We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance. machine learning | image analysis | software

SUMMARY Dietary potassium deficiency, common in Western diets, raises blood pressure and enhances salt sensitivity. Potassium homeostasis requires a molecular switch in the distal convoluted tubule (DCT), which fails in familial hyperkalemic hypertension (pseudohypoaldosteronism type 2), activating the thiazide-sensitive NaCl cotransporter, NCC. Here, we show that dietary potassium deficiency activates NCC, even in the setting of high salt intake, thereby causing sodium retention and a rise in blood pressure. The effect is dependent on plasma potassium, which modulates DCT cell membrane voltage and, in turn, intracellular chloride. Low intracellular chloride stimulates WNK kinases to activate NCC, limiting potassium losses, even at the expense of increased blood pressure. These data show that DCT cells, like adrenal cells, sense potassium via membrane voltage. In the DCT, hyperpolarization activates NCC via WNK kinases, whereas in the adrenal gland, it inhibits aldosterone secretion. These effects work in concert to maintain potassium homeostasis.

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