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Optimization of the Biosynthesis Conditions of Daptomycin by the Biostatistical Methodology

Overview of attention for article published in Interdisciplinary Sciences: Computational Life Sciences, November 2015
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Title
Optimization of the Biosynthesis Conditions of Daptomycin by the Biostatistical Methodology
Published in
Interdisciplinary Sciences: Computational Life Sciences, November 2015
DOI 10.1007/s12539-015-0133-8
Pubmed ID
Authors

Guanghai Yu, Guoying Wang

Abstract

Response surface methodology (RSM) was employed to optimize medium components including oxygen vector of n-dodecane of a mutant strain GC-63 of Streptomyces roseosporus NRRL 11379. The two-level Plackett-Burman design (PB factorial design) with fourteen variables including oxygen vector was used to screen the most significant factors affecting antibiotic production. Then, the RSM based on center composite design was used to identify the optimum levels of the significant variables to generate optimal response. Glucose, soybean meal, asparagine and n-dodecane were screened to significantly influence the daptomycin production. The medium composition optimized with response surface methodology was (g/L): glucose, 9.46; soluble starch, 25; dextrin, 12.5; yeast extract, 12.5; soybean meal, 21.34; peptone, 25; casein, 5; asparagine, 2.68; K2SO4, 6; (NH4)2Fe(SO4)2, 2; MgSO4, 1; CaCO3, 5; MnCl2, 0.5; n-dodecane, 7.47 % (v/v). The maximum daptomycin concentration reached 979.36 mg/L which was nearly 2.2-fold higher compared to that in the basal medium, with predicted optimal concentrations in a 7.5-L fermentor.

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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 13%
Lecturer 1 13%
Student > Doctoral Student 1 13%
Student > Bachelor 1 13%
Student > Ph. D. Student 1 13%
Other 3 38%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 38%
Engineering 2 25%
Pharmacology, Toxicology and Pharmaceutical Science 1 13%
Immunology and Microbiology 1 13%
Agricultural and Biological Sciences 1 13%
Other 0 0%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 November 2015.
All research outputs
#17,777,370
of 22,833,393 outputs
Outputs from Interdisciplinary Sciences: Computational Life Sciences
#147
of 294 outputs
Outputs of similar age
#261,993
of 386,425 outputs
Outputs of similar age from Interdisciplinary Sciences: Computational Life Sciences
#4
of 6 outputs
Altmetric has tracked 22,833,393 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 294 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 386,425 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.