The fungal family Clavicipitaceae includes plant symbionts and parasites that produce several psychoactive and bioprotective alkaloids. The family includes grass symbionts in the epichloae clade (Epichloë and Neotyphodium species), which are extraordinarily diverse both in their host interactions and in their alkaloid profiles. Epichloae produce alkaloids of four distinct classes, all of which deter insects, and some—including the infamous ergot alkaloids—have potent effects on mammals. The exceptional chemotypic diversity of the epichloae may relate to their broad range of host interactions, whereby some are pathogenic and contagious, others are mutualistic and vertically transmitted (seed-borne), and still others vary in pathogenic or mutualistic behavior. We profiled the alkaloids and sequenced the genomes of 10 epichloae, three ergot fungi (Claviceps species), a morning-glory symbiont (Periglandula ipomoeae), and a bamboo pathogen (Aciculosporium take), and compared the gene clusters for four classes of alkaloids. Results indicated a strong tendency for alkaloid loci to have conserved cores that specify the skeleton structures and peripheral genes that determine chemical variations that are known to affect their pharmacological specificities. Generally, gene locations in cluster peripheries positioned them near to transposon-derived, AT-rich repeat blocks, which were probably involved in gene losses, duplications, and neofunctionalizations. The alkaloid loci in the epichloae had unusual structures riddled with large, complex, and dynamic repeat blocks. This feature was not reflective of overall differences in repeat contents in the genomes, nor was it characteristic of most other specialized metabolism loci. The organization and dynamics of alkaloid loci and abundant repeat blocks in the epichloae suggested that these fungi are under selection for alkaloid diversification. We suggest that such selection is related to the variable life histories of the epichloae, their protective roles as symbionts, and their associations with the highly speciose and ecologically diverse cool-season grasses.

Increased resilience of pasture grasses mediated by fungal Epichloë endophytes is crucial to pastoral industries. The underlying mechanisms are only partially understood and likely involve very different activities of the endophyte in different plant tissues and responses of the plant to these. We analyzed the transcriptomes of Epichloë festucae and its host, Lolium perenne, in host tissues of different function and developmental stages. The endophyte contributed approximately 10× more to the transcriptomes than to the biomass of infected tissues. Proliferating mycelium in growing host tissues highly expressed genes involved in hyphal growth. Nonproliferating mycelium in mature plant tissues, transcriptionally equally active, highly expressed genes involved in synthesizing antiherbivore compounds. Transcripts from the latter accounted for 4% of fungal transcripts. Endophyte infection systemically but moderately increased transcription of L. perenne genes with roles in hormone biosynthesis and perception as well as stress and pathogen resistance while reducing expression of genes involved in photosynthesis. There was a good correlation between transcriptome-based observations and physiological observations. Our data indicate that the fitness-enhancing effects of the endophyte are based both on its biosynthetic activities, predominantly in mature host tissues, and also on systemic alteration of the host's hormonal responses and induction of stress response genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Diagnosis codes are extracted from medical records for billing and reimbursement and for secondary uses such as quality control and cohort identification. In the US, these codes come from the standard terminology ICD-9-CM derived from the international classification of diseases (ICD). ICD-9 codes are generally extracted by trained human coders by reading all artifacts available in a patient’s medical record following specific coding guidelines. To assist coders in this manual process, this paper proposes an unsupervised ensemble approach to automatically extract ICD-9 diagnosis codes from textual narratives included in electronic medical records (EMRs). Earlier attempts on automatic extraction focused on individual documents such as radiology reports and discharge summaries. Here we use a more realistic dataset and extract ICD-9 codes from EMRs of 1000 inpatient visits at the University of Kentucky Medical Center. Using named entity recognition (NER), graph-based concept-mapping of medical concepts, and extractive text summarization techniques, we achieve an example based average recall of 0.42 with average precision 0.47; compared with a baseline of using only NER, we notice a 12% improvement in recall with the graph-based approach and a 7% improvement in precision using the extractive text summarization approach. Although diagnosis codes are complex concepts often expressed in text with significant long range non-local dependencies, our present work shows the potential of unsupervised methods in extracting a portion of codes. As such, our findings are especially relevant for code extraction tasks where obtaining large amounts of training data is difficult.

Highlights The HEALing Communities Study relies on data-driven decision-making by communities. Community-tailored dashboards display opioid overdose death data and related measures. Dashboards are co-created by researchers and community stakeholders. Access to timely data can benefit other public health issues and health disparities.