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Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.

We report the first identified outbreak of cryptosporidiosis with Cryptosporidium cuniculus following a water quality incident in Northamptonshire, UK. A standardised, enhanced Cryptosporidium exposure questionnaire was administered to all cases of cryptosporidiosis after the incident. Stool samples, water testing, microscopy slides and rabbit gut contents positive for Cryptosporidium were typed at the Cryptosporidium Reference Unit, Singleton Hospital, Swansea. Twenty-three people were microbiologically linked to the incident although other evidence suggests an excess of 422 cases of cryptosporidiosis above baseline. Most were adult females; unusually for cryptosporidiosis there were no affected children identified under the age of 5 years. Water consumption was possibly higher than in national drinking water consumption patterns. Diarrhoea duration was negatively correlated to distance from the water treatment works where the contamination occurred. Oocyst counts were highest in water storage facilities. This outbreak is the first caused by C. cuniculus infection to have been noted and it has conclusively demonstrated that this species can be a human pathogen. Although symptomatically similar to cryptosporidiosis from C. parvum or C. hominis, this outbreak has revealed some differences, in particular no children under 5 were identified and females were over-represented. These dissimilarities are unexplained although we postulate possible explanations.

\"Plutarch's essay 'How to Study Poetry' offers a set of reading practices intended to remove the potential damage that poetry can do to the moral health of young readers. It opens a window on to a world of ancient education and scholarship which can seem rather alien to those brought up in the highly sophisticated world of modern literary theory and criticism. The full Introduction and Commentary, by two of the world's leading scholars in the field, trace the origins and intellectual affiliations of Plutarch's method and fully illustrate the background to each of his examples. As such this book may serve as an introduction to the whole subject of ancient reading practices and literary criticism. The Commentary also pays particular attention to grammar, syntax and style, and sets this essay within the context of Plutarch's thought and writing more generally\"-- Provided by publisher.

The impact of public sector research is evident in many technology sectors, particularly in the agriculture field. The authors discuss the impact of public sector collaboration on agricultural intellectual property management.

This book gathers together many of the principal essays of Richard Hunter, whose work has been fundamental in the modern re-evaluation of Greek literature after Alexander and its reception at Rome and elsewhere. At the heart of Hunter's work lies the high poetry of Ptolemaic Alexandria (Callimachus, Theocritus, and Apollonius of Rhodes) and the narrative literature of later antiquity ('the ancient novel'), but comedy, mime, didactic poetry and ancient literary criticism all fall within the scope of these studies. Principal recurrent themes are the uses and recreation of the past, the modes of poetic allusion, the moral purposes of literature, the intellectual context for ancient poetry, and the interaction of poetry and criticism. What emerges is not a literature shackled to the past and cowed by an 'anxiety of influence', but an energetic and constantly experimental engagement with both past and present.

The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.