ToCo: An ontology for representing hybrid telecommunication networks

The TOUCAN project proposed an ontology for telecommunication networks with hybrid technologies – the TOUCAN Ontology (ToCo), available at http://purl.org/toco/, as well as a knowledge design pattern Device-Interface-Link (DIL) pattern. The core classes and relationships forming the ontology are discussed in detail. The ToCo ontology can describe the physical infrastructure, quality of channel, services and users in heterogeneous telecommunication networks which span multiple technology domains. The DIL pattern is observed and summarised when modelling networks with various technology domains. Examples and use cases of ToCo are presented for demonstration

MUGEN mouse database; Animal models of human immunological diseases

The MUGEN mouse database (MMdb) (www.mugen-noe.org/database/) is a database of murine models of immune processes and immunological diseases. Its aim is to share and publicize information on mouse strain characteristics and availability from participating institutions. MMdb's basic classification of models is based on three major research application categories: Models of Human Disease, Models of Immune Processes and Transgenic Tools. Data on mutant strains includes detailed information on affected gene(s), mutant allele(s) and genetic background (DNA origin, gene targeted, host and backcross strain background). Each gene/transgene index also includes IDs and direct links to Ensembl, ArrayExpress, EURExpress and NCBI's Entrez Gene database. Phenotypic description is standardized and hierarchically structured, based on MGI's mammalian phenotypic ontology terms. Availability (e.g. live mice, cryopreserved embryos, sperm and ES cells) is clearly indicated, along with handling and genotyping details (in the form of documents or hyperlinks) and all relevant contact information (including EMMA and Jax/IMSR hyperlinks where available). MMdb's design offers a user-friendly query interface and provides instant access to the list of mutant strains and genes. Database access is free of charge and there are no registration requirements for data querying

FYPO: the fission yeast phenotype ontology.

MOTIVATION: To provide consistent computable descriptions of phenotype data, PomBase is developing a formal ontology of phenotypes observed in fission yeast. RESULTS: The fission yeast phenotype ontology (FYPO) is a modular ontology that uses several existing ontologies from the open biological and biomedical ontologies (OBO) collection as building blocks, including the phenotypic quality ontology PATO, the Gene Ontology and Chemical Entities of Biological Interest. Modular ontology development facilitates partially automated effective organization of detailed phenotype descriptions with complex relationships to each other and to underlying biological phenomena. As a result, FYPO supports sophisticated querying, computational analysis and comparison between different experiments and even between species. AVAILABILITY: FYPO releases are available from the Subversion repository at the PomBase SourceForge project page (https://sourceforge.net/p/pombase/code/HEAD/tree/phenotype_ontology/). The current version of FYPO is also available on the OBO Foundry Web site (http://obofoundry.org/)

The BCD Triage Sieve outperforms all existing major incident triage tools:comparative analysis using the UK national trauma registry population

BACKGROUND: Natural disasters, conflict, and terrorism are major global causes of death and disability. Central to the healthcare response is triage, vital to ensure the right care is provided to the right patient at the right time. The ideal triage tool has high sensitivity for the highest priority (P1) patients with acceptably low over-triage. This study compared the performance of major incident triage tools in predicting P1 casualty status in adults in the prospective UK Trauma Audit and Research Network (TARN) registry. METHODS: TARN patients aged 16+ years (January 2008-December 2017) were included. Ten existing triage tools were applied using patients’ first recorded pre-hospital physiology. Patients were subsequently assigned triage categories (P1, P2, P3, Expectant or Dead) based on pre-defined, intervention-based criteria. Tool performance was assessed by comparing tool-predicted and intervention-based priority status. FINDINGS: 195,709 patients were included; mortality was 7·0% (n=13,601); median Injury Severity Score (ISS) was 9 (IQR 9–17); 97·1% sustained blunt injuries. 22,144 (11·3%) patients fulfilled intervention-based criteria for P1 status, exhibiting higher mortality (12·8% vs. 5·0%, p<0.001), increased intensive care requirement (52·4% vs 5·0%, p<0.001), and more severe injuries (median ISS 21 vs 9, p<0.001) compared with P2 patients. In 16–64 year olds, the highest performing tool was the Battlefield Casualty Drills (BCD) Triage Sieve (Prediction of P1 status: 70·4% sensitivity, over-triage 70·9%, area under the receiver operating curve (AUC) 0·068 [95%CI 0·676–0·684]). The UK National Ambulance Resilience Unit (NARU) Triage Sieve had sensitivity of 44·9%; over-triage 56·4%; AUC 0·666 (95%CI 0·662–0·670). All tools performed poorly amongst the elderly (65+ years). INTERPRETATION: The BCD Triage Sieve performed best in this nationally representative population; we recommend it supersede the NARU Triage Sieve as the UK primary major incident triage tool. Validated triage category definitions are recommended for appraising future major incidents. FUNDING: This study is funded by the National Institute for Health Research (NIHR) Surgical Reconstruction and Microbiology Research Centre. GVG also acknowledges support from the MRC Heath Data Research UK (HDRUK/CFC/01). The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care, or the Ministry of Defence

Prevalence of admission plasma glucose in 'diabetes' or 'at risk' ranges in hospital emergencies with no prior diagnosis of diabetes by gender, age and ethnicity

Aims To establish the prevalence of admission plasma glucose in 'diabetes' and 'at risk' ranges in emergency hospital admissions with no prior diagnosis of diabetes; characteristics of people with hyperglycaemia; and factors influencing glucose measurement. Methods Electronic patient records for 113 097 hospital admissions over 1 year from 2014 to 2015 included 43 201 emergencies with glucose available for 31 927 (74%) admissions, comprising 22 045 people. Data are presented for 18 965 people with no prior diagnosis of diabetes and glucose available on first attendance. Results Three quarters (14 214) were White Europeans aged 62 (43-78) years, median (IQ range); 12% (2241) South Asians 46 (32-64) years; 9% (1726) Unknown/Other ethnicities 43 (29-61) years; and 4% (784) Afro-Caribbeans 49 (33-63) years,  24 hours. Conclusions Hyperglycaemia was evident in 21% of adults admitted as an emergency; various aspects related to follow-up and initial testing, age and ethnicity need to be considered by professional bodies addressing undiagnosed diabetes in hospital admissions

Utility of HbA1c assessment in people with diabetes awaiting liver transplantation

AIMS To investigate the relationship between HbA and glucose in people with co-existing liver disease and diabetes awaiting transplant, and in those with diabetes but no liver disease. METHODS HbA and random plasma glucose data were collected for 125 people with diabetes without liver disease and for 29 people awaiting liver transplant with diabetes and cirrhosis. Cirrhosis was caused by non-alcoholic fatty liver disease, hepatitis C, alcoholic liver disease, hereditary haemochromatosis, polycystic liver/kidneys, cryptogenic/non-cirrhotic portal hypertension and α-1-antitrypsin-related disease. RESULTS The median (interquartile range) age of the diabetes with cirrhosis group was 55 (49-63) years compared to 60 (50-71) years (P=0.13) in the group without cirrhosis. In the diabetes with cirrhosis group there were 21 men (72%) compared with 86 men (69%) in the group with diabetes and no cirrhosis (P=0.82). Of the group with diabetes and cirrhosis, 27 people (93%) were of white European ethnicity, two (7%) were South Asian and none was of Afro-Caribbean/other ethnicity compared with 94 (75%), 16 (13%), 10 (8%)/5 (4%), respectively, in the group with diabetes and no cirrhosis (P=0.20). Median (interquartile range) HbA was 41 (32-56) mmol/mol [5.9 (5.1-7.3)%] vs 61 (52-70) mmol/mol [7.7 (6.9-8.6)%] (P<0.001), respectively, in the diabetes with cirrhosis group vs the diabetes without cirrhosis group. The glucose concentrations were 8.4 (7.0-11.2) mmol/l vs 7.3 (5.2-11.5) mmol/l (P=0.17). HbA was depressed by 20 mmol/mol (1.8%; P<0.001) in 28 participants with cirrhosis but elevated by 28 mmol/mol (2.6%) in the participant with α-1-antitrypsin disorder. Those with cirrhosis and depressed HbA had fewer larger erythrocytes, and higher red cell distribution width and reticulocyte count. This was reflected in the positive association of glucose with mean cell volume (r=0.39) and haemoglobin level (r=0.49) and the negative association for HbA (r=-0.28 and r=-0.26, respectively) in the diabetes group with cirrhosis. CONCLUSION HbA is not an appropriate test for blood glucose in people with cirrhosis and diabetes awaiting transplant as it reflects altered erythrocyte presentation

An angiopoietin 2, FGF23, and BMP10 biomarker signature differentiates atrial fibrillation from other concomitant cardiovascular conditions

Abstract Early detection of atrial fibrillation (AF) enables initiation of anticoagulation and early rhythm control therapy to reduce stroke, cardiovascular death, and heart failure. In a cross-sectional, observational study, we aimed to identify a combination of circulating biomolecules reflecting different biological processes to detect prevalent AF in patients with cardiovascular conditions presenting to hospital. Twelve biomarkers identified by reviewing literature and patents were quantified on a high-precision, high-throughput platform in 1485 consecutive patients with cardiovascular conditions (median age 69 years [Q1, Q3 60, 78]; 60% male). Patients had either known AF (45%) or AF ruled out by 7-day ECG-monitoring. Logistic regression with backward elimination and a neural network approach considering 7 key clinical characteristics and 12 biomarker concentrations were applied to a randomly sampled discovery cohort (n = 933) and validated in the remaining patients (n = 552). In addition to age, sex, and body mass index (BMI), BMP10, ANGPT2, and FGF23 identified patients with prevalent AF (AUC 0.743 [95% CI 0.712, 0.775]). These circulating biomolecules represent distinct pathways associated with atrial cardiomyopathy and AF. Neural networks identified the same variables as the regression-based approach. The validation using regression yielded an AUC of 0.719 (95% CI 0.677, 0.762), corroborated using deep neural networks (AUC 0.784 [95% CI 0.745, 0.822]). Age, sex, BMI and three circulating biomolecules (BMP10, ANGPT2, FGF23) are associated with prevalent AF in unselected patients presenting to hospital. Findings should be externally validated. Results suggest that age and different disease processes approximated by these three biomolecules contribute to AF in patients. Our findings have the potential to improve screening programs for AF after external validation

Harmonising knowledge for safer materials via the “NanoCommons” Knowledge Base

In mediaeval Europe, the term “commons” described the way that communities managed land that was held “in common” and provided a clear set of rules for how this “common land” was used and developed by, and for, the community. Similarly, as we move towards an increasingly knowledge-based society where data is the new oil, new approaches to sharing and jointly owning publicly funded research data are needed to maximise its added value. Such common management approaches will extend the data’s useful life and facilitate its reuse for a range of additional purposes, from modelling, to meta-analysis to regulatory risk assessment as examples relevant to nanosafety data. This “commons” approach to nanosafety data and nanoinformatics infrastructure provision, co-development, and maintenance is at the heart of the “NanoCommons” project and underpins its post-funding transition to providing a basis on which other initiatives and projects can build. The present paper summarises part of the NanoCommons infrastructure called the NanoCommons Knowledge Base. It provides interoperability for nanosafety data sources and tools, on both semantic and technical levels. The NanoCommons Knowledge Base connects knowledge and provides both programmatic (via an Application Programming Interface) and a user-friendly graphical interface to enable (and democratise) access to state of the art tools for nanomaterials safety prediction, NMs design for safety and sustainability, and NMs risk assessment, as well. In addition, the standards and interfaces for interoperability, e.g., file templates to contribute data to the NanoCommons, are described, and a snapshot of the range and breadth of nanoinformatics tools and models that have already been integrated are presented Finally, we demonstrate how the NanoCommons Knowledge Base can support users in the FAIRification of their experimental workflows and how the NanoCommons Knowledge Base itself has progressed towards richer compliance with the FAIR principles

The Human Phenotype Ontology project:linking molecular biology and disease through phenotype data

The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online

Finding Our Way through Phenotypes

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility