Constructing a biodiversity terminological inventory.

The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources, which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. This article describes the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variations are prevalent. We apply a number of distributional semantic techniques on all of the titles in the Biodiversity Heritage Library, to compute semantic similarity between species names and support the automated construction of the resource. With the construction of our biodiversity term inventory, we demonstrate that distributional semantic models are able to identify semantically similar names that are not yet recorded in existing taxonomies. Such methods can thus be used to update existing taxonomies semi-automatically by deriving semantically related taxonomic names from a text corpus and allowing expert curators to validate them. We also evaluate our inventory as a means to improve search by facilitating automatic query expansion. Specifically, we developed a visual search interface that suggests semantically related species names, which are available in our inventory but not always in other repositories, to incorporate into the search query. An assessment of the interface by domain experts reveals that our query expansion based on related names is useful for increasing the number of relevant documents retrieved. Its exploitation can benefit both users and developers of search engines and text mining applications

Computational Prediction and Experimental Verification of New MAP Kinase Docking Sites and Substrates Including Gli Transcription Factors

In order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kinase docking sites, and used this algorithm to search the human genome for novel MAP kinase substrates and regulators focused on the JNK family of MAP kinases. Predictions were tested by peptide array followed by rigorous biochemical verification with in vitro binding and kinase assays on wild-type and mutant proteins. Using this procedure, we found new ‘D-site’ class docking sites in previously known JNK substrates (hnRNP-K, PPM1J/PP2Czeta), as well as new JNK-interacting proteins (MLL4, NEIL1). Finally, we identified new D-site-dependent MAPK substrates, including the hedgehog-regulated transcription factors Gli1 and Gli3, suggesting that a direct connection between MAP kinase and hedgehog signaling may occur at the level of these key regulators. These results demonstrate that a genome-wide search for MAP kinase docking sites can be used to find new docking sites and substrates

A novel acetylation of β-tubulin by San modulates microtubule polymerization via down-regulating tubulin incorporation

We report that San, an acetyltransferase required for sister chromatid cohesion, also acetylates β-tubulin at lysine 252. The acetylation happens only on free tubulin heterodimers, and it delays the incorporation of modified tubulins into microtubules in vivo

In vivo quantification of human lumbar disc degeneration using T1ρ-weighted magnetic resonance imaging

Diagnostic methods and biomarkers of early disc degeneration are needed as emerging treatment technologies develop (e.g., nucleus replacement, total disc arthroplasty, cell therapy, growth factor therapy) to serve as an alternative to lumbar spine fusion in treatment of low back pain. We have recently demonstrated in cadaveric human discs an MR imaging and analysis technique, spin-lock T1ρ-weighted MRI, which may provide a quantitative, objective, and non-invasive assessment of disc degeneration. The goal of the present study was to assess the feasibility of using T1ρ MRI in vivo to detect intervertebral disc degeneration. We evaluated ten asymptomatic 40–60-year-old subjects. Each subject was imaged on a 1.5 T whole-body clinical MR scanner. Mean T1ρ values from a circular region of interest in the center of the nucleus pulposus were calculated from maps generated from a series of T1ρ-weighted images. The degenerative grade of each lumbar disc was assessed from conventional T2-weighted images according to the Pfirmann classification system. The T1ρ relaxation correlated significantly with disc degeneration (r=−0.51, P<0.01) and the values were consistent with our previous cadaveric study, in which we demonstrated correlation between T1ρ and proteoglycan content. The technique allows for spatial measurements on a continuous rather than an integer-based scale, minimizes the potential for observer bias, has a greater dynamic range than T2-weighted imaging, and can be implemented on a 1.5 T clinical scanner without significant hardware modifications. Thus, there is a strong potential to use T1ρ in vivo as a non-invasive biomarker of proteoglycan loss and early disc degeneration