A density-dependent model of Cirsium vulgare population dynamics using field-estimated parameter values

Two versions of a stage-structured model of Cirsium vulgare population dynamics were developed. Both incorporated density dependence at one stage in the life cycle of the plant. In version 1 density dependence was assumed to operate during germination whilst in version 2 it was included at the seedling stage. Density-dependent parameter values for the model were estimated from annual census data in a factorial grazing experiment. Version 1 of the model produced significant estimates of density dependence under field conditions. The estimated values, when included in a simulation of the dynamics, produced two-point limit cycles under conditions of hard grazing. The limit cycles were most pronounced at the early rosette stage. Comparison of the effects of density dependence at the two different stages in the life cycle revealed a strong difference in predicted dynamics. This emphasizes the importance of determining where density dependence operates under field conditions and the potential problems of arbitrarily assigning it to particular life-history stages. Version 1 of the model produced a good prediction of observed mean plant density across the different grazing treatments (r ²=0.81, P<0.001).     

Genes encoding mercuric reductases from selected gram-negative aquatic bacteria have a low degree of homology with merA of transposon Tn501.

An investigation of the Hg2+ resistance mechanism of four freshwater and four coastal marine bacteria that did not hybridize with a mer operonic probe was conducted (T. Barkay, C. Liebert, and M. Gillman, Appl. Environ. Microbiol. 55:1196-1202, 1989). Hybridization with a merA probe, the gene encoding the mercuric reductase polypeptide, at a stringency of hybridization permitting hybrid formation between evolutionarily distant merA genes (as exists between gram-positive and -negative bacteria), detected merA sequences in the genomes of all tested strains. Inducible Hg2+ volatilization was demonstrated for all eight organisms, and NADPH-dependent mercuric reductase activities were detected in crude cell extracts of six of the strains. Because these strains represented random selections of bacteria from three aquatic environments, it is concluded that merA encodes a common molecular mechanism for Hg2+ resistance and volatilization in aerobic heterotrophic aquatic communities.     

ERRATUM: New Body Mass Estimates for Omomys carteri,a Middle Eocene Primate From North America. Am J Phys Anthropol 109:41–52.

Payseur BA, Covert HA, Vinyard CJ, Dagosto M. 1999. New Body Mass Estimates for Omomys carteri, a Middle Eocene Primate From North America. Am J Phys Anthropol 109:41–52. This article included an incomplete Table 2. The final two columns, showing “Intercept” and “SEE” data were omitted. The complete Table 2, with these two columns included, is provided below.     

High definition bronchoscopy: a randomized exploratory study of diagnostic value compared to standard white light bronchoscopy and autofluorescence bronchoscopy

Background

Videobronchoscopy is an essential diagnostic procedure for evaluation of the central airways and pivotal for the diagnosis and staging of lung cancer. Technological improvements have resulted in high definition (HD) images with advanced real time image enhancement techniques (i-scan).

Objectives

In this study we aimed to explore the sensitivity of HD+ i-scan bronchoscopy for detection of epithelial changes like vascular abnormalities and suspicious preinvasive lesions, and tumors.

Methods

In patients scheduled for a therapeutic or diagnostic procedure under general anesthesia videos of the bronchial tree were made using 5 videobronchoscopy modes in random order: normal white light videobronchoscopy (WLB), HD-bronchoscopy (HD), HD bronchoscopy with surface enhancement technique (i-scan1), HD with surface- and tone enhancement technique (i-scan2) and dual mode autofluorescence videobronchoscopy (AFB). The videos were scored in random order by two independent and blinded expert bronchoscopists.

Results

In 29 patients all videos were available for analysis. Vascular abnormalities were scored most frequently in HD + i-scan2 bronchoscopy (1.33 ± 0.29 abnormal or suspicious sites per patient) as compared to 0.12 ± 0.05 site for AFB (P = 0.003). Sites suspicious for preinvasive lesions were most frequently reported using AFB (0.74 ± 0.12 sites per patient) as compared to 0.17 ± 0.06 for both WLB and HD bronchoscopy (P = 0.003). Tumors were detected equally by all modalities. The preferred modality was HD bronchoscopy with i-scan (tone- plus surface and surface enhancement in respectively 38% and 35% of cases P = 0.006).

Conclusions

This study shows that high definition bronchoscopy with image enhancement technique may result in better detection of subtle vascular abnormalities in the airways. Since these abnormalities may be related to preneoplastic lesions and tumors this is of clinical relevance. Further investigations using this technique relating imaging to histology are warranted.     

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.     

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.