Phylogeography and demographic history of macaques, fascicularis species group, in East Asia: Inferred from multiple genomic markers.

Climate changes at larger scales have influenced dispersal and range shifts of many taxa in East Asia. The fascicularis species group of macaques is composed of four species and is widely distributed in Southeast and East Asia. However, its phylogeography and demographic histories are currently poorly understood. Herein, we assembled autosomal, mitogenome, and Y-chromosome data for 106 individuals, and combined them with 174 mtDNA dloop haplotypes of this species group, with particular focus on the demographic histories and dispersal routes of Macaca fuscata, M. cyclopis, and M. mulatta. The results showed: (1) three monophyletic clades for M. fuscata, M. cyclopis, and M. mulatta based on the multiple genomics analyses; (2) the disparate demographic trajectories of the three species after their split ∼1.0 Ma revealed that M. cyclopis and M. fuscata were derived from an ancestral M. mulatta population; (3) the speciation time of M. cyclopis was later than that of M. fuscata, and their divergence time occurred at the beginning of "Ryukyu Coral Sea Stage" (1.0-0.2 Ma) when the East China Sea land bridge was completely submerged by the sea level rose; and (4) the three parallel rivers (Nujiang, Lancangjiang, and Jinshajiang) of Southwestern China divided M. mulatta into Indian and Chinese genetic populations ∼200 kya. These results shed light on understanding not only the evolutionary history of the fascicularis species group but also the formation mechanism of faunal diversity in East Asia during the Pleistocene.

Fixed-Time Aperiodic Intermittent Control for Quasi-Bipartite Synchronization of Competitive Neural Networks.

This paper concerns a class of coupled competitive neural networks, subject to disturbance and discontinuous activation functions. To realize the fixed-time quasi-bipartite synchronization, an aperiodic intermittent controller is initially designed. Subsequently, by combining the fixed-time stability theory and nonsmooth analysis, several criteria are established to ensure the bipartite synchronization in fixed time. Moreover, synchronization error bounds and settling time estimates are provided. Finally, numerical simulations are presented to verify the main results.

Synergetic Contributions of Viral VP1, VP3, and 3C to Activation of the AKT-AMPK-MAPK-MTOR Signaling Pathway for Seneca Valley Virus-Induced Autophagy.

Seneca Valley virus (SVV), a member of the Picornaviridae family, can activate autophagy via the PERK and ATF6 unfolded protein response pathways and facilitate viral replication; however, the precise molecular mechanism that regulates SVV-induced autophagy remains unclear. Here, we revealed that SVV infection inhibited the phosphorylation of mechanistic target of rapamycin kinase (MTOR) and activated phosphorylation of the serine/threonine kinase AKT. We observed that activating AMP-activated protein kinase (AMPK), extracellular signal-regulated kinase (ERK), mitogen-activated protein kinase (MAPK), and p38 MAPK signaling by SVV infection promoted autophagy induction and viral replication; additionally, the SVV-induced autophagy was independent of the ULK1 complex. We further evaluated the role of viral protein(s) in the AKT-AMPK-MAPK-MTOR pathway during SVV-induced autophagy and found that VP1 induced autophagy, as evidenced by puncta colocalization with microtubule-associated protein 1 light chain 3 (LC3) in the cytoplasm and enhanced LC3-II levels. This might be associated with the interaction of VP1 with sequestosome 1 and promoting its degradation. In addition, the expression of VP1 enhanced AKT phosphorylation and AMPK phosphorylation, while MTOR phosphorylation was inhibited. These results indicate that VP1 induces autophagy by the AKT-AMPK-MTOR pathway. Additionally, expression of VP3 and 3C was found to activate autophagy induction via the ERK1/2 MAPK-MTOR and p38 MAPK-MTOR pathway. Taken together, our data suggest that SVV-induced autophagy has finely tuned molecular mechanisms in which VP1, VP3, and 3C contribute synergistically to the AKT-AMPK-MAPK-MTOR pathway. IMPORTANCE Autophagy is an essential cellular catabolic process to sustain normal physiological processes that are modulated by a variety of signaling pathways. Invading virus is a stimulus to induce autophagy that regulates viral replication. It has been demonstrated that Seneca Valley virus (SVV) induced autophagy via the PERK and ATF6 unfolded protein response pathways. However, the precise signaling pathway involved in autophagy is still poorly understood. In this study, our results demonstrated that viral proteins VP1, VP3, and 3C contribute synergistically to activation of the AKT-AMPK-MAPK-MTOR signaling pathway for SVV-induced autophagy. These findings reveal systemically the finely tuned molecular mechanism of SVV-induced autophagy, thereby facilitating deeper insight into the development of potential control strategies against SVV infection.

Prescribed Performance Back-Stepping Tracking Control for a Class of High-Order Nonlinear Systems via a Disturbance Observer.

Due to the widespread presence of disturbances in practical engineering and widespread applications of high-order systems, this paper first pays attention to a class of high-order strict-feedback nonlinear systems subject to bounded disturbance and investigates the prescribed performance tracking control and anti-disturbance control problems. A novel composite control protocol using the technique of a disturbance observer-prescribed performance control-is designed using the back-stepping method. The disturbance observer is introduced for estimating and compensating for unknown disturbances in each step, and the prescribed performance specifications guarantee both transient and steady-state performance of the tracking error to improve the control performance and result in better disturbance rejection. Moreover, the technique of adding a power integrator is modified to tackle controller design problems for the high-order systems. The Lyapunov function method is utilized for rigorous stability analysis. It is revealed that while the control performance completely remains in the prescribed bound, all states in the closed-loop system are input-to-state stable, and the tracking error and the disturbances estimating error asymptotically converge to zero simultaneously. Then, the feasibility and effectiveness of the proposed control protocol are verified by a simulation result.

Dynamical Analysis of Hyper-ILSR Rumor Propagation Model with Saturation Incidence Rate.

With the development of the Internet, it is more convenient for people to obtain information, which also facilitates the spread of rumors. It is imperative to study the mechanisms of rumor transmission to control the spread of rumors. The process of rumor propagation is often affected by the interaction of multiple nodes. To reflect higher-order interactions in rumor-spreading, hypergraph theories are introduced in a Hyper-ILSR (Hyper-Ignorant-Lurker-Spreader-Recover) rumor-spreading model with saturation incidence rate in this study. Firstly, the definition of hypergraph and hyperdegree is introduced to explain the construction of the model. Secondly, the existence of the threshold and equilibrium of the Hyper-ILSR model is revealed by discussing the model, which is used to judge the final state of rumor propagation. Next, the stability of equilibrium is studied by Lyapunov functions. Moreover, optimal control is put forward to suppress rumor propagation. Finally, the differences between the Hyper-ILSR model and the general ILSR model are shown in numerical simulations.

Stability Analysis of a Delayed Rumor Propagation Model with Nonlinear Incidence Incorporating Impulsive Vaccination.

The presence of information asymmetry can hinder the public's ability to make well-informed decisions, resulting in unwarranted suspicion and the widespread dissemination of rumors. Therefore, it is crucial to provide individuals with consistent and dependable scientific education. Regular popular science education is considered a periodic impulsive intervention to mitigate the impact of information asymmetry and promote a more informed and discerning public. Drawing on these findings, this paper proposes a susceptible-hesitant-infected-refuting-recovered (SHIDR) rumor-spreading model to explain the spread of rumors. The model incorporates elements such as time delay, nonlinear incidence, and refuting individuals. Firstly, by applying the comparison theorem of an impulsive differential equation, we calculate two thresholds for rumor propagation. Additionally, we analyze the conditions of global attractiveness of the rumor-free periodic solution. Furthermore, we consider the condition for the rumor's permanence. Finally, numerical simulations are conducted to validate the accuracy of our findings. The results suggest that increasing the proportion of impulsive vaccination, reducing the impulsive period, or prolonging the delay time can effectively suppress rumors.

Distributed Optimization for Resource Allocation Problem with Dynamic Event-Triggered Strategy.

This study aims to unravel the resource allocation problem (RAP) by using a consensus-based distributed optimization algorithm under dynamic event-triggered (DET) strategies. Firstly, based on the multi-agent consensus approach, a novel one-to-all DET strategy is presented to solve the RAP. Secondly, the proposed one-to-all DET strategy is extended to a one-to-one DET strategy, where each agent transmits its state asynchronously to its neighbors. Furthermore, it is proven that the proposed two types of DET strategies do not have Zeno behavior. Finally, numerical simulations are provided to validate and illustrate the effectiveness of the theoretical results.

Dynamic Analysis and Optimal Control of Rumor Spreading Model with Recurrence and Individual Behaviors in Heterogeneous Networks.

This paper is devoted to investigating the impact of the recurrence of rumors and individual behaviors and control strategies related to rumor spreading in online social networks. To do this, a novel susceptible-hesitating-infected-latent-recovered (SHILR) rumor propagation model in heterogeneous networks is presented. Firstly, based on the relevant mean-field equations of the model, the threshold value is examined to demonstrate the existence and stability of rumor-free/spreading equilibrium with the help of the algebraic method. Secondly, the global stabilities of the equilibria are analyzed through applying Lyapunov stability theory and LaSalle's invariance principle. Next, the optimal control is proposed by taking advantage of Pontryagin's maximum principle for reducing the number of infected individuals with minimum cost. Moreover, some numerical examples are carried out to test the theoretical results. Finally, combined with practice, a model application is presented.

Event-Triggered Fixed-Time Integral Sliding Mode Control for Nonlinear Multi-Agent Systems with Disturbances.

In this paper, the leader-following consensus problem of first-order nonlinear multi-agent systems (FONMASs) with external disturbances is studied. Firstly, a novel distributed fixed-time sliding mode manifold is designed and a new static event-triggered protocol over general directed graph is proposed which can well suppress the external disturbances and make the FONMASs achieve leader-following consensus in fixed-time. Based on fixed-time stability theory and inequality technique, the conditions to be satisfied by the control parameters are obtained and the Zeno behavior can be avoided. In addition, we improve the proposed protocol and propose a new event-triggering strategy for the FONMASs with multiple leaders. The systems can reach the sliding mode surface and achieve containment control in fixed-time if the control parameters are designed carefully. Finally, several numerical simulations are given to show the effectiveness of the proposed protocols.

Fixed-Time Synchronization Control of Delayed Dynamical Complex Networks.

Fixed-time synchronization problem for delayed dynamical complex networks is explored in this paper. Compared with some correspondingly existed results, a few new results are obtained to guarantee fixed-time synchronization of delayed dynamical networks model. Moreover, by designing adaptive controller and discontinuous feedback controller, fixed-time synchronization can be realized through regulating the main control parameter. Additionally, a new theorem for fixed-time synchronization is used to reduce the conservatism of the existing work in terms of conditions and the estimate of synchronization time. In particular, we obtain some fixed-time synchronization criteria for a type of coupled delayed neural networks. Finally, the analysis and comparison of the proposed controllers are given to demonstrate the validness of the derived results from one numerical example.

Induction of Porcine Dermatitis and Nephropathy Syndrome in Piglets by Infection with Porcine Circovirus Type 3.

Porcine circovirus type 3 (PCV3) is an emerging porcine circovirus that has been associated with porcine dermatitis and nephropathy syndrome (PDNS)-like clinical signs, reproductive failure, cardiac pathologies, and multisystemic inflammation in piglets and sows. Many aspects of PCV3 infection biology and pathogenesis, however, remain unknown. Here, we used a PCV3 virus stock from the rescue of an infectious PCV3 DNA clone to intranasally inoculate 4- and 8-week-old specific-pathogen-free piglets for evaluation of PCV3 pathogenesis. For 4-week-old piglets, typical clinical signs resembling those of PDNS-like disease were observed when piglets were inoculated with PCV3 alone or PCV3 combined with immunostimulation by keyhole limpet hemocyanin, with a mortality of 40% (2/5) for both types of inoculated piglets during a 28-day observation period postinoculation. Both types of inoculated piglets showed similar progressive increases in viral loads in the sera and had seroconverted to PCV3 capsid antibody after inoculation. Pathological lesions and PCV3-specific antigen were detected in various tissues and organs, including the lung, heart, kidney, lymph nodes, spleen, liver, and small intestine, in both types of inoculated piglets. The levels of proinflammatory cytokines and chemokines, including interleukin 1 beta (IL-1ß), IL-6, IL-23α, gamma interferon (IFN-γ), tumor necrosis factor alpha (TNF-α), and chemokine ligand 5 (CCL5), were significantly upregulated in both groups of inoculated piglets. Eight-week-old piglets also exhibited a similar PDNS-like disease but without death after PCV3 inoculation, as evidenced by pathological lesions and PCV3 antigen in various tissues and organs. These results show for the first time successful reproduction of PDNS-like disease by PCV3 infection and further provide significant information regarding the pathogenesis of PCV3 in piglets.IMPORTANCE Porcine circovirus type 3 (PCV3), an emerging porcine circovirus, is considered the cause of porcine dermatitis and nephropathy syndrome (PDNS)-like clinical signs and other systemic diseases in piglets and sows. To evaluate the pathogenesis of PCV3 infection in vivo, we used a PCV3 virus stock from the rescue of an infectious PCV3 DNA clone to intranasally inoculate 4- and 8-week-old specific-pathogen-free piglets and demonstrated successful reproduction of PDNS-like disease in animals that were inoculated with PCV3 alone or PCV3 combined with immunostimulation by keyhole limpet hemocyanin. Both 4- and 8-week-old PCV3-inoculated piglets showed similar increases in viral loads in the sera and had seroconverted to PCV3 capsid antibody. Pathological lesions and PCV3-specific antigen were detected in various tissues and organs, while numerous proinflammatory cytokines and chemokines in the sera were significantly upregulated after PCV3 inoculation. These results will provide significant information regarding the pathogenesis of PCV3 in piglets.

The spread and control of rumors in a multilingual environment.

This paper studies the rumor propagation model with heterogeneous networks in a multilingual environment. Firstly, a rumor propagation model with two language spreaders, in which the immunologic mechanism is considered in the ignorant, is proposed on heterogeneous networks. Secondly, the basic reproduction number and the dynamic behaviors are analyzed by using the next-generation matrix method and Lyapunov stability theory, respectively. Moreover, two control strategies are designed to effectively suppress the spread of the rumor. The one is continuous control strategy. By applying real-time control to the spreaders, the rumor spreading time can be greatly reduced and the rumor can die out in a short time. The other is event-triggered impulsive control strategy, which can effectively reduce the consumption of resources and ensure the extinction of the rumor. Finally, the correctness of theoretical analysis and the feasibility of control methods are verified by numerical simulations.

Analysis of HIV/AIDS Epidemic and Socioeconomic Factors in Sub-Saharan Africa.

Sub-Saharan Africa has been the epicenter of the outbreak since the spread of acquired immunodeficiency syndrome (AIDS) began to be prevalent. This article proposes several regression models to investigate the relationships between the HIV/AIDS epidemic and socioeconomic factors (the gross domestic product per capita, and population density) in ten countries of Sub-Saharan Africa, for 2011-2016. The maximum likelihood method was used to estimate the unknown parameters of these models along with the Newton-Raphson procedure and Fisher scoring algorithm. Comparing these regression models, there exist significant spatiotemporal non-stationarity and auto-correlations between the HIV/AIDS epidemic and two socioeconomic factors. Based on the empirical results, we suggest that the geographically and temporally weighted Poisson autoregressive (GTWPAR) model is more suitable than other models, and has the better fitting results.

Bipartite synchronization of multilayer signed networks under aperiodic intermittent-based adaptive dynamic event-triggered control.

This article addresses the exponential bipartite synchronization (EBS) of multilayer signed networks with time-varying coupling (MSNs) under aperiodic intermittent-based adaptive dynamic event-triggered control (AAIDETC). Firstly, to increase the elasticity, a novel AAIDETC strategy is presented, whose superiority is that the control gains and the triggering parameters can vary with the evolution of the considered networks. Meanwhile, concerning the aperiodic intermittent control, a new definition of average control ratio (ACR) is put forward, which is more rigorous compared with the relevant results. Then, by the method of ACR, graph theory and Lyapunov approach, the simpler synchronization criterion is gained, which avoids the topology structure of MSNs. Moreover, the EBS issues of Chua's circuits and neural networks established on MSNs are studied, which are two practical applications of our theoretical results. Finally, corresponding numerical simulations are presented to verify the availability of the obtained results.

Distributed consensus of discrete time-varying linear multi-agent systems with event-triggered intermittent control.

The consensus problem of discrete time-varying linear multi-agent systems (MASs) is studied in this paper. First, an event-triggered intermittent control (ETIC) protocol is designed, aided by a class of auxiliary functions. Under this protocol, some sufficient conditions for all agents to achieve consensus are established by constructing an error dynamical system and applying the Lyapunov function. Second, in order to further reduce the communication burden, an improved event triggered intermittent control (I-ETIC) strategy is presented, along with corresponding convergence analysis. Notably, the difference between the two control protocols lies in the fact that the former protocol only determines when to control or not based on the trigger conditions, while the latter, building upon this, designs new event trigger conditions for the update of the controller during the control stage. Finally, two numerical simulation examples are provided to demonstrate the effectiveness of the theoretical results.

Prescribed-time cluster practical consensus for nonlinear multi-agent systems based on event-triggered mechanism.

This paper investigates the prescribed-time event-triggered cluster practical consensus problem for a class of nonlinear multi-agent systems with external disturbances. To begin, to reach the prescribed-time cluster practical consensus, a new time-varying function is introduced and a novel distributed continuous algorithm is designed. Based on the Lyapunov stability theory and inequality techniques, some sufficient conditions are given, ensuring the prescribed-time cluster practical consensus. Moreover, to avoid different clusters' final states overlapping, a virtual leader is considered for each cluster. In this case, an event-triggered distributed protocol is further established and some related conditions are given for achieving prescribed-time cluster practical consensus. Additionally, it is proven that the Zeno behavior can be avioded by choosing parameters appropriately. Finally, some numerical examples are presented to show the effectiveness of the theoretical results.

Projective Synchronization of Discrete-Time Variable-Order Fractional Neural Networks With Time-Varying Delays.

This article is committed to studying projective synchronization and complete synchronization (CS) issues for one kind of discrete-time variable-order fractional neural networks (DVFNNs) with time-varying delays. First, two new variable-order fractional (VF) inequalities are built by relying on nabla Laplace transform and some properties of Mittag-Leffler function, which are extensions of constant-order fractional (CF) inequalities. Moreover, the VF Halanay inequality in discrete-time sense is strictly proved. Subsequently, some sufficient projective synchronization and CS criteria are derived by virtue of VF inequalities and hybrid controllers. Finally, we exploit numerical simulation examples to verify the validity of the derived results, and a practical application of the obtained results in image encryption is also discussed.

Transcription factor regulation and cytokine expression following in vitro infection of primary chicken cell culture with low pathogenic avian influenza virus.

BACKGROUND: Avian influenza virus (AIV) induced proinflammatory cytokine expression is believed to contribute to the disease pathogenesis following infection of poultry. However, there is limited information on the avian immune response to infection with low pathogenic avian influenza virus (LPAIV). METHODS: To gain a better understanding of the early viral-host interactions of LPAIV in chickens, primary chicken embryo hepatocytes (CEH) were infected with four different LPAIVs of U.S. origin. Kinetics of virus replication, transcription factor (c-Jun, p50 and IRF-3) activation and immune response gene (IL-6, IL-1beta, IFN-alpha and Mx) expression were studied at four different time points (6, 12, 24 and 48 hours) post infection and compared to non-infected controls. RESULTS: CEH can support growth of the tested LPAIVs when with trypsin supplementation. All four immune response genes tested were upregulated following infection as were transcription factors c-Jun, p50 and IRF-3. Amplification of these genes was dependant on virus replication (e.g. inclusion of trypsin), such that immune response genes and transcription factors were upregulated as viral titers increased. CONCLUSION: The results of these studies demonstrate the requirement of virus replication for innate immune regulation and broaden our understanding of transcription factor responses related to LPAIV infection in chickens.

Distributed Fixed/Preassigned-Time Optimization Based on Piecewise Power-Law Design.

The problem of fixed-time (FXT) and preassigned-time (PAT) optimization is concerned in this article based on multiagent systems (MASs) and power-law algorithms. Under the framework of strong convexity of the cost functions, two types of piecewise algorithms are proposed, which ensure that the FXT optimization can be solved either by first achieving the FXT consensus or by first achieving local optimization. Correspondingly, the PAT optimization problem is also considered by designing several piecewise protocols, where the finished time of optimization can be arbitrary prescribed according to actual demands. Furthermore, these piecewise power-law algorithms on the weighted undirected graphs are generalized to the weighted digraphs. Finally, by providing two numerical examples, the presented algorithms are further verified.

Prediction of drug-protein interaction based on dual channel neural networks with attention mechanism.

The precise identification of drug-protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot accurately predict a large number of DPIs. Compared with traditional computing methods, deep learning methods need less domain knowledge and have strong data learning ability. In this study, we construct a DPI prediction model based on dual channel neural networks with an efficient path attention mechanism, called DCA-DPI. The drug molecular graph and protein sequence are used as the data input of the model, and the residual graph neural network and the residual convolution network are used to learn the feature representation of the drug and protein, respectively, to obtain the feature vector of the drug and the hidden vector of protein. To get a more accurate protein feature vector, the weighted sum of the hidden vector of protein is applied using the neural attention mechanism. In the end, drug and protein vectors are concatenated and input into the full connection layer for classification. In order to evaluate the performance of DCA-DPI, three widely used public data, Human, C.elegans and DUD-E, are used in the experiment. The evaluation metrics values in the experiment are superior to other relevant methods. Experiments show that our model is efficient for DPI prediction.