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Subgenome dominance has been reported in diverse allopolyploid species, where genes from one subgenome are preferentially retained and are more highly expressed than those from other subgenome(s). However, the molecular mechanisms responsible for subgenome dominance remain poorly understood. Here, we develop genome-wide map of accessible chromatin regions (ACRs) in cultivated strawberry (2n = 8x = 56, with A, B, C, D subgenomes). Each ACR is identified as an MNase hypersensitive site (MHS). We discover that the dominant subgenome A contains a greater number of total MHSs and MHS per gene than the submissive B/C/D subgenomes. Subgenome A suffers fewer losses of MHS-related DNA sequences and fewer MHS fragmentations caused by insertions of transposable elements. We also discover that genes and MHSs related to stress response have been preferentially retained in subgenome A. We conclude that preservation of genes and their cognate ACRs, especially those related to stress responses, play a major role in the establishment of subgenome dominance in octoploid strawberry.

 

Organisms that live in different environments face different evolutionary pressures. As such, organisms that have more successful phenotypes reproduce more frequently, but differing selective pressures acting at the organismal level can influence genes, and thus proteins. Understanding how proteins adapt across environments may therefore be useful in engineering proteins for specific environments as well as to improve our understanding of basic biology. In this work, we explicitly compare homologous (read: paired) proteins from different environments. While previous studies have explored the relevant evolutionary pressures in one of these environments [11], [17] and genomic responses to those pressures [1], [28], no prior computational study of their proteins has been performed. We apply ESM-2 [20] and although there is no signal in our negative control (two divergent yeast strains) as expected, we obtain near perfect prediction accuracy for our selected environmental gradient–the well-established subsurface vs. surface biome. We further show that ESM-2 is able to capture relevant fine-grained biological patterns in its embedding space, even in its smallest model. Significantly, we demonstrate that these embeddings can be interpreted using a novel visualization pipeline built using explainable AI techniques. 

Superfluid³He is a paradigm for odd-parity Cooper pairing, ranging from neutron stars to uranium-based superconducting compounds. Recently it has been shown that³He, imbibed in anisotropic silica aerogel with either positive or negative strain, preferentially selects either the chiral A-phase or the time-reversal-symmetric B-phase. This control over basic order parameter symmetry provides a useful model for understanding imperfect unconventional superconductors. For both phases, the orbital quantization axis is fixed by the direction of strain. Unexpectedly, at a specific temperatureT_x, the orbital axis flops by 90^∘, but in reverse order for A and B-phases. Aided by diffusion limited cluster aggregation simulations of anisotropic aerogel and small angle X-ray measurements, we are able to classify these aerogels as either “planar and “nematic concluding that the orbital-flop is caused by competition between short and long range structures in these aerogels.

 

Non-equilibrium inductively coupled plasmas (ICPs) operating in hydrogen are of significant interest for applications including large-area materials processing. Increasing control of spatial gas heating, which drives the formation of neutral species density gradients and the rate of gas-temperature-dependent reactions, is critical. In this study, we use 2D fluid-kinetic simulations with the Hybrid Plasma Equipment Model to investigate the spatially resolved production of atomic hydrogen in a low-pressure planar ICP operating in pure hydrogen (10–20 Pa or 0.075–0.15 Torr, 300 W). The reaction set incorporates self-consistent calculation of the spatially resolved gas temperature and 14 vibrationally excited states. We find that the formation of neutral-gas density gradients, which result from spatially non-uniform electrical power deposition at constant pressure, can drive significant variations in the vibrational distribution function and density of atomic hydrogen when gas heating is spatially resolved. This highlights the significance of spatial gas heating on the production of reactive species in relatively high-power-density plasma processing sources.

 

Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.

 

Deciduous tree cover is expected to increase in North American boreal forests with climate warming and wildfire. This shift in composition has the potential to generate biophysical cooling via increased land surface albedo. Here we use Landsat-derived maps of continuous tree canopy cover and deciduous fractional composition to assess albedo change over recent decades. We find, on average, a small net decrease in deciduous fraction from 2000 to 2015 across boreal North America and from 1992 to 2015 across Canada, despite extensive fire disturbance that locally increased deciduous vegetation. We further find near-neutral net biophysical change in radiative forcing associated with albedo when aggregated across the domain. Thus, while there have been widespread changes in forest composition over the past several decades, the net changes in composition and associated post-fire radiative forcing have not induced systematic negative feedbacks to climate warming over the spatial and temporal scope of our study.