Purification and characterization of acid trehalase from the yeast suc2 mutant.

Acid trehalase was purified from the yeast suc2 deletion mutant. After hydrophobic interaction chromatography, the enzyme could be purified to a single band or peak by a further step of either polyacrylamide gel electrophoresis, gel filtration, or isoelectric focusing. An apparent molecular mass of 218,000 Da was calculated from gel filtration. Polyacrylamide gel electrophoresis of the purified enzyme in the presence of sodium dodecyl sulfate suggested a molecular mass of 216,000 Da. Endoglycosidase H digestion of the purified enzyme resulted after sodium dodecyl sulfate gel electrophoresis in one distinct band at 41,000 Da, representing the mannose-free protein moiety of acid trehalase. The carbohydrate content of the enzyme was 86%. Amino acid analysis indicated 354 residues/molecule of enzyme including 9 cysteine moieties and only 1 methionine. The isoelectric point of the enzyme was estimated by gel electrofocusing to be approximately 4.7. The catalytic activity showed a maximum at pH 4.5. The activity of the enzyme was not inhibited by 10 mM each of HgCl2, EDTA, iodoacetic acid, phenanthrolinium chloride or phenylmethylsulfonyl fluoride. There was no activation by divalent metal ions. The acid trehalase exhibited an apparent K(m) for trehalose of 4.7 ± 0.1 mM and a V(max) of 99 μmol of trehalose min-1 x mg-1 at 37°C and pH 4.5. The acid trehalase is located in the vacuoles. The rabbit antiserum raised against acid trehalase exhibited strong cross-reaction with purified invertase. These cross-reactions were removed by affinity chromatography using invertase coupled to CNBr-activated Sepharose 4B. Precipitation of acid trehalase activity was observed with the purified antiserum

Generalizing Syllogistic Reasoning: Extending Syllogisms to General Quantifiers

Syllogistic reasoning is one of the oldest domains of reasoning research and has made great advances in understanding and modeling human reasoning processes. However, the field was mostly focused on a traditional set of quantifiers originating in first-order logic, thereby neglecting the large variety of quantifiers humans use when engaging in reasoning in their everyday life. The present work makes three main contributions: (I) we conducted a study yielding a dataset covering all traditional syllogisms and tasks containing generalized quantifiers ``most'' and ``most not'', providing a starting point for existing theories and models to transition to generalized quantifiers. (II) based on the dataset, we analyze the impact that the additional quantifiers have on the reasoning behavior. (III) We investigated the reasoning behavior with respect to the difference between traditional and generalized quantifiers, gaining insights into some of the peculiarities of the domain of generalized syllogisms