An Empirical Test of the Role of Small-Scale Transmission in Large-Scale Disease Dynamics.

A key assumption of epidemiological models is that population-scale disease spread is driven by close contact between hosts and pathogens. At larger scales, however, mechanisms such as spatial structure in host and pathogen populations and environmental heterogeneity could alter disease spread. The assumption that small-scale transmission mechanisms are sufficient to explain large-scale infection rates, however, is rarely tested. Here, we provide a rigorous test using an insect-baculovirus system. We fit a mathematical model to data from forest-wide epizootics while constraining the model parameters with data from branch-scale experiments, a difference in spatial scale of four orders of magnitude. This experimentally constrained model fits the epizootic data well, supporting the role of small-scale transmission, but variability is high. We then compare this model's performance to an unconstrained model that ignores the experimental data, which serves as a proxy for models with additional mechanisms. The unconstrained model has a superior fit, revealing a higher transmission rate across forests compared with branch-scale estimates. Our study suggests that small-scale transmission is insufficient to explain baculovirus epizootics. Further research is needed to identify the mechanisms that contribute to disease spread across large spatial scales, and synthesizing models and multiscale data are key to understanding these dynamics.

Spatially antiviral dynamics determines HCV in vivo replication and evolution.

In this article, we establish HCV in a host growth partial-differential equation model to analyze the antiviral dynamics. A numerical method to analyze the basic reproductive number of this model is established and we find that for the without drug model, the diffusion rate of the virus and liver length have seldom influence on the growth of the virus. For the with drug model, we find the different pharmic factors have different effect on the virus. Based on this with drug model, we also introduce a semi-stochastic simulation method with which to analyze the virus in host evolution. Our result shows how different drugs can drive the virus in host evolution.

Modelling the effects of cutting off infected branches and replanting on fire-blight transmission using Filippov systems.

Fire blight is one of the most devastating plant diseases in the world. This paper proposes a Filippov fire-blight model incorporating cutting off infected branches and replanting susceptible trees. The Filippov-type model is formulated by considering that no control strategy is taken if the number of infected trees is less than an infected threshold level Ic; further, we cut off infected branches once the number of infected trees exceeds Ic; meanwhile, we replant trees if the number of susceptible trees is less than a susceptible threshold level Sc. The global dynamical behaviour of the Filippov system is investigated. It is shown that model solutions ultimately converge to the positive equilibrium that lies in the region above Ic, or below Ic, or on I=Ic, as we vary the susceptible and infected threshold values Sc and Ic. Our results indicate that proper combinations of the susceptible and infected threshold values based on the threshold policy can lead the number of infected trees to an acceptable level, when complete eradication is not economically desirable.

On-chip integrated exceptional surface microlaser.

The on-chip integrated visible microlaser is a core unit of high-speed visible-light communication with huge bandwidth resources, which needs robustness against fabrication errors, compressible linewidth, reducible threshold, and in-plane emission. However, until now, it has been a great challenge to meet these requirements simultaneously. Here, we report a scalable strategy to realize a robust on-chip integrated visible microlaser with further improved lasing performances enabled by the increased orders (n) of exceptional surfaces, and experimentally verify the strategy by demonstrating the performances of a second-order exceptional surface-tailored microlaser. We further prove the potential application of the strategy by discussing an exceptional surface-tailored topological microlaser with unique performances. This work lays a foundation for further development of on-chip integrated high-speed visible-light communication and processing systems, provides a platform for the fundamental study of non-Hermitian photonics, and proposes a feasible method of joint research for non-Hermitian photonics with nonlinear optics and topological photonics.

The Global dynamics of a SIR model considering competitions among multiple strains in patchy environments.

Pandemics, such as Covid-19 and AIDS, tend to be highly contagious and have the characteristics of global spread and existence of multiple virus strains. To analyze the competition among different strains, a high dimensional SIR model studying multiple strains' competition in patchy environments is introduced in this work. By introducing the basic reproductive number of different strains, we found global stability conditions of disease-free equilibrium and persistence conditions of the model. The competition exclusion conditions of that model are also given. This work gives some insights into the properties of the multiple strain patchy model and all of the analysis methods used in this work could be used in other related high dimension systems.

A Markov based model to estimate the number of syphilis cases among floating population.

Syphilis is a sexually transmitted disease that spreads widely around the world, infecting tens of millions of people every year. In China, syphilis not only causes more than 1 million infections every year, but also has its own characteristics in spreading pattern: this disease always spreads with the migration of floating population. There have been many related investigations and studies on the transmission of syphilis with the floating population in China, but the study of quantitative modeling in this field is very limited. In this paper, based on the Markov process model and datasets collected in Zhejiang Province, China, we construct a new model to analyze the transmission and immigration process of syphilis. The results show that immigrant patients are one of the sources of infection of syphilis in Zhejiang province, and the infection rate is remarkable which should not be ignored. By using the PRCC method to analyze the relationship between parameters and infected cases, we also find two main effective measures that can control the spread of syphilis and reduce the infection rate: the self-attention of infected persons, and the use of sexual protection measures. With the increasing frequent exchanges of people among different countries and regions, studying the transmission of diseases with the floating populations has become more and more important. The method we use in this paper gives a new insight studying this issue, providing a quantitative research method using the data of diagnosed cases. All the methods and models in this paper can be extendly used in the studies of other diseases where immigrant patients should be considered.

Data augmentation based on multiple oversampling fusion for medical image segmentation.

A high-performance medical image segmentation model based on deep learning depends on the availability of large amounts of annotated training data. However, it is not trivial to obtain sufficient annotated medical images. Generally, the small size of most tissue lesions, e.g., pulmonary nodules and liver tumours, could worsen the class imbalance problem in medical image segmentation. In this study, we propose a multidimensional data augmentation method combining affine transform and random oversampling. The training data is first expanded by affine transformation combined with random oversampling to improve the prior data distribution of small objects and the diversity of samples. Secondly, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the lesion pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The LUNA16 and LiTS17 datasets were introduced to evaluate the performance of our works, where four deep neural network models, Mask-RCNN, U-Net, SegNet and DeepLabv3+, were adopted for small tissue lesion segmentation in CT images. In addition, the small tissue segmentation performance of the four different deep learning architectures on both datasets could be greatly improved by incorporating the data augmentation strategy. The best pixelwise segmentation performance for both pulmonary nodules and liver tumours was obtained by the Mask-RCNN model, with DSC values of 0.829 and 0.879, respectively, which were similar to those of state-of-the-art methods.

Topological evolution of coexpression networks by new gene integration maintains the hierarchical and modular structures in human ancestors.

We analyze the global structure and evolution of human gene coexpression networks driven by new gene integration. When the Pearson correlation coefficient is greater than or equal to 0.5, we find that the coexpression network consists of 334 small components and one "giant" connected subnet comprising of 6317 interacting genes. This network shows the properties of power-law degree distribution and small-world. The average clustering coefficient of younger genes is larger than that of the elderly genes (0.6685 vs. 0.5762). Particularly, we find that the younger genes with a larger degree also show a property of hierarchical architecture. The younger genes play an important role in the overall pivotability of the network and this network contains few redundant duplicate genes. Moreover, we find that gene duplication and orphan genes are two dominant evolutionary forces in shaping this network. Both the duplicate genes and orphan genes develop new links through a "rich-gets-richer" mechanism. With the gradual integration of new genes into the ancestral network, most of the topological structure features of the network would gradually increase. However, the exponent of degree distribution and modularity coefficient of the whole network do not change significantly, which implies that the evolution of coexpression networks maintains the hierarchical and modular structures in human ancestors.