Use of Indocyanine Green Fluorescence Angiography and Prophylactic Endoscopic Stenting in Esophagectomy for Acute Esophageal Necrosis.

Acute esophageal necrosis (AEN) is a rare condition with high mortality. We describe a case of an AEN accompanied with multiple esophageal perforations in a 40-year-old patient. This was addressed via emergency Ivor-Lewis-esophagectomy with intraoperative Indocyanine-Green-Angiography (ICGA) and prophylactic placement of a self-expanding metal-stent (SEMS).

Computational exploration of molecular receptive fields in the olfactory bulb reveals a glomerulus-centric chemical map.

Progress in olfactory research is currently hampered by incomplete knowledge about chemical receptive ranges of primary receptors. Moreover, the chemical logic underlying the arrangement of computational units in the olfactory bulb has still not been resolved. We undertook a large-scale approach at characterising molecular receptive ranges (MRRs) of glomeruli in the dorsal olfactory bulb (dOB) innervated by the MOR18-2 olfactory receptor, also known as Olfr78, with human ortholog OR51E2.

Decoding odor quality and intensity in the Drosophila brain.

To internally reflect the sensory environment, animals create neural maps encoding the external stimulus space. From that primary neural code relevant information has to be extracted for accurate navigation. We analyzed how different odor features such as hedonic valence and intensity are functionally integrated in the lateral horn (LH) of the vinegar fly, Drosophila melanogaster.

Automatic segmentation of odor maps in the mouse olfactory bulb using regularized non-negative matrix factorization.

Segmentation of functional parts in image series of functional activity is a common problem in neuroscience. Here we apply regularized non-negative matrix factorization (rNMF) to extract glomeruli in intrinsic optical signal (IOS) images of the olfactory bulb. Regularization allows us to incorporate prior knowledge about the spatio-temporal characteristics of glomerular signals.

Physicochemical vs. Vibrational Descriptors for Prediction of Odor Receptor Responses.

Responses of olfactory receptors (ORs) can be predicted by applying machine learning methods on a multivariate encoding of an odorant's chemical structure. Physicochemical descriptors that encode features of the molecular graph are a popular choice for such an encoding. Here, we explore the EVA descriptor set, which encodes features derived from the vibrational spectrum of a molecule.