A major bottleneck in limiting the application of the existing methods of ship classification in synthetic aperture radar (SAR) images is the inadequate amount of labeled data available for training a classifier. However, generating ground truth involves expensive and time-consuming ground campaigns or is costly, since a high number of SAR image acquisition will be necessary. In contrast, an automatic identification system (AIS), which is an automatic tracking system used for monitoring maritime ships, can provide plenty of labeled ship samples that is relatively easier to be obtained. Inspired by these facts, this letter proposes to improve ship classification in SAR images by transferring AIS knowledge. We propose an improved multiclass adaptive support vector machine, combined with the naive geometric features (NGFs), to achieve transfer learning between the AIS domain and the SAR image domain. The experiments prove that the traditional method can be significantly improved by AIS information transfer, especially when only a few training samples in the SAR domain are available. In addition, it also shows that after feature selection, the performance of the proposed method can be close to that of the state of the art, even if by only using simpler NGFs and few training samples.

Circular RNA (circRNA) is a novel class of non-coding RNA generated by pre-mRNA back splicing, which is characterized by a closed-loop structure. Although circRNAs were firstly reported decades ago, their regulatory roles have not been discovered until recently. In this review, we discussed the putative biogenesis pathways and regulatory functions of circRNAs. Recent studies showed that circRNAs are abundant in skeletal muscle tissue, and their expression levels are regulated during muscle development and aging. We, thus, characterized the expression profile of circRNAs in skeletal muscle and discussed regulatory functions and mechanism-of-action of specific circRNAs in myogenesis. The future investigation into the roles of circRNAs in both physiological and pathological conditions may provide novel insights in skeletal muscle development and provide new therapeutic strategies for muscular diseases.

The emerging data indicates that non-coding RNAs (ncRNAs) epresent more than the “junk sequences” of the genome. Both miRNAs and long non-coding RNAs (lncRNAs) are involved in fundamental biological processes, and their deregulation may lead to oncogenesis and other diseases. As an important RNA-binding protein (RBP), heterogeneous nuclear ribonucleoprotein K (hnRNPK) is known to regulate gene expression through the RNA-binding domain involved in various pathways, such as transcription, splicing, and translation. HnRNPK is a highly conserved gene that is abundantly expressed in mammalian cells. The interaction of hnRNPK and ncRNAs defines the novel way through which ncRNAs affect the expression of protein-coding genes and form autoregulatory feedback loops. This review summarizes the interactions of hnRNPK and ncRNAs in regulating gene expression at transcriptional and post-transcriptional levels or by changing the genomic structure, highlighting their involvement in carcinogenesis, glucose metabolism, stem cell differentiation, virus infection and other cellular functions. Drawing connections between such discoveries might provide novel targets to control the biological outputs of cells in response to different stimuli.

There are still many challenges to be resolved in the task of ship classification in synthetic aperture radar (SAR) images, such as limited number of labeled samples in SAR domain, large variance in the same subcategory, small variance among different subcategories, etc. Transfer metric learning (TML) has the potential to mitigate those issues in the domain of interest (target domain, TD) by leveraging knowledge/information from other related domains (source domain, SD). In this article, we proposed a novel TML method, termed as geometric transfer metric learning (GTML), which achieves discriminative information preservation (DIP), geometric structure preservation (GSP), and handles the domain shift (DS) simultaneously by integrating pairwise constraints (PC), joint distribution adaptation (JDA), and manifold regularization (MR) into a unified optimization function, aiming to make full use of their complementarity to improve SAR ship classification performance. In practice, we proposed two simple but effective optimization strategies, termed as GTML-A and GTML-R, to construct optimization function. We also proposed two solutions for two typical real-world application scenarios, that is, the task of: 1) zero-labeled sample (ZLS) and 2) scarce-labeled samples (SLS) in SAR domain. The experiments conducted on both tasks show that the proposed GTML outperforms most of state-of-the-art methods. Code is available at https://github.com/sky-Yongjie-Xu/geometric-transfer-metric-learning.

Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful properties of the original data. It is used in various applications, such as image recognition, single-cell sequencing analysis, and biomarker discovery. However, contemporary parametric-free and parametric DR techniques suffer from several significant shortcomings, such as the inability to preserve global and local features and the poor generalisation performance. On the other hand, regarding explainability, it is crucial to comprehend the embedding process, especially the contribution of each part to the embedding process, while understanding how each feature affects the embedding results that identify critical components and help diagnose the embedding process. To address these problems, we have developed a deep neural network method called DMT-EV, which provides not only excellent performance in structural maintainability but also explainability to the DR therein. DMT-EV starts with data augmentation and a manifold-based loss function to improve embedding performance. The explanation is based on saliency maps and aims to examine the trained DMT-EV parameters and contributions of components during the embedding process. The proposed techniques are integrated with a visual interface to help the user to adjust DMT-EV to achieve better DR performance and explainability. The interactive visual interface makes it easier to illustrate the data features, compare different DR techniques, and investigate DR. An in-depth experimental comparison shows that DMT-EV consistently outperforms the state-of-the-art methods in both performance measures and explainability.

Recent years, the role of the space-borne synthetic aperture radar (SAR) in maritime ship monitoring has become widely accepted. The increase of the number of moderate- to high-resolution SAR images with the advent of the new generation of satellite missions makes it possible to further identify the type of ship (i.e. ship classification) beyond normally provide geographic location of the ship target (i.e. ship detection). However, ship classification in SAR images is by no means a simple task. It remains understudied and still has many challenges to be resolved, such as limited discriminative information provided by SAR images, large variance in the same subcategory and small variance among different subcategories, etc. In this paper, we proposed a novel DML method, termed as distribution shift metric learning (DML-ds), which improves the original Laplacian regularized metric learning (LRML) by adding an inter-class distribution shift (ICDS) regularization term. Extensive experiments and in-depth analysis demonstrate that the proposed DML-ds can effectively increase the inter-class separability and the intra-class compactness, thereby improving the fine-grained ship classification performance in SAR images, and outperforms most of state-of-the-art methods.