In vitro synthesis of heparosan using recombinant Pasteurella multocida heparosan synthase PmHS2

In vertebrates and bacteria, heparosan the precursor of heparin is synthesized by glycosyltransferases via the stepwise addition of UDP-N-acetylglucosamine and UDP-glucuronic acid. As heparin-like molecules represent a great interest in the pharmaceutical area, the cryptic Pasteurella multocida heparosan synthase PmHS2 found to catalyze heparosan synthesis using substrate analogs has been studied. In this paper, we report an efficient way to purify PmHS2 and to maintain its activity stable during 6 months storage at −80 °C using His-tag purification and a desalting step. In the presence of 1 mM of each nucleotide sugar, purified PmHS2 synthesized polymers up to an average molecular weight of 130 kDa. With 5 mM of UDP-GlcUA and 5 mM of UDP-GlcNAc, an optimal specific activity, from 3 to 6 h of incubation, was found to be about 0.145 nmol/μg/min, and polymers up to an average of 102 kDa were synthesized in 24 h. In this study, we show that the chain length distribution of heparosan polymers can be controlled by change of the initial nucleotide sugar concentration. It was observed that low substrate concentration favors the formation of high molecular weight heparosan polymer with a low polydispersity while high substrate concentration did the opposite. Similarities in the polymerization mechanism between PmHS2, PmHS1, and PmHAS are discussed

Towards Comprehensive Foundations of Computational Intelligence

Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for such meta-learning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformation-based systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented.

Burst Format Design for Optimum Joint Estimation of Doppler-Shift and Doppler-Rate in Packet Satellite Communications

This paper considers the problem of optimizing the burst format of packet transmission to perform enhanced-accuracy estimation of Doppler-shift and Doppler-rate of the carrier of the received signal, due to relative motion between the transmitter and the receiver. Two novel burst formats that minimize the Doppler-shift and the Doppler-rate Cramér-Rao bounds (CRBs) for the joint estimation of carrier phase/Doppler-shift and of the Doppler-rate are derived, and a data-aided (DA) estimation algorithm suitable for each optimal burst format is presented. Performance of the newly derived estimators is evaluated by analysis and by simulation, showing that such algorithms attain their relevant CRBs with very low complexity, so that they can be directly embedded into new-generation digital modems for satellite communications at low SNR