Importance of localized skin infection in tick-borne encephalitis virus transmission

Arboviruses are transmitted to vertebrates by the "bite" of infected arthropods. Events at the site of virus deposition are largely unknown despite increasing evidence that blood-sucking arthropods immunomodulate their skin site of feeding. This question is particularly relevant for ixodid ticks that feed for several days. To examine events under conditions mimicking tick-borne encephalitis (TBE) virus transmission in nature (i.e., infected and uninfected Ixodes ricinus ticks feeding on the same animal), infected adult and uninfected nymphal ticks were placed in one retaining chamber (skin site A) and uninfected nymphs were placed within a second chamber posteriorly (skin site B) on two natural host species, yellow-necked field mice (Apodemus flavicollis) and bank voles (Clethrionomys glareolus). Virus transmission from infected to uninfected cofeeding ticks was correlated with infection in the skin site of tick feeding. Furthermore, virus was recruited preferentially to the site in which ticks were feeding compared with uninfested skin sites. Viremia did not correspond with a generalized infection of the skin; virus was not detected in an uninfested skin site (C) of 12/13 natural hosts that had viremia levels > or = 2.0 log10 ic mouse LD50/0.02 ml blood. To characterize infected cells, laboratory mouse strains were infested with infected ticks and then explants were removed from selected skin sites and floated on culture medium. Numerous leukocytes were found to migrate from the skin explants of tick feeding sites. Two-color immunocytochemistry revealed viral antigen in both migratory Langerhans cells and neutrophils; in addition, the migratory monocyte/macrophages were shown to produce infectious virus. The results indicate that the local skin site of tick feeding is an important focus of viral replication early after TBE virus transmission by ticks. Cellular infiltration of tick feeding sites, and the migration of cells from such sites, may provide a vehicle for transmission between infected and uninfected cofeeding ticks that is independent of a patent viremia. The data support the hypothesis that viremia is a product, rather than a prerequisite, of tick-borne virus transmission.     

Socio-technical IS design science research: developing design theory for IS integration management

Design science research is an essential part of IS research since the field should not only try to understand how the world is, but also how to change it. We argue that the aim of IS design science research should be to develop practical knowledge not only for the design of novel information technology (IT), but also for IS governance and management. Whereas at least some methodological support exists for researchers engaged in IT-centric design science research, limited support is available for researchers who want to develop design knowledge and theory for IS governance and management. To overcome this shortcoming, we suggest a socio-technical IS design science research approach. The approach has four main activities: (1) identifying problem situations and desired outcomes, (2) reviewing extant theories, knowledge and data, (3) proposing/refining design theory and knowledge, and (4) testing design theory and knowledge. The applicability and usefulness of the proposed approach is shown by means of a design science research project concerning IS integration management in the context of mergers and acquisitions.     

Distinguishing attacks on stream ciphers based on arrays of pseudo-random words

In numerous modern stream ciphers, the internal state consists of a large array of pseudo-random words, while the output key-stream is a relatively simple function of the state. It has been heuristically shown in several situations [3], [8], [9], [10], [11] and [14] that this structure may lead to distinguishing attacks on the cipher. In this note we present a more rigorous treatment of this structural attack. First, we present a rigorous proof of the main probabilistic claim behind it in the basic cases. We then apply it concretely to the cipher sn3 [12], and demonstrate that the heuristic assumptions of the attack are remarkably precise in more complicated cases.     

Efficient high order matching

We present a computational approach to high-order matching of data sets in IR(d). Those are matchings based on data affinity measures that score the matching of more than two pairs of points at a time. High-order affinities are represented by tensors and the matching is then given by a rank-one approximation of the affinity tensor and a corresponding discretization. Our approach is rigorously justified by extending Zass and Shashua's hypergraph matching to high-order spectral matching. This paves the way for a computationally efficient dual-marginalization spectral matching scheme. We also show that, based on the spectral properties of random matrices, affinity tensors can be randomly sparsified while retaining the matching accuracy. Our contributions are experimentally validated by applying them to synthetic as well as real data sets.     

Textures on Rank‐1 Lattices

Storing textures on orthogonal tensor product lattices is predominant in computer graphics, although it is known that their sampling efficiency is not optimal. In two dimensions, the hexagonal lattice provides the maximum sampling efficiency. However, handling these lattices is difficult, because they are not able to tile an arbitrary rectangular region and have an irrational basis. By storing textures on rank‐1 lattices, we resolve both problems: Rank‐1 lattices can closely approximate hexagonal lattices, while all coordinates of the lattice points remain integer. At identical memory footprint texture quality is improved as compared to traditional orthogonal tensor product lattices due to the higher sampling efficiency. We introduce the basic theory of rank‐1 lattice textures and present an algorithmic framework which easily can be integrated into existing off‐line and real‐time rendering systems.     

Clustering in ordered dissimilarity data

This paper presents a new technique for clustering either object or relational data. First, the data are represented as a matrix D of dissimilarity values. D is reordered to D * using a visual assessment of cluster tendency algorithm. If the data contain clusters, they are suggested by visually apparent dark squares arrayed along the main diagonal of an image I( D *) of D *. The suggested clusters in the object set underlying the reordered relational data are found by defining an objective function that recognizes this blocky structure in the reordered data. The objective function is optimized when the boundaries in I( D *) are matched by those in an aligned partition of the objects. The objective function combines measures of contrast and edginess and is optimized by particle swarm optimization. We prove that the set of aligned partitions is exponentially smaller than the set of partitions that needs to be searched if clusters are sought in D . Six numerical examples are given to illustrate various facets of the algorithm. © 2009 Wiley Periodicals, Inc.     

Identification of statistical patterns in complex systems via symbolic time series analysis

Identification of statistical patterns from observed time series of spatially distributed sensor data is critical for performance monitoring and decision making in human-engineered complex systems, such as electric power generation, petrochemical, and networked transportation. This paper presents an information-theoretic approach to identification of statistical patterns in such systems, where the main objective is to enhance structural integrity and operation reliability. The core concept of pattern identification is built upon the principles of Symbolic Dynamics, Automata Theory, and Information Theory. To this end, a symbolic time series analysis method has been formulated and experimentally validated on a special-purpose test apparatus that is designed for data acquisition and real-time analysis of fatigue damage in polycrystalline alloys.     

High-level user interfaces for transfer function design with semantics

Many sophisticated techniques for the visualization of volumetric data such as medical data have been published. While existing techniques are mature from a technical point of view, managing the complexity of visual parameters is still difficult for non-expert users. To this end, this paper presents new ideas to facilitate the specification of optical properties for direct volume rendering. We introduce an additional level of abstraction for parametric models of transfer functions. The proposed framework allows visualization experts to design high-level transfer function models which can intuitively be used by non-expert users. The results are user interfaces which provide semantic information for specialized visualization problems. The proposed method is based on principal component analysis as well as on concepts borrowed from computer animation.     

Relational mountain (density) clustering method and web log analysis

The mountain clustering method and the subtractive clustering method are useful methods for finding cluster centers based on local density in object data. These methods have been extended to shell clustering. In this article, we propose a relational mountain clustering method (RMCM), which produces a set of (proto) typical objects as well as a crisp partition of the objects generating the relation, using a new concept that we call relational density. We exemplify RMCM by clustering several relational data sets that come from object data. Finally, RMCM is applied to web log analysis, where it produces useful user profiles from web log data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 375–392, 2005.