Foundational studies of chloroplast genome (plastome) evolution in parasitic plants have focused on broad trends across large clades, particularly among the Orobanchaceae, a species-rich and ecologically diverse family of root parasites. However, the extent to which such patterns and processes of plastome evolution, such as stepwise gene loss following the complete loss of photosynthesis (shift to holoparasitism), are detectable at shallow evolutionary time scale is largely unknown. We used genome skimming to assemble eight chloroplast genomes representing complete taxonomic sampling of sect. a small clade within the Orobanchaceae that evolved approximately 6 Ma, long after the origin of holoparasitism. We show substantial plastome reduction occurred in the stem lineage, but subsequent change in plastome size, gene content, and structure has been relatively minimal, albeit detectable. This lends additional fine-grained support to existing models of stepwise plastome reduction in holoparasitic plants. Additionally, we report phylogenetic evidence based on an gene tree and assembled 60+ kb fragments of the mitochondrial genome indicating host-to-parasite horizontal gene transfers (hpHGT) of several genes originating from the plastome of an ancient host into the mitochondrial genome of a recent common ancestor of Ecologically, this evidence of hpHGT suggests that the host-parasite associations between and have been stable at least since its subspecies diverged hundreds of thousands of years ago.

Clinical notes provide important documentation critical to medical care, as well as billing and legal needs. Too little information degrades quality of care; too much information impedes care. Training for clinical note documentation is highly variable, depending on institutions and programs. In this work, we introduce the problem of automatic evaluation of note creation through rubric-based content grading, which has the potential for accelerating and regularizing clinical note documentation training. To this end, we describe our corpus creation methods as well as provide simple feature-based and neural network baseline systems. We further provide tagset and scaling experiments to inform readers of plausible expected performances. Our baselines show promising results with content point accuracy and kappa values at 0.86 and 0.71 on the test set.

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