The estA gene encoding a novel cytoplasmic carboxylesterase from Arthrobacter nitroguajacolicus Rü61a was expressed in Escherichia coli. Sequence analysis and secondary structure predictions suggested that EstA belongs to the family VIII esterases, which are related to class C beta-lactamases. The S-x-x-K motif that in beta-lactamases contains the catalytic nucleophile, and a putative active-site tyrosine residue are conserved in EstA. The native molecular mass of hexahistidine-tagged (His6) EstA, purified by metal chelate affinity chromatography, was estimated to be 95 kDa by gel filtration, whereas the His6EstA peptide has a calculated molecular mass of 42.1 kDa. The enzyme catalyzes the hydrolysis of short-chain phenylacyl esters and triglycerides, and shows weak activity toward 2-hydroxy- and 2-nitroacetanilide. Its catalytic activity was inhibited by the serine-specific effector phenylmethylsulfonyl fluoride, and by Cd2+ and Hg2+ ions. Maximum activity of His6EstA was observed at a pH of 9.5 and a temperature of 50 degrees C to 60 degrees C. The enzyme was fairly thermostable. After 19 days at 50 degrees C and after 24 hours at 60 degrees C, its residual relative esterase activity toward phenylacetate was still 53% and 30%, respectively. Exposure of His6EstA to buffer-solvent mixtures showed that the enzyme was inactivated by several high log P (hydrophobic) solvents, whereas it showed remarkable stability and activity in up to 30% (by volume) of polar (low log P) organic solvents such as dimethylsulfoxide, methanol, acetonitrile, acetone, and propanol.

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When looking at learning processes from an interactionist perspective, interaction between individuals is seen as central for learning. In order to understand and describe these interactions and therefore the learning processes, a method was developed in mathematics education grounded in the theory of Symbolic Interactionism and Ethnomethodology-the Interactional Analysis. In this chapter, at first the underlying theory for the Interactional Analysis is presented, before the steps of the method are explained, giving an example for each step. Findings of research using this method have been widely published, however, the method has not been described in depth in English yet. Therefore, this chapter makes a valuable contribution for enabling this method to be more accessible for an international research community, as well as helping international researchers understand the findings produced by using this method more clearly.

In the context of refinement of frameworks over the past decades within the domain of mathematics education research on language, the development of more nuanced theories is a challenge. In this issue of ZDM, a number of researchers present their work of exploration and elaboration of theories for the study and understanding of language in mathematics education. Since various relevant frameworks are present in the collection of papers, we use them to consider and evaluate the existing ontology. We aim to answer the following questions: What theories and concepts are visible in the papers? What are the works of some of the authors and terms that seem to be interpreted differently? What does this complexity imply for research in mathematics education? From the answers to these questions, we conclude that the domain can be characterised by its complexity, diversity, and contention. All three phenomena together seem to have the potential to be a strength for the progress of the domain.