Ancient DNA and RNA are valuable data sources for a wide range of disciplines. Within the field of ancient metagenomics, the number of published genetic datasets has risen dramatically in recent years, and tracking this data for reuse is particularly important for large-scale ecological and evolutionary studies of individual taxa and communities of both microbes and eukaryotes. AncientMetagenomeDir (archived at 10.5281/zenodo.3980833) is a collection of annotated metagenomic sample lists derived from published studies that provide basic, standardised metadata and accession numbers to allow rapid data retrieval from online repositories. These tables are community-curated and span multiple sub-disciplines to ensure adequate breadth and consensus in metadata definitions, as well as longevity of the database. Internal guidelines and automated checks facilitate compatibility with established sequence-read archives and term-ontologies, and ensure consistency and interoperability for future meta-analyses. This collection will also assist in standardising metadata reporting for future ancient metagenomic studies.
Ancient DNA and RNA are valuable data sources for a wide range of disciplines. Within the field of ancient metagenomics, the number of published genetic datasets has risen dramatically in recent years, and tracking this data for reuse is particularly important for large-scale ecological and evolutionary studies of individual microbial taxa, microbial communities, and metagenomic assemblages. AncientMetagenomeDir (archived at https://doi.org/10.5281/zenodo.3980833) is a collection of indices of published genetic data deriving from ancient microbial samples that provides basic, standardised metadata and accession numbers to allow rapid data retrieval from online repositories. These collections are community-curated and span multiple sub-disciplines in order to ensure adequate breadth and consensus in metadata definitions, as well as longevity of the database. Internal guidelines and automated checks to facilitate compatibility with established sequence-read archives and term-ontologies ensure consistency and interoperability for future meta-analyses. This collection will also assist in standardising metadata reporting for future ancient metagenomic studies.
Background: Quantitative T 2 * MRI is the standard of care for the assessment of iron overload. However, patient motion corrupts T 2 * estimates. Purpose: To develop and evaluate a motion-robust, simultaneous cardiac and liver T 2 * imaging approach using non-Cartesian, rosette sampling and a model-based reconstruction as compared to clinical-standard Cartesian MRI. Study Type: Prospective. Phantom/Population: Six ferumoxytol-containing phantoms (26-288 μg/mL). Eight healthy subjects and 18 patients referred for clinically indicated iron overload assessment. Field Strength/Sequence: 1.5T, 2D Cartesian and rosette gradient echo (GRE) Assessment: GRE T 2 * values were validated in ferumoxytol phantoms. In healthy subjects, test-retest and spatial coefficient of variation (CoV) analysis was performed during three breathing conditions. Cartesian and rosette T 2 * were compared using correlation and Bland-Altman analysis. Images were rated by three experienced radiologists on a 5-point scale. Statistical Tests: Linear regression, analysis of variance (ANOVA), and paired Student's t-testing were used to compare reproducibility and variability metrics in Cartesian and rosette scans. The Wilcoxon rank test was used to assess reader score comparisons and reader reliability was measured using intraclass correlation analysis. Results: Rosette R2* (1/T 2 *) was linearly correlated with ferumoxytol concentration (r 2 = 1.00) and not significantly different than Cartesian values (P = 0.16). During breath-holding, ungated rosette liver and heart T 2 * had lower spatial CoV (liver: 18.4 AE 9.3% Cartesian, 8.8% AE 3.4% rosette, P = 0.02, heart: 37.7% AE 14.3% Cartesian, 13.4% AE 1.7% rosette, P = 0.001) and higher-quality scores (liver: 3.3 [3.0-3.6] Cartesian, 4.7 [4.1-4.9] rosette, P = 0.005, heart: 3.0 [2.3-3] Cartesian, 4.5 [3.8-5.0] rosette, P = 0.005) compared to Cartesian values. During free-breathing and failed breath-holding, Cartesian images had very poor to average image quality with significant artifacts, whereas rosette remained very good, with minimal artifacts (P = 0.001). Data Conclusion: Rosette k-sampling with a model-based reconstruction offers a clinically useful motion-robust T 2 * mapping approach for iron quantification.
We propose a physics-inspired, unrolled-deep-learning framework for off-resonance correction. Our forward model includes coil sensitivities, multi-frequency bins, and non-uniform Fourier transforms hence compatible with fat/water imaging and parallel imaging acceleration. The network, which includes data-consistency terms and CNN modules serving as proximal operators, is trained end-to-end using only synthetic random field maps, coil sensitivities, and noise-like images with statistics (smoothness) mimicking natural signals. Our aim is to train the network to reverse off-resonance irrespective of the type of imaging, and hence generalizable to any anatomy and contrast without retraining. We demonstrate initial results in simulations, phantom, and in-vivo data.
For fast T1-weighted imaging, zero echo time (ZTE) imaging provides rapid sampling of 3D k-space, but lacks T1 contrast due to its small flip angle excitation with short hard pulses. To efficiently increase T1 contrast, longer phase-modulated pulses can be used. However, longer pulses lead to larger dead-time gaps and pulse profile-weighted images. Here, we formulate an inverse problem to directly reconstruct profile-compensated images from multi-coil data without any intermediate step for dead-time infilling. We demonstrate that leveraging coil sensitivities and alternating phase-modulated excitations sufficiently condition the inverse problem, allowing for iterative reconstructions of T1-weighted acquisitions.
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