Multiple linear regression analysis of scintillation gamma-ray spectra: Automatic candidate selection

Quantitative Analysis of Diubiquitin Isomers Using Ion Mobility Mass Spectrometry

The diversity of ubiquitin modifications calls for methods to better characterize ubiquitin chain linkage, length, and morphology. Here, we use multiple linear regression analysis coupled with ion mobility mass spectrometry (IM-MS) to quantify the relative abundance of different ubiquitin dimer isomers. We demonstrate the utility and robustness of this approach by quantifying the relative abundance of different ubiquitin dimers in complex mixtures and comparing the results to the standard, bottom-up ubiquitin AQUA method. Our results provide a foundation for using multiple linear regression analysis and IM-MS to characterize more complex ubiquitin chain architectures.

Radioactive hot-spot localisation and identification using deep learning

The detection of radioactive hot-spots and the identification of the radionuclides present have been a challenge for the security sector, especially in situations involving chemical, biological, radiological, nuclear and explosive threats, as well as naturally occurring radioactive materials. This work proposes a solution based on Machine Learning techniques, with a focus on artificial neural networks (NNs), in order to localise, quantify and identify radioactive sources. Firstly, the created RHLnet model uses observations of radiological intensity counts and corresponding localisations to estimate the number, location and activity of unknown radioactive sources present in a given scenario. Then, another model (RHIdnet) gets the gamma spectrum of the sources to perform the identification of the corresponding radionuclides. For this, a training data set composed of simulated data is used during the training process, and so, using algorithms with the models already trained, fast and accurate predictions are achieved, ensuring the reliability of such a NN-based approach. The proposed solution is tested in simulated and real scenarios, with multiple sources, providing a low number of limitations, related to possible false negatives and false positives. Besides, the results have shown that the algorithm is scalable for very large regions, as well as for very small scenarios. Single and multiple isotope identification on each sample is explored, highlighting the benefits as well as possible improvements. Thus, NNs have demonstrated the capability of being an emerging tool with the potential to make a difference in the nuclear field, by helping in the development of novel techniques and new solutions in order to safeguard human lives.

A quantitative spectral component analysis method based on maximum likelihood

In spectroscopy, the compositional analysis of the spectrum is important, such as extracting information about the species of spectral objects contributing to spectral data from an emission spectrum of photon energy. A quantitative spectral component analysis method based on Maximum Likelihood Estimation using Expectation Maximization (MLEM) is developed, which could quantitatively decompose out the components of the measured spectrum of low counts and surpass conventional techniques which belong to classification or regression. Abundant experimental and simulated spectra data on gamma-ray spectrum of radionuclides are presented to demonstrate and evaluate this method, while the ingredient radionuclides in the mixed spectrum are identified accurately with high precision. It will be a powerful and alternative method recommended for the circumstances needing fast and quantitative spectral analysis, including radionuclide identification (gamma-ray spectra), biomass or mineral composition (near-infrared spectra), laser-induced breakdown spectra and other spectroscopy scenarios.

Improved intracellular PHA determinations with novel spectrophotometric quantification methodologies based on Sudan black dye

The presence of intracellular polyhydroxyalkanoates (PHAs) is usually studied using Sudan black dye solution (SB). In a previous work it was shown that the PHA could be directly quantified using the absorbance of SB fixed by PHA granules in wet cell samples. In the present paper, the optimum SB amount and the optimum conditions to be used for SB assays were determined following an experimental design by hybrid response surface methodology and desirability-function. In addition, a new methodology was developed in which it is shown that the amount of SB fixed by PHA granules can also be determined indirectly through the absorbance of the supernatant obtained from the stained cell samples. This alternative methodology allows a faster determination of the PHA content (involving 23 and 42 min for indirect and direct determinations, respectively), and can be undertaken by means of basic laboratory equipment and reagents. The correlation between PHA content in wet cell samples and the spectra of the SB stained supernatant was determined by means of multivariate and linear regression analysis. The best calibration adjustment (R² = 0.91, RSE: 1.56%), and the good PHA prediction obtained (RSE = 1.81%), shows that the proposed methodology constitutes a reasonably precise way for PHA content determination. Thus, this methodology could anticipate the probable results of the above mentioned direct PHA determination. Compared with the most used techniques described in the scientific literature, the combined implementation of these two methodologies seems to be one of the most economical and environmentally friendly, suitable for rapid monitoring of the intracellular PHA content.

Factor analysis of trace-element data from tree-bark samples in The Netherlands

Monte Carlo-assisted factor analysis has been applied to a data set of 20 trace-element concentrations in tree-bark samples obtained from 123 locations in The Netherlands, with the aim to investigate the suitability of bark as a biomonitor for air pollution. A Monte Carlo approach was used to give more insight to the uncertainties and significance levels of the factor analysis results. Notwith-standing a rather strong influence of soil material on the concentration levels, factor analysis enabled the identification of five significant pollution source types, all of which corresponded with source types found in an earlier biomonitoring study in The Netherlands using epiphytic lichens. A more detailed comparison with the lichen results showed a remarkable difference in lead concentrations between bark and lichen. It was concluded that bark can be successfully employed as a biomonitor for air pollution. The power of factor analysis to adequately determine the soil contribution may render extensive sample washing procedures superfluous.

A microcomputer-based whole-body counter for personnel routine monitoring

The paper describes a cost-effective NaI(Tl) whole-body counter developed for routine examinations of worker intakes at an isotope production facility. Signal processing, data analysis and system operation are microcomputer-controlled for minimum human interactions. The pulse height analyzer is developed as an microcomputer add-on card for easy manipulation. The scheme for radionuclide analysis is aimed for fast running according to a knowledge base established from background samples and phantom experiments in conjunction with a multivariate regression analysis. Long-term stability and calibration with standards and in vivo measurements are reported.