Passivity and passification of fractional-order memristive neural networks with time delays.

A class of fractional-order memristive neural networks (FMNNs) with time delays is studied. At first, the original network system is converted to fractional-order uncertain one to simplify the analysis by a variable transformation. Successively, some new LMIs-based passivity criteria are derived by differential inclusions, set-valued maps, inequality techniques and linear matrix inequality approach. Furthermore, a feedback control protocol is designed to solve the passification problem for the considered system, whose feedback control effect on different neurons can be changed artificially, which can be better applied to neural networks. The obtained results include some existing ones as special cases. A numerical example is proposed to illustrate the theoretical results.

Global Dissipativity and Quasi-Mittag-Leffler Synchronization of Fractional-Order Discontinuous Complex-Valued Neural Networks.

This article is concerned with fractional-order discontinuous complex-valued neural networks (FODCNNs). Based on a new fractional-order inequality, such system is analyzed as a compact entirety without any decomposition in the complex domain which is different from a common method in almost all literature. First, the existence of global Filippov solution is given in the complex domain on the basis of the theories of vector norm and fractional calculus. Successively, by virtue of the nonsmooth analysis and differential inclusion theory, some sufficient conditions are developed to guarantee the global dissipativity and quasi-Mittag-Leffler synchronization of FODCNNs. Furthermore, the error bounds of quasi-Mittag-Leffler synchronization are estimated without reference to the initial values. Especially, our results include some existing integer-order and fractional-order ones as special cases. Finally, numerical examples are given to show the effectiveness of the obtained theories.

Adaptive tracking synchronization for coupled reaction-diffusion neural networks with parameter mismatches.

In this paper, tracking synchronization for coupled reaction-diffusion neural networks with parameter mismatches is investigated. For such a networked control system, only local neighbor information is used to compensate the mismatch characteristic termed as parameter mismatch, uncertainty or external disturbance. Different from the general boundedness hypothesis, the parameter mismatches are permitted to be unbounded. For the known parameter mismatches, parameter-dependent controller and parameter-independent adaptive controller are respectively designed. While for fully unknown network parameters and parameter mismatches, a distributed adaptive controller is proposed. By means of partial differential equation theories and differential inequality techniques, the tracking synchronization errors driven by these nonlinear controllers are proved to be uniformly ultimately bounded and exponentially convergent to some adjustable bounded domains. Finally, three numerical examples are given to test the effectiveness of the proposed controllers.

Peptidoglycan-Associated Cyclic Lipopeptide Disrupts Viral Infectivity.

Enteric viruses exploit bacterial components, including lipopolysaccharides (LPS) and peptidoglycan (PG), to facilitate infection in humans. Because of their origin in the bat enteric system, we wondered if severe acute respiratory syndrome coronavirus (SARS-CoV) or Middle East respiratory syndrome CoV (MERS-CoV) also use bacterial components to modulate infectivity. To test this question, we incubated CoVs with LPS and PG and evaluated infectivity, finding no change following LPS treatment. However, PG from Bacillus subtilis reduced infection >10,000-fold, while PG from other bacterial species failed to recapitulate this. Treatment with an alcohol solvent transferred inhibitory activity to the wash, and mass spectrometry revealed surfactin, a cyclic lipopeptide antibiotic, as the inhibitory compound. This antibiotic had robust dose- and temperature-dependent inhibition of CoV infectivity. Mechanistic studies indicated that surfactin disrupts CoV virion integrity, and surfactin treatment of the virus inoculum ablated infection in vivo Finally, similar cyclic lipopeptides had no effect on CoV infectivity, and the inhibitory effect of surfactin extended broadly to enveloped viruses, including influenza, Ebola, Zika, Nipah, chikungunya, Una, Mayaro, Dugbe, and Crimean-Congo hemorrhagic fever viruses. Overall, our results indicate that peptidoglycan-associated surfactin has broad viricidal activity and suggest that bacteria by-products may negatively modulate virus infection.IMPORTANCE In this article, we consider a role for bacteria in shaping coronavirus infection. Taking cues from studies of enteric viruses, we initially investigated how bacterial surface components might improve CoV infection. Instead, we found that peptidoglycan-associated surfactin is a potent viricidal compound that disrupts virion integrity with broad activity against enveloped viruses. Our results indicate that interactions with commensal bacterial may improve or disrupt viral infections, highlighting the importance of understanding these microbial interactions and their implications for viral pathogenesis and treatment.

Robust Finite-Time Stabilization of Fractional-Order Neural Networks With Discontinuous and Continuous Activation Functions Under Uncertainty.

This paper is concerned with robust finite-time stabilization for a class of fractional-order neural networks (FNNs) with two types of activation functions (i.e., discontinuous and continuous activation function) under uncertainty. It is worth noting that there exist few results about FNNs with discontinuous activation functions, which is mainly because classical solutions and theories of differential equations cannot be applied in this case. Especially, there is no relevant finite-time stabilization research for such system, and this paper makes up for the gap. The existence of global solution under the framework of Filippov for such system is guaranteed by limiting discontinuous activation functions. According to set-valued analysis and Kakutani's fixed point theorem, we obtain the existence of equilibrium point. In particular, based on differential inclusion theory and fractional Lyapunov stability theory, several new sufficient conditions are given to ensure finite-time stabilization via a novel discontinuous controller, and the upper bound of the settling time for stabilization is estimated. In addition, we analyze the finite-time stabilization of FNNs with Lipschitz-continuous activation functions under uncertainty. The results of this paper improve corresponding ones of integer-order neural networks with discontinuous and continuous activation functions. Finally, three numerical examples are given to show the effectiveness of the theoretical results.

Projective synchronization of nonidentical fractional-order neural networks based on sliding mode controller.

This paper investigates global projective synchronization of nonidentical fractional-order neural networks (FNNs) based on sliding mode control technique. We firstly construct a fractional-order integral sliding surface. Then, according to the sliding mode control theory, we design a sliding mode controller to guarantee the occurrence of the sliding motion. Based on fractional Lyapunov direct methods, system trajectories are driven to the proposed sliding surface and remain on it evermore, and some novel criteria are obtained to realize global projective synchronization of nonidentical FNNs. As the special cases, some sufficient conditions are given to ensure projective synchronization of identical FNNs, complete synchronization of nonidentical FNNs and anti-synchronization of nonidentical FNNs. Finally, one numerical example is given to demonstrate the effectiveness of the obtained results.

Global Mittag-Leffler synchronization of fractional-order neural networks with discontinuous activations.

This paper is concerned with the global Mittag-Leffler synchronization for a class of fractional-order neural networks with discontinuous activations (FNNDAs). We give the concept of Filippov solution for FNNDAs in the sense of Caputo's fractional derivation. By using a singular Gronwall inequality and the properties of fractional calculus, the existence of global solution under the framework of Filippov for FNNDAs is proved. Based on the nonsmooth analysis and control theory, some sufficient criteria for the global Mittag-Leffler synchronization of FNNDAs are derived by designing a suitable controller. The proposed results enrich and enhance the previous reports. Finally, one numerical example is given to demonstrate the effectiveness of the theoretical results.

Finite time stabilization of delayed neural networks.

In this paper, the problem of finite time stabilization for a class of delayed neural networks (DNNs) is investigated. The general conditions on the feedback control law are provided to ensure the finite time stabilization of DNNs. Then some specific conditions are derived by designing two different controllers which include the delay-dependent and delay-independent ones. In addition, the upper bound of the settling time for stabilization is estimated. Under fixed control strength, discussions of the extremum of settling time functional are made and a switched controller is designed to optimize the settling time. Finally, numerical simulations are carried out to demonstrate the effectiveness of the obtained results.

Evidence for autophagic gridlock in aging and neurodegeneration.

Autophagy is essential to neuronal homeostasis, and its impairment is implicated in the development of neurodegenerative pathology. However, the underlying mechanisms and consequences of this phenomenon remain a matter of conjecture. We show that misexpression of human tau in Drosophila induces accumulation of autophagic intermediates with a preponderance of large vacuoles, which we term giant autophagic bodies (GABs), which are reminiscent of dysfunctional autophagic entities. Lowering basal autophagy reduces GABs, whereas increasing autophagy decreases mature autolysosomes. Induction of autophagy is also associated with rescue of the tauopathy phenotype, suggesting that formation of GABs may be a compensatory mechanism rather than a trigger of neurodegeneration. Last, we show that the peculiar Biondi bodies observed in the choroid epithelium of both elderly and Alzheimer's disease human brains express immunoreactive markers similar to those of GABs. Collectively, these data indicate that autophagic gridlock contributes to the development of pathology in aging and neurodegeneration.