A Bionic Localization Memristive Circuit Based on Spatial Cognitive Mechanisms of Hippocampus and Entorhinal Cortex.

In this article, a bionic localization memristive circuit is proposed, which mainly consists of head direction cell module, grid cell module, place cell module and decoding module. This work modifies the two-dimensional Continuous Attractor Network (CAN) model of grid cells into two one-dimensional models in X and Y directions. The head direction cell module utilizes memristors to integrate angular velocity and represents the real orientation of an agent. The grid cell module uses memristors to sense linear velocity and orientation signals, which are both self-motion cues, and encodes the position in space by firing in a periodic mode. The place cell module receives the grid cell module's output and fires in a specific position. The decoding module decodes the angle or place information and transfers the neuron state to a 'one-hot' code. This proposed circuit completes the localizing task in space and realizes in-memory computing due to the use of memristors, which can shorten the execution time. The functions mentioned above are implemented in LTSPICE. The simulation results show that the proposed circuit can realize path integration and localization. Moreover, it is shown that the proposed circuit has good robustness and low area overhead. This work provides a possible application idea in a prospective robot platform to help the robot localize and build maps.

Prescribed-Time Network-Based Deployment of Nonlinear Multiagent Systems: A Discrete-Space PDE Method.

This article attempts to design the prescribed-time time-varying deployment schemes for first-order and second-order nonlinear multiagent systems (MASs). We assume that all agents can obtain the information of their current and final relative positions with their neighbors, and the final absolute velocities (as well as their current and final relative velocities, the final absolute accelerations for the second-order MASs) through a communication network, whereas two boundary agents are able to obtain their current and final absolute positions (as well as their current and final absolute velocities for the second-order MASs). The neighbor relationship of all agents is described by a spatial variable and two static-feedback controllers are introduced, which can be expressed as a second-order space difference of the spatial variable. Then, the deployment of MASs can be transformed into the stabilization of discrete-space partial differential equation (PDE) systems. Three virtual agents are introduced to constitute the Dirchlet and Neumann boundary conditions. Several algebraic inequality criteria are derived to guarantee that the prescribed-time time-varying deployment can be achieved within a prescribed time under the Dirchlet and mixed boundary conditions. Unlike the published results, our results are derived based on the discrete-space PDE systems instead of continuous-space PDE systems, which is consistent with the discrete spatial distribution of agents. Finally, two numerical examples are given to illustrate the effectiveness of our results.

Pinning Control of Multiplex Dynamical Networks Using Spectral Graph Theory.

Pinning control has been attracting wide attention for the study of various complex networks for decades. This article explores grounded theory on the pinning synchronization of the emerging multiplex dynamical networks. The multiplex dynamical networks under study can describe many real-world scenarios, in which different layers have distinct individual dynamics of node. In this work, we build the bridge between multiplex structures and network dynamics by using the Lyapunov stability theory and the spectral graph theory. Furthermore, by analyzing spectral properties of the grounded super-Laplacian matrices, we set up several graph-based synchronization criteria for multiplex networks via pinning control. In addition, we overcome the difficulties induced by distinct node dynamics in different layers, and find that interlayer coupling strengths promote intralayer synchronization of multiplex networks. Finally, a collection of numerical simulations verifies the effectiveness of theoretical results.

Exponential Boundary Control for 2-D Spatial Distributed Parameter Systems Under Boundary Collocated and Planar Linear Measurements.

For a class of 2-D spatial distributed parameter systems (DPSs) with space-dependent diffusivity, this article aims to achieve exponential realization of their desired profiles. To reduce the number of required sensors and actuators, a planar output feedback boundary control strategy is proposed with combining two nonfull-domain measurement methods, boundary collocated measurement and planar linear measurement, in which only two boundaries of the considered 2-D spatial DPSs are controlled and a little output information is measured. Moreover, by employing the Poincaré-Wirtinger inequality and variable substitution dexterously, the final exponential convergence criteria of the error system can be obtained with method of "Diverse treatment for same term." Finally, we provide a general numerical example and an application example in 2-D heat conduction systems to illustrate the effectiveness and practicability of the proposed measurement and control schemes.

An Accelerated Approach on Adaptive Gradient Neural Network for Solving Time-Dependent Linear Equations: A State-Triggered Perspective.

To improve the acceleration performance, a hybrid state-triggered discretization (HSTD) is proposed for the adaptive gradient neural network (AGNN) for solving time-dependent linear equations (TDLEs). Unlike the existing approaches that use an activation function or a time-varying coefficient for acceleration, the proposed HSTD is uniquely designed from a control theory perspective. It comprises two essential components: adaptive sampling interval state-triggered discretization (ASISTD) and adaptive coefficient state-triggered discretization (ACSTD). The former addresses the gap in acceleration methods related to the variable sampling period, while the latter considers the underlying evolutionary dynamics of the Lyapunov function to determine coefficients greedily. Finally, compared with commonly used discretization methods, the acceleration performance and computational advantages of the proposed HSTD are substantiated by the numerical simulations and applications to robotics.

The Prognostic Value of Serum Calcium Levels in Elderly Dilated Cardiomyopathy Patients.

Background: It is unclear whether serum calcium on admission is associated with clinical outcomes in dilated cardiomyopathy (DCM). In this study, we conducted a retrospective study spanning a decade to investigate the prognostic value of baseline calcium in elderly patients with DCM. Methods: A total of 1,089 consecutive elderly patients (age ≥60 years) diagnosed with DCM were retrospectively enrolled from January 2010 to December 2019. Univariate and multivariate analyses were performed to investigate the association of serum calcium with their clinical outcomes. Results: In this study, the average age of the subjects was 68.36 ± 6.31 years. Receiver operating characteristic (ROC) curve analysis showed that serum calcium level had a great sensitivity and specificity for predicting in-hospital death, with an AUC of 0.732. Kaplan-Meier survival analysis showed that patients with a serum calcium >8.62 mg/dL had a better prognosis than those with a serum calcium ≤8.62 mg/dL (log-rank χ2 40.84, p < 0.001). After adjusting for several common risk factors, a serum calcium ≤8.62 mg/dL was related to a higher risk of long-term mortality (HR: 1.449; 95% CI: 1.115~1.882; p = 0.005). Conclusions: Serum calcium level could be served as a simple and affordable tool to evaluate patients' prognosis in DCM.

Quasi-synchronization for variable-order fractional complex dynamical networks with hybrid delay-dependent impulses.

This paper focuses on addressing the problem of quasi-synchronization in heterogeneous variable-order fractional complex dynamical networks (VFCDNs) with hybrid delay-dependent impulses. Firstly, a mathematics model of VFCDNs with short memory is established under multi-weighted networks and mismatched parameters, which is more diverse and practical. Secondly, under the framework of variable-order fractional derivative, a novel fractional differential inequality has been proposed to handle the issue of quasi-synchronization with hybrid delay-dependent impulses. Additionally, the quasi-synchronization criterion for VFCDNs is developed using differential inclusion theory and Lyapunov method. Finally, the practicality and feasibility of this theoretical analysis are demonstrated through numerical examples.

Convergence-Rate-Based Event-Triggered Mechanisms for Quasi-Synchronization of Delayed Nonlinear Systems on Time Scales.

Most of the existing event-triggered mechanisms (ETMs) were designed according to the difference between the quadratic form of measurement errors and the quadratic form of sampling states (or real-time states). In order to reduce the amount of data transmission and develop ETMs for continuous-time and discrete-time delayed nonlinear systems (NSs) simultaneously, this article investigates quasi-synchronization (QS) of NSs on time scales based on a novel ETM, which is designed according to the convergence rate instead of measurement errors of the addressed systems. First, a novel ETM is designed under known nonlinear dynamics, and it is demonstrated that QS with given convergence rate and error level can be achieved under matrix inequality criteria. Second, if the nonlinear functions are unknown, we adapt our ETM to handle this special case. Not only QS but also complete synchronization with given convergence rate can be achieved under the ETMs. If the constructed Lyapunov functions passes through 0, the designed ETM will keep it at the origin. In this case, finite-time synchronization is achieved. Third, under the designed ETMs, it is proved that Zeno behavior can be excluded. At last, four numerical simulations are presented to demonstrate the feasibility and the advantage of the designed ETMs in this article.

Quasi-Synchronization of Fractional Multiweighted Coupled Neural Networks via Aperiodic Intermittent Control.

This article investigates the quasi-synchronization for fractional multiweighted coupled neural networks (FMCNNs) with discontinuous activation functions and mismatched parameters. First, under the generalized Caputo fractional-order derivative operator, a novel piecewise fractional differential inequality is established to study the convergence of fractional systems, which significantly extends some related published results. Subsequently, by exploiting the new inequality and Lyapunov stability theory, some sufficient quasi-synchronization conditions of FMCNNs are presented by aperiodic intermittent control. Meanwhile, the exponential convergence rate and synchronization error's bound are given explicitly. Finally, the validity of theoretical analysis is confirmed by numerical examples and simulations.

Memristive Bionic Memory Circuit Implementation and Its Application in Multisensory Mutual Associative Learning Networks.

Memory is vital and indispensable for organisms and brain-inspired intelligence to gain complete sensation and cognition of the environment. In this work, a memristive bionic memory circuit inspired by human memory model is proposed, which includes 1) receptor and sensory neuron (SN), 2) short-term memory (STM) module, and 3) long-term memory (LTM) module. By leveraging the in-memory computing characteristic of memristors, various functions such as sensation, learning, forgetting, recall, consolidation, reconsolidation, retrieval, and reset are realized. Besides, a multisensory mutual associative learning network is constructed with several bionic memory units to memorize and associate sensory information of different modalities bidirectionally. Except for association establishment, enhancement, and extinction, we also mimicked multisensory integration to manifest the synthetic process of information from different sensory channels. According to the simulation results in PSPICE, the proposed circuit performs high robustness, low area overhead, and low power consumption. Combining associative memory with human memory model, this work provides a possible idea for further research in associative learning networks.

Multiple Mittag-Leffler Stability of Almost Periodic Solutions for Fractional-Order Delayed Neural Networks: Distributed Optimization Approach.

This article proposes new theoretical results on the multiple Mittag-Leffler stability of almost periodic solutions (APOs) for fractional-order delayed neural networks (FDNNs) with nonlinear and nonmonotonic activation functions. Profited from the superior geometrical construction of activation function, the considered FDNNs have multiple APOs with local Mittag-Leffler stability under given algebraic inequality conditions. To solve the algebraic inequality conditions, especially in high-dimensional cases, a distributed optimization (DOP) model and a corresponding neurodynamic solving approach are employed. The conclusions in this article generalize the multiple stability of integer-or fractional-order NNs. Besides, the consideration of the DOP approach can ameliorate the excessive consumption of computational resources when utilizing the LMI toolbox to deal with high-dimensional complex NNs. Finally, a simulation example is presented to confirm the accuracy of the theoretical conclusions obtained, and an experimental example of associative memories is shown.

Characteristics of the gut microbiome and metabolic profile in elderly patients with sarcopenia.

Introduction: There is growing evidence of research indicating that the gut microbiota is involved in the development of sarcopenia. Nevertheless, there exists a notable deficiency in comprehension concerning the connection between irregularities in the intestinal microbiome and metabolic processes in older individuals suffering from sarcopenia. Methods: To analyze fecal samples obtained from a cohort of 30 older patients diagnosed with sarcopenia as well as 30 older patients without sarcopenia, this study employed 16S rDNA sequencing and liquid chromatography-mass spectrometry (LC-MS)-based non-targeted metabolomics profiling techniques. Results: As a result, we found that 29 genera and 172 metabolites were significantly altered in the sarcopenic patients. Among them, Blautia, Lachnospiraceae_unclassified, and Subdoligranulum were the bacteria with a potential diagnostic value for sarcopenia diagnosis. Correlation analysis between clinical indices and these gut bacteria suggested that the IL-6 level was negatively correlated with Blautia. Function prediction analysis demonstrated that 17 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways differ significantly between sarcopenic and non-sarcopenic patients. The primary classes of metabolites identified in the study included lipids and lipid-like molecules, organic acids and derivatives, and organoheterocyclic compounds. KEGG enrichment analysis showed that purine metabolism, arginine and proline metabolism, alanine, aspartate, and glutamate metabolism, butanoate metabolism, and histidine metabolism may contribute to the development of sarcopenia. The correlation study on gut microbiota and metabolites found that Lachnospiraceae_unclassified was positively associated with seven metabolites that were more abundant in the non-sarcopenia group and negatively correlated with three metabolites that were more abundant in the sarcopenia group. In addition, Subdoligranulum was positively correlated with seven metabolites that were lacking in sarcopenia and negatively correlated with two metabolites that were enriching in sarcopenia. Moreover, Blautia was positively associated with xanthosine. Discussion: We conducted a study on the intestinal microbiota and metabolic profile of elderly individuals with sarcopenia, offering a comprehensive analysis of the overall ecosystem. Through this investigation, we were able to validate existing research on the gut-muscle axis and further investigate potential pathogenic processes and treatment options for sarcopenia.

Adaptive Drive-Response Synchronization of Timescale-Type Neural Networks With Unbounded Time-Varying Delays.

In recent years, adaptive drive-response synchronization (DRS) of two continuous-time delayed neural networks (NNs) has been investigated extensively. For two timescale-type NNs (TNNs), how to develop adaptive synchronization control schemes and demonstrate rigorously is still an open problem. This article concentrates on adaptive control design for synchronization of TNNs with unbounded time-varying delays. First, timescale-type Barbalat lemma and novel timescale-type inequality techniques are first proposed, which provides us practical methods to investigate timescale-type nonlinear systems. Second, using timescale-type calculus, novel timescale-type inequality, and timescale-type Barbalat lemma, we demonstrate that global asymptotic synchronization can be achieved via adaptive control under algebraic and matrix inequality criteria even if the time-varying delays are unbounded and nondifferentiable. Adaptive DRS is discussed for TNNs, which implies our control schemes are suitable for continuous-time NNs, their discrete-time counterparts, and any combination of them. Finally, numerical examples on TNNs and timescale-type chaotic Ikeda-like oscillator with unbounded time-varying delays are carried out to verify the adaptive control schemes.

Finite/fixed-time synchronization of inertial memristive neural networks by interval matrix method for secure communication.

This paper investigates the finite/fixed-time synchronization problem of delayed inertial memristive neural networks (DIMNNs) using interval matrix-based methods within a unified control framework. By employing set-valued mapping and differential inclusion theory, two distinct methods are applied to handle the switching behavior of memristor parameters: the maximum absolute value method and the interval matrix method. Based on these different approaches, two control strategies are proposed to select appropriate control parameters, enabling the system to achieve finite and fixed-time synchronization, respectively. Additionally, the resulting theoretical criteria differ based on the chosen control strategy, with one expressed in algebraic form and the other in the form of linear matrix inequalities (LMIs). Numerical simulations demonstrate that the interval matrix method outperforms the maximum absolute value method in terms of handling memristor parameter switching, achieving faster finite/fixed-time synchronization. Furthermore, the theoretical results are extended to the field of image encryption, where the response system is utilized for decryption and expanding the keyspace.

Serum albumin and prognosis in elderly patients with nonischemic dilated cardiomyopathy.

AIMS: Hypoalbuminemia was extensively used to diagnose malnutrition in older adults. Malnutrition was associated with mortality in elderly patients with cardiovascular diseases. The relationship between hypoalbuminemia and clinical outcomes in elderly patients with nonischemic dilated cardiomyopathy (NIDCM) remains unknown. METHODS: A total of 1058 consecutive patients with NIDCM (age ≥60 years) were retrospectively enrolled from January 2010 to December 2019. Univariate and multivariate analyses were performed to assess the association of hypoalbuminemia with clinical outcomes. RESULTS: Patients with hypoalbuminemia were older (69.29 ±â€Š6.67 vs. 67.61 ±â€Š5.90 years, P  < 0.001) and had higher prevalence of in-hospital and long-term death than those without (6.9 vs. 1.7%, 50.7 vs. 35.2%, P  < 0.001). Logistic regression analysis showed that hypoalbuminemia was significantly related to in-hospital death [odds ratio (OR): 4.334, 95% confidence interval (CI): 2.185-8.597, P  < 0.001]. Kaplan-Meier survival analysis showed that patients with hypoalbuminemia had worse prognosis than those with nonhypoalbuminemia (log-rank χ2 28.96, P  < 0.001). After adjusting for age, serum creatinine, HDL-C, AST/ALT hypoalbuminemia, LVEF and diabetes, hypoalbuminemia remained an independent predictor for long-term death (hazard ratio 1.322, 95% CI 0.046-1.670, P  = 0.019). CONCLUSION: Hypoalbuminemia was associated with increased risk of in-hospital and long-term mortality in elderly patients with NIDCM.

Synthesis of Imidazo[1,2-a]pyridine-Fused 1,3-Benzodiazepine Derivatives with Anticancer Activity via a One-Pot Cascade GBB-3CR/Pd(II)-Catalyzed Azide-Isocyanide Coupling/Cyclization Process.

A new one-pot synthesis of imidazo[1,2-a]pyridine-fused 1,3-benzodiazepine derivatives via a sequential GBB-3CR/Pd(II)-catalyzed azide-isocyanide coupling/cyclization process was developed. The Groebke-Blackburn-Bienaymé three-component reactions (GBB-3CR) of 2-aminopyridine, 2-azidobenzaldehydes, and isocyanides in the presence of a catalytic amount of p-toluenesulfonic acid gave azide intermediates without separation. The reaction was followed by using another molecule of isocyanides to produce imidazo[1,2-a]pyridine-fused 1,3-benzodiazepine derivatives in good yields by the Pd(II)-catalyzed azide-isocyanide coupling/cyclization reaction. The synthetic approach produces novel nitrogen-fused polycyclic heterocycles under mild reaction conditions. The preliminary biological evaluation demonstrated that compound 6a inhibited glioma cells efficiently, suggesting potentially broad applications of the approach for synthesis and medicinal chemistry.

Muricauda okinawensis sp. Nov. and Muricauda yonaguniensis sp. Nov., Two Marine Bacteria Isolated from the Sediment Core near Hydrothermal Fields of Southern Okinawa Trough.

Two strains, 81s02T and 334s03T, were isolated from the sediment core near the hydrothermal field of southern Okinawa Trough. The cells of both strains were observed to be rod-shaped, non-gliding, Gram-staining negative, yellow-pigmented, facultatively anaerobic, catalase and oxidase positive, and showing optimum growth at 30 °C and pH 7.5. The strains 81s02T and 334s03T were able to tolerate up to 10% and 9% (w/v) NaCl concentration, respectively. Based on phylogenomic analysis, the average nucleotide identity (ANI) and the digital DNA-DNA hybridization (dDDH) values between the two strains and the nearest phylogenetic neighbors of the genus Muricauda were in range of 78.0-86.3% and 21.5-33.9%, respectively. The strains 81s02T and 334s03T shared 98.1% 16S rRNA gene sequence similarity to each other but were identified as two distinct species based on 81.4-81.5% ANIb, 85.5-85.6% ANIm and 25.4% dDDH values calculated using whole genome sequences. The strains 81s02T and 334s03T shared the highest 16S rRNA gene sequence similarity to M. lutimaris SMK-108T (98.7%) and M. aurea BC31-1-A7T (98.8%), respectively. The major fatty acid of strains 81s02T and 334s03T were identified similarly as iso-C15:0, iso-C17:0 3-OH and iso-C15:1 G, and the major polar lipids of the both strains consisted of phosphatidylethanolamine and two unidentified lipids. The strains contained MK-6 as their predominant menaquinone. The genomic G+C contents of strains 81s02T and 334s03T were determined to be 41.6 and 41.9 mol%, respectively. Based on the phylogenetic and phenotypic characteristics, both strains are considered to represent two novel species of the genus Muricauda, and the names Muricauda okinawensis sp. nov. and Muricauda yonaguniensis sp. nov. are proposed for strains 81s02T (=KCTC 92889T = MCCC 1K08502T) and 334s03T (=KCTC 92890T = MCCC 1K08503T).

EEG-Based Brain-Computer Interfaces are Vulnerable to Backdoor Attacks.

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This paper proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which makes adversarial attacks much easier to implement. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it.

Neural Network-Based Fixed-Time Tracking and Containment Control of Second-Order Heterogeneous Nonlinear Multiagent Systems.

This study concentrates on the fixed-time tracking consensus and containment control of second-order heterogeneous nonlinear multiagent systems (MASs) with and without measurable velocity under directed topology. By defining a time-varying scaling function and approximating the unknown nonlinear dynamics with radial basis function neural networks (RBFNNs), a novel distributed protocol for solving the fixed-time tracking consensus and containment control problems of second-order heterogeneous nonlinear MASs with full states available is proposed based on a nonsingular sliding-mode control method constructed by designing a prescribed-time convergent sliding surface. For the scenario of immeasurable velocity, a fixed-time convergent states' observer is designed to reveal the velocity information when the unknown linearity is bounded. Subsequently, a distributed fixed-time consensus protocol based on observed velocity information is proposed for the extended results. Ultimately, the acquired results are verified by three simulation examples.

Federated Multiagent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multimicrogrid Energy Management.

The utilization of large-scale distributed renewable energy (RE) promotes the development of the multimicrogrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy sufficiency. The multiagent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this article tackles this practical yet challenging issue by proposing a federated MADRL (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm, thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data are not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge National Laboratory distributed energy control communication laboratory MG (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.