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The forests of coastal Alaska and British Columbia are globally significant for their high carbon storage capacity and complex forest structure, hosting some of the densest values of aboveground biomass in the world. These ecosystems support biodiversity, provide critical habitat, and serve as long-term carbon sinks, offering resilience to climate change. However, comprehensive, spatially continuous estimates of forest structure across this region have been limited, particularly across political boundaries. In this study, we used a Gradient Nearest Neighbor (GNN) modeling approach to integrate extensive forest inventory plot data with satellite-derived environmental variables. This approach enabled us to produce moderate-resolution (30-meter) maps of aboveground biomass, species biomass, forest age, basal area, and additional structural attributes. Our results indicated that climate and topography accounted for the majority of the explainable variation across all modeling regions. Predictions of aboveground live biomass were higher than previous estimates, particularly in Southeast Alaska, where estimates were 30–53 % greater than previous studies. Forest structure varied across the region, with older forests found in Southeast Alaska and higher tree densities in British Columbia. Collectively, the coastal forests of Alaska and British Columbia store approximately 3.58 petagrams of carbon. These spatially explicit maps offer critical insights for carbon monitoring, forest management, and biodiversity conservation across this ecologically diverse and politically fragmented landscape. 

Abstract The genusTauschiahas long been a source of taxonomic consternation for researchers. The group of species currently included in this genus are distributed primarily across the western United States and Mexico, but a few species occur in Central America and northern South America. Its circumscription is highly problematic, and its species have been moved countless times between more than a dozen genera. The advent of molecular phylogenetics has allowed some testing of generic boundaries inTauschiaand related taxa, but the sampling of previous studies was limited to a few species representing too small of a range to sort out the confusion. Here, we expand the sample size to include plants from throughout the range of the genus and use this to examine relationships among species ofTauschia, as well as to the larger clades to which it belongs within tribe Selineae. We also detail the complex taxonomic history ofTauschiaand related genera, provide a complete synonymy of the genus as it is currently defined, and confirm the polyphyly ofTauschiavia phylogenetic analysis of nuclear and cpDNA sequences. 

Just as a phylogeny encodes the evolutionary relationships among a group of organisms, a cophylogeny represents the coevolutionary relationships among symbiotic partners. Both are primarily reconstructed using computational analysis of biomolecular sequence data. The most widely used cophylogenetic reconstruction methods utilize an important simplifying assumption: species phylogenies for each set of coevolved taxa are required as input and assumed to be correct. Many studies have shown that this assumption is rarely – if ever – satisfied, and the consequences for cophylogenetic studies are poorly understood. To address this gap, we conduct a comprehensive performance study that quantifies the relationship between species tree estimation error and downstream cophylogenetic estimation accuracy. We study the performance of state-of-the-art methods for cophylogenetic reconstruction using in silico model-based simulations. Our investigation also assessed cophylogenetic reproducibility using genomic sequence data from two important models of symbiosis: soil-associated fungi and their endosymbiotic bacteria, and bobtail squid and their bioluminescent bacterial symbionts. Our findings conclusively demonstrate the major impact that upstream phylogenetic estimation error has on downstream cophylogenetic reconstruction. Relative to other experimental factors such as cophylogenetic estimation method choice and coevolutionary event costs, phylogenetic estimation error ranked highest in importance based on a random forest-based variable importance assessment. We conclude with practical guidance and future research directions. Among the many considerations needed for accurate cophylogenetic reconstruction – choice of computational method, method settings, sampling design, and others – just as much attention must be paid to careful species phylogeny estimation using modern best practices. 

Abstract Mismatch repair (MMR) is a highly conserved DNA repair pathway that recognizes mispairs that occur spontaneously during DNA replication and coordinates their repair. In Saccharomyces cerevisiae, Msh2-Msh3 and Msh2-Msh6 initiate MMR by recognizing and binding insertion or deletion (in/del) loops up to ∼17 nucleotides (nt.) and base–base mispairs, respectively; the 2 complexes have overlapping specificity for small (1–2 nt.) in/dels. The DNA-binding specificity for the 2 complexes resides in their respective mispair binding domains (MBDs) and has distinct DNA-binding modes. Msh2-Msh3 also plays a role in promoting CAG/CTG trinucleotide repeat (TNR) expansions, which underlie many neurodegenerative diseases such as Huntington's disease and myotonic dystrophy type 1. Models for Msh2-Msh3's role in promoting TNR tract expansion have invoked its specific DNA-binding activity and predict that the TNR structure alters its DNA binding and downstream activities to block repair. Using a chimeric Msh complex that replaces the MBD of Msh6 with the Msh3 MBD, we demonstrate that Msh2-Msh3 DNA-binding activity is not sufficient to promote TNR expansions. We propose a model for Msh2-Msh3-mediated TNR expansions that requires a fully functional Msh2-Msh3 including DNA binding, coordinated ATP binding, and hydrolysis activities and interactions with Mlh complexes that are analogous to those required for MMR. 

Active learning facilitated adsorption predictions in porous materials by incorporating “alchemical” molecules, fugacity, and material features (PC1, PC2) using Gaussian process regression.