Chromosome-level reference genome of tetraploid Isoetes sinensis provides insights into evolution and adaption of lycophytes.

BACKGROUND: The Lycophyta species are the extant taxa most similar to early vascular plants that were once abundant on Earth. However, their distribution has greatly diminished. So far, the absence of chromosome-level assembled lycophyte genomes has hindered our understanding of evolution and environmental adaption of lycophytes. FINDINGS: We present the reference genome of the tetraploid aquatic quillwort, Isoetes sinensis, a lycophyte. This genome represents the first chromosome-level assembled genome of a tetraploid seed-free plant. Comparison of genomes between I. sinensis and Isoetestaiwanensis revealed conserved and different genomic features between diploid and polyploid lycophytes. Comparison of the I. sinensis genome with those of other species representing the evolutionary lineages of green plants revealed the inherited genetic tools for transcriptional regulation and most phytohormones in I. sinensis. The presence and absence of key genes related to development and stress responses provide insights into environmental adaption of lycophytes. CONCLUSIONS: The high-quality reference genome and genomic analysis presented in this study are crucial for future genetic and environmental studies of not only I. sinensis but also other lycophytes.

Optimization methods for decision making in disease prevention and epidemic control.

This paper investigates problems of disease prevention and epidemic control (DPEC), in which we optimize two sets of decisions: (i) vaccinating individuals and (ii) closing locations, given respective budgets with the goal of minimizing the expected number of infected individuals after intervention. The spread of diseases is inherently stochastic due to the uncertainty about disease transmission and human interaction. We use a bipartite graph to represent individuals' propensities of visiting a set of location, and formulate two integer nonlinear programming models to optimize choices of individuals to vaccinate and locations to close. Our first model assumes that if a location is closed, its visitors stay in a safe location and will not visit other locations. Our second model incorporates compensatory behavior by assuming multiple behavioral groups, always visiting the most preferred locations that remain open. The paper develops algorithms based on a greedy strategy, dynamic programming, and integer programming, and compares the computational efficacy and solution quality. We test problem instances derived from daily behavior patterns of 100 randomly chosen individuals (corresponding to 195 locations) in Portland, Oregon, and provide policy insights regarding the use of the two DPEC models.