307 resultados para GENE


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A modified mRNA differential display method has been applied to studying differential expression of protein kinase genes in oocytes between natural gynogenetic silver crucian carp and amphimictic crucian carp. Total RNA was reverse transcribed using downstream 3' primers T(12)MA, T(12)MG and T12MC respectively. Then the reverse transcription products were amplified using upstream 5' kinase-specific primer designed according to protein kinase conserved sequence. The PCR products had different patterns and numbers of: cDNA bands on polyacrylamide:gel. Totally 21 cDNAs fragments were recovered and cloned. Two of them were confirmed to be particularly expressed in oocytes of amphimictic crucian carp, and another was specific for gynogenetic silver crucian carp.

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Transgenic common carp, Cyprinus carpio, produced by the microinjection of fertilized eggs with a linearized chimeric plasmid pMThGH, a human growth hormone (hGH) gene with a mouse metallothionein-I (MT) gene promoter in pBR322, were used to produce F1 and F2 transgenics. Following hypophysectomy of the transgenic F2 common carp, non-transgenic common carp and non-transgenic crucian carp, growth was monitored for up to 110 days. In addition, recombinant hGH was injected subcutaenously into a group of the non-transgenic crucian carp. Growth rate analyses indicated that (1) hypophysectomy of non-transgenic common carp and crucian carp results in the cessation of growth, (2) hGH administration can stimulate the growth of hypophysectomized crucian carp and (3) hypophysectomized hGH-transgenic common carp continue to grow in the absence of their own growth hormone, suggesting that the hGH-transgene is being expressed in tissues other than the pituitary.

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The accurate recognition of cancer subtypes is very significant in clinic. Especially, the DNA microarray gene expression technology is applied to diagnosing and recognizing cancer types. This paper proposed a method of that recognized cancer subtypes based on geometrical learning. Firstly, the cancer genes expression profiles data was pretreated and selected feature genes by conventional method; then the expression data of feature genes in the training samples was construed each convex hull in the high-dimensional space using training algorithm of geometrical learning, while the independent test set was tested by the recognition algorithm of geometrical learning. The method was applied to the human acute leukemia gene expression data. The accuracy rate reached to 100%. The experiments have proved its efficiency and feasibility.