Search

Multiple new variants of SARS-CoV-2 have been identified as the COVID-19 pandemic spreads across the globe. However, most epidemic models view the virus as static and unchanging and thus fail to address the consequences of the potential evolution of the virus. Here, we built a competitive susceptible-infected-removed (coSIR) model to simulate the competition between virus strains of differing severities or transmissibility under various virus control policies. The coSIR model predicts that although the virus is extremely unlikely to evolve into a "super virus" that causes an increased fatality rate, virus variants with less severe symptoms can lead to potential new outbreaks and can cost more lives over time. The present model also demonstrates that the protocols restricting the transmission of the virus, such as wearing masks and social distancing, are the most effective strategy in reducing total mortality. A combination of adequate testing and strict quarantine is a powerful alternative to policies such as mandatory stay-at-home orders, which may have an enormous negative impact on the economy. In addition, building Mobile Cabin Hospitals can be effective and efficient in reducing the mortality rate of highly infectious virus strains. [ABSTRACT FROM AUTHOR]

Abstract Background The advent of rapid and inexpensive sequencing technology allows scientists to decipher heterogeneity within primary tumours, between primary and metastatic sites, and between metastases. Charting the evolutionary history of individual tumours has revealed drivers of tumour heterogeneity and highlighted its impact on therapeutic outcomes. Discussion Scientists are using improved sequencing technologies to characterise and address the challenge of tumour heterogeneity, which is a major cause of resistance to therapy and relapse. Heterogeneity may fuel metastasis through the selection of rare, aggressive, somatically altered cells. However, extreme levels of chromosomal instability, which contribute to intratumour heterogeneity, are associated with improved patient outcomes, suggesting a delicate balance between high and low levels of genome instability. Conclusions We review evidence that intratumour heterogeneity influences tumour evolution, including metastasis, drug resistance, and the immune response. We discuss the prevalence of tumour heterogeneity, and how it can be initiated and sustained by external and internal forces. Understanding tumour evolution and metastasis could yield novel therapies that leverage the immune system to control emerging tumour neo-antigens.

This paper proposes an effective ensemble framework (EF) for tackling multiobjective optimization problems, by combining the advantages of various evolutionary operators and selection criteria that are run on multiple populations. A simple ensemble algorithm is realized as a prototype to demonstrate our proposed framework. Two mechanisms, namely competition and cooperation, are employed to drive the running of the ensembles. Competition is designed by adaptively running different evolutionary operators on multiple populations. The operator that better fits the problem’s characteristics will receive more computational resources, being rewarded by a decomposition-based credit assignment strategy. Cooperation is achieved by a cooperative selection of the offspring generated by different populations. In this way, the promising offspring from one population have chances to migrate into the other populations to enhance their convergence or diversity. Moreover, the population update information is further exploited to build an evolutionary potentiality model, which is used to guide the evolutionary process. Our experimental results show the superior performance of our proposed ensemble algorithms in solving most cases of a set of 31 test problems, which corroborates the advantages of our EF. [ABSTRACT FROM AUTHOR]

Background: The advent of rapid and inexpensive sequencing technology allows scientists to decipher heterogeneity within primary tumours, between primary and metastatic sites, and between metastases. Charting the evolutionary history of individual tumours has revealed drivers of tumour heterogeneity and highlighted its impact on therapeutic outcomes.Discussion: Scientists are using improved sequencing technologies to characterise and address the challenge of tumour heterogeneity, which is a major cause of resistance to therapy and relapse. Heterogeneity may fuel metastasis through the selection of rare, aggressive, somatically altered cells. However, extreme levels of chromosomal instability, which contribute to intratumour heterogeneity, are associated with improved patient outcomes, suggesting a delicate balance between high and low levels of genome instability.Conclusions: We review evidence that intratumour heterogeneity influences tumour evolution, including metastasis, drug resistance, and the immune response. We discuss the prevalence of tumour heterogeneity, and how it can be initiated and sustained by external and internal forces. Understanding tumour evolution and metastasis could yield novel therapies that leverage the immune system to control emerging tumour neo-antigens. [ABSTRACT FROM AUTHOR]

Abstract: Inversion of Rayleigh wave dispersion curves is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this paper, we proposed and tested a new Rayleigh wave dispersion curve inversion scheme called the shuffled complex evolution (SCE) approach, which is based on a synthesis of four global optimization strategies that have proved successful for global optimization: (a) combination of probabilistic and deterministic approaches; (b) the strategy of clustering; (c) systematic evolution of a complex of points spanning the space, in the direction of global improvement; and (d) competitive evolution. Incorporating these four global optimization strategies into the inverse procedure greatly enhances the performance of the SCE method because these steps not only can effectively locate the promising areas in the solution space for a global minimum but also avoid its wandering near the global minimum in the final stage of search. The proposed inverse procedure was applied to nonlinear inversion of fundamental-mode Rayleigh wave dispersion curves for near-surface shear (S)-wave velocity profiles. The calculation efficiency and stability of the inversion scheme are tested on four synthetic models and a real-world example from a waste disposal site in NE Italy. A comparative analysis with genetic algorithms (GA), marginal posterior probability density (MPPD) estimation, and simulated annealing (SA) is also made in the present study to further evaluate the performance of the proposed approach. Results from both synthetic and actual field data demonstrate that shuffled complex evolution algorithm promises to be robust, effective, and efficient for high-frequency surface wave analysis. [Copyright &y& Elsevier]

Coevolution (i.e., the evolution of two or more competing populations with coupled fitness) has several features that may potentially enhance the power of adaptation of artificial evolution. In particular, as discussed by Dawkins and Krebs, competing populations may reciprocally drive one another to increasing levels of complexity by producing an evolutionary "arms race." In this article we will investigate the role of coevolution in the context of evolutionary robotics. In particular, we will try to understand in what conditions coevolution can lead to "arms races." Moreover, we will show that in some cases artificial coevolution has a higher adaptive power than simple evolution. Finally, by analyzing the dynamics of coevolved populations, we will show that in some circumstances well-adapted individuals would be better advised to adopt simple but easily modifiable strategies suited for the current competitor strategies rather than incorporate complex and general strategies that may be effective against a wide range of opposing counter-strategies. [ABSTRACT FROM AUTHOR]

I employ a mathematical model integrating the population and co-adaptive dynamics of an insect host and its specialist parasitoid wasp toinvestigate the competitive evolution of two forms of host resistance: concealment from adult parasitoid location and encapsulation of parasitoid eggs. When only one form of resistance is permitted to evolve, concealment always evolves to frequencies equal to or higher than encapsulation. When both forms of resistance evolve, the outcome depends on the capacity of the parasitoid to evolve counter-measures. Evolution of the host in the presence of the most virulent parasitoid clone results in no differences between the equilibrium frequencies of the two forms of resistance, unless host clone densities are somehow unequally perturbed. When the parasitoid is allowed to co-evolve, thefrequency of hosts concealing themselves exceeds the frequency of those capable of encapsulating parasitoid eggs. At equilibrium, there is generally a negative genetic correlation between resistance measures in the host population, and a positive one for counter-measures in the parasitoid population. The mechanism driving the asymmetries between the two forms of host defense is the loss of more reproductive effort by parasitoids when the host encapsulates as compared to when anencounter is missed due to concealment. I predict that hosts should pay greater costs to maintain concealment as compared to encapsulation when they are relatively common and/or parasitism rates relatively low; that is, when regulation of the host population tends to be influenced by other density dependent forces. [ABSTRACT FROM AUTHOR]