Writing clinical practice guidelines in controlled natural language

Clinicians could benefit from decision support systems incorporating the knowledge contained in clinical practice guidelines. However, the unstructured form of these guidelines makes them unsuitable for formal representation. To address this challenge we translated a complete set of pediatric guideline recommendations into Attempto Controlled English (ACE). One experienced pediatrician, one physician and a knowledge engineer assessed that a suitably extended version of ACE can accurately and naturally represent the clinical concepts and the proposed actions of the guidelines. Currently, we are developing a systematic and replicable approach to authoring guideline recommendations in ACE

Germline MC1R status influences somatic mutation burden in melanoma

The major genetic determinants of cutaneous melanoma risk in the general population are disruptive variants (R alleles) in the melanocortin 1 receptor (MC1R) gene. These alleles are also linked to red hair, freckling, and sun sensitivity, all of which are known melanoma phenotypic risk factors. Here we report that in melanomas and for somatic C>T mutations, a signature linked to sun exposure, the expected single-nucleotide variant count associated with the presence of an R allele is estimated to be 42% (95% CI, 15-76%) higher than that among persons without an R allele. This figure is comparable to the expected mutational burden associated with an additional 21 years of age. We also find significant and similar enrichment of non-C>T mutation classes supporting a role for additional mutagenic processes in melanoma development in individuals carrying R alleles

Innate acting memory Th1 cells modulate heterologous diseases

Through immune memory, infections have a lasting effect on the host. While memory cells enable accelerated and enhanced responses upon rechallenge with the same pathogen, their impact on susceptibility to unrelated diseases is unclear. We identify a subset of memory T helper 1 (Th1) cells termed innate acting memory T (TIA) cells that originate from a viral infection and produce IFN-γ with innate kinetics upon heterologous challenge in vivo. Activation of memory TIA cells is induced in response to IL-12 in combination with IL-18 or IL-33 but is TCR independent. Rapid IFN-γ production by memory TIA cells is protective in subsequent heterologous challenge with the bacterial pathogen Legionella pneumophila. In contrast, antigen-independent reactivation of CD4+ memory TIA cells accelerates disease onset in an autoimmune model of multiple sclerosis. Our findings demonstrate that memory Th1 cells can acquire additional TCR-independent functionality to mount rapid, innate-like responses that modulate susceptibility to heterologous challenges

Automatic extraction of candidate nomenclature terms using the doublet method

BACKGROUND: New terminology continuously enters the biomedical literature. How can curators identify new terms that can be added to existing nomenclatures? The most direct method, and one that has served well, involves reading the current literature. The scholarly curator adds new terms as they are encountered. Present-day scholars are severely challenged by the enormous volume of biomedical literature. Curators of medical nomenclatures need computational assistance if they hope to keep their terminologies current. The purpose of this paper is to describe a method of rapidly extracting new, candidate terms from huge volumes of biomedical text. The resulting lists of terms can be quickly reviewed by curators and added to nomenclatures, if appropriate. The candidate term extractor uses a variation of the previously described doublet coding method. The algorithm, which operates on virtually any nomenclature, derives from the observation that most terms within a knowledge domain are composed entirely of word combinations found in other terms from the same knowledge domain. Terms can be expressed as sequences of overlapping word doublets that have more specific meaning than the individual words that compose the term. The algorithm parses through text, finding contiguous sequences of word doublets that are known to occur somewhere in the reference nomenclature. When a sequence of matching word doublets is encountered, it is compared with whole terms already included in the nomenclature. If the doublet sequence is not already in the nomenclature, it is extracted as a candidate new term. Candidate new terms can be reviewed by a curator to determine if they should be added to the nomenclature. An implementation of the algorithm is demonstrated, using a corpus of published abstracts obtained through the National Library of Medicine's PubMed query service and using "The developmental lineage classification and taxonomy of neoplasms" as a reference nomenclature. RESULTS: A 31+ Megabyte corpus of pathology journal abstracts was parsed using the doublet extraction method. This corpus consisted of 4,289 records, each containing an abstract title. The total number of words included in the abstract titles was 50,547. New candidate terms for the nomenclature were automatically extracted from the titles of abstracts in the corpus. Total execution time on a desktop computer with CPU speed of 2.79 GHz was 2 seconds. The resulting output consisted of 313 new candidate terms, each consisting of concatenated doublets found in the reference nomenclature. Human review of the 313 candidate terms yielded a list of 285 terms approved by a curator. A final automatic extraction of duplicate terms yielded a final list of 222 new terms (71% of the original 313 extracted candidate terms) that could be added to the reference nomenclature. CONCLUSION: The doublet method for automatically extracting candidate nomenclature terms can be used to quickly find new terms from vast amounts of text. The method can be immediately adapted for virtually any text and any nomenclature. An implementation of the algorithm, in the Perl programming language, is provided with this article

Extended P-I diagram method

The pressure-impulse (P-I) diagram method is used in practice (for civilian and military applications) for predicting the level of damage sustained by structures when subjected to blast loads and for assessing the imposed loading regime. Each P-I curve is associated with a certain structural configuration as well as a specific form of blast load and level of damage sustained. When assessing the effect of different parameters (associated with the form of the imposed load and the design of the structure considered) on structural performance, a series of new P-I curves need to be derived. This paper presents an extended P-I diagram method, which is based on derivation of complementary diagrams that can define the effect of two parameters (e.g., the level of axial loading imposed onto a column and the level of damage sustained) on the quasi-static and impulsive asymptotes, thus governing the positions of P-I curves in the diagram plane. The extended P-I diagram method is presented in dimensional and normalised forms. The dimensional form simplifies the derivation of new P-I curves, while the normalised form simplifies the procedure adopted for assessing the behaviour of a certain structure when subjected to a new set of loads. The application of the proposed method is demonstrated in both forms using a typical reinforced concrete (RC) column subjected to a blast load. The column is modelled using finite element analysis capable of accounting for the nonlinear behaviour of concrete and steel. A novel method is proposed for material modelling of concrete. The new material model is validated at both material and structural levels against relevant experimental data. P-I diagrams are initially derived for the axially unloaded column, while complementary diagrams are derived for the column loaded by different axial forces. The framework of the extended P-I diagram method employed for the derivation of new P-I curves and the assessment of the level of damage sustained by the column when subjected to different loading conditions is provided herein.</p

A scalable machine-learning approach to recognize chemical names within large text databases

MOTIVATION: The use or study of chemical compounds permeates almost every scientific field and in each of them, the amount of textual information is growing rapidly. There is a need to accurately identify chemical names within text for a number of informatics efforts such as database curation, report summarization, tagging of named entities and keywords, or the development/curation of reference databases. RESULTS: A first-order Markov Model (MM) was evaluated for its ability to distinguish chemical names from words, yielding ~93% recall in recognizing chemical terms and ~99% precision in rejecting non-chemical terms on smaller test sets. However, because total false-positive events increase with the number of words analyzed, the scalability of name recognition was measured by processing 13.1 million MEDLINE records. The method yielded precision ranges from 54.7% to 100%, depending upon the cutoff score used, averaging 82.7% for approximately 1.05 million putative chemical terms extracted. Extracted chemical terms were analyzed to estimate the number of spelling variants per term, which correlated with the total number of times the chemical name appeared in MEDLINE. This variability in term construction was found to affect both information retrieval and term mapping when using PubMed and Ovid

Shear analysis of concrete with brittle reinforcement

The design of steel-reinforced concrete relies on lower-bound plasticity theory, which allows an equilibrium-state to be postulated without considering compatibility. This is of particular benefit in shear design, due to the complexity of shear-transfer, where simplified models such as the truss analogy are used. Lower-bound plasticity theory, however, relies on stress- redistribution. If brittle reinforcement [such as fiber-reinforced-plastic (FRP)] is used in concrete, lower-bound plasticity theory cannot be applied. This paper studies how compatibility, equilibrium, and the material constitutive laws can be combined to establish the actual conditions within an FRP-reinforced beam subjected to shear. A crack-based analysis is proposed to model shear failure in a beam with brittle reinforcement. The analysis is used to illustrate the importance of satisfying compatibility requirements, and the results are contrasted with the current shear design proposals for FRP-reinforced concrete