Epidemic spreading on multi-layer networks with active nodes.

Investigations on spreading dynamics based on complex networks have received widespread attention these years due to the COVID-19 epidemic, which are conducive to corresponding prevention policies. As for the COVID-19 epidemic itself, the latent time and mobile crowds are two important and inescapable factors that contribute to the significant prevalence. Focusing on these two factors, this paper systematically investigates the epidemic spreading in multiple spaces with mobile crowds. Specifically, we propose a SEIS (Susceptible-Exposed-Infected-Susceptible) model that considers the latent time based on a multi-layer network with active nodes which indicate the mobile crowds. The steady-state equations and epidemic threshold of the SEIS model are deduced and discussed. And by comprehensively discussing the key model parameters, we find that (1) due to the latent time, there is a "cumulative effect" on the infected, leading to the "peaks" or "shoulders" of the curves of the infected individuals, and the system can switch among three states with the relative parameter combinations changing; (2) the minimal mobile crowds can also cause the significant prevalence of the epidemic at the steady state, which is suggested by the zero-point phase change in the proportional curves of infected individuals. These results can provide a theoretical basis for formulating epidemic prevention policies.

Repulsive synchronization in complex networks.

Synchronization in complex networks characterizes what happens when an ensemble of oscillators in a complex autonomous system become phase-locked. We study the Kuramoto model with a tunable phase-lag parameter α in the coupling term to determine how phase shifts influence the synchronization transition. The simulation results show that the phase frustration parameter leads to desynchronization. We find two global synchronization regions for α∈[0,2π) when the coupling is sufficiently large and detect a relatively rare network synchronization pattern in the frustration parameter near α=π. We call this frequency-locking configuration as "repulsive synchronization," because it is induced by repulsive coupling. Since the repulsive synchronization cannot be described by the usual order parameter r, the parameter frequency dispersion is introduced to detect synchronization.

Suppressing epidemic spreading by optimizing the allocation of resources between prevention and treatment.

The rational allocation of resources is crucial to suppress the outbreak of epidemics. Here, we propose an epidemic spreading model in which resources are used simultaneously to prevent and treat disease. Based on the model, we study the impacts of different resource allocation strategies on epidemic spreading. First, we analytically obtain the epidemic threshold of disease using the recurrent dynamical message passing method. Then, we simulate the spreading of epidemics on the Erdos-Rényi (ER) network and the scale-free network and investigate the infection density of disease as a function of the disease infection rate. We find hysteresis loops in the phase transition of the infection density on both types of networks. Intriguingly, when different resource allocation schemes are adopted, the phase transition on the ER network is always a first-order phase transition, while the phase transition on the scale-free network transforms from a hybrid phase transition to a first-order phase transition. Particularly, through extensive numerical simulations, we find that there is an optimal resource allocation scheme, which can best suppress epidemic spreading. In addition, we find that the degree heterogeneity of the network promotes the spreading of disease. Finally, by comparing theoretical and numerical results on a real-world network, we find that our method can accurately predict the spreading of disease on the real-world network.

Cooperative epidemics spreading under resource control.

The input and allocation of public resources are of crucial importance to suppressing the outbreak of infectious diseases. However, in the research on multi-disease dynamics, the impact of resources has never been taken into account. Here, we propose a two-epidemic spreading model with resource control, in which the amount of resources is introduced into the recovery rates of diseases and the allocation of resources between two diseases is regulated by a parameter. Using the dynamical message passing method, we obtain resource thresholds of the two diseases and validate them on ER networks and scale-free networks. By comparing the results on scale-free networks with different power-law exponents, we find that the heterogeneity of the network promotes the spreading of both diseases. Especially, we find optimal allocation coefficients at different resource levels. And, we get a counterintuitive conclusion that when the available resources are limited, it is a better strategy to preferentially suppress the disease with lower infection rate. In addition, we investigate the effect of interaction strength and find that great interaction strength between diseases makes two diseases with different infectivity tend to be homogeneous.

Immunogenicity consistency and safety with different production scales of recombinant adenovirus type-5 vectored COVID-19 vaccine in healthy adults: a randomized, double-blinded, immunobridging trial.

BACKGROUND: The certification of immunogenicity consistency at different production scales is indispensable for the quality control of vaccines. RESEARCH DESIGN AND METHODS: A randomized, double-blind immunobridging trial in healthy adults aged 18-59 was divided into Scale A (50 L and 800 L) and Scale B (50 L and 500 L) based on vaccine manufacturing scales. Eligible participants in Scale A were randomly assigned to receive the single-dose recombinant adenovirus type-5 vectored COVID-19 vaccine (Ad5-nCoV) of different scales at a 1:1 ratio, as was Scale B. The primary endpoint was the geometric mean titer (GMT) of anti-live SARS-CoV-2-specific neutralizing antibodies (NAb) 28 days post-vaccination. RESULTS: 1,012 participants were enrolled, with 253 (25%) per group. The post-vaccination GMTs of NAb were 10.72 (95% CI: 9.43, 12.19) and 13.23 (11.64, 15.03) in Scale A 50 L and 800 L, respectively; 11.64 (10.12, 13.39) and 12.09 (10.48, 13.95) in Scale B 50 L and 500 L, respectively. GMT ratios in Scale A and B have a 95% CI of 0.67-1.5. Most adverse reactions were mild or moderate. 17 of 18 participants reported non-vaccination-related serious adverse reactions. CONCLUSIONS: The Ad5-nCoV in the scale-up production of 500 L and 800 L showed consistent immunogenicity with the original 50 L production scale, respectively.

A competition model for prediction of admission scores of colleges and universities in Chinese college entrance examination.

Predicting the admission scores of colleges and universities is significant for high school graduates in the College Entrance Examination in China (which is also called "Gaokao" for short). The practice of parallel application for the students after Gaokao not only puts forward a question about how students could make the best of their scores and make the best choice, but also results in the strong competition among different colleges and universities, with the institutions all striving to admit high-performing students in this examination. However, existing prevailing prediction algorithms and models of the admission score of the colleges and universities based on machine learning methods do not take such competitive relationship into consideration, but simply make predictions for individual college or university, causing low predication accuracy and poor generalization capability. This paper intends to analyze such competitive relationship by extracting the important features (e.g., project, location and score discrepancy) of colleges and universities. A novel competition model incorporating the coarse clustering is thus proposed to make the predictions for colleges and universities in a same cluster. By using Gaokao data of Shanxi province in China from 2016 to 2019, we testify the proposed model in comparison with several benchmark methods. The experimental results show that the precision within the error of 3 points and 5 points are 7.3% and 2.8% higher respectively than the second-best algorithm. It has proven that the competition model has the capability to fit the competitive relationship, thus improving the predication accuracy to a large extent. Theoretically, the method proposed could provide a more advanced and comprehensive view about the analysis of factors that may influence the admission score of higher institutions. Practically, the model proposed with high accuracy could help the students make the best of their scores and apply for the college and universities more scientifically.

Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks.

Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients' families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.

Center of mass in complex networks.

Network dynamics is always a big challenge in nonlinear dynamics. Although great advancements have been made in various types of complex systems, an universal theoretical framework is required. In this paper, we introduce the concept of center of 'mass' of complex networks, where 'mass' stands for node importance or centrality in contrast to that of particle systems, and further prove that the phase transition and evolutionary state of the system can be characterized by the activity of center of 'mass'. The steady states of several complex networks (gene regulatory networks and epidemic spreading systems) are then studied by analytically calculating the decoupled equation of the dynamic activity of center of 'mass', which is derived from the dynamic equation of the complex networks. The limitations of this method are also pointed out, such as the dynamical problems that related with the relative activities among components, and those systems that consist of oscillatory or chaotic motions.

Advanced Algorithms for Local Routing Strategy on Complex Networks.

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 〈T〉 decreases by 70-90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks.

Correction: Advanced Algorithms for Local Routing Strategy on Complex Networks.

[This corrects the article DOI: 10.1371/journal.pone.0156756.].

[Detection of neutralizing antibodies and DNA of human papillomavirus 16, 18 in women aged 18-45 years in Funing, Jiangsu province].

OBJECTIVE: To understand the distributions of DNA and neutralizing antibodies of human papillomavirus (HPV)16, 18 in 18-45 year-old women. METHODS: Totally, 1494 women were enrolled through multistage random sampling in Funing, Jiangsu province. Cervical exfoliated cells were collected from them for HPV DNA testing, and serum samples were taken from them for the detection of HPV16, 18 neutralizing antibodies by using pseudovirion-based neutralization assay(PBNA). RESULTS: Among the 1494 women, 28(1.9%) and 188(12.6%) were positive for DNA and neutralizing antibody of HPV16 respectively, and 15(1.0%) and 60(4.0%) were positive for DNA and neutralizing antibody of HPV18, respectively. There were no significant differences in the detection rates of DNA and neutralizing antibody of HPV16, 18 among different age groups. About 16.7% of the women were infected with HPV16, 18, or both. CONCLUSION: In Funing county of Jiangsu province, most women aged 18-45 years has no immunity to HPV16 and 18, indicating that they are appropriate targets for HPV 16/18 vaccination.