OVERVIEW OF c-KIT AND ITS RTKs FAMILY c-KIT or CD117 is a member of class III transmembrane receptor tyrosine kinases (RTKs) along with platelet-derived growth factor receptors (PDGFRs), fms like tyrosine kinase 3 (FLT3)/ CD135, and macrophage colony stimulating factor receptors (M-CSFRs). It was discovered in 1987 as a cellular homologue of viral oncogene v-kit, which was isolated from a feline retro-virus. 1,2 A variety of cell types were identified to express c-KIT including hematopoietic cells, germ cells, gastrointestinal (GI) tract Cajal cells, melanoma cells, B cell progenitors, and mast cells. Wild-type c-KIT protein contains 976 amino acids (aa) divided into three main regions including an extracellular ligand-binding domain with 519 aa, a hydrophobic transmembrane domain with 23 aa, and an intracellular tail (Fig. 1). 3,4 The extracellular domain consists of five immunoglobulin-like domains D1-D5, in which D1-D3 is responsible for stem cell factor (SCF) binding while D4-D5 contains motif for receptor dimerization. The 433 aa cytoplasmic region includes a juxtamembrane domain and a tyrosine kinase domain with an insertion of approximately 80 residues. Most of phosphorylation sites on c-KIT are located at the juxta-membrane region, the kinase insertion domain, and the C-terminal tail. Human c-KIT is encoded by a proto-oncogene located on the chromosome 4 at position of q11-12. 5 This 90 kb gene is transcribed and translated into a core protein of 110 kDa, which is subsequently heterogeneously N-linked glycosylated, mainly in the extra
The key obstacle to communicating images over wireless sensor networks has been the lack of suitable processing architecture and communication strategies to deal with the large volume of data. High packet error rates and the need for retransmission make it inefficient in terms of energy and bandwidth. This paper presents novel architecture and protocol for energy efficient image processing and communication over wireless sensor networks. Practical results show the effectiveness of these approaches to make image communication over wireless sensor networks feasible, reliable and efficient.
Magnetic sensing technology offers an attractive alternative for in vivo tracking with much better performance than RF and ultrasound technologies. In this paper, an efficient in vivo magnetic tracking system is presented. The proposed system is intended to localize an endoscopic capsule which delivers biomarkers around specific locations of the gastrointestinal (GI) tract. For efficiently localizing a magnetic marker inside the capsule, a mathematical model has been developed for the magnetic field around a cylindrical magnet and used with a localization algorithm that provides minimum error and fast computation. The proposed tracking system has much reduced complexity compared to the ones reported in the literature to date. Laboratory tests and in vivo animal trials have demonstrated the suitability of the proposed system for tracking a magnetic marker with expected accuracy.
We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality.
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