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Two proteins with myosin light chain kinase activity and electrophoretic molecular weights of 155,000 and 130,000 were each isolated from bovine stomach smooth muscle [Kuwayama, H., Suzuki, M., Koga, R., & Ebashi, S. (1988) J. Biochem. 104, 862-866]. The 155 kDa component showed a much higher superprecipitation-inducing activity than the 130 kDa component, when compared on the basis of equivalent myosin light chain kinase activity. In this study, we isolated a cDNA for the entire coding region of the 155 kDa protein. The deduced amino acid sequence revealed a high degree of similarity to those of chicken and rabbit smooth muscle myosin light chain kinases. Multiple motifs, such as three repeats of an immunoglobulin C2-like domain, a fibronectin type III domain, and unusual 20 repeats of 12 amino acids were detected in the sequence. Part of the amino-terminal sequence was similar to that of the actin- and calmodulin-binding domain of smooth muscle caldesmon. These observations suggest that the 155 kDa protein has additional functions other than its enzymatic activity. Two mRNAs of 6.0 and 2.6 kb in length in the bovine stomach smooth muscle RNAs were hybridized with cDNA probes. The 2.6-kb RNA probably encodes telokin, which is the carboxyl terminus of smooth muscle myosin light chain kinase. mRNAs with identical lengths were also detected in bovine aorta.

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In this study, we consider a summarization method using the document level similarity based on embeddings, or distributed representations of words, where we assume that an embedding of each word can represent its "meaning." We formalize our task as the problem of maximizing a submodular function defined by the negative summation of the nearest neighbors' distances on embedding distributions, each of which represents a set of word embeddings in a document. We proved the submodularity of our objective function and that our problem is asymptotically related to the KL-divergence between the probability density functions that correspond to a document and its summary in a continuous space. An experiment using a real dataset demonstrated that our method performed better than the existing method based on sentence-level similarity.