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Background An increase in work on the full text of journal articles and the growth of PubMedCentral have the opportunity to create a major paradigm shift in how biomedical text mining is done. However, until now there has been no comprehensive characterization of how the bodies of full text journal articles differ from the abstracts that until now have been the subject of most biomedical text mining research. Results We examined the structural and linguistic aspects of abstracts and bodies of full text articles, the performance of text mining tools on both, and the distribution of a variety of semantic classes of named entities between them. We found marked structural differences, with longer sentences in the article bodies and much heavier use of parenthesized material in the bodies than in the abstracts. We found content differences with respect to linguistic features. Three out of four of the linguistic features that we examined were statistically significantly differently distributed between the two genres. We also found content differences with respect to the distribution of semantic features. There were significantly different densities per thousand words for three out of four semantic classes, and clear differences in the extent to which they appeared in the two genres. With respect to the performance of text mining tools, we found that a mutation finder performed equally well in both genres, but that a wide variety of gene mention systems performed much worse on article bodies than they did on abstracts. POS tagging was also more accurate in abstracts than in article bodies. Conclusions Aspects of structure and content differ markedly between article abstracts and article bodies. A number of these differences may pose problems as the text mining field moves more into the area of processing full-text articles. However, these differences also present a number of opportunities for the extraction of data types, particularly that found in parenthesized text, that is present in article bodies but not in article abstracts.

Background We introduce the linguistic annotation of a corpus of 97 full-text biomedical publications, known as the Colorado Richly Annotated Full Text (CRAFT) corpus. We further assess the performance of existing tools for performing sentence splitting, tokenization, syntactic parsing, and named entity recognition on this corpus. Results Many biomedical natural language processing systems demonstrated large differences between their previously published results and their performance on the CRAFT corpus when tested with the publicly available models or rule sets. Trainable systems differed widely with respect to their ability to build high-performing models based on this data. Conclusions The finding that some systems were able to train high-performing models based on this corpus is additional evidence, beyond high inter-annotator agreement, that the quality of the CRAFT corpus is high. The overall poor performance of various systems indicates that considerable work needs to be done to enable natural language processing systems to work well when the input is full-text journal articles. The CRAFT corpus provides a valuable resource to the biomedical natural language processing community for evaluation and training of new models for biomedical full text publications.

This case study examines strategies used to leverage the library’s existing journal licenses to obtain a large collection of full-text journal articles in extensible markup language (XML) format; the right to text mine the collection; and the right to use the collection and the data mined from it for grant-funded research to develop biomedical natural language processing (BNLP) tools. Researchers attempted to obtain content directly from PubMed Central (PMC). This attempt failed due to limits on use of content in PMC. Next researchers and their library liaison attempted to obtain content from contacts in the technical divisions of the publishing industry. This resulted in an incomplete research data set. Then researchers, the library liaison, and the acquisitions librarian collaborated with the sales and technical staff of a major science, technology, engineering, and medical (STEM) publisher to successfully create a method for obtaining XML content as an extension of the library’s typical acquisition process for electronic resources. Our experience led us to realize that text mining rights of full-text articles in XML format should routinely be included in the negotiation of the library’s licenses.

Bio-molecular event extraction from literature is recognized as an important task of bio text mining and, as such, many relevant systems have been developed and made available during the last decade. While such systems provide useful services individually, there is a need for a meta-service to enable comparison and ensemble of such services, offering optimal solutions for various purposes. We have integrated nine event extraction systems in the U-Compare framework, making them intercompatible and interoperable with other U-Compare components. The U-Compare event meta-service provides various meta-level features for comparison and ensemble of multiple event extraction systems. Experimental results show that the performance improvements achieved by the ensemble are significant. While individual event extraction systems themselves provide useful features for bio text mining, the U-Compare meta-service is expected to improve the accessibility to the individual systems, and to enable meta-level uses over multiple event extraction systems such as comparison and ensemble.

Background Ontological concepts are useful for many different biomedical tasks. Concepts are difficult to recognize in text due to a disconnect between what is captured in an ontology and how the concepts are expressed in text. There are many recognizers for specific ontologies, but a general approach for concept recognition is an open problem. Results Three dictionary-based systems (MetaMap, NCBO Annotator, and ConceptMapper) are evaluated on eight biomedical ontologies in the Colorado Richly Annotated Full-Text (CRAFT) Corpus. Over 1,000 parameter combinations are examined, and best-performing parameters for each system-ontology pair are presented. Conclusions Baselines for concept recognition by three systems on eight biomedical ontologies are established (F-measures range from 0.14–0.83). Out of the three systems we tested, ConceptMapper is generally the best-performing system; it produces the highest F-measure of seven out of eight ontologies. Default parameters are not ideal for most systems on most ontologies; by changing parameters F-measure can be increased by up to 0.4. Not only are best performing parameters presented, but suggestions for choosing the best parameters based on ontology characteristics are presented.

The aim of this work was the development of successful cell therapy techniques for cartilage engineering. This will depend on the ability to monitor non-invasively transplanted cells, especially mesenchymal stem cells (MSCs) that are promising candidates to regenerate damaged tissues. MSCs were labeled with superparamagnetic iron oxide particles (SPIO). We examined the effects of long-term labeling, possible toxicological consequences and the possible influence of progressive concentrations of SPIO on chondrogenic differentiation capacity. No influence of various SPIO concentrations was noted on human bone marrow MSC viability or proliferation. We demonstrated long-term (4 weeks) in vitro retention of SPIO by human bone marrow MSCs seeded in collagenic sponges under TGF-β1 chondrogenic conditions, detectable by Magnetic Resonance Imaging (MRI) and histology. Chondrogenic differentiation was demonstrated by molecular and histological analysis of labeled and unlabeled cells. Chondrogenic gene expression (COL2A2, ACAN, SOX9, COL10, COMP) was significantly altered in a dose-dependent manner in labeled cells, as were GAG and type II collagen staining. As expected, SPIO induced a dramatic decrease of MRI T2 values of sponges at 7T and 3T, even at low concentrations. This study clearly demonstrates (1) long-term in vitro MSC traceability using SPIO and MRI and (2) a deleterious dose-dependence of SPIO on TGF-β1 driven chondrogenesis in collagen sponges. Low concentrations (12.5-25 µg Fe/mL) seem the best compromise to optimize both chondrogenesis and MRI labeling.

To attract pollinators, individuals of the threatened and food-deceptive orchid Cypripedium calceolus (L.) employ visual and olfactory signals, notably volatile organic compounds (VOCs). However, habitat fragmentation has disrupted its population into patches of still relatively large sizes, but also small sizes. We hypothesized that small orchid populations inhabit low-biodiversity areas with fewer pollinators, potentially leading to extinction. To investigate whether local biodiversity and olfactory signals vary with population size, we analysed site-specific vegetation characteristics, insect and flower visitor diversity, plant growth traits and floral VOC profiles in small and large C. calceolus populations. Our results revealed that smaller populations occupy habitats with lower biodiversity, consist of smaller plants with fewer flowers and exhibit reduced flowering success compared to larger populations. VOC profiles also varied between population sizes. However, fruit production and reproductive success did not differ, indicating that these chemical and ecological differences do not necessarily affect reproductive output. These results highlight that population size is linked to variation in plant traits and floral scent but does not directly predict reproductive success under current conditions.

Transcription in eukaryotic nuclei is carried out by DNA-dependent RNA polymerases I, II, and III. Human RNA polymerase III (Pol III) transcribes small untranslated RNAs that include tRNAs, 5S RNA, U6 RNA, and some microRNAs. Increased Pol III transcription has been reported to accompany or cause cell transformation. Here we describe a Pol III subunit (RPC32β) that led to the demonstration of two human Pol III isoforms (Pol IIIα and Pol IIIβ). RPC32β-containing Pol IIIβ is ubiquitously expressed and essential for growth of human cells. RPC32α-containing Pol IIIα is dispensable for cell survival, with expression being restricted to undifferentiated ES cells and to tumor cells. In this regard, and most importantly, suppression of RPC32α expression impedes anchorage-independent growth of HeLa cells, whereas ectopic expression of RPC32α in IMR90 fibroblasts enhances cell transformation and dramatically changes the expression of several tumor-related mRNAs and that of a subset of Pol III RNAs. These results identify a human Pol III isoform and isoform-specific functions in the regulation of cell growth and transformation.

Polypyrimidine tract-binding protein 1 (PTBP1) is a heterogeneous nuclear ribonucleoprotein primarily known for its alternative splicing activity. It shuttles between the nucleus and cytoplasm via partially overlapping N-terminal nuclear localization (NLS) and export (NES) signals. Despite its fundamental role in cell growth and differentiation, its involvement in human disease remains poorly understood. We identified 27 individuals from 25 families harboring de novo or inherited pathogenic variants - predominantly start-loss (89%) and, to a lesser extent, missense (11%) - affecting NES/NLS motifs. Affected individuals presented with a syndromic neurodevelopmental disorder and variable skeletal dysplasia with disproportionate short stature with short limbs. Intellectual functioning ranged from normal to moderately delayed. Start-loss variants led to translation initiation from an alternative downstream in-frame methionine, resulting in loss of the NES and the first half of the bipartite NLS, and increased cytoplasmic stability. Start-loss and missense variants shared a DNA methylation episignature in peripheral blood and altered nucleocytoplasmic distribution in vitro and in vivo with preferential accumulation in processing bodies, causing aberrant gene expression but normal RNA splicing. Transcriptomic analysis of patient-derived fibroblasts revealed dysregulated pathways involved in osteochondrogenesis and neurodevelopment. Overall, our findings highlight a cytoplasmic role for PTBP1in RNA stability and disease pathogenesis.