Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:3.4.11.18 (MAP)
 7,412 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The major acute-phase protein (alpha 1-MAP) of rat serum is induced in response to inflammation. This induction may be attributed to a corresponding increase in the level of translatable mRNA for the protein. Using in vitro and in vivo systems, various biosynthetic processing intermediates of this glycoprotein have been isolated. alpha 1-MAP is translated in a rabbit reticulocyte system as a preprotein with an amino-terminal signal peptide and an apparent molecular weight of 51,000. Translation of rough microsomes yields a product with a mass of 57,000 Da, representing the core glycosylated form of alpha 1-MAP. Cotranslational glycosylation appears to occur in a stepwise fashion, since three glycosylated forms of alpha 1-MAP (51,000, 54,000, and 57,000 Da) were detected in polysome translations; these products were digested by endoglycosidase H to a 48,000-Da protein. Two intracellular forms of alpha 1-MAP were observed in vivo, a 57,000-Da (core carbohydrate sidechains) and a 66,000-Da protein (mature complex carbohydrate side-chains); the latter was the only component secreted into the culture medium. To extend our studies on this protein, a cDNA clone specific for alpha 1-MAP was isolated. The recombinant was positively identified by hybrid selection procedures and contains a 1.55-kb insert. Partial radiosequence analysis of the primary translation product indicated the distribution of Leu, Ile, Cys, and Met in the amino-terminal region of this protein. To relate the location of these amino acids with the nucleotide sequence, cDNA was analyzed by the method of Maxam and Gilbert. These results indicate that the cDNA insert contains the 3' poly(A) tail, and alignment of the 5' end of the cDNA with the available amino acid sequence of the primary translation product corroborated that the insert encodes the entire alpha 1-MAP protein except for the first four amino acids of the signal peptide.
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PMID:Rat major acute-phase protein: biosynthesis and characterization of cDNA clone. 620 75

In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.
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PMID:Review on solving the inverse problem in EEG source analysis. 1899 Feb 57