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Effective feature representations play a decisive role in content-based remote sensing image retrieval (CBRSIR). Recently, learning-based features have been widely used in CBRSIR and they show powerful ability of feature representations. In addition, a significant effort has been made to improve learning-based features from the perspective of the network structure. However, these learning-based features are not sufficiently discriminative for CBRSIR. In this paper, we propose two effective schemes for generating discriminative features for CBRSIR. In the first scheme, the attention mechanism and a new attention module are introduced to the Convolutional Neural Networks (CNNs) structure, causing more attention towards salient features, and the suppression of other features. In the second scheme, a multi-task learning network structure is proposed, to force learning-based features to be more discriminative, with inter-class dispersion and intra-class compaction, through penalizing the distances between the feature representations and their corresponding class centers. Then, a new method for constructing more challenging datasets is first used for remote sensing image retrieval, to better validate our schemes. Extensive experiments on challenging datasets are conducted to evaluate the effectiveness of our two schemes, and the comparison of the results demonstrate that our proposed schemes, especially the fusion of the two schemes, can improve the baseline methods by a significant margin.

A synapse is the junction across which a nerve impulse passes from an axon terminal to a neuron, muscle cell or gland cell. The functions and building molecules of the synapse are essential to almost all neurobiological processes. To describe synaptic structures and functions, we have developed Synapse Ontology (SynO), a hierarchical representation that includes 177 terms with hundreds of synonyms and branches up to eight levels deep. associated 125 additional protein keywords and 109 InterPro domains with these SynO terms. Using a combination of automated keyword searches, domain searches and manual curation, we collected 14 000 non-redundant synapse-related proteins, including 3000 in human. We extensively annotated the proteins with information about sequence, structure, function, expression, pathways, interactions and disease associations and with hyperlinks to external databases. The data are stored and presented in the Synapse protein DataBase (SynDB, ). SynDB can be interactively browsed by SynO, Gene Ontology (GO), domain families, species, chromosomal locations or Tribe-MCL clusters. It can also be searched by text (including Boolean operators) or by sequence similarity. SynDB is the most comprehensive database to date for synaptic proteins.

The Advanced Space-based Solar Observatory (ASO-S) is a mission proposed for the 25th solar maximum by the Chinese solar community. The scientific objectives are to study the relationships between the solar magnetic field, solar flares and coronal mass ejections (CMEs). Three payloads are deployed: the Full-disk vector MagnetoGraph (FMG), the Lyman-α Solar Telescope (LST) and the Hard X-ray Imager (HXI). ASO-S will perform the first simultaneous observations of the photospheric vector magnetic field, non-thermal imaging of solar flares, and the initiation and early propagation of CMEs on a single platform. ASO-S is scheduled to be launched into a 720 km Sun-synchronous orbit in 2022. This paper presents an overview of the mission till the end of Phase-B and the beginning of Phase-C.