Journal: Viruses

Article Title: HIV RGB: Automated Single-Cell Analysis of HIV-1 Rev-Dependent RNA Nuclear Export and Translation Using Image Processing in KNIME

doi: 10.3390/v14050903

Figure Lengend Snippet: NR-SAT single-cell segmentation and tracking scheme. (Phase 1) The input nuclear channel is illumination-corrected using background subtraction, followed by local contrast enhancement. The resulting images then undergo thresholding (Mean method) and are dilated and then labeled. An object filter is applied to remove threshold artifacts prior to converting the images into binary images and filling holes. Finally, the data is passed to the Wählby Cell Clump Splitter, which separates nuclei that are in proximity to one another. (Phase 2) These data are then passed to the nuclei tracking nodes where the ImageJ plugin TrackMate is implemented to track each cell, accounting for splitting and merging events. (Phase 3) After the nuclei are tracked, the nuclear mask is duplicated and dilated (number of dilations is user-defined and applied to all images in the same manner, 10× in this example) to generate two masks, where the newly dilated mask is larger than the original source mask. These two masks (the newly larger dilated mask and the smaller original mask) are then segmented via the Voronoi segmentation node, generating cytoplasmic rings that are approximately a fixed pixel-width. (Phase 4) Finally these cytoplasmic rings, along with the nuclear masks, are applied to the measurement channel(s) and written to a CSV output file.

Article Snippet: NR-SAT represents an intuitive open-source method for single-cell tracking, reliable nucleus vs. cytoplasm cell segmentation, and fluorescence intensity measurements using ImageJ plugins [ , ] integrated into the open source KNIME analytics platform [ ].

Techniques: Labeling

Journal: PLoS Computational Biology

Article Title: Biomedical Discovery Acceleration, with Applications to Craniofacial Development

doi: 10.1371/journal.pcbi.1000215

Figure Lengend Snippet: A system diagram describing the modules of the Hanalyzer. Reading methods (green) take external sources of knowledge (blue) and extract information from them, either by parsing structured data or biomedical language processing to extract information from unstructured data. Reading modules are responsible for tracking the provenance of all knowledge. Reasoning methods (yellow) enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. All knowledge sources, read or reasoned, are assigned a reliability score, and all are combined using that score into a knowledge network (orange) that represents the integration of all sorts of relationship between a pair of genes and a combined reliability score. A data network (also orange) is created from experimental results to be analyzed. The reporting modules (pink) integrate the data and knowledge networks, producing visualizations that can be queried with the associated drill-down tool.

Article Snippet: The Hanalyzer, including the experts and the Cytoscape plugin for visualization is available as open source software via SourceForge at hanalyzer.sourceforge.net.

Techniques: