(4S,5S,6S)-4-Hy­droxy-3-meth­oxy-5-methyl-5,6-ep­oxy­cyclo­hex-2-en-1-one

The title compound, C8H10O4, was isolated from culture extracts of the endophytic fungus Xylaria sp. (PB-30). The cyclo­hexenone ring exhibits a flattened boat conformation. In the crystal structure, mol­ecules related by translation along the b axis are linked into chains through O—H⋯O hydrogen bonds. Weak non-classical C—H⋯O contacts are also observed in the structure

Differential bait preference and rate of attraction by Argentine ants (Linepithema humile Mayr) at freshwater and saltwater marsh sites in southern California

Ants are a type of foraging insect species which harvests food resources based on availability. When ants locate food resources that are scarce within their habitat, they tend to be more strongly attracted to that food resource. This study used protein, carbohydrate and control based baits to examine if there was a deficiency in resources demonstrated by the ants at two different wetland habitats. We sampled Argentine ants (Linepithema humileMayr) within the saltwater and freshwater marshes of Ballona Wetlands in Los Angeles, CA. We found significant differences in the rapid deployment of Argentine ants towards protein baits over carbohydrate and control baits. We saw more Argentine ants at the protein baits in the saltwater marshes than in the freshwater marshes. We propose that a protein limitation exists in both wetland habitats with increased protein limitation in the saltwater marshes

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-