Binding of tissue plasminogen activator to endothelial cells. The effect on functional properties. Localization of a ligand in the B-chain of tPA

The binding of 125I-labelled tissue plasminogen activator (tPA), the tPA A- or B-chain to endothelial cells (EC) were studied in suspensions of cultured human umbilical vein EC (HUVEC) or immortalized microvascular EC (HMEC). By determinations of the concentration-dependent binding it was shown that both the A-chain and the B-chain, which were isolated after partial reduction of two-chain tPA, contain ligands for binding to EC. The affinity for the B-chain was much higher than for the A-chain according to Scatchard analysis (Kd 24 and 515 nM, respectively), whereas the number of binding sites was higher for the A-chain than for the B-chain (Bmax 8 x 10(5) and 1.2 x 10(5), respectively). There were no cross interactions between the A- and B-chains and their binding sites. The binding of tPA to EC induced an almost 100-fold increase of the activation rate when compared to the same amount of enzyme in free solution, which in contrast to the fibrin-induced stimulation was not inhibited by antibodies against fibrin. The enzymatic activity of the B-chain was much less affected by the association to the cells. Both tPA and the tPA B-chain were largely protected against inhibition by an excess plasminogen activator type-1 (PAI-1) when bound to EC, whereas the same amount of free tPA was totally inactivated. The competition studies strongly indicated that an N-terminal segment in the B-chain, AKHRRSPGER, may be the ligand part of the B-chain. It is interesting to note that this polypeptide segment also participates in a binding site for PAI-1, necessary for effective inhibition. This implies a possible competition between PAI-1 and a tPA-receptor for binding of tPA. High molecular weight urokinase had no quenching effect on the binding of the B-chain to EC.     

A sorter-based architecture for a parallel implementation of communication intensive algorithms

This paper deals with the parallel execution of algorithms with global and/or irregular data dependencies on a regularly and locally connected processor array. The associated communication problems are solved by the use of a two-dimensional sorting algorithm. The proposed architecture, which is based on a two-dimensional sorting network, offers a high degree of flexibility and allows an efficient mapping of many irregularly structured algorithms. In this architecture a one-dimensional processor array performs all required control and arithmetic operations, whereas the sorter solves complex data transfer problems. The storage capability of the sorting network is also used as memory for data elements. The algorithms for sparse matrix computations, fast Fourier transformation and for the convex hull problem, which are mapped onto this architecture, as well as the simulation of a shared-memory computer show that the utilization of the most complex components, the processors, is O(1).     

An empirical InSAR-optical fusion approach to mapping vegetation canopy height

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km² in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service-Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r = 0.88) was achieved for the prototype mapping zone.     

Estimation in Random Coefficient Autoregressive Models

Abstract. We propose the quasi‐maximum likelihood method to estimate the parameters of an RCA(1) process, i.e. a random coefficient autoregressive time series of order 1. The strong consistency and the asymptotic normality of the estimators are derived under optimal conditions.