2,629 research outputs found
Processing at the 3'-end of M1 RNA, the catalytic component of Escherichia coli RNase P, is essential for protection against a poly (A)-dependent RNA degradation system
No full textThe sequence variation of the rne-dependent site affects the specificity and the pathway of the 3'-processing of M1 RNA, the RNA component
No full textRefinement of DDRT-PCR with non-poly (A) RNA by a length-restriction polyadenylation technique
No full textDirect involvement of RNase E in processing at the 3' end of precursors of M1 RNA, the catalytic subunit of Escherichia coli RNase P
No full textPathways and mechanisms of 3' processing of M1 RNA, the RNA component of Escherichia coli RNase P
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Processing of M1 RNA at the 3′ end is essential for the protection of its primary transcript from degradation
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3′-end processing of precursor M1 RNA by the N-terminal half of RNase E
No full textAbstractM1 RNA, the catalytic component of Escherichia coli RNase P, is derived from the 3′-end processing of precursor M1 RNA, a major transcript of the rnpB gene. In this study, we investigated the mechanism of 3′-end processing of M1 RNA using the recombinant N-terminal half RNase E. The cleavage site preference of RNase E differed from that of the 40% ammonium sulfate precipitate (ASP-40), a partially purified cell extract containing processing activity. However, the addition of a trace amount of ASP-40 changed the cleavage site preference of RNase E to that of ASP-40 suggesting the involvement of a soluble factor in cleavage site preference
Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning
No full textThis article presents an efficient learning-based method to solve the <italic>inverse kinematic</italic> (IK) problem on soft robots with highly nonlinear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Materials and ManufacturingMechatronic Desig
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