The rate of nondiagnostic US-guided FNAB is heavily dependent on the operator's experience. We estimated that at least 200 procedures need to be performed in order to achieve the levels of diagnostic accuracy reported in the literature. We therefore suggest specific training before operators routinely perform this procedure in clinical practice.

Superfluid Helium, a state of matter existing at low temperatures, shows many remarkable properties. One example is the so called Fountain Effect, where a heater can produce a jet of helium. This converts heat into mechanical motion; a machine with no moving parts, but working only below 2 K. Allen and Jones first demonstrated the effect in 1938, but their work was basically qualitative. We now present data of a quantitative version of the experiment. We have measured the heat supplied, the temperature and the height of the jet produced. We also develop equations, based on the two fluid model of Superfluid Helium, that give a satisfactory fit to the data. The experiment has been performed by advanced undergraduate students in our home institution, and illustrates in a vivid way some of the striking properties of the superfluid state.

Extracting quantitative information of neuronal signals by non-invasive imaging is an outstanding challenge for understanding brain function and pathology. However, state-of-the-art techniques offer low sensitivity to deep electrical sources. Stimulus induced rotary saturation is a recently proposed magnetic resonance imaging sequence that detects oscillatory magnetic fields using a spin-lock preparation. Phantom experiments and simulations proved its efficiency and sensitivity, but the susceptibility of the method to field inhomogeneities is still not well understood. In this study, we simulated and analyzed the dynamic of three spin-lock preparations and their response to field inhomogeneities in the presence of a resonant oscillating field. We show that the composite spin-lock preparation is more robust against field variations within the double resonance effect. In addition, we tested the capability of the chosen composite spin-lock preparation to recover information about the spectral components of a composite signal. This study sets the bases to move one step further towards the clinical application of MR-based neuronal current imaging.

We implemented a mathematical representation of a prior-knowledge model in a Neural Network using a Tensorflow. The trainables tensors are directly the free parameters of the model and we do metabolite quantification by overfitting the output to the signal that we want to replicate. We found that this way of fitting has a relatively low performance in time domain but similar to the state-of-the-art (TDFDFit) when using frequency domain. In addition we have a faster method and it can be used in future works as a component of a more complex network.