Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology.

The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.

White Guinea yam (Dioscorea rotundata Poir.) landraces trait profiling and setting benchmark for breeding programs in the Republic of Benin.

To meet the high demand for white Guinea yam, there is a need to develop and release improved varieties to farmers. Unfortunately, low rate of adoption of most of the improved yam varieties by both producers and consumers was observed. Information regarding agronomic characteristics and food qualities of popular white Guinea yam landraces with high market value are not available to establish minimum standards to be considered by breeding programs. To fill this gap, surveys using rural appraisal tools were carried out in 20 villages and 16 markets throughout Benin. Data on the agronomic performance suggested that for an improved variety to be adopted by Beninese farmers it should have a minimum yield of 4.16 ± 0.15 kg per mound, and average number of marketable tubers of 1.23 ± 0.05, a mean tuber length of 36.41 ± 1.22 cm, and a minimum diameter of 25.44 ± 1.16 cm. The sensorial attributes for boiled and pounded tubers of this improved variety should have minimum score of 3.16 for texture, 0.75 for softness, 3.75 for elasticity, and 1.34 for colour during the sensory evaluation. The improved variety must also have a minimum average severity score of 1.1 for yam mosaic virus disease, 1.33 for anthracnose and 1 for nematodes. Landraces Amoula, Laboko, and Djilaadja should be considered as the standard for yield, sensory attributes, and tolerance to pest and diseases while landraces Danwari, Kodjewe, Mondji, and Gnidou should be characterized as possessing good flowering and fruit setting capacities for breeding programs.

NCI Imaging Data Commons.

The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloud-based data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of deidentified imaging data and to support integrated analyses with nonimaging data. We achieve this goal by colocating versatile imaging collections with cloud-based computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020 and is being continuously populated with radiology and histopathology collections. IDC provides access to curated imaging collections, accompanied by documentation, a user forum, and a growing number of analysis use cases that aim to demonstrate the value of a data commons framework applied to cancer imaging research. SIGNIFICANCE: This study introduces NCI Imaging Data Commons, a new repository of the NCI Cancer Research Data Commons, which will support cancer imaging research on the cloud.