Fast and robust quantification of uncertainty in non-linear diffusion MRI models.

Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.

The profiling of microbiota in vaginal swab samples using 16S rRNA gene sequencing and IS-pro analysis.

BACKGROUND: 16S rRNA gene sequencing is currently the most common way of determining the composition of microbiota. This technique has enabled many new discoveries to be made regarding the relevance of microbiota to the health and disease of the host. However, compared to other diagnostic techniques, 16S rRNA gene sequencing is fairly costly and labor intensive, leaving room for other techniques to improve on these aspects. RESULTS: The current study aimed to compare the output of 16S rRNA gene sequencing to the output of the quick IS-pro analysis, using vaginal swab samples from 297 women of reproductive age. 16S rRNA gene sequencing and IS-pro analyses yielded very similar vaginal microbiome profiles, with a median Pearson's R2 of 0.97, indicating a high level of similarity between both techniques. CONCLUSIONS: We conclude that the results of 16S rRNA gene sequencing and IS-pro are highly comparable and that both can be used to accurately determine the vaginal microbiota composition, with the IS-pro analysis having the benefit of rapidity.