A data-driven epidemic model with human mobility and vaccination protection for COVID-19 prediction.

Epidemiological models allow for quantifying the dynamic characteristics of large-scale outbreaks. However, capturing detailed and accurate epidemiological information often requires consideration of multiple kinetic mechanisms and parameters. Due to the uncertainty of pandemic evolution, such as pathogen variation, host immune response and changes in mitigation strategies, the parameter evaluation and state prediction of complex epidemiological models are challenging. Here, we develop a data-driven epidemic model with a generalized SEIR mechanistic structure that includes new compartments, human mobility and vaccination protection. To address the issue of model complexity, we embed the epidemiological model dynamics into physics-informed neural networks (PINN), taking the observed series of time instances as direct input of the network to simultaneously infer unknown parameters and unobserved dynamics of the underlying model. Using actual data during the COVID-19 outbreak in Australia, Israel, and Switzerland, our model framework demonstrates satisfactory performance in multi-step ahead predictions compared to several benchmark models. Moreover, our model infers time-varying parameters such as transmission rates, hospitalization ratios, and effective reproduction numbers, as well as calculates the latent period and asymptomatic infection count, which are typically unreported in public data. Finally, we employ the proposed data-driven model to analyze the impact of different mitigation strategies on COVID-19.

Phase I study of a non-S2P SARS-CoV-2 mRNA vaccine LVRNA009 in Chinese adults.

BACKGROUND: COVID-19 caused by SARS-CoV-2 is a great threat to public health. We present the safety and immunogenicity data from a phase I trial in China of an mRNA vaccine (LVRNA009). METHODS: In the single-centre, double-blind, placebo-controlled and dose-escalation study, 72 healthy unvaccinated adults aged 18-59 years were randomized (3:1) to receive LVRNA009 with one of three vaccine dosage (25, 50 and 100 µg) or placebo, to evaluate for the safety, tolerability and immunogenicity of LVRNA009. RESULTS: All these participants received two injections 28 days apart. No adverse events higher than grade 2 were reported during the study. A total of 30 participants (42 %) reported solicited adverse reactions during the first 14 days after vaccinations. Of the events reported, fever (n = 11, 15 %) was the most common systemic adverse reaction, and pain at the injection site (n = 17, 24 %) was the most frequent solicited local adverse reaction. Anti-S-protein IgG and neutralising antibodies were observed to have been induced 14 days after the first dose, significantly increased 7 days after the second dose, and remained at a high level 28 days after the second dose. Specific T-cell responses peaked 7 days and persisted 28 days after second vaccination. CONCLUSION: LVRNA009 has demonstrated promising results in safety and tolerability at all three dose levels among Chinese adults. LVRNA009 at three dose levels could rapidly induce strong humoral and cellular immune responses, including binding and neutralising antibody production and IFN- γ secretion, which showed good immunogenicity. CLINICAL TRIAL REGISTRATION NUMBER: Clinicaltrials.gov NCT05364047; Chictr.org.cn ChiCTR2100049349.

Kinematic modelling and experimental testing of a particle-jamming soft robot based on a DEM-FEM coupling method.

Particle-jamming soft robots are characterised by high flexibility in motion and high stiffness when executing a task. Regarding particle jamming of soft robots, the discrete element method (DEM)-finite element method (FEM) coupling was used for modelling and control. At first, a real-time particle-jamming soft actuator was proposed by integrating advantages of the driving Pneu-Net and the driven particle-jamming mechanism. Then, DEM and FEM were separately employed to determine the force-chain structure of the particle-jamming mechanism and the bending deformation performance of the pneumatic actuator. Furthermore, the piecewise constant curvature method was adopted for forward and inverse kinematic modelling of the particle-jamming soft robot. Finally, a prototype of the coupled particle-jamming soft robot was prepared, and a visual tracking test platform was established. The adaptive control method was proposed to compensate for the accuracy of motion trajectories. The variable-stiffness performance of the soft robot was verified by conducting stiffness tests and bending tests. The results provide novel theoretical and technical support for the modelling and control of variable-stiffness soft robots.

[Expression level and clinical significance of CD44 and CD33 in 77 patients with benign lymphoadenosis of oral mucosa].

PURPOSE: To investigate the expression and clinical significance of CD44 and CD33 in benign lymphoadenosis of oral mucosa(BLOM). METHODS: From January 2017 to March 2020, seventy-seven BLOM wax blocks from the Department of Pathology of Qingdao Traditional Chinese Medicine Hospital were selected as the experimental group, and 63 cases of normal oral mucosal tissue wax blocks during the same period were selected as the control group. Immunohistochemical method was used to detect the positive expression of CD44 and CD33 in the two groups.Spearman correlation analysis was used to analyze the correlation between the positive expression of CD33 and the positive expression of CD44 in the diseased tissues of BLOM patients.The general information about patients were collected.The relationship between the expression of CD33 and CD44 in the diseased tissues of BLOM patients and the clinicopathological characteristics of BLOM patients were analyzed. SPSS 21.0 software package was used for statistical analysis of the data. RESULTS: The positive expression rates of CD33 in the control group and the experimental group were 95.24% and 63.64%, respectively, and the difference was statistically significant(P＜0.05). The positive expression rates of CD44 in the control group and the experimental group were 93.65% and 67.53%, respectively, and the difference was statistically significant(P＜0.05). The results of Spearman correlation analysis showed that the positive expression of CD33 in the diseased tissues of BLOM patients was positively correlated with the positive expression of CD44 (r=0.834, P=0.002). The expression of CD33 and CD44 in the diseased tissues of patients with BLOM were related to clinical type, degree of inflammation, presence or absence of lymphoid follicles, and lymphocyte infiltration(P＜0.05), but not related to age, gender, course of disease, location, and epithelial surface keratinization(P＞0.05). CONCLUSIONS: The positive expression rate of CD33 and CD44 in the BLOM tissues decreased, which was closely related to the clinical type, degree of inflammation, presence or absence of lymphoid follicles, and lymphocyte infiltration.

Reactive-diffusion epidemic model on human mobility networks: Analysis and applications to COVID-19 in China.

The complex dynamics of human mobility, combined with sporadic cases of local outbreaks, make assessing the impact of large-scale social distancing on COVID-19 propagation in China a challenge. In this paper, with the travel big dataset supported by Baidu migration platform, we develop a reactive-diffusion epidemic model on human mobility networks to characterize the spatio-temporal propagation of COVID-19, and a novel time-dependent function is incorporated into the model to describe the effects of human intervention. By applying the system control theory, we discuss both constant and time-varying threshold behavior of proposed model. In the context of population mobility-mediated epidemics in China, we explore the transmission patterns of COVID-19 in city clusters. The results suggest that human intervention significantly inhibits the high correlation between population mobility and infection cases. Furthermore, by simulating different population flow scenarios, we reveal spatial diffusion phenomenon of cases from cities with high infection density to cities with low infection density. Finally, our model exhibits acceptable prediction performance using actual case data. The localized analytical results verify the ability of the PDE model to correctly describe the epidemic propagation and provide new insights for controlling the spread of COVID-19.

Consensus-Based Distributed Reduced-Order Observer Design for LTI Systems.

In this article, we refocus on the distributed observer construction of a continuous-time linear time-invariant (LTI) system, which is called the target system, by using a network of observers to measure the output of the target system. Each observer can access only a part of the component information of the output of the target system, but the consensus-based communication among them can make it possible for each observer to estimate the full state vector of the target system asymptotically. The main objective of this article is to simplify the distributed reduced-order observer design for the LTI system on the basis of the consensus communication pattern. For observers interacting on a directed graph, we first address the problem of the distributed reduced-order observer design for the detectable target system and provide sufficient conditions involving the topology information to guarantee the existence of the distributed reduced-order observer. Then, the dependence on the topology information in the sufficient conditions will be eliminated by using the adaptive strategy and so that a completely distributed reduced-order observer can be designed for the target system. Finally, some numerical simulations are proposed to verify the theoretical results.

Consensus of Continuous-Time Linear Multiagent Systems With Discrete Measurements.

This article concerns the robust consensus problem of continuous-time linear multiagent systems (MASs) with uncertainty and discrete-time measurement information, where the output measurement information is in the data-sampled form. Distributed output-feedback protocol with or without controller interaction is proposed for each agent. Specifically, the output-feedback protocol runs in continuous time with an output error correction term mixed with the discrete-time measurement information. The concrete algorithm is given for the construction of the feedback matrices. Then, by using the delay-input approach, sufficient conditions are provided for the robust consensus of this kind of MASs interacting over networks described by the directed graphs. Finally, numerical simulations are given to illustrate the theoretical results.

Observer-Based Multiagent Bipartite Consensus With Deterministic Disturbances and Antagonistic Interactions.

This article studies the multiagent bipartite consensus in networks with deterministic disturbances and antagonistic interactions. An observer-based output-feedback controller design is provided to guarantee the bipartite consensus with deterministic disturbances that satisfy the matching condition. Then, by considering that the bandwidths of communication channels are limited in practical systems, the event-triggered scenario of the proposed output controller for the bipartite consensus is further studied; the node-based broadcast updating fashion is utilized and the Zeno behavior is ruled out. Simulations are also offered to support the theoretical results of protocol designs.

An iterative Q-learning based global consensus of discrete-time saturated multi-agent systems.

This paper addresses the consensus problem of discrete-time multiagent systems (DTMASs), which are subject to input saturation and lack of the information of agent dynamics. In the previous works, the DTMASs with input saturation can achieve semiglobal consensus by utilizing the low gain feedback (LGF) method, but computing the LGF matrices by solving the modified algebraic Riccati equation requires the knowledge of agent dynamics. In this paper, motivated by the reinforcement learning method, we propose a model-free Q-learning algorithm to obtain the LGF matrices for the DTMASs achieving global consensus. Firstly, we define a Q-learning function and deduce a Q-learning Bellman equation, whose solution can work out the LGF matrix. Then, we develop an iterative Q-learning algorithm to obtain the LGF matrix without the requirement of the knowledge about agent dynamics. Moreover, the DTMASs can achieve global consensus. Lastly, some simulation results are proposed to validate the effectiveness of the Q-learning algorithm and show the effect on the rate of convergence from the initial states of agents and the input saturation limit.

Novel epidemic models on PSO-based networks.

This paper proposes two spatio-temporal epidemic network models based on popularity and similarity optimization (PSO), called r-SI and r-SIS, respectively, in which new connections take both popularity and similarity into account. In the spatial dimension, the epidemic process is described by the diffusion equation; in the time dimension, the growth of an epidemic is described by the logistic map. Both models are represented by partial differential equations, and can be easily solved. Simulations are performed on both artificial and real networks, demonstrating the effectiveness of the two models.

A New Model for Complex Dynamical Networks Considering Random Data Loss.

Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost states in the coupling term. By employing Lyapunov stability theory and stochastic analysis, a sufficient condition is derived to guarantee the compensation values finally equal to the lost values, namely, the influence of data loss is finally eliminated in the proposed model. Moreover, we generalize the modeling method to output-coupling complex dynamical networks. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed model.

Modeling and analysis of epidemic spreading on community networks with heterogeneity.

A large number of real world networks exhibit community structure, and different communities may often possess heterogeneity. In this paper, considering the heterogeneity among communities, we construct a new community network model in which the communities show significant differences in average degree. Based on this heterogeneous community network, we propose a novel mathematical epidemic model for each community and study the epidemic dynamics in this network model. We find that the location of the initial infection node only affects the spreading velocity and barely influences the epidemic prevalence. And the epidemic threshold of entire network decreases with the increase of heterogeneity among communities. Moreover, the epidemic prevalence increases with the increase of heterogeneity around the epidemic threshold, while the converse situation holds when the infection rate is much greater than the epidemic threshold.

An Adaptive Primal-Dual Subgradient Algorithm for Online Distributed Constrained Optimization.

In this paper, we consider the problem of solving distributed constrained optimization over a multiagent network that consists of multiple interacting nodes in online setting, where the objective functions of nodes are time-varying and the constraint set is characterized by an inequality. Through introducing a regularized convex-concave function, we present a consensus-based adaptive primal-dual subgradient algorithm that removes the need for knowing the total number of iterations in advance. We show that the proposed algorithm attains an [where ] regret bound and an bound on the violation of constraints; in addition, we show an improvement to an regret bound when the objective functions are strongly convex. The proposed algorithm allows a novel tradeoffs between the regret and the violation of constraints. Finally, a numerical example is provided to illustrate the effectiveness of the algorithm.

Distributed Finite Time Consensus of Secondorder Multi-agent Systems via Pinning Control (August 2018).

The robust distributed finite time consensus of second-order multi-agent systems via pinning control has been investigated in this paper. A new nonsingular finite time TSM control method is proposed for second-order single system with disturbances. Based on the pinning error function vector, robust distributed finite time consensus of second-order multi-agent systems via pinning control method is given. Simulations results are performed to validate the effectiveness of the theoretical results.

State Estimation for General Complex Dynamical Networks with Incompletely Measured Information.

Estimating uncertain state variables of a general complex dynamical network with randomly incomplete measurements of transmitted output variables is investigated in this paper. The incomplete measurements, occurring randomly through the transmission of output variables, always cause the failure of the state estimation process. Different from the existing methods, we propose a novel method to handle the incomplete measurements, which can perform well to balance the excessively deviated estimators under the influence of incomplete measurements. In particular, the proposed method has no special limitation on the node dynamics compared with many existing methods. By employing the Lyapunov stability theory along with the stochastic analysis method, sufficient criteria are deduced rigorously to ensure obtaining the proper estimator gains with known model parameters. Illustrative simulation for the complex dynamical network composed of chaotic nodes are given to show the validity and efficiency of the proposed method.

Inhibitory effects of transcription factor Ikaros on the expression of liver cancer stem cell marker CD133 in hepatocellular carcinoma.

CD133 is a cellular surface glycoprotein that has been reported as a marker for the enrichment of cancer stem cells (CSCs). However, the regulatory mechanism of CD133 remains unknown. CSCs have been proposed to contribute to radioresistance and multi-drug resistance. The elucidation of key regulators of CD133 and CSCs is critical for the development of CSC-targeted therapy. In this study, we showed that Ikarosinhibited the expression of CD133 via direct binding to the CD133 P1 promoter and repressed the tumorigenic and self-renewal capacity of CD133(+) cancer stem-like cells in hepatocellular carcinoma (HCC). We found that Ikaros interacted with CtBP as a transcription repressor complex, which inhibited CD133 expression in HCC. We also demonstrated that Ikaros expression was up-regulated by ETS1 which activity was regulated by MAPKs pathway. Furthermore, decreased expression of Ikaroswas significantly associated with poor survival in HCC patients. Overall, our study identifies that Ikaros plays a role as a transcription repressor in HCC and is a new reactivated therapeutic target for the treatment of HCC. Meanwhile, our findings provide evidence that Ikaros could be an attractive inhibitor of the target gene CD133, which reactivates anticancer mechanisms in targeted CSC therapy.

A new method for evaluating tricuspid valve displacement in children with Ebstein's anomaly: using the annulus and coronary sinus as a reference point.

This study aimed to investigate the reference point for the downward displacement of the posterior and anterior leaflets of the tricuspid valve using echocardiography in children with Ebstein's anomaly. This study enrolled 25 patients with Ebstein's anomaly. The extent of downward displacement of the posterior and anterior leaflets of the tricuspid valve was evaluated by echocardiography using the tricuspid annulus and the coronary sinus as reference points. These results were compared with the surgical findings. The findings showed displacement of the simple septal leaflet in 1 patient, displacement of both the septal and posterior leaflets in 22 patients, displacement of both the anterior and posterior leaflets in 1 patient, and displacement of all the leaflets in 1 patient. Because the septal and posterior leaflets were close to the apex or because the posterior leaflet was nearly absent, the displacement distance of the septal and posterior leaflets could not be measured accurately in two patients. The displacement distance of the septal and posterior leaflets in the remaining 22 patients were 2.08 ± 1.15 and 2.58 ± 1.06 cm, respectively. The displacement distances of the anterior leaflet in two patients were respectively 1.0 and 2.2 cm. These results were similar to those measured during surgery. The direction of the valvular regurgitation flow was anterolateral in the apical four-chamber and apical right heart two-chamber views in patients with the downward displacement of the anterior leaflet. The tricuspid valve annulus and the coronary sinus are ideal reference points for evaluating the downward displacement of the posterior and anterior leaflets of the tricuspid valve. It is critical to evaluate the downward displacement of the anterior leaflet that the direction of the tricuspid regurgitation flow is changed.

Noninvasive perioperative evaluation of right ventricular function in children with tetralogy of Fallot.

Early and accurate noninvasive means of identifying right ventricular (RV) dysfunction in children with tetralogy of Fallot (TOF) are needed. RV function was examined using tissue Doppler imaging (TDI), strain rate (SR), and strain analysis (SA) in children before (N = 37) and after (6-12 months; N = 32) TOF repair, and in a control group of children (N = 37). Plasma concentrations of N-terminal pro-brain natriuretic peptide (NT-proBNP) and matrix metalloproteinase 9 (MMP-9) were measured. TDI, SR, and SA revealed that RV systolic and diastolic function indices were lower preoperatively in the TOF group compared with the control group, and did not improve after TOF repair. Plasma NT-proBNP concentrations were significantly higher in the TOF group pre- and postoperatively compared with the control group. In the preoperative TOF group, NT-proBNP concentration was significantly correlated with peak systolic SR and systolic strain in the mid segments of RV free wall. Plasma MMP-9 concentrations were significantly increased in the preoperative TOF group compared with the control group, and significantly correlated with plasma NT-proBNP and logNT-proBNP concentrations. RV function correlated with plasma NT-proBNP concentrations in children with TOF. Assessment of this noninvasive measure may help identify RV dysfunction in patients with TOF before they become clinically symptomatic.

Epidemic spreading in metapopulation networks with heterogeneous infection rates.

In this paper, we study epidemic spreading in metapopulation networks wherein each node represents a subpopulation symbolizing a city or an urban area and links connecting nodes correspond to the human traveling routes among cities. Differently from previous studies, we introduce a heterogeneous infection rate to characterize the effect of nodes' local properties, such as population density, individual health habits, and social conditions, on epidemic infectivity. By means of a mean-field approach and Monte Carlo simulations, we explore how the heterogeneity of the infection rate affects the epidemic dynamics, and find that large fluctuations of the infection rate have a profound impact on the epidemic threshold as well as the temporal behavior of the prevalence above the epidemic threshold. This work can refine our understanding of epidemic spreading in metapopulation networks with the effect of nodes' local properties.

Diagnostic value of parasternal pulmonary artery short-axis view for the anomalous origin of the left coronary artery from the pulmonary artery.

OBJECTIVE: To evaluate the diagnostic value of parasternal pulmonary artery (PA) short-axis view for the anomalous origin of left coronary artery (LCA) from the pulmonary artery by echocardiography. METHODS: A total of 13 patients (3 boys) aged from 2 months to 12 years were enrolled. Transthoracic echocardiography, including cross-sectional imaging and color Doppler flow imaging, were performed, and their diagnoses were confirmed by operation. RESULTS: Among the 13 patients, 7 had LCA originated from the left posterior wall of PA, 2 from the posterior wall, and 4 from the right posterior wall. The PA short-axis view could visualize the anomalous origin of the LCA from left posterior or posterior wall of PA clearly. The LCA and aortic wall were overlapping at 3-4 o'clock at PA short-axis view in the patients with LCA originating from the right posterior wall of PA. It was similar with the image of the LCA originating from the aorta. But the blood flow was opposite to that of LCA with normal origin. CONCLUSIONS: The parasternal PA short-axis view is a good view to visualize the anomalous origin of the LCA.