© 2015 IEEE. A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. On a set of 30 GB fusion simulation data, we observed linear speedup on 1,024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Throughout 2021, ESnet and the Office of Fusion Energy Sciences (FES) of the DOE SC organized an ESnet requirements review of FES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about their relationship to the FES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

The stem cells that maintain and repair the postnatal skeleton remain undefined. One model suggests that perisinusoidal mesenchymal stem cells (MSCs) give rise to osteoblasts, chondrocytes, marrow stromal cells, and adipocytes, although the existence of these cells has not been proven through fate-mapping experiments. We demonstrate here that expression of the bone morphogenetic protein (BMP) antagonist gremlin 1 defines a population of osteochondroreticular (OCR) stem cells in the bone marrow. OCR stem cells self-renew and generate osteoblasts, chondrocytes, and reticular marrow stromal cells, but not adipocytes. OCR stem cells are concentrated within the metaphysis of long bones not in the perisinusoidal space and are needed for bone development, bone remodeling, and fracture repair. Grem1 expression also identifies intestinal reticular stem cells (iRSCs) that are cells of origin for the periepithelial intestinal mesenchymal sheath. Grem1 expression identifies distinct connective tissue stem cells in both the bone (OCR stem cells) and the intestine (iRSCs).