Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models.

Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is much more strenuous than in 2D images. For 4D time-series tomograms, this is usually handled by segmenting the constituent tomograms independently through time with 3D convolutional neural networks.

Positioning the Root Elongation Zone Is Saltatory and Receives Input from the Shoot.

In the root, meristem and elongation zone lengths remain stable, despite growth and division of cells. To gain insight into zone stability, we imaged individual Arabidopsis thaliana roots through a horizontal microscope and used image analysis to obtain velocity profiles. For a root, velocity profiles obtained every 5 min over 3 h coincided closely, implying that zonation is regulated tightly.

Specifically Sized Hyaluronan (35 kDa) Prevents Ethanol-Induced Disruption of Epithelial Tight Junctions Through a layilin-Dependent Mechanism in Caco-2 Cells.

Background: Specific-sized species of the carbohydrate hyaluronan elicit a variety of cellular responses mediating tissue integrity and repair, as well as regulating inflammatory responses. Orally provided hyaluronan with an average molecular weight of 35 kDa (HA35) protects mice from short-term ethanol (EtOH)-induced liver injury. This protection was associated with maintenance of the colocalization of zonula occludens-1 (ZO-1) and occludin at tight junctions in the proximal colon.

A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram.

X-ray computed tomography and, specifically, time-resolved volumetric tomography data collections (4D datasets) routinely produce terabytes of data, which need to be effectively processed after capture. This is often complicated due to the high rate of data collection required to capture at sufficient time-resolution events of interest in a time-series, compelling the researchers to perform data collection with a low number of projections for each tomogram in order to achieve the desired `frame rate'. It is common practice to collect a representative tomogram with many projections, after or before the time-critical portion of the experiment without detrimentally affecting the time-series to aid the analysis process.