Evolution of swarming behavior is shaped by how predators attack

Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. In the past decade, researchers have begun using evolutionary computation to study the evolutionary effects of these selection pressures in predator-prey models. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of ``domains of danger.'' Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work corroborates Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.Comment: 25 pages, 11 figures, 5 tables, including 2 Supplementary Figures. Version to appear in "Artificial Life

Identifying Structural Variation in Haploid Microbial Genomes from Short-Read Resequencing Data Using Breseq

Mutations that alter chromosomal structure play critical roles in evolution and disease, including in the origin of new lifestyles and pathogenic traits in microbes. Large-scale rearrangements in genomes are often mediated by recombination events involving new or existing copies of mobile genetic elements, recently duplicated genes, or other repetitive sequences. Most current software programs for predicting structural variation from short-read DNA resequencing data are intended primarily for use on human genomes. They typically disregard information in reads mapping to repeat sequences, and significant post-processing and manual examination of their output is often required to rule out false-positive predictions and precisely describe mutational events. Results: We have implemented an algorithm for identifying structural variation from DNA resequencing data as part of the breseq computational pipeline for predicting mutations in haploid microbial genomes. Our method evaluates the support for new sequence junctions present in a clonal sample from split-read alignments to a reference genome, including matches to repeat sequences. Then, it uses a statistical model of read coverage evenness to accept or reject these predictions. Finally, breseq combines predictions of new junctions and deleted chromosomal regions to output biologically relevant descriptions of mutations and their effects on genes. We demonstrate the performance of breseq on simulated Escherichia coli genomes with deletions generating unique breakpoint sequences, new insertions of mobile genetic elements, and deletions mediated by mobile elements. Then, we reanalyze data from an E. coli K-12 mutation accumulation evolution experiment in which structural variation was not previously identified. Transposon insertions and large-scale chromosomal changes detected by breseq account for similar to 25% of spontaneous mutations in this strain. In all cases, we find that breseq is able to reliably predict structural variation with modest read-depth coverage of the reference genome (>40-fold). Conclusions: Using breseq to predict structural variation should be useful for studies of microbial epidemiology, experimental evolution, synthetic biology, and genetics when a reference genome for a closely related strain is available. In these cases, breseq can discover mutations that may be responsible for important or unintended changes in genomes that might otherwise go undetected.U.S. National Institutes of Health R00-GM087550U.S. National Science Foundation (NSF) DEB-0515729NSF BEACON Center for the Study of Evolution in Action DBI-0939454Cancer Prevention & Research Institute of Texas (CPRIT) RP130124University of Texas at Austin startup fundsUniversity of Texas at AustinCPRIT Cancer Research TraineeshipMolecular Bioscience

A Core Outcome Set for Pediatric Critical Care

Objectives: More children are surviving critical illness but are at risk of residual or new health conditions. An evidence-informed and stakeholder-recommended core outcome set is lacking for pediatric critical care outcomes. Our objective was to create a multinational, multistakeholder-recommended pediatric critical care core outcome set for inclusion in clinical and research programs.Design: A two-round modified Delphi electronic survey was conducted with 333 invited research, clinical, and family/advocate stakeholders. Stakeholders completing the first round were invited to participate in the second. Outcomes scoring greater than 69% “critical” and less than 15% “not important” advanced to round 2 with write-in outcomes considered. The Steering Committee held a virtual consensus conference to determine the final components.Setting: Multinational survey.Patients: Stakeholder participants from six continents representing clinicians, researchers, and family/advocates.Measurements and Main Results: Overall response rates were 75% and 82% for each round. Participants voted on seven Global Domains and 45 Specific Outcomes in round 1, and six Global Domains and 30 Specific Outcomes in round 2. Using overall (three stakeholder groups combined) results, consensus was defined as outcomes scoring greater than 90% “critical” and less than 15% “not important” and were included in the final PICU core outcome set: four Global Domains (Cognitive, Emotional, Physical, and Overall Health) and four Specific Outcomes (Child Health-Related Quality of Life, Pain, Survival, and Communication). Families (n = 21) suggested additional critically important outcomes that did not meet consensus, which were included in the PICU core outcome set—extended.Conclusions: The PICU core outcome set and PICU core outcome set—extended are multistakeholder-recommended resources for clinical and research programs that seek to improve outcomes for children with critical illness and their families