The gene encoding the multifunctional enzyme enniatin synthetase from Fusarium scirpi (esyn1) was isolated and characterized by transcriptional mapping and expression studies in Escherichia coli. This is the first example of a gene encoding an N-methyl peptide synthetase. The nucleotide sequence revealed an open reading frame of 9393 bp encoding a protein of 3131 amino acids (M(r) 346,900). Two domains designated EA and EB within the protein were identified which share similarity to each other and to microbial peptide synthetase domains. In contrast to the N-terminal domain EA, the carboxyl terminal domain EB is interrupted by a 434-amino-acid portion which shows local similarity to a motif apparently conserved within adenine and cytosine RNA and DNA methyltransferases and therefore seems to harbour the N-methyl-transferase function of the multienzyme.

Constraint sets can become inconsistent in different contexts. For example, during a configuration session the set of customer requirements can become inconsistent with the configuration knowledge base. Another example is the engineering phase of a configuration knowledge base where the underlying constraints can become inconsistent with a set of test cases. In such situations we are in the need of techniques that support the identification of minimal sets of faulty constraints that have to be deleted in order to restore consistency. In this paper we introduce a divide and conquer-based diagnosis algorithm (FastDiag) that identifies minimal sets of faulty constraints in an overconstrained problem. This algorithm is specifically applicable in scenarios where the efficient identification of leading (preferred) diagnoses is crucial. We compare the performance of FastDiag with the conflict-directed calculation of hitting sets and present an in-depth performance analysis that shows the advantages of our approach.

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Abstract. In contrast to customers of bricks and mortar stores, users of online selling environments are not supported by human sales experts. In such situations recommender applications help to identify the products and/or services that fit the user's wishes and needs. In order to successfully apply recommendation technologies we have to develop an in-depth understanding of decision strategies of users. These decision strategies are explained in different models of human decision making. In this paper we provide an overview of selected models and discuss their importance for recommender system development. Furthermore, we provide an outlook on future research issues.

Requirements engineering (RE) is considered as one of the most critical phases in the software life-cycle, and poorly implemented RE processes are among the major risks for project failure. Stakeholders are often faced with the challenge that the complexity of information outstrips their capability to survey it and to decide about which requirements should be taken into account. Additionally, preferences regarding a set of requirements are typically not known beforehand but constructed within the scope of a decision making process. In this paper we introduce a simple application scenario and discuss recommendation and decision technologies which can be exploited for proactively supporting stakeholders in their decision making.