Truncated human endothelin receptor A produced by alternative splicing and its expression in melanoma

In this study, reverse transcriptase polymerase chain reaction was used to amplify human endothelin receptor A (ETA) and ETB receptor mRNA. A truncated ETA receptor transcript with exons 3 and 4 skipped was found. The skipping of these two exons results in 109 amino acids being deleted from the receptor. The truncated receptor was expressed in all tissues and cells examined, but the level of expression varied. In melanoma cell lines and melanoma tissues, the truncated receptor gene was the major species, whereas the wild-type ETA was predominant in other tissues. A 1.9-kb ETA transcript was identified in melanoma cell lines by Northern blot, which was much smaller than the transcript in heart and in other tissues reported previously (4.3 kb). The cDNA coding regions of the truncated and wild-type ETA receptors were stably transfected into Chinese hamster ovary (CHO) cells. The truncated ETA receptor-transfected CHO cells did not show binding affinity to endothelin 1 (ET-1) or endothelin 3 (ET-3). The function and biological significance of this truncated ETA receptor is not clear, but it may have regulatory roles for cell responses to ETs

Incremental Knowledge Base Construction Using DeepDive

Populating a database with unstructured information is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration. Recent names used for this problem include dealing with dark data and knowledge base construction (KBC). In this work, we describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems, and we present techniques to make the KBC process more efficient. We observe that the KBC process is iterative, and we develop techniques to incrementally produce inference results for KBC systems. We propose two methods for incremental inference, based respectively on sampling and variational techniques. We also study the tradeoff space of these methods and develop a simple rule-based optimizer. DeepDive includes all of these contributions, and we evaluate DeepDive on five KBC systems, showing that it can speed up KBC inference tasks by up to two orders of magnitude with negligible impact on quality