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The COVID-19 pandemic has caused more than 1,000,000 reported deaths globally, of which more than 200,000 have been reported in the United States as of October 1, 2020. Public health interventions have had significant impacts in reducing transmission and in averting even more deaths. Nonetheless, in many jurisdictions, the decline of cases and fatalities after apparent epidemic peaks has not been rapid. Instead, the asymmetric decline in cases appears, in most cases, to be consistent with plateau- or shoulder-like phenomena—a qualitative observation reinforced by a symmetry analysis of US state-level fatality data. Here we explore a model of fatality-driven awareness in which individual protective measures increase with death rates. In this model, fast increases to the peak are often followed by plateaus, shoulders, and lag-driven oscillations. The asymmetric shape of model-predicted incidence and fatality curves is consistent with observations from many jurisdictions. Yet, in contrast to model predictions, we find that population-level mobility metrics usually increased from low levels before fatalities reached an initial peak. We show that incorporating fatigue and long-term behavior change can reconcile the apparent premature relaxation of mobility reductions and help understand when post-peak dynamics are likely to lead to a resurgence of cases.

Background Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks. Results We have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user. Conclusions We demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa . We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

Viruses that infect microbial hosts have traditionally been studied in laboratory settings with a focus on either obligate lysis or persistent lysogeny. In the environment, these infection archetypes are part of a continuum that spans antagonistic to beneficial modes. In this Review, we advance a framework to accommodate the context-dependent nature of virus–microorganism interactions in ecological communities by synthesizing knowledge from decades of virology research, eco-evolutionary theory and recent technological advances. We discuss that nuanced outcomes, rather than the extremes of the continuum, are particularly likely in natural communities given variability in abiotic factors, the availability of suboptimal hosts and the relevance of multitrophic partnerships. We revisit the ‘rules of life’ in terms of how long-term infections shape the fate of viruses and microbial cells, populations and ecosystems.In this Review, Correa and colleagues revisit the rules of life for viruses of microorganisms by advancing a conceptual framework that recognizes virus–host interactions across a continuum of infection modalities and by examining the influence of these modalities on viruses, their hosts and ecosystems.

The structure of hierarchical networks in biological and physical systems has long been characterized using the Horton-Strahler ordering scheme. The scheme assigns an integer order to each edge in the network based on the topology of branching such that the order increases from distal parts of the network (e.g., mountain streams or capillaries) to the \"root\" of the network (e.g., the river outlet or the aorta). However, Horton-Strahler ordering cannot be applied to networks with loops because they they create a contradiction in the edge ordering in terms of which edge precedes another in the hierarchy. Here, we present a generalization of the Horton-Strahler order to weighted planar reticular networks, where weights are assumed to correlate with the importance of network edges, e.g., weights estimated from edge widths may correlate to flow capacity. Our method assigns hierarchical levels not only to edges of the network, but also to its loops, and classifies the edges into reticular edges, which are responsible for loop formation, and tree edges. In addition, we perform a detailed and rigorous theoretical analysis of the sensitivity of the hierarchical levels to weight perturbations. In doing so, we show that the ordering of the reticular edges is more robust to noise in weight estimation than is the ordering of the tree edges. We discuss applications of this generalized Horton-Strahler ordering to the study of leaf venation and other biological networks.

Background The gene composition of bacteria of the same species can differ significantly between isolates. Variability in gene composition can be summarized in terms of gene frequency distributions, in which individual genes are ranked according to the frequency of genomes in which they appear. Empirical gene frequency distributions possess a U-shape, such that there are many rare genes, some genes of intermediate occurrence, and many common genes. It would seem that U-shaped gene frequency distributions can be used to infer the essentiality and/or importance of a gene to a species. Here, we ask: can U-shaped gene frequency distributions, instead, arise generically via neutral processes of genome evolution? Results We introduce a neutral model of genome evolution which combines birth-death processes at the organismal level with gene uptake and loss at the genomic level. This model predicts that gene frequency distributions possess a characteristic U-shape even in the absence of selective forces driving genome and population structure. We compare the model predictions to empirical gene frequency distributions from 6 multiply sequenced species of bacterial pathogens. We fit the model with constant population size to data, matching U-shape distributions albeit without matching all quantitative features of the distribution. We find stronger model fits in the case where we consider exponentially growing populations. We also show that two alternative models which contain a \"rigid\" and \"flexible\" core component of genomes provide strong fits to gene frequency distributions. Conclusions The analysis of neutral models of genome evolution suggests that U-shaped gene frequency distributions provide less information than previously suggested regarding gene essentiality. We discuss the need for additional theory and genomic level information to disentangle the roles of evolutionary mechanisms operating within and amongst individuals in driving the dynamics of gene distributions.

Multiple epidemiological models have been proposed to predict the spread of Ebola in West Africa. These models include consideration of counter-measures meant to slow and, eventually, stop the spread of the disease. Here, we examine one component of Ebola dynamics that is of ongoing concern – the transmission of Ebola from the dead to the living. We do so by applying the toolkit of mathematical epidemiology to analyze the consequences of post-death transmission. We show that underlying disease parameters cannot be inferred with confidence from early-stage incidence data (that is, they are not “identifiable”) because different parameter combinations can produce virtually the same epidemic trajectory. Despite this identifiability problem, we find robustly that inferences that don't account for post-death transmission tend to underestimate the basic reproductive number – thus, given the observed rate of epidemic growth, larger amounts of post-death transmission imply larger reproductive numbers. From a control perspective, we explain how improvements in reducing post-death transmission of Ebola may reduce the overall epidemic spread and scope substantially. Increased attention to the proportion of post-death transmission has the potential to aid both in projecting the course of the epidemic and in evaluating a portfolio of control strategies.

Key Points Virus-mediated lysis of host cells results in the generation of dissolved organic carbon (DOC), dissolved organic nitrogen (DON) and dissolved organic phosphorus (DOP) via a process that is known as the 'viral shunt'. Previous quantitative estimates of the contribution of the viral shunt to biogeochemical cycles focused on host cellular constituents and overlooked the contribution of virus particles. In this Analysis article, we develop a biophysical scaling model that predicts the elemental contents and compositions of virus particles. This scaling model was validated using detailed sequence and structural contents of intact bacteriophage particles. Viruses are predicted to be enriched in phosphorus, so much so that the total phosphorus content in a burst of released viruses may approach that of the phosphorus content in an uninfected host. As a consequence, cellular debris may be depleted in phosphorus compared with the stoichiometry of hosts. Furthermore, by extrapolating the model to the ecosystem scale, marine viruses are predicted to contain an important fraction (for example, >5%) of the total DOP pool in some systems (for example, in surface waters, when virus density exceeds 3.5 × 10 10 and the DOP concentration is approximately 100 nM). Weitz and colleagues use a biophysical scaling model of intact virus particles to quantify differences in the elemental stoichiometry of marine viruses compared with their microbial hosts. They propose that, under certain circumstances, marine virus populations could make a previously unrecognised and important contribution to the reservoir and cycling of oceanic phosphorus. In marine environments, virus-mediated lysis of host cells leads to the release of cellular carbon and nutrients and is hypothesized to be a major driver of carbon recycling on a global scale. However, efforts to characterize the effects of viruses on nutrient cycles have overlooked the geochemical potential of the virus particles themselves, particularly with respect to their phosphorus content. In this Analysis article, we use a biophysical scaling model of intact virus particles that has been validated using sequence and structural information to quantify differences in the elemental stoichiometry of marine viruses compared with their microbial hosts. By extrapolating particle-scale estimates to the ecosystem scale, we propose that, under certain circumstances, marine virus populations could make an important contribution to the reservoir and cycling of oceanic phosphorus.

Bacteriophage (or ‘phage’ – viruses that infect and kill bacteria) are increasingly considered as a therapeutic alternative to treat antibiotic-resistant bacterial infections. However, bacteria can evolve resistance to phage, presenting a significant challenge to the near- and long-term success of phage therapeutics. Application of mixtures of multiple phages ( i.e. , ‘cocktails’) has been proposed to limit the emergence of phage-resistant bacterial mutants that could lead to therapeutic failure. Here, we combine theory and computational models of in vivo phage therapy to study the efficacy of a phage cocktail, composed of two complementary phages motivated by the example of Pseudomonas aeruginosa facing two phages that exploit different surface receptors, LUZ19v and PAK_P1. As confirmed in a Luria-Delbrück fluctuation test, this motivating example serves as a model for instances where bacteria are extremely unlikely to develop simultaneous resistance mutations against both phages. We then quantify therapeutic outcomes given single- or double-phage treatment models, as a function of phage traits and host immune strength. Building upon prior work showing monophage therapy efficacy in immunocompetent hosts, here we show that phage cocktails comprised of phage targeting independent bacterial receptors can improve treatment outcome in immunocompromised hosts and reduce the chance that pathogens simultaneously evolve resistance against phage combinations. The finding of phage cocktail efficacy is qualitatively robust to differences in virus-bacteria interactions and host immune dynamics. Altogether, the combined use of theory and computational analysis highlights the influence of viral life history traits and receptor complementarity when designing and deploying phage cocktails in immunocompetent and immunocompromised hosts.

Background The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data. While previous methods have focused primarily on supervised learning involving extrinsic data, a first-principles statistical model combined with a self-training fitting method has not yet been developed. Results We derive an unsupervised, maximum-likelihood formalism for clustering short sequences by their taxonomic origin on the basis of their k -mer distributions. The formalism is implemented using a Markov Chain Monte Carlo approach in a k -mer feature space. We introduce a space transformation that reduces the dimensionality of the feature space and a genomic fragment divergence measure that strongly correlates with the method's performance. Pairwise analysis of over 1000 completely sequenced genomes reveals that the vast majority of genomes have sufficient genomic fragment divergence to be amenable for binning using the present formalism. Using a high-performance implementation, the binner is able to classify fragments as short as 400 nt with accuracy over 90% in simulations of low-complexity communities of 2 to 10 species, given sufficient genomic fragment divergence. The method is available as an open source package called LikelyBin. Conclusion An unsupervised binning method based on statistical signatures of short environmental sequences is a viable stand-alone binning method for low complexity samples. For medium and high complexity samples, we discuss the possibility of combining the current method with other methods as part of an iterative process to enhance the resolving power of sorting reads into taxonomic and/or functional bins.

The riveting account of how asymptomatic transmission drove COVID-19's global spread and catalyzed interventions to control it. Why was COVID-19 so difficult to contain and so devastating to people and economies worldwide? In Asymptomatic, author Joshua S. Weitz explains how silent transmission enabled COVID-19's massive and tragic global impact. Weaving the science of viral infections together with an insider's look at response efforts, Weitz guides readers through the shockwaves of successive epidemic waves as public health officials and academic research teams confronted the rise and risk of what was then a burgeoning global pandemic. The discovery of asymptomatic spread also fueled competing narratives: either COVID-19 was about to dissipate as quickly as it had emerged or completely disrupt life as we knew it. Weitz, a physicist-turned-biologist who directs a quantitative viral dynamics research group and has been immersed in COVID-19 response efforts, explains both why and how scientists tried to wade through competing narratives and warn the public of COVID-19's profound risk. As explored through a careful analysis of local outbreaks, accessible descriptions of virus dynamics, and the use of predictive models to guide response efforts, Asymptomatic provides readers a unique look into the secret ingredient that allowed COVID-19 to spread across borders and the high-impact interventions needed to fight it and future pandemics.