Exosomes, which are nanosized endocytic vesicles that are secreted by most cells, contain an abundant cargo of different RNA species that can modulate the behavior of recipient cells and may be used as circulating biomarkers for diseases. Here, we develop a web-accessible database (http://www.exoRBase.org), exoRBase, which is a repository of circular RNA (circRNA), long non-coding RNA (lncRNA) and messenger RNA (mRNA) derived from RNA-seq data analyses of human blood exosomes. Experimental validations from the published literature are also included. exoRBase features the integration and visualization of RNA expression profiles based on normalized RNA-seq data spanning both normal individuals and patients with different diseases. exoRBase aims to collect and characterize all long RNA species in human blood exosomes. The first release of exoRBase contains 58 330 circRNAs, 15 501 lncRNAs and 18 333 mRNAs. The annotation, expression level and possible original tissues are provided. exoRBase will aid researchers in identifying molecular signatures in blood exosomes and will trigger new exosomal biomarker discovery and functional implication for human diseases.

A better understanding of global ruminal microbiota and metabolites under extensive feeding conditions is a prerequisite for optimizing rumen function and improving ruminant feed efficiency. Furthermore, the gap between the information on the ruminal microbiota and metabolites needs to be bridged. The aim of this study was to investigate the effects of a wide range of forage to concentrate ratios (F:C) on changes and interactions of ruminal microbiota and metabolites. Four diets with different F:C (80:20, 60:40, 40:60, and 20:80) were limit-fed to 24 Holstein heifers, and Illumina MiSeq sequencing and gas chromatography time-of-flight/mass spectrometry were used to investigate the profile changes of the ruminal microbes and metabolites, and the interaction between them. The predominant bacterial phyla in the rumen were Bacteroidetes (57.2 ± 2.6%) and Firmicutes (26.8 ± 1.6%), and the predominant anaerobic fungi were Neocallimastigomycota (64.3 ± 3.8%) and Ascomycota (22.6 ± 2.4%). In total, 44, 9, 25, and 2 genera, respectively, were identified as the core rumen bacteria, ciliate protozoa, anaerobic fungi, and archaea communities across all samples. An increased concentrate level linearly decreased the relative abundance of cellulolytic bacteria and ciliates, namely Fibrobacter, Succinimonas, Polyplastron, and Ostracodinium (q < 0.05), and linearly increased the relative abundance of Entodinium (q = 0.04), which is a non-fibrous carbohydrate degrader. Dietary F:C had no effect on the communities of anaerobic fungi and archaea. Rumen metabolomics analysis revealed that ruminal amino acids, lipids, organic acids, and carbohydrates were altered significantly by altering the dietary F:C. With increasing dietary concentrate levels, the proportions of propionate and butyrate linearly increased in the rumen (P ≤ 0.01). Correlation analysis revealed that there was some utilization relationship or productive association between candidate metabolites and affected microbe groups. This study provides a better understanding of ruminal microbiota and metabolites under a wide range of dietary F:C, which could further reveal integrative information of rumen function and lead to an improvement in ruminant production.

Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with previous works based on low-level features, our approach is able to represent the complicated appearance of the FASP and hence achieve better classification performance. More importantly, in order to reduce the overfitting problem caused by the small amount of training samples, we propose a transfer learning strategy, which transfers the knowledge in the low layers of a base CNN trained from a large database of natural images to our task-specific CNN. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method for the FASP localization as well as the CNN only trained on the limited US training samples. The proposed approach can be easily extended to other similar medical image computing problems, which often suffer from the insufficient training samples when exploiting the deep CNN to represent high-level features.

Recurrent chromosomal aberrations have led to the discovery of oncogenes or tumour suppressors involved in carcinogenesis. Here we characterized an oncogenic long intergenic non-coding RNA in the frequent DNA-gain regions in hepatocellular carcinoma (HCC), LINC01138 (long intergenic non-coding RNA located on 1q21.2). The LINC01138 locus is frequently amplified in HCC; the LINC01138 transcript is stabilized by insulin like growth factor-2 mRNA-binding proteins 1/3 (IGF2BP1/IGF2BP3) and is associated with the malignant features and poor outcomes of HCC patients. LINC01138 acts as an oncogenic driver that promotes cell proliferation, tumorigenicity, tumour invasion and metastasis by physically interacting with arginine methyltransferase 5 (PRMT5) and enhancing its protein stability by blocking ubiquitin/proteasome-dependent degradation in HCC. The discovery of LINC01138, a promising prognostic indicator, provides insight into the molecular pathogenesis of HCC, and the LINC01138/PRMT5 axis is an ideal therapeutic target for HCC treatment.

TSLNC8 is a promising prognostic predictor for patients with HCC, and the TSLNC8-transketolase-STAT3 axis is a potential therapeutic target for HCC treatment. (Hepatology 2018;67:171-187).