A deep-learning model of prescient ideas demonstrates that they emerge from the periphery.

Where do prescient ideas-those that initially challenge conventional assumptions but later achieve widespread acceptance-come from? Although their outcomes in the form of technical innovation are readily observed, the underlying ideas that eventually change the world are often obscured. Here, we develop a novel method that uses deep learning to unearth the markers of prescient ideas from the language used by individuals and groups. Our language-based measure identifies prescient actors and documents that prevailing methods would fail to detect. Applying our model to corpora spanning the disparate worlds of politics, law, and business, we demonstrate that it reliably detects prescient ideas in each domain. Moreover, counter to many prevailing intuitions, prescient ideas emanate from each domain's periphery rather than its core. These findings suggest that the propensity to generate far-sighted ideas may be as much a property of contexts as of individuals.

Systematic identification and quantification of phase variation in commensal and pathogenic Escherichia coli.

Bacteria have been shown to generate constant genetic variation in a process termed phase variation. We present a tool based on whole genome sequencing that allows detection and quantification of coexisting genotypes mediated by genomic inversions in bacterial cultures. We tested our method on widely used strains of Escherichia coli, and detected stable and reproducible phase variation in several invertible loci. These are shown here to be responsible for maintaining constant variation in populations grown from a single colony. Applying this tool on other bacterial strains can shed light on how pathogens adjust to hostile environments by diversifying their genomes.

Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations.

The great therapeutic achievements of antibiotics have been dramatically undercut by the evolution of bacterial strategies that overcome antibiotic stress. These strategies fall into two classes. 'Resistance' makes it possible for a microorganism to grow in the constant presence of the antibiotic, provided that the concentration of the antibiotic is not too high. 'Tolerance' allows a microorganism to survive antibiotic treatment, even at high antibiotic concentrations, as long as the duration of the treatment is limited. Although both resistance and tolerance are important reasons for the failure of antibiotic treatments, the evolution of resistance is much better understood than that of tolerance. Here we followed the evolution of bacterial populations under intermittent exposure to the high concentrations of antibiotics used in the clinic and characterized the evolved strains in terms of both resistance and tolerance. We found that all strains adapted by specific genetic mutations, which became fixed in the evolved populations. By monitoring the phenotypic changes at the population and single-cell levels, we found that the first adaptive change to antibiotic stress was the development of tolerance through a major adjustment in the single-cell lag-time distribution, without a change in resistance. Strikingly, we found that the lag time of bacteria before regrowth was optimized to match the duration of the antibiotic-exposure interval. Whole genome sequencing of the evolved strains and restoration of the wild-type alleles allowed us to identify target genes involved in this antibiotic-driven phenotype: 'tolerance by lag' (tbl). Better understanding of lag-time evolution as a key determinant of the survival of bacterial populations under high antibiotic concentrations could lead to new approaches to impeding the evolution of antibiotic resistance.

Neither ideologues nor agnostics: alternative voters' belief system in an age of partisan politics.

How do Americans organize their political beliefs? This article argues that party polarization and the growing prominence of moral issues in recent decades have catalyzed different responses by different groups of Americans. The article investigates systematic heterogeneity in the organization of political attitudes using relational class analysis, a graph-based method for detecting multiple patterns of opinion in survey data. Three subpopulations, each characterized by a distinctive way of organizing its political beliefs, are identified: ideologues, whose political attitudes strongly align with either liberal or conservative categories; alternatives, who are instead morally conservative but economically liberal, or vice versa; and agnostics, who exhibit weak associations between political beliefs. Individuals' sociodemographic profiles, particularly their income, education, and religiosity, lie at the core of the different ways in which they understand politics. Results show that while ideologues have gone through a process of issue alignment, alternatives have grown increasingly apart from the political agendas of both parties. The conflictual presence of conservative and liberal preferences has often been resolved by alternative voters in favor of the Republican Party.

Whack-an-E. coli with the morbidostat.

Using a device termed the 'morbidostat', a recent study sheds new light on the determinism of genetic and phenotypic trajectories leading to high antibiotic resistance.