Auroras are beautiful phenomena and attract many people. However, its physical model still remains a subject of dispute because it is caused by the interaction of diverse areas, such as solar wind, magnetosphere, and ionosphere, and it is difficult to simultaneously obtain data in such wide areas. This paper is devoted to forecasting the onset of brightening of auroras followed by poleward expansion, called auroral substorms. We adopt a data-driven approach, instead of physical models of auroras. This approach requires labeled data, which shows when auroras appeared. However, this is challenging because there exist a wide variety of observed data from diverse areas while they are not tied with onset time of auroras. We identified auroral substorms using all-sky images obtained at Tromso, Norway. Then, we chose solar wind and geomagnetic field data as the first attempt toward the goal, out of many types of data, and associated them with the onset times of the identified auroral substorms. We trained a classifier of the support vector machine, which is a typical supervised learning algorithm, using the constructed data, and the classifier achieves around 78% classification accuracy at 5-fold cross validation.

MOTIVATION: Genome projects have produced large amounts of data on the sequences of new genes whose functions are as yet unknown. The functions of new genes are usually inferred by comparing their sequences with those of known genes, but evaluation of the sequence homology of individual genes does not make the most of the available sequence information. Therefore, new methods and tools for extracting more biological information from homology searches would be advantageous. RESULTS: We have developed a computational tool, ORI-GENE, to analyze the results of sequence homology searches from the perspective of the evolution of selected sets of new genes. ORI-GENE has a graphical interface and accomplishes two important tasks: first, based on the output of homology searches, it identifies species with similar genes and displays their pattern of distribution on the phylogenetic tree. This function enables one to infer the way in which a given gene may have propagated among species over time. Second, from the distribution patterns, it predicts the point at which a given gene may have been first acquired (i.e. its 'origin'), then classifies the gene on that basis. Because it makes use of available evolutionary information to show the way in which genes cluster among species, ORI-GENE should be an effective tool for the screening and classification of new genes revealed by genome analysis. AVAILABILITY: ORI-GENE is retrievable via the Internet at: http://www.rtc.riken.go.jp/jouhou/ORI-GENE.