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71.
Ovaries of adult have recently been shown to produce impressive amounts of ecdysone together with low polarity ecdysteroids, some of which cross-react with ecdysone in our RIA. A gas chromatographic-mass spectrometric analysis of extracts from ovaries of has shown the presence of following compounds (less polar than ecdysone): 2-deoxy-ecdysone, 2,22-bis-deoxy-ecdysone, 2, 22, 2 5-tri-deoxy-ecdysone, 2, 14, 22, 2 5-tetra-deoxy-ecdysone. No other related ecdysteroids were present in our extracts. Cholesterol is used by ovaries as a precursor for ecdysone biosynthesis, as our previous studies with labelled products have shown, and we propose that the compounds detected in the present work represent biosynthetic intermediates between cholesterol and ecdysone in ovaries. 相似文献
72.
Full length cDNA structure and deduced amino acid sequence of human 3 beta-hydroxy-5-ene steroid dehydrogenase 总被引:3,自引:0,他引:3
V Luu The Y Lachance C Labrie G Leblanc J L Thomas R C Strickler F Labrie 《Molecular endocrinology (Baltimore, Md.)》1989,3(8):1310-1312
Polyclonal antibodies raised against 3 beta-hydroxysteroid dehydrogenase isolated from human placenta were used to screen a lambda gt11 expression cDNA library from the same tissue. The protein deduced from cDNA sequences contains 372 amino acids with a calculated mol wt of 42,216. Since 3 beta-hydroxysteroid dehydrogenase is the enzyme catalyzing the formation of all classes of hormonal steroids, the availability of the cDNA encoding this enzyme opens new possibilities for a detailed investigation of the factors regulating the expression and activity of this crucial enzyme in adrenal, gonadal as well as peripheral tissues. 相似文献
73.
Small trypanosome RNA-binding proteins TbUBP1 and TbUBP2 influence expression of F-box protein mRNAs in bloodstream trypanosomes 总被引:2,自引:0,他引:2
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Hartmann C Benz C Brems S Ellis L Luu VD Stewart M D'Orso I Busold C Fellenberg K Frasch AC Carrington M Hoheisel J Clayton CE 《Eukaryotic cell》2007,6(11):1964-1978
74.
Luo J Deng ZL Luo X Tang N Song WX Chen J Sharff KA Luu HH Haydon RC Kinzler KW Vogelstein B He TC 《Nature protocols》2007,2(5):1236-1247
Recombinant adenoviruses provide a versatile system for gene expression studies and therapeutic applications. We have developed an approach that simplifies the generation and production of such viruses called the AdEasy system. A recombinant adenoviral plasmid is generated with a minimum of enzymatic manipulations, employing homologous recombination in bacteria rather than in eukaryotic cells. After transfection of such plasmids into a mammalian packaging cell line, viral production is conveniently followed with the aid of GFP encoded by a gene incorporated into the viral backbone. This system has expedited the process of generating and testing recombinant adenoviruses for a variety of purposes. In this protocol, we describe the practical aspects of using the AdEasy system for generating recombinant adenoviruses. The full protocol usually takes 4-5 weeks to complete. 相似文献
75.
Zachary D. Stephens Skylar Y. Lee Faraz Faghri Roy H. Campbell Chengxiang Zhai Miles J. Efron Ravishankar Iyer Michael C. Schatz Saurabh Sinha Gene E. Robinson 《PLoS biology》2015,13(7)
Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade.We compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Astronomy has faced the challenges of Big Data for over 20 years and continues with ever-more ambitious studies of the universe. YouTube burst on the scene in 2005 and has sparked extraordinary worldwide interest in creating and sharing huge numbers of videos. Twitter, created in 2006, has become the poster child of the burgeoning movement in computational social science [6], with unprecedented opportunities for new insights by mining the enormous and ever-growing amount of textual data [7]. Particle physics also produces massive quantities of raw data, although the footprint is surprisingly limited since the vast majority of data are discarded soon after acquisition using the processing power that is coupled to the sensors [8]. Consequently, we do not include the domain in full detail here, although that model of rapid filtering and analysis will surely play an increasingly important role in genomics as the field matures.To compare these four disparate domains, we considered the four components that comprise the “life cycle” of a dataset: acquisition, storage, distribution, and analysis (
Data Phase
Astronomy
Twitter
YouTube
Genomics
Acquisition
25 zetta-bytes/year 0.5–15 billion tweets/year 500–900 million hours/year 1 zetta-bases/year
Storage
1 EB/year 1–17 PB/year 1–2 EB/year 2–40 EB/year
Analysis
In situ data reduction Topic and sentiment mining Limited requirements Heterogeneous data and analysis Real-time processing Metadata analysis Variant calling, ~2 trillion central processing unit (CPU) hours Massive volumes All-pairs genome alignments, ~10,000 trillion CPU hours
Distribution
Dedicated lines from antennae to server (600 TB/s) Small units of distribution Major component of modern user’s bandwidth (10 MB/s) Many small (10 MB/s) and fewer massive (10 TB/s) data movement