Analysis of readability and structural accuracy in SNOMED CT

Background The increasing adoption of ontologies in biomedical research and the growing number of ontologies available have made it necessary to assure the quality of these resources. Most of the well-established ontologies, such as the Gene Ontology or SNOMED CT, have their own quality assurance processes. These have demonstrated their usefulness for the maintenance of the resources but are unable to detect all of the modelling flaws in the ontologies. Consequently, the development of efficient and effective quality assurance methods is needed. Methods Here, we propose a series of quantitative metrics based on the processing of the lexical regularities existing in the content of the ontology, to analyse readability and structural accuracy. The readability metrics account for the ratio of labels, descriptions, and synonyms associated with the ontology entities. The structural accuracy metrics evaluate how two ontology modelling best practices are followed: (1) lexically suggest locally define (LSLD), that is, if what is expressed in natural language for humans is available as logical axioms for machines; and (2) systematic naming, which accounts for the amount of label content of the classes in a given taxonomy shared. Results We applied the metrics to different versions of SNOMED CT. Both readability and structural accuracy metrics remained stable in time but could capture some changes in the modelling decisions in SNOMED CT. The value of the LSLD metric increased from 0.27 to 0.31, and the value of the systematic naming metric was around 0.17. We analysed the readability and structural accuracy in the SNOMED CT July 2019 release. The results showed that the fulfilment of the structural accuracy criteria varied among the SNOMED CT hierarchies. The value of the metrics for the hierarchies was in the range of 0–0.92 (LSLD) and 0.08–1 (systematic naming). We also identified the cases that did not meet the best practices. Conclusions We generated useful information about the engineering of the ontology, making the following contributions: (1) a set of readability metrics, (2) the use of lexical regularities to define structural accuracy metrics, and (3) the generation of quality assurance information for SNOMED CT.

The Natural History Museum Fossil Porifera Collection

This article provides updated information about the Porifera Collection at The Natural History Museum (NHM), London. With very little information available regarding fossil sponge digitization or any similar initiative, this paper covers the type and figured specimens and drawer label content data of the Porifera Collection and also describes the collection and its research potential. With approximately 71,000 specimens, of which more than 60% are Mesozoic, the NHM holdings offer the best Mesozoic sponge collection in the world and one of the most important due to its breadth and depth. The Porifera Collection covers all stratigraphic periods and all taxonomic groups and includes almost 3000 cited and figured specimens including types. Although most of the specimens come from the British Isles, worldwide samples are also present, with abundant specimens from other Commonwealth countries and from Antarctica.

A Content-Based Model for Tag Recommendation in Software Information Sites

Developers use software information sites such as Stack Overflow to get and give information on various subjects. These sites allow developers to label content with tags as a short description. Tags, then, are used to describe, categorize and search the posted content. However, tags might be noisy, and postings may become poorly categorized since people tag a posting based on their knowledge of its content and other existing tags. To keep the content well organized, tag recommendation systems can help users by suggesting appropriate tags for their posted content. In this paper, we propose a tag recommendation scheme that uses the textual content of already tagged postings to recommend suitable tags for newly posted content. Our approach combines multi-label classification and textual similarity techniques to improve the performance of tag recommendation. We evaluate the performance of the proposed scheme on 11 software information sites from the Stack Exchange network. The results show a significant improvement over TagCombine, TagMulRec and FastTagRec, which are well-known tag recommendation systems. On average, the proposed model outperforms TagCombine, TagMulRec and FastTagRec by 26.2, 15.9 and 13.8% in terms of Recall@5 and by 16.9, 12.4 and 9.4% in terms of Recall@10, respectively.

Eco-labelling

This chapter examines whether and how eco-labelling schemes, either public or private, can generate environmental benefits. Eco-labels are a form of informational regulation that have emerged as an alternative to traditional command and control regulation. Eco-labels have some potential to improve environmental outcomes, in part by educating consumers about environmental attributes of products and incentivizing product greening. The chapter first considers different types of eco-labels, focusing on label content and label governance, using examples from around the world. It then discusses the challenges that a labelling scheme must overcome in order to be a successful force for environmental change. In particular, it explores whether an eco-label will work (issues relating to labelling methodology, label legitimacy, and consumer behaviour) and concludes with an analysis of eco-labelling normative concerns (equity, consumerism, and ‘strong’ versus ‘weak’ sustainability).

How Stereotypes Are Shared Through Language: A Review and Introduction of the Social Categories and Stereotypes Communication (SCSC) Framework

Language use plays a crucial role in the consensualization of stereotypes within cultural groups. Based on an integrative review of the literature on stereotyping and biased language use, we propose the Social Categories and Stereotypes Communication (SCSC) framework. The framework integrates largely independent areas of literature and explicates the linguistic processes through which social-category stereotypes are shared and maintained. We distinguish two groups of biases in language use that jointly feed and maintain three fundamental cognitive variables in (shared) social-category cognition: perceived category entitativity, stereotype content, and perceived essentialism of associated stereotypic characteristics. These are: (1) Biases in linguistic labels used to denote categories, within which we discuss biases in (a) label content and (b) linguistic form of labels; (2) Biases in describing behaviors and characteristics of categorized individuals, within which we discuss biases in (a) communication content (i.e., what information is communicated), and (b) linguistic form of descriptions (i.e., how is information formulated). Together, these biases create a self-perpetuating cycle in which social-category stereotypes are shared and maintained. The framework allows for a better understanding of stereotype maintaining biases in natural language. We discuss various opportunities for further research.

Dietary Supplement Ingredient Database (DSID) and the Application of Analytically Based Estimates of Ingredient Amount to Intake Calculations

Objective We describe the purpose of the Dietary Supplement Ingredient Database (DSID), the statistical methodology underlying online calculators of analytically verified supplement content estimates, and the application and significance of DSID label adjustments in nutritional epidemiology. Background and History During dietary supplement (DS) manufacturing, many ingredients are added at higher than declared label amounts, but overages are not standardized among manufacturers. As a result, researchers may underestimate nutrient intakes from DSs. The DSID provides statistical tools on the basis of the results of chemical analysis to convert label claims into analytically predicted ingredient amounts. These adjustments to labels are linked to DS products reported in NHANES. Rationale Tables summarizing the numbers of NHANES DS products with ingredient overages and below label content show the importance of DSID adjustments to labels for accurate intake calculations. Recent Developments We show the differences between analytically based estimates and labeled content for vitamin D, calcium, iodine, caffeine, and omega-3 (n–3) fatty acids and their potential impact on the accuracy of intake assessments in large surveys. Analytical overages >20% of label levels are predicted for several nutrients in 50–99% of multivitamin-mineral products (MVMs) reported in NHANES: for iodine and selenium in adult MVMs, for iodine and vitamins D and E in children's MVMs, and for iodine, chromium, and potassium in nonprescription prenatal MVMs. Predicted overages of 10–20% for calcium can be applied to most MVMs and overages >10% for folic acid in the vast majority of adult and children's MVMs. Future Directions DSID studies are currently evaluating ingredient levels in prescription prenatal MVMs and levels of constituents in botanical DSs. Conclusions We estimate that the majority of MVM products reported in NHANES have significant overages for several ingredients. It is important to account for nonlabeled additional nutrient exposure from DSs to better evaluate nutritional status in the United States.

Challenges in Developing Analytically Validated Laboratory-Derived Dietary Supplement Databases

The Dietary Supplement Label Database (DSLD) is sponsored by the Office of Dietary Supplements (ODS) and the National Library of Medicine (NLM). It provides a searchable, free database of the contents of ∼65,000 supplement labels. A companion database of analytically verified product labels [the Dietary Supplement Ingredient Database (DSID)] was created by ODS, NLM, and the USDA. There are considerable challenges to populating both databases, but the DSID faces unique analytic chemistry challenges. This article describes the challenges to creating analytically verified marketplace surveys of dietary supplement (DS) product content claims for inclusion in public databases. Nutritionists and public health scientists require information on actual exposures to DS constituents because labeled content may not match labeled product content. Analytic verification of composition of DSs provides a link to actual exposure. A public database of analytically derived DS content was developed to provide more accurate estimates of dietary intake in population-based epidemiologic studies. The DSID has conducted surveys of several types of vitamin- and mineral-containing DSs. Results showing label content claims as analytically derived values are available in the current DSID. A recent pilot project explored the feasibility of adding botanical DS products to the DSID. Candidates for future botanical DSID studies will be based on sales volume, potential public health impacts, and the availability of validated analytic methods and reference materials. Databases like DSID and the DSLD are essential for researchers and clinicians to evaluate dietary ingredient intakes in population-based epidemiologic studies. Together, these databases provide a picture of the DS marketplace. The DSID provides an analytic survey of marketed DSs. However, selection of future botanical supplements for DSID evaluation involves analytic challenges. Even when appropriate resources are available, method selection and data evaluation are resource- and time-consuming.

Effect of message congruency on attention and recall in pictorial health warning labels

ObjectiveThe nine pictorial health warning labels (PWLs) proposed by the US Food and Drug Administration vary in format and feature of visual and textual information. Congruency is the degree to which visual and textual features reflect a common theme. This characteristic can affect attention and recall of label content. This study investigates the effect of congruency in PWLs on smoker’s attention and recall of label content.Methods120 daily smokers were randomly assigned to view either congruent or incongruent PWLs, while having their eye movements recorded. Participants were asked to recall label content immediately after exposure and 5 days later.ResultsOverall, the image was viewed more and recalled better than the text. Smokers in the incongruent condition spent more time focusing on the text than smokers in the congruent condition (p=0.03), but dwell time of the image did not differ. Despite lower dwell time on the text, smokers in the congruent condition were more likely to correctly recall it on day 1 (p=0.02) and the risk message of the PWLs on both day 1 (p=0.01) and day 5 (p=0.006) than smokers in the incongruent condition.ConclusionsThis study identifies an important design feature of PWLs and demonstrates objective differences in how smokers process PWLs. Our results suggest that message congruency between visual and textual information is beneficial to recall of label content. Moreover, images captured and held smokers’ attention better than the text.