Search

The overall goal of IMSI is to explore and test methods for use of new satellite earth observation data in sea ice monitoring and improve the utilisation of these observations in a wider user community. To reach these objectives IMSI consists of four major tasks: 1. Study of user requirements to define which new satellite products should be developed for use in ice monitoring. 2. Development of new ice products, derived from EO data covering three spatial scales: global scale, regional scale and local scale. The data used are synthetic aperture radar (SAR), passive microwave, scatterometer and side-looking radar (SLR) data. The products are developed for the Northern Sea Route, the Baltic Sea region, the Greenland waters, and for the whole Arctic and Antarctic region. Integrated products, which make use of several types of satellite data, are also developed. 3. User demonstration and product dissemination. The data products developed in Task 2 are disseminated to many users in several countries of Europe where sea ice is relevant. Specific demonstrations are carried out during field expeditions where in situ observations are obtained for validation of the satellite data products. 4. User assessment, evaluation and recommendation. The users who have received satellite data and data products have assessed the usefulness of the information which can be retrieved from satellite data. The benefits for the users have been studied and recommendations have been suggested for future use of satellite data in ice monitoring., NERSC Technical Report no. 170. Funded by the European Union through Contract no. ENV4-CT96-0361

The overall objective of the Ice Routes project is to demonstrate the feasibility of using satellite data in ice charting and routing for safer and more efficient ship transport in ice-covered waters. The specific objectives of the SAR ice charting demonstration and validation activities are: 1. To acquire for the first time a large amount of RADARSAT ScanSAR data (more than 30 scenes) for analysis and interpretation of ice conditions in the NSR 2. To demonstrate Artificial Intelligence technologies in generating high-resolution ice classification charts for ship routing using RADARSAT ScanSAR data 3. To evaluate the benefits of using ScanSAR data in support of ice navigation The study has showed that ice charts from RADARSAT images can be useful both for the Marine Operations Headquarters and onboard icebreakers, but the automatically derived ice charts need to be corrected and improved by an ice expert before they can be used in navigation. Estimates for usage of space-derived high-resolution images (up to 100 m) predict a 150-200% increase of the operating speed during normal ice conditions. The commercial prices of RADARSAT SAR data are currently far too high for implementation of these data in the Russian ice services. Only commercial users such as oil and shipping companies are expected to use SAR data in the NSR in the near future. Unless there is considerable new funding available, it is not realistic that RADARSAT or other high-cost SAR data can be used on regular basis in the Northern Sea Route., NERSC Technical report no. 150. Funded by the European Union, Project no. WA-96-AM-1136

Hetnets, short for "heterogeneous networks", contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes - including genes, diseases, drugs, pathways, and anatomical structures - with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the GJA1 gene might be involved in insomnia. We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any two nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). We find that predictions are broadly similar to those from previously described supervised approaches for certain node type pairs. Scoring of individual paths is based on the most specific paths of a given type. Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. We implemented the method on Hetionet and provide an online interface at https://het.io/search . We provide an open source implementation of these methods in our new Python package named hetmatpy .

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Degree's predictive performance diminishes when the networks used for training and testing-despite measuring the same biological relationships-were generated using distinct techniques and hence have large differences in degree distribution. We introduce the permutation-derived edge prior as the probability that an edge exists based only on degree. The edge prior shows excellent discrimination and calibration for 20 biomedical networks (16 bipartite, 3 undirected, 1 directed), with AUROCs frequently exceeding 0.85. Researchers seeking to predict new or missing edges in biological networks should use the edge prior as a baseline to identify the fraction of performance that is nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).