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Quantitative magnetic resonance imaging (MRI) techniques are emerging as non-invasive alternatives to biopsy for assessment of diffuse liver diseases of iron overload, steatosis and fibrosis. For testing and validating the accuracy of these techniques, phantoms are often used as stand-ins to human tissue to mimic diffuse liver pathologies. However, currently, there is no standardization in the preparation of MRI-based liver phantoms for mimicking iron overload, steatosis, fibrosis or a combination of these pathologies as various sizes and types of materials are used to mimic the same liver disease. Liver phantoms that mimic specific MR features of diffuse liver diseases observed in vivo are important for testing and calibrating new MRI techniques and for evaluating signal models to accurately quantify these features. In this study, we review the liver morphology associated with these diffuse diseases, discuss the quantitative MR techniques for assessing these liver pathologies, and comprehensively examine published liver phantom studies and discuss their benefits and limitations.

Submarine-melting of ice at the glacier-ocean interface accounts for a large portion of the ice-loss at tidewater glaciers and produces sound via bubble-release. The sound production is dominant in the sub-surface region near the glacier-ocean interface. This depth-dependence of the sound is studied by melting ice blocks in a glacial bay at various depths up to 20 m and recording their acoustics over a large frequency range. The acoustic energy decreases with depth in line with expectations from the physics of the phenomenon and is fit to an exponentially decaying curve. The estimated variation will be useful for interpreting the sound in marine-terminating glaciers bays in terms of the submarine-melting activity.

Absorption cross-sections of 1,1,1,2,3,3-hexafluoropropane (HFC-236ea) and 1,1,2,2, 3-pentafluoropropane (HFC-245ca) were derived from Fourier transform infrared spectra recorded from 550 to 3400 cm − 1 with a resolution of 0.1 cm − 1 over a temperature range of 300–365 K. Good agreement was found between the experimentally derived results, theoretical spectra from density functional theory (DFT) calculations, and previously published experimental measurements. The only temperature dependence observed was in the amplitude of some of the absorption peaks due to the changing ratio of the stable conformations of both molecules studied. This temperature dependence does not result in a significant trend in integrated band strength as a function of temperature. The average value for the integrated band strength of HFC-236ea was found to be (1.982 ± 0.012)x10 − 16 cm molecule − 1 over the spectral range of 550 to 3026 cm − 1 . The average value for the integrated band strength of HFC-245ca was found to be (1.348 ± 0.35)x10 − 16 cm molecule − 1 over the spectral range of 570 to 3035 cm − 1 . Radiative efficiency (RE) and the global warming potential (GWP) for both molecules were also derived, accounting for corrections due to atmospheric lifetimes and stratospheric temperature adjustment. A RE of 0.256 ± 0.034 Wm − 2 ppbv − 1 was obtained for HFC-236ea, which leads to a GWP100 of 1141 ± 402 for 550–3000 cm − 1 . For HFC-245ca, a RE of 0.197 ± 0.023 Wm − 2 ppbv − 1 was derived, which leads to a GWP100 of 588 ± 205 for 550–3000 cm − 1 . The DFT calculation was linearly adjusted to match the experimental spectrum. Using this adjusted DFT spectrum to expand the range below 550 to 0 cm − 1 increases the RE to 0.265 ± 0.035 Wm − 2 ppbv − 1 and the GWP100 to 1181 ± 417 for HFC-236ea; while for HFC-245ca, the RE increases to 0.202 ± 0.027 Wm − 2 ppbv − 1 and the GWP100 to 602 ± 210.

Lipid profiling by 1 H-NMR has gained increasing utility in many fields because of its intrinsically quantitative, nondestructive nature and the ability to differentiate small molecules based on their spectral location. Most nuclear magnetic resonance (NMR) techniques for metabolite quantification use frequency domain analysis that involves many user-dependent steps such as phase and baseline correction and quantification by either manual integration or peak fitting. Recently, Bayesian analysis of time-domain NMR data has been shown to reduce operator bias and increase automation in NMR spectroscopy. In this study, we demonstrate the use of CRAFT (complete reduction to amplitude-frequency table), a Bayesian-based approach to automate processing in NMR-based lipidomics using lipid standards and tissue samples of healthy and tumor-bearing mice supplemented with leucine. Complex mixtures of lipid standards were prepared and examined using CRAFT to validate it against conventional Fourier transform (FT)-NMR and derive a fingerprint to be used for analyzing lipid profiles of serum and liver samples. CRAFT and FT-NMR were comparable in accuracy, with CRAFT achieving higher correlation in quantifying several lipid species. Analysis of the serum lipidome of tumor-bearing mice revealed hyperlipidemia and no signs of hepatic triglyceride accumulation compared with that of the healthy group demonstrating that the tumor-bearing mice were in a state of precachexia. Leucine-supplementation was associated with minimal changes in the lipid profile in both tissues. In conclusion, our study demonstrates that the CRAFT method can accurately identify and quantify lipids in complex lipid mixtures and murine tissue samples and, hence, will increase automation and reproducibility in NMR-based lipidomics.