Summary The discovery of RNAs (e.g. mRNAs, non-coding RNAs) in sperm has opened the possibility that sperm may function in delivering additional paternal information aside from solely providing the DNA1. Increasing evidence now suggests that sperm small non-coding RNAs (sncRNAs) can mediate intergenerational transmission of paternally acquired phenotypes, including mental stress2, 3 and metabolic disorders4–6. How sperm sncRNAs encode paternal information remains unclear, but the mechanism may involve RNA modifications. Here we show that deletion of a mouse tRNA methyltransferase, DNMT2, abolished sperm sncRNA-mediated transmission of high-fat diet (HFD)-induced metabolic disorders to offspring. Dnmt2 deletion prevented the elevation of RNA modifications (m5C, m2G) in sperm 30–40nt RNA fractions that are induced by HFD. Also, Dnmt2 deletion altered the sperm small RNA expression profile, including levels of tRNA-derived small RNAs (tsRNAs) and rRNA-derived small RNAs (rsRNA-28S), which might be essential in composing a sperm RNA ‘coding signature’ that is needed for paternal epigenetic memory. Finally, we show that Dnmt2-mediated m5C contributes to the secondary structure and biological properties of sncRNAs, implicating sperm RNA modifications as an additional layer of paternal hereditary information.

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Motivation Drug-drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not. The studies revealed that DDIs could cause different subsequent events, and predicting drug-drug interaction-associated events is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions. Results In this paper, we collect DDIs from DrugBank database, and extract 65 categories of DDI events by dependency analysis and events trimming. We propose a multimodal deep learning framework named DDIMDL that combines diverse drug features with deep learning to build a model for predicting drug-drug interaction-associated events. DDIMDL first constructs deep neural network-based sub-models by respectively using four types of drug features: chemical substructures, targets, enzymes and pathways, and then adopts a joint DNN framework to combine the sub-models to learn cross-modality representations of drug-drug pairs and predict DDI events. In computational experiments, DDIMDL produces high-accuracy performances and has high efficiency. Moreover, DDIMDL outperforms state-of-the-art DDI event prediction methods and baseline methods. Among all the features of drugs, the chemical substructures seem to be the most informative. With the combination of substructures, targets and enzymes, DDIMDL achieves an accuracy of 0.8852 and an area under the precision-recall curve of 0.9208. Availability The source code and data are available at https://github.com/YifanDengWHU/DDIMDL Supplementary information Supplementary data are available at Bioinformatics online.

Although high-throughput RNA sequencing (RNA-seq) has greatly advanced small non-coding RNA (sncRNA) discovery, the currently widely used complementary DNA library construction protocol generates biased sequencing results. This is partially due to RNA modifications that interfere with adapter ligation and reverse transcription processes, which prevent the detection of sncRNAs bearing these modifications. Here, we present PANDORA-seq (panoramic RNA display by overcoming RNA modification aborted sequencing), employing a combinatorial enzymatic treatment to remove key RNA modifications that block adapter ligation and reverse transcription. PANDORA-seq identified abundant modified sncRNAs-mostly transfer RNA-derived small RNAs (tsRNAs) and ribosomal RNA-derived small RNAs (rsRNAs)-that were previously undetected, exhibiting tissue-specific expression across mouse brain, liver, spleen and sperm, as well as cell-specific expression across embryonic stem cells (ESCs) and HeLa cells. Using PANDORA-seq, we revealed unprecedented landscapes of microRNA, tsRNA and rsRNA dynamics during the generation of induced pluripotent stem cells. Importantly, tsRNAs and rsRNAs that are downregulated during somatic cell reprogramming impact cellular translation in ESCs, suggesting a role in lineage differentiation.RNA modifications, warrant future extensive investigations in different systems.

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The inner cell mass (ICM) in blastocyst is the origin of all somatic and germ cells in mammals and pluripotent stem cells (PSCs) in vitro. As the conserved principles between pig and human, here we performed comprehensive single‐cell RNA‐seq for porcine early embryos from oocyte to early blastocyst (EB). We show the specification of the ICM and trophectoderm in morula and the molecular signature of the precursors. We demonstrate the existence of naïve pluripotency signature in morula and ICM of EB, and the specific pluripotent genes and the activity of signalling pathways highlight the characteristics of the naïve pluripotency. We observe the absence of dosage compensation with respect to X‐chromosome (XC) in morula, and incomplete dosage compensation in the EB. However, the dynamics of dosage compensation may be independent of the expression of XIST induced XC inactivation. Our study describes molecular landmarks of embryogenesis in pig that will provide a better strategy for derivation of porcine PSCs and improve research in regenerative medicine.

This paper considers the master and slave synchronization control for bilateral teleoperation systems with time delay and backlash-like hysteresis. Based on radial basis functions neural networks' approximation capabilities, two improved adaptive neural control approaches are developed. By Lyapunov stability analysis, the position and velocity tracking errors are guaranteed to converge to a small neighborhood of the origin. The contributions of this paper can be summarized as follows: 1) by using the matrix norm established using the weight vector of neural networks as the estimated parameters, two novel control schemes are developed and 2) the hysteresis inverse is not required in the proposed controllers. The simulations are performed, and the results show the effectiveness of the proposed method.

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