An embedding is a function that maps entities from one algebraic structure into another while preserving certain characteristics. Embeddings are being used successfully for mapping relational data or text into vector spaces where they can be used for machine learning, similarity search, or similar tasks. We address the problem of finding vector space embeddings for theories in the Description Logic EL ++ that are also models of the TBox. To find such embeddings, we define an optimization problem that characterizes the model-theoretic semantics of the operators in EL ++ within R n , thereby solving the problem of finding an interpretation function for an EL ++ theory given a particular domain ∆. Our approach is mainly relevant to large EL ++ theories and knowledge bases such as the ontologies and knowledge graphs used in the life sciences. We demonstrate that our method can be used for improved prediction of protein-protein interactions when compared to semantic similarity measures or knowledge graph embeddings.

Motivation Infectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. Results We developed DeepViral, a deep learning based method that predicts protein–protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Availability Code and datasets for reproduction and customization are available at https://github.com/bio-ontology-research-group/DeepViral. Prediction results for 14 virus families are available at https://doi.org/10.5281/zenodo.4429824.

Background/Aims: Previous studies have suggested that myricetin (Myr) could promote the expression and nuclear translocation of nuclear factor (erythroid-derived 2)-like (Nrf2). This study aimed to investigate whether Myr could attenuate diabetes-associated kidney injuries and dysfunction in wild-type (WT) and Nrf2 knockdown (Nrf2-KD) mice. Methods: Lentivirus-mediated Nrf2-KD and WT mice were used to establish type 1 diabetes mellitus (DM) by streptozotocin (STZ) injection. WT and Nrf2-KD mice were then randomly allocated into four groups: control (CON), Myr, STZ, and STZ + Myr. Myr (100 mg/kg/day) or vehicle was administered for 6 months. Kidneys were harvested and weighed at the end of the experiment. Hematoxylin and eosin staining and Masson’s trichrome staining were used to assess the morphology and fibrosis of the kidneys, respectively. Urinary albumin-to-creatinine ratio was used to test renal function. Western blotting was performed to determine oxidative-stress- or inflammation-associated signaling pathways. Real-time polymerase chain reaction (RT-PCR) was performed to detect the expression of fibrosis or inflammatory cytokines at the message Ribonucleic Acid (mRNA) level. Results: In WT mice, Myr alleviated DM-induced renal dysfunction, fibrosis, and oxidative damage and enhanced the expression of Nrf2 and its downstream genes. After knockdown of Nrf2, Myr treatment partially but significantly mitigated DM-induced renal dysfunction and fibrosis, which might be associated with inhibition of the I-kappa-B (IκB)/nuclear factor-κB (NF-κB) (P65) signaling pathway. Conclusions: This study showed that Myr prevented DM-associated decreased expression of Nrf2 and inhibited IκB/NF-κB (P65) signaling pathway. Moreover, inhibition of IκB/NF-κB (P65) signaling pathway is independent of the regulation of Nrf2. Thus, Myr could be a potential treatment for preventing the development and progression of DM-associated kidney injuries and dysfunction.

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Motivation: Direct RNA sequencing (dRNA-seq) on the Oxford Nanopore Technology platforms has become increasingly popular in recent years, with promising outlook to transform the field of epitranscriptomics. Reads produced from dRNA-seq can cover up to full-length gene transcripts while containing decipherable information about RNA base modifications and poly-A tail lengths. Although many studies have been published exploring and expanding the potential of dRNA-seq, the sequencing accuracy and error patterns remain understudied and less characterized compared to DNA sequencing. Results: We evaluated the sequencing accuracy and characterized the systematic error patterns of dRNA-seq on public datasets including native RNA samples from diverse organisms, as well as synthetic in vitro transcribed RNAs. The median read accuracy is about 90% for most organisms, although some species are more challenging. Deletions account for the majority of errors and are twice as common as mismatches or insertions. Apart from the well-known homopolymer sequencing errors, there are systematic biases across all organisms at both single nucleotide and 2-mer motif level. In general, cytosines and uracils are more likely to be erroneous than guanines and adenines. Moreover, the systematic errors are found to be strongly dependent on the local sequence contexts, with complex interactions between adjacent positions. We further evaluated the accuracy of sequencing homopolymers, read quality scores as an estimate of error rates, and the consequences of failing to detect the DNA adaptors. Lastly, we discuss the relevance of such error patterns for the downstream applications of dRNA-seq data, such as transcript identification and base modification detection.