Protective effect of S-allyl cysteine against cerebral ischemia/reperfusion injury in experimental rats

Purpose: To investigate the protective effect of S-allyl cysteine (SAC) against cerebral ischemiareperfusion injury (CRI) in rats.Methods: The protective effect of SAC was determined in a male Wistar rat model of middle cerebral artery occlusion (MCAO)-stimulated transient focal ischemia, followed by reperfusion. Cerebral ischemia reperfusion injury was induced via 90 min of MCAO, followed by 24-h reperfusion. The cerebral infarct size was determined by staining with 2,3,5- triphenyl tetrazolium chloride. The onset of cellular derangement, neurological deficit score and neuronal oedema were determined. In addition, the expressions of CRI markers and inflammatory cytokines were measured by enzyme-linked immunosorbent assay (ELISA).Results: Rats subjected to CRI showed marked increases in cellular oxidative stress, as evidenced by significant increase in the levels of inflammatory markers, including MDA (p < 0.05), MPO (p < 0.05) and nitric oxide (p < 0.01). In addition, CRI increased the mRNA expression levels of the marker genes TLR4, syndecan-1, CSF, aquaporin-1, OCT3, and RFX1. In contrast, rats pre-treated with SAC prior to CRI displayed reduced levels of inflammatory cytokines, with a near-normal cellular arrangement. SAC treatment significantly reduced the mRNA expression levels of the marker genes in CRI rats.Conclusion: These findings suggest that SAC may protect the brain of rats from cerebral ischemiareperfusion injury caused by amplification of oxidative stress and inflammatory signaling. Thus, S-allyl cysteine is a potential therapy for the management of CRI

Finite-Time Boundedness of Impulsive Delayed Reaction–Diffusion Stochastic Neural Networks

Considering the impulsive delayed reaction&amp;#x2013;diffusion stochastic neural networks (IDRDSNNs) with hybrid impulses, the finite-time boundedness (FTB) and finite-time contractive boundedness (FTCB) are investigated in this article. First, a novel delay integral inequality is presented. By integrating this inequality with the comparison principle, some sufficient conditions that ensure the FTB and FTCB of IDRDSNNs are obtained. This study demonstrates that the FTB of neural networks with hybrid impulses can be maintained, even in the presence of impulsive perturbations. And for a system that is not FTB due to impulsive perturbations, achieving FTB is possible through the implementation of appropriate impulsive control and optimization of the average impulsive intervals. In addition, to validate the practicality of our results, three illustrative examples are provided. In the end, these theoretical findings are successfully applied to image encryption.</p

Finite-Time Boundedness of Impulsive Delayed Reaction–Diffusion Stochastic Neural Networks

Considering the impulsive delayed reaction&amp;#x2013;diffusion stochastic neural networks (IDRDSNNs) with hybrid impulses, the finite-time boundedness (FTB) and finite-time contractive boundedness (FTCB) are investigated in this article. First, a novel delay integral inequality is presented. By integrating this inequality with the comparison principle, some sufficient conditions that ensure the FTB and FTCB of IDRDSNNs are obtained. This study demonstrates that the FTB of neural networks with hybrid impulses can be maintained, even in the presence of impulsive perturbations. And for a system that is not FTB due to impulsive perturbations, achieving FTB is possible through the implementation of appropriate impulsive control and optimization of the average impulsive intervals. In addition, to validate the practicality of our results, three illustrative examples are provided. In the end, these theoretical findings are successfully applied to image encryption.</p

Dynamical Behavior of Nonautonomous Stochastic Reaction-Diffusion Neural Network Models

This brief investigates nonautonomous stochastic reaction-diffusion neural-network models with S-type distributed delays. First, the existence and uniqueness of mild solution are studied under the Lipschitz condition without the linear growth condition. Due to the existence of a nonautonomous reaction-diffusion term and the infinite dimensional Wiener process, the criteria for the well-posedness of the models are established based on the evolution system theory. Then, the S-type distributed delay, which is an infinite delay, is handled by the truncation method, and sufficient conditions for the global exponential stability are obtained by constructing a simple Lyapunov-Krasovskii functional candidate. Finally, neural-network examples and an illustrative example are given to show the applications of the obtained results.</p

Exponential Stability for a Class of Stochastic Reaction-Diffusion Hopfield Neural Networks with Delays

This paper studies the asymptotic behavior for a class of delayed reaction-diffusion Hopfield neural networks driven by finite-dimensional Wiener processes. Some new sufficient conditions are established to guarantee the mean square exponential stability of this system by using Poincaré’s inequality and stochastic analysis technique. The proof of the almost surely exponential stability for this system is carried out by using the Burkholder-Davis-Gundy inequality, the Chebyshev inequality and the Borel-Cantelli lemma. Finally, an example is given to illustrate the effectiveness of the proposed approach, and the simulation is also given by using the Matlab

STKE: Temporal Knowledge Graph Embedding in the Spherical Coordinate System

Knowledge graph embedding (KGE) aims to learn the representation of entities and predicates in low-dimensional vector spaces which can complete the missing parts of the Knowledge Graphs (KGs). Nevertheless, temporal knowledge graphs (TKGs) that include time information are more consistent with real-world application scenarios. Meanwhile, the facts with time constraints make the results of reasoning over time more accurate. Because of these, we propose a novel temporal knowledge graph embedding (TKGE) model, namely Spherical Temporal Knowledge Graph Embedding (STKE), which embeds facts into a spherical coordinate system. We treat each fact as a rotation from the subject to the object. The entities and predicates in STKE are divided into three parts--the radial part, the azimuth part, and the polar part. The radial part aims to resize the modulus between two entities. The azimuth part is mainly used to distinguish entities with the same module length and the polar part aims to represent the transformation of the time embedding with the change of polar angle. We evaluate the proposed model via the link prediction task on four typical temporal datasets. Experiments demonstrate that STKE achieves a significant surpass compared with the state-of-the-art static knowledge graph embedding (SKGE) model and TKGE model. In addition, we analyze the representation ability of different facts in the spherical coordinate system and confirm that our model can better represent time-constrained facts

Mean Square Almost Periodic Solutions for Impulsive Stochastic Differential Equations with Delays

We establish a result on existence and uniqueness on mean square almost periodic solutions for a class of impulsive stochastic differential equations with delays, which extends some earlier works reported in the literature