Alterations in dihydropyridine receptors in dystrophin-deficient cardiac muscle

The deficiency of dystrophin, a critical membrane stabilizing protein, in the mdx mouse causes an elevation in intracellular calcium in myocytes. One mechanism that could elicit increases in intracellular calcium is enhanced influx via the L-type calcium channels. This study investigated the effects of the dihydropyridines BAY K 8644 and nifedipine and alterations in dihydropyridine receptors in dystrophin-deficient mdx hearts. A lower force of contraction and a reduced potency of extracellular calcium (P < 0.05) were evident in mdx left atria. The dihydropyridine agonist BAY K 8644 and antagonist nifedipine had 2.7- and 1.9-fold lower potencies in contracting left atria (P < 0.05). This corresponded with a 2.0-fold reduction in dihydropyridine receptor affinity evident from radioligand binding studies of mdx ventricular homogenates (P < 0.05). Increased ventricular dihydropyridine receptor protein was evident from both radioligand binding studies and Western blot analysis and was accompanied by increased mRNA levels (P < 0.05). Patch-clamp studies in isolated ventricular myocytes showed no change in L-type calcium current density but revealed delayed channel inactivation (P < 0.05). This study indicates that a deficiency of dystrophin leads to changes in dihydropyridine receptors and L-type calcium channel properties that may contribute to enhanced calcium influx. Increased influx is a potential mechanism for the calcium overload observed in dystrophin-deficient cardiac muscle.

Multiresolution spatial databases: Making web-based spatial advances faster

Spatial data has now been used extensively in the Web environment, providing online customized maps and supporting map-based applications. The full potential of Web-based spatial applications, however, has yet to be achieved due to performance issues related to the large sizes and high complexity of spatial data. In this paper, we introduce a multiresolution approach to spatial data management and query processing such that the database server can choose spatial data at the right resolution level for different Web applications. One highly desirable property of the proposed approach is that the server-side processing cost and network traffic can be reduced when the level of resolution required by applications are low. Another advantage is that our approach pushes complex multiresolution structures and algorithms into the spatial database engine. That is, the developer of spatial Web applications needs not to be concerned with such complexity. This paper explains the basic idea, technical feasibility and applications of multiresolution spatial databases.

Semantic caching for multiresolution spatial query processing in mobile environments

Spatial data are particularly useful in mobile environments. However, due to the low bandwidth of most wireless networks, developing large spatial database applications becomes a challenging process. In this paper, we provide the first attempt to combine two important techniques, multiresolution spatial data structure and semantic caching, towards efficient spatial query processing in mobile environments. Based on the study of the characteristics of multiresolution spatial data (MSD) and multiresolution spatial query, we propose a new semantic caching model called Multiresolution Semantic Caching (MSC) for caching MSD in mobile environments. MSC enriches the traditional three-category query processing in semantic cache to five categories, thus improving the performance in three ways: 1) a reduction in the amount and complexity of the remainder queries; 2) the redundant transmission of spatial data already residing in a cache is avoided; 3) a provision for satisfactory answers before 100% query results have been transmitted to the client side. Our extensive experiments on a very large and complex real spatial database show that MSC outperforms the traditional semantic caching models significantly

Semantic caching for web-based spatial applications

Client-side caching of spatial data is an important yet very much under investigated issue. Effective caching of vector spatial data has the potential to greatly improve the performance of spatial applications in the Web and wireless environments. In this paper, we study the problem of semantic spatial caching, focusing on effective organization of spatial data and spatial query trimming to take advantage of cached data. Semantic caching for spatial data is a much more complex problem than semantic caching for aspatial data. Several novel ideas are proposed in this paper for spatial applications. A number of typical spatial application scenarios are used to generate spatial query sequences. An extensive experimental performance study is conducted based on these scenarios using real spatial data. We demonstrate a significant performance improvement using our ideas.