Patterns of Genetic Variation in Southern Appalachian Populations of Athyrium filix‐femina var. asplenioides (Dryopteridaceae)

Allozyme variation (17 loci coding 11 enzymes) was investigated in 14 populations of the fern Athyrium filix‐femina var. asplenioides arrayed at differing elevations and latitudes in the southern Appalachians. Allozyme fingerprints showed that asplenioides individuals comprise meandering, overlapping clones usually ≤3 m in extent, occasionally forming larger clones of up to 17 m. Levels of genetic variability in populations (means: , , ) were near the averages for both ferns and seed plants. General conformance to Hardy‐Weinberg expectations indicated a predominantly outcrossing mating system. Hierarchical F statistic analysis and occasional deficits and excesses of heterozygotes indicated population substructure. Similar allele frequencies across all populations resulted in low to moderate values (mean ; ) and high values of genetic similarity (mean ; mean ). Hierarchical analysis indicated that neither regional proximity ( ) nor elevation ( ) contributed substantially to divergence among populations ( ), a result corroborated by UPGMA analysis that clustered together populations from different regions and of different elevational class. Southern Appalachian asplenioides differed from more eastern asplenioides populations of the piedmont and coastal plain in having higher frequencies of Pgm‐2c and Tpi‐2B, alleles characteristic of the more northern variety angustum. Nonetheless, genetic distinctness of the two varieties was maintained. We hypothesize that higher frequencies of angustum alleles in the southern Appalachian asplenioides populations are the result of introgression from angustum that persisted at high elevations as both taxa migrated northward following the retreat of the Wisconsinan glacier

A framework for future national pediatric pandemic respiratory disease severity triage: The HHS pediatric COVID-19 data challenge

Abstract Introduction: With persistent incidence, incomplete vaccination rates, confounding respiratory illnesses, and few therapeutic interventions available, COVID-19 continues to be a burden on the pediatric population. During a surge, it is difficult for hospitals to direct limited healthcare resources effectively. While the overwhelming majority of pediatric infections are mild, there have been life-threatening exceptions that illuminated the need to proactively identify pediatric patients at risk of severe COVID-19 and other respiratory infectious diseases. However, a nationwide capability for developing validated computational tools to identify pediatric patients at risk using real-world data does not exist. Methods: HHS ASPR BARDA sought, through the power of competition in a challenge, to create computational models to address two clinically important questions using the National COVID Cohort Collaborative: (1) Of pediatric patients who test positive for COVID-19 in an outpatient setting, who are at risk for hospitalization? (2) Of pediatric patients who test positive for COVID-19 and are hospitalized, who are at risk for needing mechanical ventilation or cardiovascular interventions? Results: This challenge was the first, multi-agency, coordinated computational challenge carried out by the federal government as a response to a public health emergency. Fifty-five computational models were evaluated across both tasks and two winners and three honorable mentions were selected. Conclusion: This challenge serves as a framework for how the government, research communities, and large data repositories can be brought together to source solutions when resources are strapped during a pandemic

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present