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Microsoft word - pgala_t sequence

pGala-T Vector Positions of various elements: Vector size (bp) 2727 T7 RNA polymerase promoter 396-418 Multiple cloning site 420-496 Cloning Site 459 LacZ α-peptide 146-510 Ampicillin resistance gene 1667-2521 pUC origin 908-1496 primer binding sites: BcaBEST Sequencing Primer M13-47 binding site 352-375 BcaBEST Sequencing Primer RV-M binding site 484-542 TCGCGCGTTT CGGTGATGAC GGTGAAAACC TCTGACACAT GCAGCTCCCG 50 GAGACGGTCA CAGCTTGTCT GTAAGCGGAT GCCGGGAGCA GACAAGCCCG 100 TCAGGGCGCG TCAGCGGGTG TTGGCGGGTG TCGGGGCTGG CTTAACTATG 150 CGGCATCAGA GCAGATTGTA CTGAGAGTGC ACCATATGCG GTGTGAAATA 200 CCGCACAGAT GCGTAAGGAG AAAATACCGC ATCAGGCGCC ATTCGCCATT 250 CAGGCTGCGC AACTGTTGGG AAGGGCGATC GGTGCGGGCC TCTTCGCTAT 300 TACGCCAGCT GGCGAAAGGG GGATGTGCTG CAAGGCGATT AAGTTGGGTA 350 ACGCCAGGGT TTTCCCAGTC ACGACGTTGT AAAACGACGG CCAGTGTAAT 400 ACGACTCACT ATAGGGCGAA AGCTTTATTG CCAAGCTTGC ATGCCTGCAG 450 GTCGACGATT ATCTCTAGAG GATCCCCGGG TACCGAGCTC GAATTCGTAA 500 TCATGGTCAT AGCTGTTTCC TGTGTGAAAT TGTTATCCGC TCACAATTCC 550 ACACAACATA CGAGCCGGAA GCATAAAGTG TAAAGCCTGG GGTGCCTAAT 600 GAGTGAGCTA ACTCACATTA ATTGCGTTGC GCTCACTGCC CGCTTTCCAG 650 TCGGGAAACC TGTCGTGCCA GCTGCATTAA TGAATCGGCC AACGCGCGGG 700 GAGAGGCGGT TTGCGTATTG GGCGCTCTTC CGCTTCCTCG CTCACTGACT 750 CGCTGCGCTC GGTCGTTCGG CTGCGGCGAG CGGTATCAGC TCACTCAAAG 800 GCGGTAATAC GGTTATCCAC AGAATCAGGG GATAACGCAG GAAAGAACAT 850 GTGAGCAAAA GGCCAGCAAA AGGCCAGGAA CCGTAAAAAG GCCGCGTTGC 900 TGGCGTTTTT CCATAGGCTC CGCCCCCCTG ACGAGCATCA CAAAAATCGA 950 CGCTCAAGTC AGAGGTGGCG AAACCCGACA GGACTATAAA GATACCAGGC 1000 GTTTCCCCCT GGAAGCTCCC TCGTGCGCTC TCCTGTTCCG ACCCTGCCGC 1050 TTACCGGATA CCTGTCCGCC TTTCTCCCTT CGGGAAGCGT GGCGCTTTCT 1100 CATAGCTCAC GCTGTAGGTA TCTCAGTTCG GTGTAGGTCG TTCGCTCCAA 1150 GCTGGGCTGT GTGCACGAAC CCCCCGTTCA GCCCGACCGC TGCGCCTTAT 1200 CCGGTAACTA TCGTCTTGAG TCCAACCCGG TAAGACACGA CTTATCGCCA 1250 CTGGCAGCAG CCACTGGTAA CAGGATTAGC AGAGCGAGGT ATGTAGGCGG 1300 TGCTACAGAG TTCTTGAAGT GGTGGCCTAA CTACGGCTAC ACTAGAAGAA 1350 CAGTATTTGG TATCTGCGCT CTGCTGAAGC CAGTTACCTT CGGAAAAAGA 1400 GTTGGTAGCT CTTGATCCGG CAAACAAACC ACCGCTGGTA GCGGTGGTTT 1450 TTTTGTTTGC AAGCAGCAGA TTACGCGCAG AAAAAAAGGA TCTCAAGAAG 1500 ATCCTTTGAT CTTTTCTACG GGGTCTGACG CTCAGTGGAA CGAAAACTCA 1550 CGTTAAGGGA TTTTGGTCAT GAGATTATCA AAAAGGATCT TCACCTAGAT 1600 CCTTTTAAAT TAAAAATGAA GTTTTAAATC AATCTAAAGT ATATATGAGT 1650 AAACTTGGTC TGACAGTTAC CAATGCTTAA TCAGTGAGGC ACCTATCTCA 1700 GCGATCTGTC TATTTCGTTC ATCCATAGTT GCCTGACTCC CCGTCGTGTA 1750 GATAACTACG ATACGGGAGG GCTTACCATC TGGCCCCAGT GCTGCAATGA 1800 TACCGCGAGA CCCACGCTCA CCGGCTCCAG ATTTATCAGC AATAAACCAG 1850 CCAGCCGGAA GGGCCGAGCG CAGAAGTGGT CCTGCAACTT TATCCGCCTC 1900 CATCCAGTCT ATTAATTGTT GCCGGGAAGC TAGAGTAAGT AGTTCGCCAG 1950 TTAATAGTTT GCGCAACGTT GTTGCCATTG CTACAGGCAT CGTGGTGTCA 2000 CGCTCGTCGT TTGGTATGGC TTCATTCAGC TCCGGTTCCC AACGATCAAG 2050 GCGAGTTACA TGATCCCCCA TGTTGTGCAA AAAAGCGGTT AGCTCCTTCG 2100 GTCCTCCGAT CGTTGTCAGA AGTAAGTTGG CCGCAGTGTT ATCACTCATG 2150 GTTATGGCAG CACTGCATAA TTCTCTTACT GTCATGCCAT CCGTAAGATG 2200 CTTTTCTGTG ACTGGTGAGT ACTCAACCAA GTCATTCTGA GAATAGTGTA 2250 TGCGGCGACC GAGTTGCTCT TGCCCGGCGT CAATACGGGA TAATACCGCG 2300 CCACATAGCA GAACTTTAAA AGTGCTCATC ATTGGAAAAC GTTCTTCGGG 2350 GCGAAAACTC TCAAGGATCT TACCGCTGTT GAGATCCAGT TCGATGTAAC 2400 CCACTCGTGC ACCCAACTGA TCTTCAGCAT CTTTTACTTT CACCAGCGTT 2450 TCTGGGTGAG CAAAAACAGG AAGGCAAAAT GCCGCAAAAA AGGGAATAAG 2500 GGCGACACGG AAATGTTGAA TACTCATACT CTTCCTTTTT CAATATTATT 2550 GAAGCATTTA TCAGGGTTAT TGTCTCATGA GCGGATACAT ATTTGAATGT 2600 ATTTAGAAAA ATAAACAAAT AGGGGTTCCG CGCACATTTC CCCGAAAAGT 2650 GCCACCTGAC GTCTAAGAAA CCATTATTAT CATGACATTA ACCTATAAAA 2700 ATAGGCGTAT CACGAGGCCC TTTCGTC 2727

Source: http://www.galaxybio.cn/upload/SEQUENCE1.pdf

Ghostscript wrapper for c:\documents and settings\winkel\my documents\teaching\bs3bht10(ds)\bs3b_lecture_notes2010_3.pdf

Assignment 6 – BS3b Statistical Lifetime-Models – Oxford HT 2013Model testing, proportional-hazards, accelerated life(a) Supppose that we have a random sample which includes right-censored data (censoring as-sumed non-informative). We wish to decide whether or not a Weibull distribution is appro-priate. Using an estimator of the survival function how might we graphically investigate theappr

Lena

TIMP-1 over-expression confers resistance of MCF-7 breast cancer cells to fulvestrant Lena Vinther1, Christina Bjerre1, Kirstine Belling2, Anne-Sofie Schrohl Rasmussen1, Jian Li3,4, Xue Lin3,4, Zujing Han4, Jun Wang4, Lars Bolund3,4, Vibeke Jensen1, Birgitte Sander Nielsen1, Rolf Søkilde5, Ramneek Gupta2, Ulrik Lademann1, Nils Brünner1 and Jan Stenvang1 Sino-Danish Breast Cancer Resea

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