Tuning Yu-Shiba-Rusinov States in a Quantum Dot

We present transport spectroscopy of sub-gap states in a bottom gated InAs nanowire coupled to a normal lead and a superconducting aluminium lead. The device shows clearly resolved sub-gap states which we can track as the coupling parameters of the system are tuned and as the gap is closed by means of a magnetic field. We systematically extract system parameters by using numerical renormalization group theory fits as a level of the quantum dot is tuned through a quantum phase transition electrostatically and magnetically. We also give an intuitive description of sub-gap excitations.Comment: 9 pages, 10 figure

The role of aquaporins in the kidney of euryhaline teleosts

Water balance in teleost fish is maintained with contributions from the major osmoregulatory organs: intestine, gills and kidney. Overall water fluxes have been studied in all of these organs but not until recently has it become possible to approach the mechanisms of water transport at the molecular level. This mini-review addresses the role of the kidney in osmoregulation with special emphasis on euryhaline teleosts. After a short review of current knowledge of renal functional morphology and regulation, we turn the focus to recent molecular investigations of the role of aquaporins in water and solute transport in the teleost kidney. We conclude that there is much to be achieved in understanding water transport and its regulation in the teleost kidney and that effort should be put into systematic mapping of aquaporins to their tubular as well as cellular localization

Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data

Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a non-parametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find that the number of states extracted are driven by subject variability and preprocessing differences while the individual states are almost purely defined by either task or rest. This questions how we in general interpret dynamic FC and points to the need for more research on what drives dynamic FC.Comment: 8 pages, 1 figure. Presented at the Machine Learning and Interpretation in Neuroimaging Workshop (MLINI-2015), 2015 (arXiv:1605.04435