Hyperpolarized 13C magnetic resonance spectroscopy (MRS) provides unprecedented opportunities to obtain clinical diagnostic information through in vivo monitoring of metabolic pathways. The continuing advancement of this field relies on the identification of molecular probes that can effectively interrogate pathways critical to disease. In this report, we describe the synthesis, development, and in vivo application of sodium [1-13C]-glycerate ([13C]-Glyc) as a novel probe for evaluating glycolysis using hyperpolarized 13C MRS. This agent was prepared by a concise synthetic route and formulated for dynamic nuclear polarization. [13C]-Glyc displayed a high level of polarization and long spin–lattice relaxation time—both of which are necessary for future clinical investigations. In vivo spectroscopic studies with hyperpolarized [13C]-Glyc in rat liver furnished metabolic products, [13C]-labeled pyruvate and lactate, originating from glycolysis. The levels of production and relative intensities of these metabolites were directly correlated with the induced glycolytic state (fasted versus fed groups). This work establishes hyperpolarized [13C]-Glyc as a novel agent for clinically relevant 13C MRS studies of energy metabolism and further provides opportunities for evaluating intracellular redox states in biochemical investigations.

Recent spherical nanoindentation protocols have proven robust at capturing the local elastic-plastic response of polycrystalline metal samples at length scales much smaller than the grain size. In this work, we extend these protocols to length scales that include multiple grains to recover microindentation stress-strain curves. These new protocols are first established in this paper and then demonstrated for Al-6061 by comparing the measured indentation stress-strain curves with the corresponding measurements from uniaxial tension tests. More specifically, the scaling factors between the uniaxial yield strength and the indentation yield strength was determined to be about 1.9, which is significantly lower than the value of 2.8 used commonly in literature. The reasons for this difference are discussed. Second, the benefits of these new protocols in facilitating high throughput exploration of process-property relationships are demonstrated through a simple case study.

This paper presents a two-step Bayesian framework for the estimation of the intrinsic single crystal elastic stiffness parameters from the measurements of spherical indentation stress-strain responses in multiple individual grains of a polycrystalline sample, whose crystal lattice orientations have been measured using electron back-scattered diffraction technique. The first step requires the establishment of the functional dependence of the indentation elastic modulus given the lattice orientation and the intrinsic single crystal elastic stiffness parameters. Previous efforts for this step required a large database of computationally expensive finite element (FE) simulations in order to establish this function with adequate accuracy. In this paper, it is shown that the introduction of a Bayesian framework can greatly reduce the number of simulations necessary to establish this function, while introducing practically useful measures of uncertainty which can guide the selection of specific additional simulations that are expected to best improve the predictive accuracy of the function. The second step involves a Markov Chain Monte-Carlo (MCMC) sampling of the distribution of possible values for the single crystal elastic stiffness parameters based on a given set of experimentally measured elastic indentation moduli in individual grains of different lattice orientations. This second step is accomplished by calibrating the available experimental data to the function established in the first step. This novel framework is presented and demonstrated in this paper for an as-cast cubic polycrystalline Fe-3% Si sample and a hexagonal polycrystalline commercially pure (CP-Ti) titanium sample.11 12 44 Literature a 225 135 124 Previous Study b 216 132 122 Current Study 223 132 114 a. Simmons and Wang [40] b. Patel et al. [9]

No abstract

No abstract