SummaryThe Arabidopsis trichome is a model system for studying cell development, cell differentiation and the cell cycle in plants. Our previous studies have shown that the ZINC FINGER PRO-TEIN5 (ZFP5) controls shoot maturation and epidermal cell fate through GA signaling in Arabidopsis.We have identified a novel C2H2 zinc finger protein ZINC FINGER PROTEIN 6 (ZFP6) which plays a key role in regulating trichome development in Arabidopsis.Overexpression of ZFP6 results in ectopic trichomes on carpels and other inflorescence organs. Gain-and loss-of-function analyses have shown that the zfp6 mutant exhibits a reduced number of trichomes in sepals of flowers, cauline leaves, lateral branch and main inflorescence stems in comparison to wild-type plants.Molecular and genetic analyses suggest that ZFP6 functions upstream of GIS, GIS2, ZFP8, ZFP5 and key trichome initiation regulators GL1 and GL3.We reveal that ZFP6 and ZFP5 mediate the regulation of trichome initiation by integrating GA and cytokinin signaling in Arabidopsis. These findings provide new insights into the molecular mechanism of plant hormone control of epidermal trichome patterning through C2H2 transcriptional factors.

In this paper, a class of neutral type high-order Hopfield neural networks with mixed time-varying delays and leakage delays on time scales is proposed. Based on the exponential dichotomy of linear dynamic equations on time scales, Banach's fixed point theorem and the theory of calculus on time scales, some sufficient conditions are obtained for the existence and global exponential stability of pseudo almost periodic solutions for this class of neural networks. Our results are completely new. Finally, we present an example to illustrate our results are effective. Our example also shows that the continuous-time neural network and its discrete-time analogue have the same dynamical behaviors for the pseudo almost periodicity.

Spinal cord injury (SCI) causes a high rate of morbidity and disability. The clinical features of SCI are divided into acute, subacute, and chronic phases according to its pathophysiological events. The mammalian target of rapamycin (mTOR) signaling pathway plays an important role in cell death and inflammation in the acute phase and neuroregeneration in the subacute/chronic phases at different times. Resveratrol has the potential of regulating cell growth, proliferation, metabolism, and angiogenesis through the mTOR signaling pathway. Herein, we explicate the role of resveratrol in the repair of SCI through the inhibition of the mTOR signaling pathway. The inhibition of the mTOR pathway by resveratrol has the potential of serving as a neuronal restorative mechanism following SCI.

We propose a class of neutral type quaternion-valued neural networks with delays in the leakage term on time scales that can unify the discrete-time and the continuous-time neural networks. In order to avoid the difficulty brought by the noncommutativity of quaternion multiplication, we first decompose the quaternion-valued system into four real-valued systems. Then, by applying the exponential dichotomic theory of linear dynamic equations on time scales, Banach's fixed point theorem, the theory of calculus on time scales, and inequality techniques, we obtain some sufficient conditions on the existence and global exponential stability of pseudo almost periodic solutions for this class of neural networks. Our results are completely new even for both the case of the neural networks governed by differential equations and the case of the neural networks governed by difference equations and show that, under a simple condition, the continuous-time quaternion-valued network and its corresponding discrete-time quaternionvalued network have the same dynamical behavior for the pseudo almost periodicity. Finally, a numerical example is given to illustrate the feasibility of our results.

This paper focuses on the global exponential almost periodic synchronization of quaternion-valued neural networks with timevarying delays. By virtue of the exponential dichotomy of linear differential equations, Banach's fixed point theorem, Lyapunov functional method, and differential inequality technique, some sufficient conditions are established for assuring the existence and global exponential synchronization of almost periodic solutions of the delayed quaternion-valued neural networks, which are completely new. Finally, we give one example with simulation to show the applicability and effectiveness of our main results.