Abstract: Connectomes of human cortical gray matter require high-contrast homogeneously stained samples sized at least 2-3 mm on a side, and a whole-mouse brain connectome requires samples sized at least 5-10 mm on a side. Here, en-bloc staining and postprocessing protocols are reported, including dehydrating and embedding of neuronal samples, for dense neuronal circuit reconstruction and other applications.

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.