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Abstract The formation of transcription regulatory complexes by the association of Smad4 with Smad2 and Smad3 (Smad2/3) is crucial in the canonical TGFβ pathway. Although the central requirement of Smad4 as a common mediator is emphasized in regulating TGFβ signaling, it is not obligatory for all responses. The role of Smad2/3 independently of Smad4 remains understudied. Here, we introduce a stepwise paradigm in which Smad2/3 regulate the lineage priming and differentiation of mouse embryonic stem cells (mESCs) by collaboration with different effectors. During the naïve-to-primed transition, Smad2/3 upregulate DNA methyltransferase 3b (Dnmt3b), which establishes the proper DNA methylation patterns and, in turn, enables Smad2/3 binding to the hypomethylated centers of promoters and enhancers of epiblast marker genes. Consequently, in the absence of Smad2/3, Smad4 alone cannot initiate epiblast-specific gene transcription. When primed epiblast cells begin to differentiate, Dnmt3b becomes less actively engaged in global genome methylation, and Smad4 takes over the baton in this relay race, forming a complex with Smad2/3 to support mesendoderm induction. Thus, mESCs lacking Smad4 can undergo the priming process but struggle with the downstream differentiation. This work sheds light on the intricate mechanisms underlying TGFβ signaling and its role in cellular processes.

Current studies on intelligent bearing fault diagnosis based on transfer learning have been fruitful. However, these methods mainly focus on transfer fault diagnosis of bearings under different working conditions. In engineering practice, it is often difficult or even impossible to obtain a large amount of labeled data from some machines, and an intelligent diagnostic method trained by labeled data from one machine may not be able to classify unlabeled data from other machines, strongly hindering the application of these intelligent diagnostic methods in certain industries. In this study, a deep transfer learning method for bearing fault diagnosis, domain separation reconstruction adversarial networks (DSRAN), was proposed for the transfer fault diagnosis between machines. In DSRAN, domain-difference and domain-invariant feature extractors are used to extract and separate domain-difference and domain-invariant features, respectively Moreover, the idea of generative adversarial networks (GAN) was used to improve the network in learning domain-invariant features. By using domain-invariant features, DSRAN can adopt the distribution of the data in the source and target domains. Six transfer fault diagnosis experiments were performed to verify the effectiveness of the proposed method, and the average accuracy reached 89.68%. The results showed that the DSRAN method trained by labeled data obtained from one machine can be used to identify the health state of the unlabeled data obtained from other machines.