Scanner calibration revisited

<p>Abstract</p> <p>Background</p> <p>Calibration of a microarray scanner is critical for accurate interpretation of microarray results. Shi et al. (<it>BMC Bioinformatics</it>, 2005, <b>6</b>, Art. No. S11 Suppl. 2.) reported usage of a Full Moon BioSystems slide for calibration. Inspired by the Shi et al. work, we have calibrated microarray scanners in our previous research. We were puzzled however, that most of the signal intensities from a biological sample fell below the sensitivity threshold level determined by the calibration slide. This conundrum led us to re-investigate the quality of calibration provided by the Full Moon BioSystems slide as well as the accuracy of the analysis performed by Shi et al.</p> <p>Methods</p> <p>Signal intensities were recorded on three different microarray scanners at various photomultiplier gain levels using the same calibration slide from Full Moon BioSystems. Data analysis was conducted on raw signal intensities without normalization or transformation of any kind. Weighted least-squares method was used to fit the data.</p> <p>Results</p> <p>We found that initial analysis performed by Shi et al. did not take into account autofluorescence of the Full Moon BioSystems slide, which led to a grossly distorted microarray scanner response. Our analysis revealed that a power-law function, which is explicitly accounting for the slide autofluorescence, perfectly described a relationship between signal intensities and fluorophore quantities.</p> <p>Conclusions</p> <p>Microarray scanners respond in a much less distorted fashion than was reported by Shi et al. Full Moon BioSystems calibration slides are inadequate for performing calibration. We recommend against using these slides.</p

Was the Scanner Calibration Slide used for its intended purpose?

In the article, Scanner calibration revisited, BMC Bioinformatics 2010, 11:361, Dr. Pozhitkov used the Scanner Calibration Slide, a key product of Full Moon BioSystems to generate data in his study of microarray scanner PMT response and proposed a mathematic model for PMT response [1]. In the end, the author concluded that "Full Moon BioSystems calibration slides are inadequate for performing calibration," and recommended "against using these slides." We found these conclusions are seriously flawed and misleading, and his recommendation against using the Scanner Calibration Slide was not properly supported

The Zeta Herculis binary system revisited. Calibration and seismology

We have revisited the calibration of the visual binary system Zeta Herculis with the goal to give the seismological properties of the G0 IV sub-giant Zeta Her A. We have used the most recent physical and observational data. For the age we have obtained 3387 Myr, for the masses respectively 1.45 and 0.98 solar mass, for the initial helium mass fraction 0.243, for the initial mass ratio of heavy elements to hydrogen 0.0269 and for the mixing-length parameters respectively 0.92 and 0.90 using the Canuto & Mazitelli (1991, 1992) convection theory. Our results do not exclude that Zeta Her A is itself a binary sub-system; the mass of the hypothetical unseen companion would be smaller than 0.05 solar mass. The adiabatic oscillation spectrum of Zeta Her A is found to be a complicated superposition of acoustic and gravity modes; some of them have a dual character. This greatly complicates the classification of the non-radial modes. The echelle diagram used by the observers to extract the frequencies will work for ell=0, 2, 3. The large difference is found to be of the order of 42 mu Hz, in agreement with the Martic et al. (2001) seismic observations.Comment: 12 pages, A&A in pres

Asteroseismology and calibration of alpha Cen binary system

Using the oscillation frequencies of alpha Cen A recently discovered by Bouchy & Carrier, the available astrometric, photometric and spectroscopic data, we tried to improve the calibration of the visual binary system alpha Cen. With the revisited masses of Pourbaix et al. (2002) we do not succeed to obtain a solution satisfying all the seismic observational constraints. Relaxing the constraints on the masses, we have found an age t_alpha Cen=4850+-500 Myr, an initial helium mass fraction Y_i = 0.300+-0.008, and an initial metallicity (Z/X)_i=0.0459+-0.0019, with M_A=1.100+-0.006M_o and M_B=0.907+-0.006M_o for alpha Cen A&B.Comment: accepted for publication as a letter in A&

Rabin’s calibration theorem revisited

We simplify and refine the theoretical results behind Rabin’s famous calibration theorem for expected utility preferences and present the resulting tightened versions of his numerical illustrations

The calibration of nanoindenters revisited

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The Cost of Business Cycles Under Endogenous Growth

In his famous monograph, Lucas (1987) put forth an argument that the welfare gains from reducing the volatility of aggregate consumption are negligible. Subsequent work that revisited Lucas' calculation continued to find only small benefits from reducing the volatility of consumption, further reinforcing the perception that business cycles don't matter. This paper argues instead that fluctuations can affect welfare by affecting the growth rate of consumption. I present an argument for why fluctuations can reduce growth starting from a given initial consumption, which could imply substantial welfare effects as Lucas (1987) already observed in his calculation. Empirical evidence and calibration exercises suggest that the welfare effects are likely to be substantial, about two orders of magnitude greater than Lucas' original estimates.

A Markov chain approach to ABM calibration

Agent based model are nowadays widely used, however the lack of general methods and rules for their calibration still prevent to exploit completely their potentiality. Rarely such a kind of models can be studied analytically, more often they are studied by using simulation. Reference [1] show that many computer simulation models, like ABM, can be represented as Markov Chains. Exploting such an idea we illustrate an example of how to calibrate an ABM when it can be revisited as a Markov chain