Benford's law and metabolomics: A tale of numbers and blood

The Newcomb-Benford law - also known as the "law of anomalous numbers" or, more commonly, Benford's law - predicts that the distribution of the first significant digit of random numbers obtained from mixed probability distributions follows a predictable pattern and reveals some universal behavior. Specifically, given a dataset of empirical measures, the likelihood of the first digit of any number being 1 is ∼30 %, ∼18 % for 2, 12.5 % for 3 and so on, with a decreasing probability all the way to number 9. If the digits were distributed uniformly, all the numbers 1 through 9 would have the same probability to appear as the first digit in any given empirical random measurement. However, this is not the case, as this law defies common sense and seems to apply seamlessly to large data. The use of omics technologies and, in particular, metabolomics has generated a wealth of big data in the field of transfusion medicine. In the present meta-analysis, we focused on previous big data from metabolomics studies of relevance to transfusion medicine: one on the quality of stored red blood cells, one on the phenotypes of transfusion recipients, i.e. trauma patients suffering from trauma and hemorrhage, and one of relevance to the 2020 SARS-COV-2 global pandemic. We show that metabolomics data follow a Benford's law distribution, an observation that could be relevant for future application of the "law of anomalous numbers" in the field of quality control processes in transfusion medicine.

Systematic influences on the areas of peaks in gamma-ray spectra that have a large statistical uncertainty

A method is presented for calculating the expected number of counts in peaks that have a large relative peak-area uncertainty and appear in measured gamma-ray spectra. The method was applied to calculations of the correction factors for peaks occurring in the spectra of radon daughters. It was shown that the factors used for correcting the calculated peak areas to their expected values decrease with an increasing relative peak-area uncertainty. The accuracy of taking the systematic influence inducing the correction factors into account is given by the dispersion of the correction factors corresponding to specific peaks. It was shown that the highest accuracy is obtained in the peak analyses with the GammaVision and Gamma-W software.

A simple method for activity determination of ¹³⁴Cs and ¹³⁷Cs in foodstuffs using NaI(Tl) scintillation spectrometer

A simplified peak fitting technique for the analysis of the overlapped pulse-height spectra of (134)Cs and (137)Cs γ-rays obtained with a NaI(Tl) scintillation spectrometer was studied. In this analysis, nearly an upper half of 662 keV (137)Cs peak data was employed for fitting of the Gaussian peak using the least squares algorithm. Consistent results were obtained as compared with the reference value of test samples mixed with (134)Cs and (137)Cs standard solutions.