Electron photodetachment dissociation of DNA polyanions in a quadrupole ion trap mass spectrometer

We hereby explore the effects of irradiating DNA polyanions stored in a quadrupole ion trap mass spectrometer with an optical parametric oscillator laser between 250 and 285 nm. We studied DNA 6-20-mer single strands and 12-base pair double strands. In all cases, laser irradiation causes electron detachment from the multiply charged DNA anions. Electron photodetachment efficiency directly depends on the number of guanines in the strand, and maximum efficiency is observed between 260 and 275 nm. Subsequent collision-induced dissociation (CID) of the radical anions produced by electron photodetachment results in extensive fragmentation. In addition to neutral losses, a large number of fragments from the w, d, a*, and z* ion series are obtained, contrasting with the w and (a-base) ion series observed in regular CID. The major advantage of this technique, coined electron photodetachment dissociation (EPD) is the absence of internal fragments, combined with good sequence coverage. EPD is therefore a highly promising approach for de novo sequencing of oligonucleotides. EPD of nucleic acids is also expected to give specific radical-induced strand cleavages, with conservation of other fragile bonds, including noncovalent bonds. In effect, preliminary results on a DNA hairpin and on double strands suggest that EPD could also be used to probe intra- and intermolecular interactions in nucleic acids.     

Validation of a stochastic modelling approach for Listeria monocytogenes growth in refrigerated foods

A stochastic modelling approach was developed to describe the distribution of Listeria monocytogenes contamination in foods throughout their shelf life. This model was designed to include the main sources of variability leading to a scattering of natural contaminations observed in food portions: the variability of the initial contamination, the variability of the biological parameters such as cardinal values and growth parameters, the variability of individual cell behaviours, the variability of pH and water activity of food as well as portion size, and the variability of storage temperatures. Simulated distributions of contamination were compared to observed distributions obtained on 5 day-old and 11 day-old cheese curd surfaces artificially contaminated with between 10 and 80 stressed cells and stored at 14 °C, to a distribution observed in cold smoked salmon artificially contaminated with approximately 13 stressed cells and stored at 8 °C, and to contaminations observed in naturally contaminated batches of smoked salmon processed by 10 manufacturers and stored for 10 days a 4 °C and then for 20 days at 8 °C. The variability of simulated contaminations was close to that observed for artificially and naturally contaminated foods leading to simulated statistical distributions properly describing the observed distributions. This model seems relevant to take into consideration the natural variability of processes governing the microbial behaviour in foods and is an effective approach to assess, for instance, the probability to exceed a critical threshold during the storage of foods like the limit of 100 CFU/g in the case of L. monocytogenes.     

Microwave-Assisted Extraction of Secoisolariciresinol Diglucoside��Method Development

An optimized microwave-assisted extraction (MAE) method was developed for extracting secoisolariciresinol diglucoside (SDG) from flaxseed. This paper presents the optimization of factors for maximizing the extraction yield of SDG. This work was conducted using the experimental domain identified in a previous study by means of screening designs, that is, samples of 1 g defatted flaxseed meal (DFM) were extracted with 50 ml NaOH of concentration of 0.5–1 M, at microwave power levels of 60–360 W, for 3–9 min, with the microwave power applied intermittently (power on 30 s/min) and continuously (power on 60 s/min). The MAE of SDG was maximized when 1 g DFM was extracted with 50 ml 0.5 M NaOH, at 135 W, for 3 min in intermittent power mode (power on 30 s/min). The optimized MAE achieved a 6% increase in the extraction yield (21.45 mg SDG per gram DFM) as opposed to a direct hydrolysis method (20.22 mg SDG per gram DFM). The MAE of SDG was governed by the microwave–NaOH interaction, which had a curvilinear dependence on the microwave power level, and linear dependence on the NaOH concentration. The microwave-induced effects accounted for a 10% increase in the SDG extraction yield (21.45 mg SDG per gram DFM) as opposed to a microwaveless control method (19.45 mg SDG per gram DFM). The optimized MAE method has good repeatability, a 97% recovery of the target compound; it is fast and efficient and can be used for precise quantification of SDG in flaxseed.     

A Possible Mechanism for Protrusions Formation at the Metal/Oxide Interface During Short Time Oxidation of Ferritic Stainless Steel

High temperature oxidation of ferritic stainless steel for short durations leads to the formation of an original morphology at the metal/oxide interface. This interface is composed of metallic protrusions localized in a chromium-rich oxide layer through a discontinuous silica film. In this paper we propose a mechanism based on preferential diffusion paths for the oxygen through the oxide that are governed by the distribution of the hydrostatic pressure in this layer. We point out that the mechanical contrast between the oxide and the metal subjected to creep can be critical for the hydrostatic pressure gradient magnitude inside the oxide layer. This observation is likely to promote the formation of protrusions for specific conditions of temperature and time of exposure to oxidation.     

Artificial neural network to predict the power number of agitated tanks fed by CFD simulations

The power consumption of the agitator is a critical variable to consider in the design of a mixing system. It is generally evaluated through a dimensionless number known as the power number $N_p$. Multiple empirical equations exist to calculate the power number based on the Reynolds number $Re$ and dimensionless geometrical variables that characterize the tank, the impeller, and the height of the fluid. However, correlations perform poorly outside of the conditions in which they were established. We create a rich database of 100 k computational fluid dynamics (CFD) simulations. We simulate paddle and pitched blade turbines in unbaffled tanks from $Re$ 1 to 100 and use an artificial neural network (ANN) to create a robust and accurate predictor of the power number. We perform a mesh sensitivity analysis to verify the precision of the $N_p$ values given by the CFD simulations. To sample the 100 k mixers by their geometrical and physical properties, we use the Latin hypercube sampling (LHS) method. We then normalize the data with a MinMax transformation to put all features in the same scale and thus avoid bias during the ANN's training. Using a grid search cross-validation, we find the best architecture of the ANN that prevents overfitting and underfitting. Finally, we quantify the performance of the ANN by extracting 30% of the database, predicting the $N_p$ using the ANN, and evaluating the mean absolute percentage error. The mean absolute error in the ANN prediction is 0.5%, and its accuracy surpasses correlations even for untrained geometries.