The BioLexicon: a large-scale terminological resource for biomedical text mining

Background

Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.

Results

This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.

Conclusions

The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.     

Social class differences in secular trends in established coronary risk factors over 20 years: a cohort study of British men from 1978-80 to 1998-2000

Background

Coronary heart disease (CHD) mortality in the UK since the late 1970s has declined more markedly among higher socioeconomic groups. However, little is known about changes in coronary risk factors in different socioeconomic groups. This study examined whether changes in established coronary risk factors in Britain over 20 years between 1978–80 and 1998–2000 differed between socioeconomic groups.

Methods and Findings

A socioeconomically representative cohort of 7735 British men aged 40–59 years was followed-up from 1978–80 to 1998–2000; data on blood pressure (BP), cholesterol, body mass index (BMI) and cigarette smoking were collected at both points in 4252 survivors. Social class was based on longest-held occupation in middle-age. Compared with men in non-manual occupations, men in manual occupations experienced a greater increase in BMI (mean difference = 0.33 kg/m²; 95%CI 0.14–0.53; p for interaction = 0.001), a smaller decline in non-HDL cholesterol (difference in mean change = 0.18 mmol/l; 95%CI 0.11–0.25, p for interaction≤0.0001) and a smaller increase in HDL cholesterol (difference in mean change = 0.04 mmol/l; 95%CI 0.02–0.06, p for interaction≤0.0001). However, mean systolic BP declined more in manual than non-manual groups (difference in mean change = 3.6; 95%CI 2.1–5.1, p for interaction≤0.0001). The odds of being a current smoker in 1978–80 and 1998–2000 did not differ between non-manual and manual social classes (p for interaction = 0.51).

Conclusion

Several key risk factors for CHD and type 2 diabetes showed less favourable changes in men in manual occupations. Continuing priority is needed to improve adverse cardiovascular risk profiles in socially disadvantaged groups in the UK.     

The generation and maintenance of diversity in microbial communities

Microorganisms play a central role in the regulation of ecosystem processes, and they comprise the vast majority of species on Earth. With recent developments in molecular methods, it has become tractable to quantify the extent of microbial diversity in natural environments. Here we examine this revolution in our understanding of microbial diversity, and we explore the factors that contribute to the seemingly astounding numbers of microbial taxa found within individual environmental samples. We conducted a meta-analysis of bacterial richness estimates from a variety of ecosystems. Nearly all environments contained hundreds to thousands of bacterial taxa, and richness levels increased with the number of individuals in a sample, a pattern consistent with those reported for nonmicrobial taxa. A cursory comparison might suggest that bacterial richness far exceeds the richness levels typically observed for plant and animal taxa. However, the apparent diversity of bacterial communities is influenced by phylogenetic breadth and allometric scaling issues. When these features are taken into consideration, the levels of microbial diversity may appear less astounding. Although the fields of ecology and biogeography have traditionally ignored microorganisms, there are no longer valid excuses for neglecting microorganisms in surveys of biodiversity. Many of the concepts developed to explain plant and animal diversity patterns can also be applied to microorganisms once we reconcile the scale of our analyses to the scale of the organisms being observed. Furthermore, knowledge from microbial systems may provide insight into the mechanisms that generate and maintain species richness in nonmicrobial systems.     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.     

Depth-Dependent Differences in Community Structure of the Human Colonic Microbiota in Health

Objective

The aims of this study were to develop techniques for spatial microbial assessment in humans and to establish colonic luminal and mucosal spatial ecology, encompassing longitudinal and cross-sectional axes.

Design

A microbiological protected specimen brush was used in conjunction with a biopsy forceps to sample the colon in nine healthy volunteers undergoing colonoscopy. Terminal Restriction Fragment Length Polymorphism analysis was used to determine the major variables in the spatial organization of the colonic microbiota.

Results

Protected Specimen Brush sampling retrieved region-specific, uncontaminated samples that were enriched for bacterial DNA and depleted in human DNA when compared to biopsy samples. Terminal Restriction Fragment Length Polymorphism analysis revealed a segmentation of bacterial communities between the luminal brush and biopsy-associated ecological niches with little variability across the longitudinal axis of the colon and reduced diversity in brush samples.

Conclusion

These results support the concept of a microbiota with little longitudinal variability but with some degree of segregation between luminal and mucosal communities.     

Diversity and metabolism of marine bacteria cultivated on dissolved DNA

Dissolved DNA (dDNA) is a potentially important source of energy and nutrients in aquatic ecosystems. However, little is known about the identity, metabolism, and interactions of the microorganisms capable of consuming dDNA. Bacteria from Eel Pond (Woods Hole, MA) were cultivated on low-molecular-weight (LMW) or high-molecular-weight (HMW) dDNA, which served as the primary source of carbon, nitrogen, and phosphorus. Cloning and sequencing of 16S rRNA genes revealed that distinct bacterial assemblages with comparable levels of taxon richness developed on LMW and HMW dDNA. Since the LMW and HMW dDNA used in this study were stoichiometrically identical, the results confirm that the size alone of dissolved organic matter can influence bacterial community composition. Variation in the growth and metabolism of isolates provided insight into mechanisms that may have generated differences in bacterial community composition. For example, bacteria from LMW dDNA enrichments generally grew better on LMW dDNA than on HMW dDNA. In contrast, bacteria isolated from HMW dDNA enrichments were more effective at degrading HMW dDNA than bacteria isolated from LMW dDNA enrichments. Thus, marine bacteria may experience a trade-off between their ability to compete for LMW dDNA and their ability to access HMW dDNA via extracellular nuclease production. Together, the results of this study suggest that dDNA turnover in marine ecosystems may involve a succession of microbial assemblages with specialized ecological strategies.     

Development of a novel PCR assay for detection of Prevotella oris in clinical specimens

Prevotella oris is a nonpigmented, Gram-negative, anaerobic bacterium that has been associated with several serious oral and systemic infections. Prevotella oris has been identified in clinical specimens by bacterial culture and biochemical tests, which are generally unreliable. The aim of this study was to develop a PCR assay for the direct detection of P. oris in clinical specimens. PCR primers specific for P. oris were identified by alignment of bacterial 16S rRNA genes from closely related species and selection of PCR primers specific for P. oris at their 3' ends. Amplification of a 1110-bp product indicated PCR positivity for P. oris. The primers were shown to be specific for P. oris DNA, because no PCR products were obtained when DNA from other oral bacteria, including closely related Prevotella species, were used as test species, and this was confirmed by digestion of PCR products with RsaI and MnlI. Prevotella oris DNA was detected in 17 (36.2%) of 47 pus samples from subjects with dentoalveolar abscesses and in all three pus samples from subjects with spreading odontogenic infections. This PCR assay provides a sensitive, specific and reliable method for identifying P. oris in clinical specimens.     

Diversity and Metabolism of Marine Bacteria Cultivated on Dissolved DNA

Dissolved DNA (dDNA) is a potentially important source of energy and nutrients in aquatic ecosystems. However, little is known about the identity, metabolism, and interactions of the microorganisms capable of consuming dDNA. Bacteria from Eel Pond (Woods Hole, MA) were cultivated on low-molecular-weight (LMW) or high-molecular-weight (HMW) dDNA, which served as the primary source of carbon, nitrogen, and phosphorus. Cloning and sequencing of 16S rRNA genes revealed that distinct bacterial assemblages with comparable levels of taxon richness developed on LMW and HMW dDNA. Since the LMW and HMW dDNA used in this study were stoichiometrically identical, the results confirm that the size alone of dissolved organic matter can influence bacterial community composition. Variation in the growth and metabolism of isolates provided insight into mechanisms that may have generated differences in bacterial community composition. For example, bacteria from LMW dDNA enrichments generally grew better on LMW dDNA than on HMW dDNA. In contrast, bacteria isolated from HMW dDNA enrichments were more effective at degrading HMW dDNA than bacteria isolated from LMW dDNA enrichments. Thus, marine bacteria may experience a trade-off between their ability to compete for LMW dDNA and their ability to access HMW dDNA via extracellular nuclease production. Together, the results of this study suggest that dDNA turnover in marine ecosystems may involve a succession of microbial assemblages with specialized ecological strategies.     

Quirks of dye nomenclature. 3. Trypan blue

Trypan blue is colorant from the 19^th century that has an association with Africa as a chemotherapeutic agent against protozoan (Trypanosomal) infections, which cause sleeping sickness. The dye still is used for staining biopsies, living cells and organisms, and it also has been used as a colorant for textiles.