding spin environment, such as nuclear spins. Our aim is to give a general method for truncating the model to another one, valid at low energies, in which a two-level system interacts with the environmental spins, and higher energy terms are absorbed into a new set of couplings. This is done using an instanton technique. We then study the accuracy of this technique, by comparing the results for the low energy effective Hamiltonian, with results derived for the original giant spin, coupled to a macroscopic spin, using exact diagonalization techniques. We find that the low energy central spin effective Hamiltonian gives very accurate results (with increasing accuracy for large S), provided the typical coupling energies between the giant spin and the microscopic spins are not too large, and provided temperature and external field are sufficiently low. The essential limitation to the applicability of the low-energy effective Hamiltonian is just the semiclassical WKB approximation itself, which inevitably fails for very small S. Our results thus justify previous use of this effective Hamiltonian in calculations of the effects of nuclear spins on the dynamics of nanomagnetic systems.