Quantification of y-aminobutyric acid (GABA) in 1 H MRS volumes composed heterogeneously of grey and white matter

Quantification of γ-aminobutyric acid (GABA) concentration using localized MRS suffers from partial volume effects related to differences in the intrinsic concentration of GABA in grey matter (GM) and white matter (WM). These differences can be represented as a ratio between intrinsic GABA in GM and WM, rM. Individual differences in GM tissue volume can therefore potentially drive apparent concentration differences. Here, a quantification method that corrects for these effects is formulated and empirically validated. Quantification using tissue water as an internal concentration reference has previously been described. Partial volume effects attributed to rM can be accounted for by incorporating into this established method an additional multiplicative correction factor based on measured or literature values of rM weighted by the proportion of GM and WM within tissue-segmented MRS volumes. Simulations were performed to test the sensitivity of this correction using different assumptions of rM taken from previous studies. The tissue correction method was then validated by applying it to an independent dataset of in vivo GABA measurements using an empirically measured value of rM. It is shown that incorrect assumptions of rM can lead to overcorrection and inflation of GABA concentration measurements quantified in volumes composed predominantly of WM. For the independent dataset, GABA concentration was linearly related to GM tissue volume when only the water signal was corrected for partial volume effects. Performing a full correction that additionally accounts for partial volume effects ascribed to rM successfully removed this dependency. With appropriate assumption of the ratio of intrinsic GABA concentration in GM and WM, GABA measurements can be corrected for partial volume effects, potentially leading to a reduction in between-participant variance, increased power in statistical tests and better discriminability of true effects

Stress-induced adaptive morphogenesis in bacteria

Bacteria thrive in virtually all environments. Like all other living organisms, bacteria may encounter various types of stresses, to which cells need to adapt. In this chapter, we describe how cells cope with stressful conditions and how this may lead to dramatic morphological changes. These changes may not only allow harmless cells to withstand environmental insults but can also benefit pathogenic bacteria by enabling them to escape from the immune system and the activity of antibiotics. A better understanding of stress-induced morphogenesis will help us to develop new approaches to combat such harmful pathogens.Microbial Biotechnolog

Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models

Background: Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes. Objective: This study integrated multiple approaches to develop new models to estimate within-city spatial variations in annual median (i.e. average) outdoor UFP and BC concentrations as well as mean UFP size in Canada’s two largest cities, Montreal and Toronto. Methods: We conducted year-long mobile monitoring campaigns in each city that included evenings and weekends. We developed generalized additive models trained on land use parameters and deep Convolutional Neural Network (CNN) models trained on satellite-view images. Using predictions from these models, we developed final combined models. Results: In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm3, 33.7 nm, and 1225 ng/m3, respectively. In Montreal, the median observed UFP concentration, UFP size, and BC concentration values were 14,702pt/cm3, 29.7 nm, and 1060 ng/m3, respectively. For all pollutants in both cities, the proportion of spatial variation explained (i.e., R2) was slightly greater (1–2 percentage points) for the combined models than the generalized additive models and a greater (approximately 10 percentage points) than the deep CNN models. The Toronto combined model R2 values in the test set were 0.73, 0.55, and 0.61 for UFP concentrations, UFP size, and BC concentration, respectively. The Montreal combined model R2 values were 0.60, 0.49, and 0.60 for UFP concentration, UFP size, and BC concentration models respectively. For each pollutant, predictions from the combined, deep CNN, and generalized additive models were highly correlated with each other and differences between models were explored in sensitivity analyses. Conclusion: Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC