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- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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- Lin, Jing, et al.
(författare)
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Bayesian analysis for randomly truncated constant-stress accelerated life testing
- 2007
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Ingår i: Journal of Systems Engineering and Electronics. - 1001-506X. ; 29:2, s. 320-323
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Tidskriftsartikel (refereegranskat)abstract
- Aimed at the fault of the traditional numeration methods, the Weibull model, which is used widely in the family of Bayesian accelerated failure-time model was discussed. Markov chain Monte Carlo method based on Gibbs sampling was discussed, which were used to simulate dynamically the Markov Chain of the parameters’ posterior distribution. Also, the parameters’ Bayesian estimations were given out with prior suppose for its parameters. What’s more, the results of the data’s simulation were utilized to show the process of setting the model by using the BUGS package. It proves the objectivity and validity of the model.
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| 9. |
- Lin, Jing, et al.
(författare)
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Bayesian analysis of constant stress AFT for Weibull distribution using Gibbs sampling
- 2007
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Ingår i: 2007 IEEE International Conference on Grey Systems and Intelligent Services. - Piscataway, NJ : IEEE Communications Society. - 1424412943 ; , s. 1492-1496
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Konferensbidrag (refereegranskat)abstract
- To mitigate the limitations of the traditional methods for reliability analysis such as BLUE, the model of Weibull constant-stress accelerated failure test (AFT)is considered here. Based on the theory of Bayesian survival analysis and informative prior hypothesis for model’s parameters, Markov chain Monte Carlo(MCMC) method based on Gibbs sampling is used to dynamically simulate the Markov chain of the parameters’ posterior distribution, under the conditionthat the lifetime’s distribution is Weibull. Through that, the parameters’ Bayesian estimations in the constant-stress AFT model were given. BUGS package is used here as an example. The example data is first used by Shisong Mao (2003). And, two result sets are presented based on two assumptions, one of which is that the truncated data in the AFT is viewed as the right censored data in survival analysis. The other assumption is that the truncated data in the AFT is viewed as just failure data but not truncated. The two data sets are used to be compared with the results provided by Mao using BLUE method. The results indicate that, the estimation given by BLUE is close to the results given under the assumptionthat the truncated data is just failure data. However, when we consider the truncated data, the results far differ from the former. The limitations of BLUE will be presented once more, and the significance and the effectiveness of the model will be illustrated.
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