No abstract

No abstract

No abstract

In this paper, we consider a class of Clifford-valued neutral-type neural networks with leakage delays on time scales. We do not decompose the networks under consideration into real-valued systems, but we directly study the Clifford-valued networks. We first establish the existence of weighted pseudo almost periodic solutions of this class of neural networks by the theory of calculus on time scales and the Banach fixed point theorem. Then, we study the global exponential stability of weighted pseudo almost periodic solutions of this class of neural networks by using inequality techniques and the proof by contradiction. Finally, we give an example to illustrate the feasibility of the obtained results.

At present, the research on discrete-time Clifford-valued neural networks is rarely reported. However, the discrete-time neural networks are an important part of the neural network theory. Because the time scale theory can unify the study of discrete- and continuous-time problems, it is not necessary to separately study continuous- and discrete-time systems. Therefore, to simultaneously study the pseudo almost periodic oscillation and synchronization of continuous- and discrete-time Clifford-valued neural networks, in this paper, we consider a class of Clifford-valued fuzzy cellular neural networks on time scales. Based on the theory of calculus on time scales and the contraction fixed point theorem, we first establish the existence of pseudo almost periodic solutions of neural networks. Then, under the condition that the considered network has pseudo almost periodic solutions, by designing a novel state-feedback controller and using reduction to absurdity, we obtain that the drive-response structure of Clifford-valued fuzzy cellular neural networks on time scales with pseudo almost periodic coefficients can realize the global exponential synchronization. Finally, we give a numerical example to illustrate the feasibility of our results.

No abstract

Inflammation in gestational tissues plays critical role in parturition initiation. We sought to investigate the leukocyte infiltration and cytokine profile in uterine tissues to understand the inflammation during term and preterm labor in the mouse model. Preterm birth was induced by the administration of lipopolysaccharide (LPS) or RU38486. The populations of leukocytes were determined by flow cytometry. Macrophages were the largest population in the myometrium and decidua in late gestation. The macrophage population was significantly changed in the myometrium and decidua from late pregnancy to term labor and significantly changed at LPS- and RU386-induced preterm labor. Neutrophils, T cells, and NKT cells were increased in LPS- and RU38486-induced preterm labor. The above changes were accompanied by the increased expression of cytokines and chemokines. In late gestation, M2 macrophages were the predominant phenotype in gestational tissues. M1 macrophages significantly increased in these tissues at term and preterm labor. IL-6 and NLRP3 expression was significantly increased in macrophages at labor, supporting that macrophages exhibit proinflammatory phenotypes. NLRP3 inflammasome inhibitor MCC950 mainly suppressed macrophage infiltration in the myometrium at term labor and preterm labor. Our data suggest that the M1 polarization of macrophages contributes to inflammation linked to term and preterm labor initiation in gestational tissues.

A class of quaternion-valued fuzzy cellular neural networks with time-varying delays on time scales is proposed. Based on inequality analysis techniques on time scales, a fixed point theorem and the theory of calculus on time scales, the existence, and global exponential stability of anti-periodic solutions for this class of neural networks are established. The obtained results are completely new and supplement to the known results. Finally, a numerical example is given to illustrate the feasibility of our results.