Use of a predictive protocol to measure the antimicrobial resistance risks associated with biocidal product usage

Background In this study we assessed the propensity of biocide exposure in the development of antimicrobial resistance in bacteria. Methods Our protocol is based on reporting changes in established antimicrobial susceptibility profiles in biocides and antibiotics after during use exposure to a product. The during use exposure reflects worse conditions of product use during application. It differs from the term low concentration, which usually reflects a concentration below the minimal inhibitory concentration, but not necessarily a concentration that occurs in practice. Results Our results showed that exposure to triclosan (0.0004%) was associated with a high risk of developing resistance and cross-resistance in Staphylococcus aureus and Escherichia coli. This was not observed with exposure to chlorhexidine (0.00005%) or a hydrogen peroxide–based biocidal product (in during use conditions). Interestingly, exposure to a low concentration of hydrogen peroxide (0.001%) carried a risk of emerging resistance to antibiotics if the presence of the oxidizing agent was maintained. We observed a number of unstable clinical resistances to antibiotics after exposure to the cationic biocide and oxidizing agent, notably to tobramycin and ticarcillin–clavulanic acid. Conclusions Using a decision tree based on the change in antimicrobial susceptibility test results, we were able to provide information on the effect of biocide exposure on the development of bacterial resistance to antimicrobials. Such information should address the call from the U.S. Food and Drug Administration and European Union Biocidal Products Regulation for manufacturers to provide information on antimicrobial resistance and cross-resistance in bacteria after the use of their product

A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: III. Disentangling Multiple Components in H ii regions

International audienceIn the first two papers of this series (Rhea et al. 2020b; Rhea et al. 2021), we demonstrated the dynamism of machine learning applied to optical spectral analysis by using neural networks to extract kinematic parameters and emission-line ratios directly from the spectra observed by the SITELLE instrument located at the Canada-France-Hawai'i Telescope. In this third installment, we develop a framework using a convolutional neural network trained on synthetic spectra to determine the number of line-of-sight components present in the SN3 filter (656-683nm) spectral range of SITELLE. We compare this methodology to standard practice using Bayesian Inference. Our results demonstrate that a neural network approach returns more accurate results and uses less computational resources over a range of spectral resolutions. Furthermore, we apply the network to SITELLE observations of the merging galaxy system NGC2207/IC2163. We find that the closest interacting sector and the central regions of the galaxies are best characterized by two line-of-sight components while the outskirts and spiral arms are well-constrained by a single component. Determining the number of resolvable components is crucial in disentangling different galactic components in merging systems and properly extracting their respective kinematics