@article{21955,
  abstract     = {AgRP neurons cause hunger, the drive to seek and consume food. Their activation by fasting is key for survival and is thought to be triggered by feedback when energy stores are low. However, we know that environmental cues can also regulate AgRP neurons since cues that predict future food intake rapidly inhibit AgRP neurons, but is the converse true: can the prediction of future fasting rapidly activate AgRP neurons? Here, we show in mice that such rapid fasting activation of AgRP neurons does occur. This rapid activation is driven by excitatory input from paraventricular hypothalamic (PVH) neurons expressing Sim2, which are bidirectionally sensitive to predictions of future energy state. Thus, cognitively processed contextual information conveyed by PVHSim2 neurons strongly activates AgRP neurons. Lastly, chronic silencing of PVHSim2 neurons causes persistent hypophagia. This PVHSim2-to-AgRP-neuron circuit, by anticipating and preventing negative energy balance, provides an important new dimension of hunger regulation.},
  author       = {Walker, Samuel J. and Lowenstein, Elijah D. and Douglass, Amelia May Barnett and Thomas, Callum M.P. and Madara, Joseph C. and Kucukdereli, Hakan and Barbosa-Meillon, Eunice A. and Tao, Jenkang and Resch, Jon M. and Lowell, Bradford B.},
  issn         = { 1097-4199},
  journal      = {Neuron},
  keywords     = {hunger, hypothalamus, AGRP neurons, neuroscience, metabolism, homeostasis, feeding, food intake, energy balance, appetite},
  publisher    = {Elsevier},
  title        = {{A hypothalamic circuit for anticipating future changes in energy balance}},
  doi          = {10.1016/j.neuron.2026.05.010},
  year         = {2026},
}

@inproceedings{9943,
  abstract     = {Segmentation is the process of partitioning digital images into meaningful regions. The analysis of biological high content images often requires segmentation as a first step. We propose ilastik as an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way. ilastik learns from labels provided by the user through a convenient mouse interface. Based on these labels, ilastik infers a problem specific segmentation. A random forest classifier is used in the learning step, in which each pixel's neighborhood is characterized by a set of generic (nonlinear) features. ilastik supports up to three spatial plus one spectral dimension and makes use of all dimensions in the feature calculation. ilastik provides realtime feedback that enables the user to interactively refine the segmentation result and hence further fine-tune the classifier. An uncertainty measure guides the user to ambiguous regions in the images. Real time performance is achieved by multi-threading which fully exploits the capabilities of modern multi-core machines. Once a classifier has been trained on a set of representative images, it can be exported and used to automatically process a very large number of images (e.g. using the CellProfiler pipeline). ilastik is an open source project and released under the BSD license at www.ilastik.org.},
  author       = {Sommer, Christoph M and Straehle, Christoph and Köthe, Ullrich and Hamprecht, Fred A.},
  booktitle    = {2011 IEEE International Symposium on Biomedical Imaging: from Nano to Micro},
  isbn         = {978-1-4244-4127-3},
  issn         = {1945-8452},
  keywords     = {image segmentation, biomedical imaging, three dimensional displays, neurons, retina, observers, image color analysis},
  location     = {Chicago, Illinois, USA},
  publisher    = {Institute of Electrical and Electronics Engineers},
  title        = {{Ilastik: Interactive learning and segmentation toolkit}},
  doi          = {10.1109/isbi.2011.5872394},
  year         = {2011},
}