A Target Restricted Assembly Method (TRAM) for Phylogenomics

While next generation sequencing technology can produce sequences covering the entire genome, assembly and annotation are still prohibitive steps for many phylogenomics applications. Here we describe a method of Target Restricted Assembly (TRAM) of a single lane of Illumina sequences for genes of relevance to phylogeny reconstruction, i.e. single copy protein-coding genes. This method has the potential to produce a data set of hundreds of genes using only one Illumina lane per taxon

Influence of season and method of topkill on resprouting characteristics and biomass of Quercus nigra saplings from a southeastern U.S. pine-grassland ecosystem

© 2014, Springer Science+Business Media Dordrecht. The resprouting ability of woody plants in frequently burned ecosystems may be influenced by the season and method of topkill. We conducted an experiment to test for the effects of season and method of topkill on aboveground biomass, belowground biomass, and mortality of hardwoods found in a southeastern U.S. pine-grassland. We predicted that topkill occurring during the growing season and topkill by fire would have greater negative impacts on resprouting and root growth and result in greater mortality. We conducted a shadehouse experiment in north Florida in which we applied topkill treatments (burn, clip, and no-topkill) in three seasons (dormant, early growing, and mid growing) to Quercus nigra (water oak) saplings. Plants were destructively sampled 12 months post-treatment to measure aboveground and belowground biomass. Saplings topkilled in the early and mid growing seasons had reduced growth and greater mortality one-year post-treatment compared to plants topkilled in the dormant season. While there was no difference in one-year post-treatment biomass or mortality of saplings between the two methods of topkill, clipped plants had more stems and shorter average stem height than plants topkilled by fire. Root growth continued despite topkilling for all seasons and was greatest for no-topkill plants. These results suggest that while topkill reduces biomass, hardwoods have evolved to maintain belowground biomass reserves, enabling genets to resprout following subsequent topkilling and to persist through frequent disturbances

Phenotopic Plasticity of Leaf Shape Along a Temperature Gradient in \u3cem\u3eAcer Rubrum\u3c/em\u3e

Both phenotypic plasticity and genetic determination can be important for understanding how plants respond to environmental change. However, little is known about the plastic response of leaf teeth and leaf dissection to temperature. This gap is critical because these leaf traits are commonly used to reconstruct paleoclimate from fossils, and such studies tacitly assume that traits measured from fossils reflect the environment at the time of their deposition, even during periods of rapid climate change. We measured leaf size and shape in Acer rubrum derived from four seed sources with a broad temperature range and grown for two years in two gardens with contrasting climates (Rhode Island and Florida). Leaves in the Rhode Island garden have more teeth and are more highly dissected than leaves in Florida from the same seed source. Plasticity in these variables accounts for at least 6–19 % of the total variance, while genetic differences among ecotypes probably account for at most 69–87 %. This study highlights the role of phenotypic plasticity in leaf-climate relationships. We suggest that variables related to tooth count and leaf dissection in A. rubrum can respond quickly to climate change, which increases confidence in paleoclimate methods that use these variables

Seeking Evolution of Dark Energy

We study how observationally to distinguish between a cosmological constant (CC) and an evolving dark energy with equation of state $\omega(Z)$. We focus on the value of redshift Z* at which the cosmic late time acceleration begins and $\ddot{a}(Z^{*}) = 0$. Four $\omega(Z)$ are studied, including the well-known CPL model and a new model that has advantages when describing the entire expansion era. If dark energy is represented by a CC model with $\omega \equiv -1$, the present ranges for $\Omega_{\Lambda}(t_0)$ and $\Omega_m(t_0)$ imply that Z* = 0.743 with 4% error. We discuss the possible implications of a model independent measurement of Z* with better accuracy.Comment: 9 pages, LaTeX, 5 figure

A review of Bayesian perspectives on sample size derivation for confirmatory trials

Sample size derivation is a crucial element of the planning phase of any confirmatory trial. A sample size is typically derived based on constraints on the maximal acceptable type I error rate and a minimal desired power. Here, power depends on the unknown true effect size. In practice, power is typically calculated either for the smallest relevant effect size or a likely point alternative. The former might be problematic if the minimal relevant effect is close to the null, thus requiring an excessively large sample size. The latter is dubious since it does not account for the a priori uncertainty about the likely alternative effect size. A Bayesian perspective on the sample size derivation for a frequentist trial naturally emerges as a way of reconciling arguments about the relative a priori plausibility of alternative effect sizes with ideas based on the relevance of effect sizes. Many suggestions as to how such `hybrid' approaches could be implemented in practice have been put forward in the literature. However, key quantities such as assurance, probability of success, or expected power are often defined in subtly different ways in the literature. Starting from the traditional and entirely frequentist approach to sample size derivation, we derive consistent definitions for the most commonly used `hybrid' quantities and highlight connections, before discussing and demonstrating their use in the context of sample size derivation for clinical trials

Cambial Phenology Informs Tree-Ring Analysis of Fire Seasonality in Coastal Plain Pine Savannas

© 2018, The Author(s). Understanding of historical fire seasonality should facilitate development of concepts regarding fire as an ecological and evolutionary process. In tree-ring based fire-history studies, the seasonality of fire scars can be classified based on the position of the fire scar within or between growth rings. Cambial phenology studies are needed to precisely relate a fire-scar position to months within a year because the timing of dormancy, earlywood production, and latewood production varies by species and location. We examined cambial phenology patterns of longleaf pine (Pinus palustris Mill.), slash pine (P. elliottii Engelm.), and South Florida slash pine (P. densa [Little & K.W Dorman] Silba) at sites in southern Georgia and south-central and northern Florida, USA. We developed long-term (2.5 yr to 12 yr) datasets of monthly growth and dormancy and determined when trees transitioned from producing early-wood to producing latewood each year. Most trees were dormant for a period of 1 to 2 months in the winter and transitioned from earlywood to latewood in June. Given the annual growth ring morphology of the pines that we studied and the timing of the lightning-fire season in our study area, we propose a new classification system for assigning seasonality to fire scars found in the three native upland pine species that we studied. This new system, which we name the Coastal Plain Pine System, accounts for the large proportion of latewood typical of these pines and includes a position (the transition position) that corresponds with the time of year when lightning fires occur most frequently. Our findings demonstrate how cambial phenology data can improve interpretation of fire-scar data for determining historical fire seasonality

IgG antibody production and persistence to 6 months following SARS-CoV-2 vaccination: a Northern Ireland observational study

BACKGROUND: This study evaluates spike protein IgG antibody response following Oxford-AstraZeneca COVID-19 vaccination using the AbC-19™ lateral flow device. METHODS: Plasma samples were collected from n=111 individuals from Northern Ireland. The majority were >50 years old and/or clinically vulnerable. Samples were taken at five timepoints from pre-vaccination until 6-months post-first dose. RESULTS: 20.3% of participants had detectable IgG responses pre-vaccination, indicating prior COVID-19. Antibodies were detected in 86.9% of participants three weeks after the first vaccine dose, falling to 74.7% immediately prior to the second dose, and rising to 99% three weeks post-second vaccine. At 6-months post-first dose, this decreased to 90.5%. At all timepoints, previously infected participants had significantly higher antibody levels than those not previously infected. CONCLUSION: This study demonstrates that strong anti-spike protein antibody responses are evoked in almost all individuals that receive two doses of Oxford-AstraZeneca vaccine, and largely persist beyond six months after first vaccination