Investigation of discrete imaging models and iterative image reconstruction in differential X-ray phase-contrast tomography.

Differential X-ray phase-contrast tomography (DPCT) refers to a class of promising methods for reconstructing the X-ray refractive index distribution of materials that present weak X-ray absorption contrast. The tomographic projection data in DPCT, from which an estimate of the refractive index distribution is reconstructed, correspond to one-dimensional (1D) derivatives of the two-dimensional (2D) Radon transform of the refractive index distribution. There is an important need for the development of iterative image reconstruction methods for DPCT that can yield useful images from few-view projection data, thereby mitigating the long data-acquisition times and large radiation doses associated with use of analytic reconstruction methods. In this work, we analyze the numerical and statistical properties of two classes of discrete imaging models that form the basis for iterative image reconstruction in DPCT. We also investigate the use of one of the models with a modern image reconstruction algorithm for performing few-view image reconstruction of a tissue specimen.

More Money, Fewer Problems? Cross-Level Effects of Economic Deprivation on Political Representation

While equal political representation of all citizens is a fundamental democratic goal, it is hampered empirically in a multitude of ways. This study examines how the societal level of economic inequality affects the representation of relatively poor citizens by parties and governments. Using CSES survey data for citizens' policy preferences and expert placements of political parties, empirical evidence is found that in economically more unequal societies, the party system represents the preferences of relatively poor citizens worse than in more equal societies. This moderating effect of economic equality is also found for policy congruence between citizens and governments, albeit slightly less clear-cut.

Falsification and corroboration of conceptual hydrological models using geophysical data

Geophysical data may provide crucial information about hydrological properties, states, and processes that are difficult to obtain by other means. Large data sets can be acquired over widely different scales in a minimally invasive manner and at comparatively low costs, but their effective use in hydrology makes it necessary to understand the fidelity of geophysical models, the assumptions made in their construction, and the links between geophysical and hydrological properties. Geophysics has been applied for groundwater prospecting for almost a century, but it is only in the last 20 years that it is regularly used together with classical hydrological data to build predictive hydrological models. A largely unexplored venue for future work is to use geophysical data to falsify or rank competing conceptual hydrological models. A promising cornerstone for such a model selection strategy is the Bayes factor, but it can only be calculated reliably when considering the main sources of uncertainty throughout the hydrogeophysical parameter estimation process. Most classical geophysical imaging tools tend to favor models with smoothly varying property fields that are at odds with most conceptual hydrological models of interest. It is thus necessary to account for this bias or use alternative approaches in which proposed conceptual models are honored at all steps in the model building process.

In Vivo Longitudinal (1)H MRS Study of Transgenic Mouse Models of Prion Disease in the Hippocampus and Cerebellum at 14.1 T.

In vivo (1)H MR spectroscopy allows the non invasive characterization of brain metabolites and it has been used for studying brain metabolic changes in a wide range of neurodegenerative diseases. The prion diseases form a group of fatal neurodegenerative diseases, also described as transmissible spongiform encephalopathies. The mechanism by which prions elicit brain damage remains unclear and therefore different transgenic mouse models of prion disease were created. We performed an in vivo longitudinal (1)H MR spectroscopy study at 14.1 T with the aim to measure the neurochemical profile of Prnp -/- and PrPΔ32-121 mice in the hippocampus and cerebellum. Using high-field MR spectroscopy we were able to analyze in details the in vivo brain metabolites in Prnp -/- and PrPΔ32-121 mice. An increase of myo-inositol, glutamate and lactate concentrations with a decrease of N-acetylaspartate concentrations were observed providing additional information to the previous measurements.