webKnossos: efficient online 3D data annotation for connectomics.

We report webKnossos, an in-browser annotation tool for 3D electron microscopic data. webKnossos provides flight mode, a single-view egocentric reconstruction method enabling trained annotator crowds to reconstruct at a speed of 1.5 ± 0.6 mm/h for axons and 2.1 ± 0.9 mm/h for dendrites in 3D electron microscopic data from mammalian cortex. webKnossos accelerates neurite reconstruction for connectomics by 4- to 13-fold compared with current state-of-the-art tools, thus extending the range of connectomes that can realistically be mapped in the future.

Enzyme pretreatment of fats, oil and grease from restaurant waste to prolong septic soil treatment system effectiveness.

When a fast-food restaurant's wastewater containing fats, oil and grease (FOG) is discharged into a collection system, it builds up over time and clogs pipes. Similarly, when such wastewater flows into a septic soil treatment system, it adheres to the surface of inlet pipes, gravel/distribution media and soil, restricting the flow and eventually clogging the septic soil treatment system. In this study, an enzymatic pretreatment system was tested on wastewater from a fast-food restaurant to determine its effectiveness in preventing septic soil treatment system clogging. This system used aeration equipment, baffles and a one-time inoculum that excretes enzymes to reduce the molecular weight and number of double bonds associated with FOG. FOG containing triglycerides having lower molecular weights and fewer double bonds are less sticky. The enzymatic pretreatment system was found to cause these changes as verified by measuring the types of triglycerides (compounds in FOG) using liquid chromatography/mass spectrometry. A unique bench-scale septic soil treatment system (soil trench) was also used. Each contained six soil moisture sensors to enable the determination of moisture saturation trends among the five tested conditions: sanitary wastewater only, a combination of sanitary and kitchen wastewater, enzymatically pretreated sanitary and kitchen wastewater, kitchen wastewater, and enzymatically pretreated kitchen wastewater. For all influent types, a significant amount of FOG and other pollutants were removed, regardless of the initial concentrations. Moisture sensor readings showed differences among the tested conditions, indicating that septic soil treatment system clogging was delayed. Inspection of the influent pipe and gravel at the end of testing verified these differences as did the measurements of volatile solids.

Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.

Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on manual tracing by trained experts, which is labor-intensive, time-consuming, and prone to inter- and intra-rater variability. Here, we present a fully automated ischemic stroke lesion segmentation method for mouse T2-weighted MRI data. As an end-to-end deep learning approach, the automated lesion segmentation requires very little preprocessing and works directly on the raw MRI scans. We randomly split a large dataset of 382 MRI scans into a subset (n = 293) to train the automated lesion segmentation and a subset (n = 89) to evaluate its performance. We compared Dice coefficients and accuracy of lesion volume against manual segmentation, as well as its performance on an independent dataset from an open repository with different imaging characteristics. The automated lesion segmentation produced segmentation masks with a smooth, compact, and realistic appearance that are in high agreement with manual segmentation. We report dice scores higher than the agreement between two human raters reported in previous studies, highlighting the ability to remove individual human bias and standardize the process across research studies and centers.