Subspace-based support vector machines for pattern classification.

In this paper, we discuss subspace-based support vector machines (SS-SVMs), in which an input vector is classified into the class with the maximum similarity. Namely, for each class we define the weighted similarity measure using the vectors called dictionaries that represent the class, and optimize the weights so that the margin between classes is maximized. Because the similarity measure is defined for each class, for a data sample the similarity measure to which the data sample belongs needs to be the largest among all the similarity measures.

A novel c-kit positive biphenotypic acute leukemia cell line, TMBL-1, carrying a p53 point mutation.

We established and characterized a c-kit positive cell line from the bone marrow of a patient with biphenotypic acute leukemia (BAL). The cell line, designated TMBL-1, carried a His-175 mutant p53. The immunophenotype of the primary leukemia cells at diagnosis was cytoplasmic CD3+, CD7+, CD13+, CD33-, interleukin-7 (IL-7) receptor+ and c-kit -.