Transfer RNA (tRNA) can produce smaller RNA fragments called tRNA-derived fragments (tRFs). tRFs play critical roles in multiple cellular programs, although the functional mechanisms of tRFs remain largely unknown in plants. In this study, we examined the phenotype associated with 5′ tRF-Ala (tRF-Ala, produced from tRNA-Ala) overexpression and knockdown lines (tDR-Ala-OE and tDR-Ala-kd, respectively) and the mechanisms by which tRF-Ala affects mRNA levels in Arabidopsis (Arabidopsis thaliana). We investigated the candidate proteins associated with tRF-Ala by quantitative proteomics and confirmed the direct interaction between tRF-Ala and the splicing factor SERINE-ARGININE RICH PROTEIN 34 (SR34). A transcriptome sequencing analysis showed that 318 genes among all the genes (786) with substantial alternative splicing (AS) variance in tDR-Ala-OE lines are targets of SR34. tRF-Ala diminished the binding affinity between SR34 and its targets by direct competition for interaction with SR34. These findings reveal the critical roles of tRF-Ala in regulating mRNA levels and splicing.
This article originally analyses intelligent robust tracking for multi-arm fruit-harvesting mobile manipulators (MAFHMMs) with delayed angle-velocity uncertainties. The MAFHMMs are composed of two parts: a crawler-type mobile platform and a four-arm harvesting manipulator. The method proposed here does not require a matching condition for the non-linear uncertainties. A fuzzy cerebellar model articulation controller (CMAC) neural network system is used to approximate an unknown controlled system from the strategic manipulation of the model following the tracking errors. In addition, an adaptive robust compensator is presented to compensate for the uncertainties. Based on the Lyapunov stability theory and neural network approximation capability, several sufficient conditions are derived, which guarantee the convergence of the closed-loop error system. Both simulation and experimental results show the superior control performance of the proposed intelligent control method.
This paper deals with delay-dependent H∞ control for discrete-time systems with time-varying state delays and input delays. A new finite-sum inequality is first established to derive a delay-dependent condition, under which the resulting closed-loop system via a state feedback is asymptotically stable with a prescribed H∞ noise attenuation level. Moreover, a modified iterative algorithm according to the previously published algorithm of Ghaoui and Oustry ( IEEE Trans. Autom. Control, 1997, 42(8), 1171–1176) involving convex optimization is proposed to obtain a suboptimal H∞ controller. Two numerical examples are presented which show the effectiveness of the proposed method.
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