Purification and characterization of L-amino acid oxidase from Agkistrodon blomhoffii ussurensis snake venom of Changbai Mountains

The L-a. a, oxidase of Agkistrodon blomhof fii ussurensis of Changbai Mountains in northeast of China has been separated by using ion-exchange and gel filtration techniques, This enzyme is composed of two subunits, the molecular weight of one subunit is about 36 000, the another is about 57 000, determined by sodium dodecyl sulfate-polyacryamide gel electrophoresis and matrix assisted laser desorption ion/time of flight mass spectrometry, The activity of L-a, a. oxidase determined using L-Leu as substrate. The optimal pH of the enzyme is 4. 5 similar to 5. 5 and 8 similar to 9. The UV-Visible absorption spectrum of L-a, a. oxidase shows the characteristics of flavor-proteins.

Anthony Ravalli, S.J., forty years a missionary in the Rocky Mountains, memoir / by L.B. Palladino.

http://www.archive.org/details/anthonyravallisj00pallrich

An account of the life of Mr. David Brainerd : missionary from the Society for Propagating Christian Knowledge, & pastor of a church of Christian Indians in New-Jersey / published by Jonathan Edwards ; with Mr. Brainerd's public journal ; to this edition is added, Mr. Beatty's mission to the westward of the Allegheny Mountains.

http://www.archive.org/details/accountoflifeofm00brairich

Efficient Nearest Neighbor Classification Using a Cascade of Approximate Similarity Measures

Nearest neighbor classification using shape context can yield highly accurate results in a number of recognition problems. Unfortunately, the approach can be too slow for practical applications, and thus approximation strategies are needed to make shape context practical. This paper proposes a method for efficient and accurate nearest neighbor classification in non-Euclidean spaces, such as the space induced by the shape context measure. First, a method is introduced for constructing a Euclidean embedding that is optimized for nearest neighbor classification accuracy. Using that embedding, multiple approximations of the underlying non-Euclidean similarity measure are obtained, at different levels of accuracy and efficiency. The approximations are automatically combined to form a cascade classifier, which applies the slower approximations only to the hardest cases. Unlike typical cascade-of-classifiers approaches, that are applied to binary classification problems, our method constructs a cascade for a multiclass problem. Experiments with a standard shape data set indicate that a two-to-three order of magnitude speed up is gained over the standard shape context classifier, with minimal losses in classification accuracy.