Abstract: The invention is based on a class of dual modulators of soluble epoxide hydrolase (sEH) and farnesoid X receptor (FXR), in particular of compounds having an activity as FXR agonist and sEH inhibitor for the treatment or prevention of kidney diseases and/or fibrotic diseases. The invention provides the compounds for use in such treatments and preventions as well as pharmaceutical compositions comprising the compounds as active ingredients.

Abstract: The present invention pertains to novel dual modulators of farnesoid X receptor (FXR) and soluble epoxide hydrolase (sEH). The modulators of the invention were designed to provide compounds which harbor a dual activity as agonists of FXR and inhibitors (antagonists) of sEH. The invention also provides methods for treating subjects suffering from diseases associated with FXR and sEH, such as metabolic disorders, in particular non-alcoholic fatty liver or nonalcoholic steatohepatitis (NASH).

Abstract: Identifying objects in images is a difficult problem, particularly in cases an original image is noisy or has areas narrow in color or grayscale gradient. A technique employing a convolutional network has been identified to identify objects in such images in an automated and rapid manner. One example embodiment trains a convolutional network including multiple layers of filters. The filters are trained by learning and are arranged in successive layers and produce images having at least a same resolution as an original image. The filters are trained as a function of the original image or a desired image labeling; the image labels of objects identified in the original image are reported and may be used for segmentation. The technique can be applied to images of neural circuitry or electron microscopy, for example. The same technique can also be applied to correction of photographs or videos.

Abstract: Identifying objects in images is a difficult problem, particularly in cases an original image is noisy or has areas narrow in color or grayscale gradient. A technique employing a convolutional network has been identified to identify objects in such images in an automated and rapid manner. One example embodiment trains a convolutional network including multiple layers of filters. The filters are trained by learning and are arranged in successive layers and produce images having at least a same resolution as an original image. The filters are trained as a function of the original image or a desired image labeling; the image labels of objects identified in the original image are reported and may be used for segmentation. The technique can be applied to images of neural circuitry or electron microscopy, for example. The same technique can also be applied to correction of photographs or videos.