Endogenous viral mutations, evolutionary selection, and containment policy design

How will the novel coronavirus evolve? I study a simple epidemiological model, in which mutations may change the properties of the virus and its associated disease stochastically and antigenic drifts allow new variants to partially evade immunity. I show analytically that variants with higher infectiousness, longer disease duration, and shorter latent period prove to be fitter. “Smart” containment policies targeting symptomatic individuals may redirect the evolution of the virus, as they give an edge to variants with a longer incubation period and a higher share of asymptomatic infections. Reduced mortality, on the other hand, does not per se prove to be an evolutionary advantage. I then implement this model as an agent-based simulation model in order to explore its aggregate dynamics. Monte Carlo simulations show that a) containment policy design has an impact on both speed and direction of viral evolution, b) the virus may circulate in the population indefinitely, provided that containment efforts are too relaxed and the propensity of the virus to escape immunity is high enough, and crucially c) that it may not be possible to distinguish between a slowly and a rapidly evolving virus by looking only at short-term epidemiological outcomes. Thus, what looks like a successful mitigation strategy in the short run, may prove to have devastating long-run effects. These results suggest that optimal containment policy must take the propensity of the virus to mutate and escape immunity into account, strengthening the case for genetic and antigenic surveillance even in the early stages of an epidemic.

Epidemiological Isolation May Explain Differences in Historical Respiratory Infectious Disease Mortality

Indigenous and aboriginal peoples of the Americas and Pacific died at enormous rates soon after joining the global pathogen pool in the seventeenth to nineteenth centuries from respiratory infections such as smallpox, measles, and influenza. It was widely assumed that this represented a selection process against primitive societies. Darwinian selection for specific genetic resistance factors seems an unlikely hypothesis given that some populations stabilized quickly over two to three generations. European-origin populations whose childhood was marked by epidemiological isolation also suffered high infectious disease mortality from respiratory pathogens. American soldiers with smallpox, South African (Boer) children with measles, and New Zealand soldiers with influenza suggest that epidemiological isolation resulting in few previous respiratory infections during childhood may be a consistent mortality risk factor. Modern studies of innate immunity following Bacillus Calmette–Guérin (BCG) in infancy point toward rapid immune adaptation rather than evolutionary selection as an explanation for excessive first contact epidemic mortality from respiratory pathogens.

Blind cavefish retain functional connectivity in the tectum despite loss of retinal input

Sensory systems display remarkable plasticity and are under strong evolutionary selection. The Mexican cavefish, Astyanax mexicanus, consists of eyed river-dwelling surface populations, and multiple independent cave populations which have converged on eye loss, providing the opportunity to examine the evolution of sensory circuits in response to environmental perturbation. Functional analysis across multiple transgenic populations expressing GCaMP6s showed that functional connectivity of the optic tectum largely did not differ between populations, except for the selective loss of negatively correlated activity within the cavefish tectum, suggesting positively correlated neural activity is resistant to an evolved loss of input from the retina. Further, analysis of surface-cave hybrid fish reveals that changes in the tectum are genetically distinct from those encoding eye-loss. Together, these findings uncover the independent evolution of multiple components of the visual system and establish the use of functional imaging in A. mexicanus to study neural circuit evolution.

Prevalent uses of exons as regulatory sequences are possible and inevitable

ABSTRACTIt has long been known that exons can encode transcriptional enhancers. However, the prevalence of such dual-use exons and related questions remain elusive. Our recently predicted highly accurate, large sets of cis-regulatory module candidates (CRMCs) and non-CRMCs in the human genome provide us an opportunity to address these questions. We find that exonic transcription factor binding sites(eTFBSs) occupy at least a third of the total exon lengths, suggesting exonic enhancers(eEHs) are more prevalent than originally thought. Moreover, active eTFBSs significantly overlap experimentally determined active eEHs, and enhance the transcription of nearby genes. Furthermore, both A/T and C/G in eTFBSs are more likely under evolutionary selection than those in non-CRMC exons, indicating the eTFBSs might be in dual-use. Interestingly, eTFBSs in codons tend to encode loops rather than more critical helices and strands in protein structures, while eTFBSs in untranslated regions (UTRs) tend to avoid positions where known UTR-related functions were located. Intriguingly, active eTFBSs are found to be in close physical proximity to distal promoters and involved in the activation of target genes. The close physical proximity between exons and promoters in topologically associating domains might render less critical exons to opt for parts of enhancers when non-exonic sequences are unavailable due to space constraints. It appears that nature avoids the dilemma of evolving a sequence for two unrelated functions by using less-critical, physically available exons for eTFBSs. Therefore, the prevalent dual-use of exons is not only possible but also inevitable.

Market sentiment and heterogeneous agents in an evolutive financial model

We study a financial market populated by heterogeneous agents, whose decisions are driven by “animal spirits”. Each agent may have either correct, optimistic or pessimistic beliefs about the fundamental value, which can change from time to time based on an evolutionary mechanism. The evolutionary selection of beliefs depends on a weighted evaluation of the general market sentiment perceived by the agents and on a profitability measure of the existent strategies. As the relevance given to the sentiment index increases, a herding phenomenon in agent behavior may occur and animal spirits can drive the market toward polarized economic regimes, which coexist and are characterized by persistent high or low levels of optimism and pessimism. This conduct is detectable from agents polarized shares and beliefs, which in turn influence the price level. Such polarized regimes can consist in stable steady states or can be characterized by endogenous dynamics, generating persistent alternating waves of optimism and pessimism, as well as return distributions displaying the typical features of financial time series, such as fat tails, excess volatility and multifractality. Moreover, we show that if the sentiment has no or low relevance on belief selection, those stylized facts are abated or are missing from the simulated time series.

A Many-Objective Evolutionary Algorithm Based on Two-Phase Selection

Aims: The main purpose of this paper is to achieve good convergence and distribution in different Pareto fronts. Background: Research in recent decades has appeared that evolutionary multi-objective optimization can effectively solve multi-objective optimization problems with no more than 3 targets. However, when solving MaOPs, the traditional evolutionary multi-objective optimization algorithm is difficult to balance convergence and diversity effectively. In order to solve these problems, many algorithms have emerged, which can be roughly divided into three types: decomposition-based, index-based, and dominance relationship-based. In addition, many algorithms introduce the idea of clustering into the environment. However, there are some disadvantages to solving different types of MaOPs. In order to take advantage of the above algorithms, this paper proposes a many-objective optimization algorithm based on two-phase evolutionary selection. Objective: To verify the comprehensive performance of the algorithm on the testing problem of different Pareto front, 18 examples of regular PF problems and irregular PF problems are used to test the performance of the algorithm proposed in this paper. Method: This paper proposes a two-phase evolutionary selection strategy. The evolution process is divided into two phases to select individuals with good quality. In the first phase, the convergence area is constructed by indicators to accelerate the convergence of the algorithm. In the second phase, the parallel distance is used to map the individuals to the hyperplane, and the individuals are clustered according to the distance on the hyperplane, and then the smallest fitness in each category is selected. Result: For regular Pareto front testing problems, MaOEA/TPS performed better than RVEA 、PREA 、CAMOEA and One by one EA in 19,21,30,26 cases, respectively, while it was only outperformed by RVEA 、PREA 、CAMOEA and One by one EA in 8,5,1,6 cases. For irregular front testing problem, MaOEA/TPS performed better than RVEA 、PREA 、CAMOEA and One by one EA in 20,17,25,21 cases, respectively, while it was only outperformed by RVEA 、PREA 、CAMOEA and One by one EA in 6,8,1,6 cases. Conclusion: The paper proposes a many-objective evolutionary algorithm based two phase selection, termed MaOEA/TPS, for solving MaOPs with different shapes of Pareto fronts. The results show that MaOEA/TPS has quite a competitive performance compared with the several algorithms on most test problems. Other: Although the algorithm in this paper has achieved good results, the optimization problem in the real environment is more difficult, so applying the algorithm proposed in this paper to real problems will be the next research direction.

Natural variations in the non-coding region of ZmNAC080308 contributes maintaining grain yield under drought stress in maize

Background Natural variations derived from both evolutionary selection and genetic recombination, presume to have important functions to respond to various abiotic stresses, which could be used to improve drought tolerance via genomic selection. Results In the present study, the NAC-encoding gene of ZmNAC080308 was cloned and sequenced in 199 inbred lines in maize. Phylogenetic analysis showed that ZmNAC080308 is closely clusteredinto the same group with other well-known NAC genes responding to improve drought tolerance. In total, 86 SNPs and 47 InDels were identified in the generic region of ZmNAC080308, 19 of these variations were associated with GY (grain yield) in different environments. Nine variations in the 5’-UTR region of ZmNAC080308 are closely linked, they might regulate the gene expression and respond to improve GY under drought condition via Sp1-mediated transactivation. Two haplotypes (Hap1 and Hap2) identified in the, 5’-UTR region using the nine variations, and Hap2 containing insertion variants, exhibited 15.47 % higher GY under drought stress condition. Further, a functional marker was developed to predict the drought stress tolerance in a US maize inbred line panel. Lines carrying Hap2 exhibited > 10 % higher GY than those carrying Hap1 under drought stress condition. In Arabidopsis, overexpression ZmNAC080308 enhanced drought tolerance. Conclusions ZmNAC080308 is an important gene responding to drought tolerance, a functional marker is developed for improving maize drought tolerance by selecting this gene.

Choice, Selection, and Learning

The chapter examines the relationship among processes of choice, selection, and learning. The notions of choice and selection differ with respect to the degree of intentionality that they suggest. However, if “choice” is viewed as the identification of preferential action over some set of latent alternatives, then processes of choice and selection can be seen as differing primarily by their level of analysis. Another critical distinction among these processes is their temporal orientation. In the case of rational choice, selection is driven by a projection of the future consequences of alternative actions. In contrast, evolutionary selection processes are driven by the contemporaneous relative fitness of alternatives. A third perspective is that of learning. Here, the preferential attraction to different alternatives is backward looking, with actions that are perceived to have been associated with more successful outcomes more likely to be enacted than those associated with less successful outcomes.