Search for: All records

Abstract Speciation processes in plants can be difficult to evaluate, but are essential to understanding evolutionary processes that lead to diversification. Determining the juncture at which a genetically and/or morphologically divergent population can be reliably considered a separate species is often challenging. This is particularly so with respect to recent divergences amongst closely related taxa wherein factors such as incomplete lineage sorting may yield confounding results. Taxa in theCymopterus terebinthinus(Apiaceae) species complex have long puzzled botanists. Named entities in this group display similar, yet apparently distinct morphologies that have been classified as varieties under various generic names highlighting long‐standing nomenclatural instability. Previous phylogenetic studies have challenged the monophyly of this complex. This study aims to clarify taxonomic boundaries and infer evolutionary relationships among the fourC. terebinthinusvarieties andC. petraeusby applying phylogenetic inference and incorporating ecological, morphological, and geographical evidence. We sampled from populations of all varieties ofC. terebinthinusandC. petraeusfor target capture with the Angiosperms353 bait kit. We performed phylogenetic analyses with maximum likelihood (RAxML and IQ‐TREE) and coalescent‐based phylogenetic analysis (ASTRAL). We also conducted principal component analysis of soil samples and climatic variables. We find thatC. terebinthinusand its varietal infrataxa comprise a monophyletic clade that includesC. petraeus. Clade groupings correspond to previous taxonomic assignments and morphology. Clades are often closely associated with geographical variables and at times correlated with ecological variables. Exceptions to this are here attributed to various evolutionary factors that often confound other phylogenetic analyses such as incomplete lineage sorting, introgression, and paralogous loci. Our findings suggests that geographical factors might play a major role in genetic and morphological differentiation in this complex. Despite finding well‐supported clades that correspond to defined morphological characters; further sampling amongC. petraeuspopulations is required to make taxonomic decisions. 

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. 

In this paper, we focus on the important yet understudied problem of Continual Federated Learning (CFL), where a server communicates with a set of clients to incrementally learn new concepts over time without sharing or storing any data. The complexity of this problem is compounded by challenges from both the Continual and Federated Learning perspectives. Specifically, models trained in a CFL setup suffer from catastrophic forgetting which is exacerbated by data heterogeneity across clients. Existing attempts at this problem tend to impose large overheads on clients and communication channels or require access to stored data which renders them unsuitable for real-world use due to privacy. In this paper, we attempt to tackle forgetting and heterogeneity while minimizing overhead costs and without requiring access to any stored data. We study this problem in the context of Vision Transformers and explore parameter-efficient approaches to adapt to dynamic distributions while minimizing forgetting. We achieve this by leveraging a prompting based approach (such that only prompts and classifier heads have to be communicated) and proposing a novel and lightweight generation and distillation scheme to consolidate client models at the server. We formulate this problem for image classification and establish strong baselines for comparison, conduct experiments on CIFAR-100 as well as challenging, large-scale datasets like ImageNet-R and DomainNet. Our approach outperforms both existing methods and our own baselines by as much as 7% while significantly reducing communication and client-level computation costs. Code available at https://github.com/shaunak27/hepco-fed. 

The movement and pathogenicity of trypanosomatid species, the causative agents of trypanosomiasis and leishmaniasis, are dependent on a flagellum that contains an axoneme of dynein-bound doublet microtubules (DMTs). In this work, we present cryo–electron microscopy structures of DMTs from two trypanosomatid species,Leishmania tarentolae and Crithidia fasciculata, at resolutions up to 2.7 angstrom. The structures revealed 27 trypanosomatid-specific microtubule inner proteins, a specialized dynein-docking complex, and the presence of paralogous proteins that enable higher-order periodicities or proximal-distal patterning. Leveraging the genetic tractability of trypanosomatid species, we quantified the location and contribution of each structure-identified protein to swimming behavior. Our study shows that proper B-tubule closure is critical for flagellar motility, exemplifying how integrating structural identification with systematic gene deletion can dissect individual protein contributions to flagellar motility. 

In the centuries following Christopher Columbus’s 1492 voyage to the Americas, transoceanic travel opened unprecedented pathways in global pathogen circulation. Yet no biological transfer is a single, discrete event. We use mathematical modeling to quantify historical risk of shipborne pathogen introduction, exploring the respective contributions of journey time, ship size, population susceptibility, transmission intensity, density dependence, and pathogen biology. We contextualize our results using port arrivals data from San Francisco, 1850 to 1852, and from a selection of historically significant voyages, 1492 to 1918. We offer numerical estimates of introduction risk across historically realistic ranges of journey time and ship population size, and show that both steam travel and shipping regimes that involved frequent, large-scale movement of people substantially increased risk of transoceanic pathogen circulation.